Visualizing The Intervention

Library

# Turn off scientific notation
options(scipen=999)

# Load packages
library(here)        # relative file paths for reproducibility
library(tidyverse)   # data wrangling
library(stringi)     # string data wrangling
library(tigris)      # US census TIGER/Line shapefiles
library(ggplot2)     # data visualization
library(cowplot)     # data visualization plotting
library(gridExtra)   # grid for data visualizations
library(biscale)     # bivariate mapping
library(kableExtra)  # table formatting
library(scales)      # palette and number formatting
library(cluster)     # clustering algorithms
library(factoextra)  # clustering algorithms & visualization

Functions

import::here( "fips_census_regions",
              "load_svi_data",
              "merge_svi_data",
              "census_division",
              "flag_summarize",
              "summarize_county_nmtc",
              "summarize_county_lihtc",
              "elbow_plot",
             # notice the use of here::here() that points to the .R file
             # where all these R objects are created
             .from = here::here("analysis/project_data_steps_dodson.R"),
             .character_only = TRUE)
census_division

Load SVI Data

# Load SVI data sets
svi_2010 <- readRDS(here::here("data/raw/Census_Data_SVI/svi_2010_trt10.rds"))
svi_2020 <- readRDS(here::here("data/raw/Census_Data_SVI/svi_2020_trt10.rds"))

# Load mapping data sets
svi_county_map2010 <- readRDS(here::here(paste0("data/wrangling/", str_replace_all(census_division, " ", "_"), "_county_svi_flags10.rds")))

svi_county_map2020 <- readRDS(here::here(paste0("data/wrangling/", str_replace_all(census_division, " ", "_"), "_county_svi_flags20.rds")))

divisional_st_sf <- readRDS(here::here(paste0("data/wrangling/", str_replace_all(census_division, " ", "_"), "_st_sf.rds")))

# Load NMTC & LIHTC Tract Eligibility Data

orig_nmtc <- readxl::read_excel(here::here("data/raw/NMTC_LIHTC_tracts/nmtc_2011-2015_lic_110217.xlsx"), sheet="NMTC LICs 2011-2015 ACS")

high_migration_nmtc <- readxl::read_excel(here::here("data/raw/NMTC_LIHTC_tracts/nmtc_2011-2015_lic_110217.xlsx"), sheet="High migration tracts", skip=1)

nmtc_awards_data <- readxl::read_excel(here::here("data/raw/NMTC_LIHTC_tracts/NMTC_Public_Data_Release_includes_FY_2021_Data_final.xlsx"), sheet = "Projects 2 - Data Set PUBLISH.P")

lihtc_eligible <- readxl::read_excel(here::here("data/raw/NMTC_LIHTC_tracts/qct_data_2010_2011_2012.xlsx"))

lihtc_projects <- read.csv(here::here("data/raw/NMTC_LIHTC_tracts/lihtcpub/LIHTCPUB.csv"))

Load 2010 Data

# National 2010 Data
svi_2010_national <- load_svi_data(svi_2010, percentile=.75)
svi_2010_national %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	FIPS_st	FIPS_county	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_10	E_HU_10	E_HH_10	E_POV150_10	ET_POVSTATUS_10	EP_POV150_10	EPL_POV150_10	F_POV150_10	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	F_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	F_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	F_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	F_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	F_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	F_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	F_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	F_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	F_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	F_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	F_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	F_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	F_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	F_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	F_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	F_NOVEH_10	E_GROUPQ_10	ET_HHTYPE_10	EP_GROUPQ_10	EPL_GROUPQ_10	F_GROUPQ_10	SPL_THEME1	RPL_THEME1	F_THEME1	SPL_THEME2	RPL_THEME2	F_THEME2	SPL_THEME3	RPL_THEME3	F_THEME3	SPL_THEME4	RPL_THEME4	F_THEME4	SPL_THEMES	RPL_THEMES	F_TOTAL
01001020100	01	001	020100	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	1809	771	696	297	1809	16.41791	0.3871	0	36	889	4.049494	0.1790	0	127	598	21.23746	0.20770	0	47	98	47.95918	0.5767	0	174	696	25.00000	0.18790	0	196	1242	15.780998	0.6093	0	186	1759	10.574190	0.3790	0	222	12.271973	0.4876	0	445	24.59923	0.5473	0	298	1335	22.32210	0.8454	1	27	545	4.954128	0.09275	0	36	1705	2.1114370	0.59040	0	385	1809	21.282477	0.4524	0	771	0	0.0000000	0.1224	0	92	11.9325551	0.8005	1	0	696	0.0000000	0.1238	0	50	696	7.183908	0.6134	0	0	1809	0	0.364	0	1.74230	0.28200	0	2.56345	0.5296	1	0.4524	0.4482	0	2.0241	0.2519	1	6.78225	0.3278	2
01001020200	01	001	020200	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	2020	816	730	495	1992	24.84940	0.5954	0	68	834	8.153477	0.5754	0	49	439	11.16173	0.02067	0	105	291	36.08247	0.3019	0	154	730	21.09589	0.09312	0	339	1265	26.798419	0.8392	1	313	2012	15.556660	0.6000	0	204	10.099010	0.3419	0	597	29.55446	0.8192	1	359	1515	23.69637	0.8791	1	132	456	28.947368	0.83510	1	15	1890	0.7936508	0.40130	0	1243	2020	61.534653	0.7781	1	816	0	0.0000000	0.1224	0	34	4.1666667	0.6664	0	13	730	1.7808219	0.5406	0	115	730	15.753425	0.8382	1	0	2020	0	0.364	0	2.70312	0.56650	1	3.27660	0.8614	3	0.7781	0.7709	1	2.5316	0.5047	1	9.28942	0.6832	6
01001020300	01	001	020300	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	3543	1403	1287	656	3533	18.56779	0.4443	0	93	1552	5.992268	0.3724	0	273	957	28.52665	0.45780	0	178	330	53.93939	0.7152	0	451	1287	35.04274	0.49930	0	346	2260	15.309734	0.5950	0	252	3102	8.123791	0.2596	0	487	13.745413	0.5868	0	998	28.16822	0.7606	1	371	2224	16.68165	0.6266	0	126	913	13.800657	0.46350	0	0	3365	0.0000000	0.09298	0	637	3543	17.979114	0.4049	0	1403	10	0.7127584	0.3015	0	2	0.1425517	0.4407	0	0	1287	0.0000000	0.1238	0	101	1287	7.847708	0.6443	0	0	3543	0	0.364	0	2.17060	0.41010	0	2.53048	0.5116	1	0.4049	0.4011	0	1.8743	0.1942	0	6.98028	0.3576	1
01001020400	01	001	020400	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	4840	1957	1839	501	4840	10.35124	0.2177	0	101	2129	4.744011	0.2447	0	310	1549	20.01291	0.17080	0	89	290	30.68966	0.2044	0	399	1839	21.69657	0.10540	0	274	3280	8.353658	0.3205	0	399	4293	9.294200	0.3171	0	955	19.731405	0.8643	1	1195	24.69008	0.5530	0	625	3328	18.78005	0.7233	0	152	1374	11.062591	0.34710	0	10	4537	0.2204100	0.22560	0	297	4840	6.136364	0.1647	0	1957	33	1.6862545	0.3843	0	25	1.2774655	0.5516	0	14	1839	0.7612833	0.3564	0	19	1839	1.033170	0.1127	0	0	4840	0	0.364	0	1.20540	0.13470	0	2.71330	0.6129	1	0.1647	0.1632	0	1.7690	0.1591	0	5.85240	0.1954	1
01001020500	01	001	020500	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	9938	3969	3741	1096	9938	11.02838	0.2364	0	188	4937	3.807981	0.1577	0	426	2406	17.70574	0.11050	0	528	1335	39.55056	0.3753	0	954	3741	25.50120	0.20140	0	293	5983	4.897209	0.1655	0	740	10110	7.319486	0.2211	0	837	8.422218	0.2408	0	3012	30.30791	0.8455	1	759	7155	10.60797	0.2668	0	476	2529	18.821669	0.63540	0	78	9297	0.8389803	0.41110	0	1970	9938	19.822902	0.4330	0	3969	306	7.7097506	0.6153	0	0	0.0000000	0.2198	0	7	3741	0.1871157	0.2535	0	223	3741	5.960973	0.5483	0	0	9938	0	0.364	0	0.98210	0.08468	0	2.39960	0.4381	1	0.4330	0.4290	0	2.0009	0.2430	0	5.81560	0.1905	1
01001020600	01	001	020600	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	3402	1456	1308	735	3402	21.60494	0.5199	0	134	1720	7.790698	0.5436	0	242	1032	23.44961	0.28010	0	62	276	22.46377	0.1035	0	304	1308	23.24159	0.14070	0	301	2151	13.993491	0.5510	0	355	3445	10.304790	0.3656	0	386	11.346267	0.4232	0	931	27.36626	0.7200	0	440	2439	18.04018	0.6912	0	143	924	15.476190	0.52900	0	4	3254	0.1229256	0.19840	0	723	3402	21.252205	0.4519	0	1456	18	1.2362637	0.3507	0	433	29.7390110	0.9468	1	16	1308	1.2232416	0.4493	0	28	1308	2.140673	0.2298	0	0	3402	0	0.364	0	2.12080	0.39510	0	2.56180	0.5288	0	0.4519	0.4477	0	2.3406	0.4048	1	7.47510	0.4314	1

# Divisional 2010 Data
svi_2010_divisional <- load_svi_data(svi_2010, rank_by = "divisional", location = census_division, percentile=.75)
svi_2010_divisional %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	FIPS_st	FIPS_county	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_10	E_HU_10	E_HH_10	E_POV150_10	ET_POVSTATUS_10	EP_POV150_10	EPL_POV150_10	F_POV150_10	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	F_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	F_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	F_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	F_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	F_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	F_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	F_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	F_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	F_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	F_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	F_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	F_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	F_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	F_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	F_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	F_NOVEH_10	E_GROUPQ_10	ET_HHTYPE_10	EP_GROUPQ_10	EPL_GROUPQ_10	F_GROUPQ_10	SPL_THEME1	RPL_THEME1	F_THEME1	SPL_THEME2	RPL_THEME2	F_THEME2	SPL_THEME3	RPL_THEME3	F_THEME3	SPL_THEME4	RPL_THEME4	F_THEME4	SPL_THEMES	RPL_THEMES	F_TOTAL
10001040100	10	001	040100	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	6468	2388	2272	868	6455	13.446940	0.2879	0	201	3230	6.222910	0.3819	0	708	2036	34.77407	0.6489	0	47	236	19.91525	0.07811	0	755	2272	33.23063	0.4315	0	691	4369	15.815976	0.5692	0	400	6594	6.066121	0.12090	0	688	10.636982	0.3706	0	1689	26.11317	0.6923	0	725	5107	14.19620	0.4566	0	209	1742	11.99770	0.3673	0	0	5993	0.0000000	0.1022	0	845	6468	13.06432	0.2341	0	2388	0	0.000000	0.1428	0	601	25.167504	0.8423	1	14	2272	0.6161972	0.3716	0	92	2272	4.049296	0.4329	0	0	6468	0.000000	0.3814	0	1.79140	0.2784	0	1.9890	0.2036	0	0.2341	0.2310	0	2.1710	0.3144	1	6.18550	0.2310	1
10001040201	10	001	040201	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	5208	1953	1809	850	5183	16.399769	0.3672	0	147	2550	5.764706	0.3364	0	385	1323	29.10053	0.4581	0	222	486	45.67901	0.49650	0	607	1809	33.55445	0.4431	0	459	3090	14.854369	0.5386	0	435	5283	8.233958	0.19610	0	454	8.717358	0.2561	0	1588	30.49155	0.8927	1	537	3716	14.45102	0.4708	0	417	1343	31.04989	0.8599	1	69	4835	1.4270941	0.5240	0	1881	5208	36.11751	0.5689	0	1953	87	4.454685	0.5392	0	148	7.578085	0.6495	0	39	1809	2.1558872	0.6471	0	121	1809	6.688778	0.6124	0	0	5208	0.000000	0.3814	0	1.88140	0.3053	0	3.0035	0.7667	2	0.5689	0.5614	0	2.8296	0.6516	0	8.28340	0.5452	2
10001040202	10	001	040202	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	11385	4350	4041	1680	10992	15.283843	0.3360	0	475	5262	9.026986	0.6217	0	1237	3491	35.43397	0.6683	0	255	550	46.36364	0.51190	0	1492	4041	36.92155	0.5546	0	751	7545	9.953612	0.3559	0	803	12478	6.435326	0.13310	0	1756	15.423803	0.6665	0	3042	26.71937	0.7280	0	1556	9021	17.24864	0.6195	0	336	3110	10.80386	0.3143	0	62	10616	0.5840241	0.3482	0	3295	11385	28.94159	0.4817	0	4350	100	2.298851	0.4559	0	478	10.988506	0.6962	0	20	4041	0.4949270	0.3450	0	192	4041	4.751299	0.4900	0	387	11385	3.399209	0.8606	1	2.00130	0.3396	0	2.6765	0.5915	0	0.4817	0.4753	0	2.8477	0.6611	1	8.00720	0.5007	1
10001040203	10	001	040203	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	4643	1865	1718	1441	4597	31.346530	0.7241	0	99	2296	4.311847	0.2007	0	362	1223	29.59935	0.4754	0	271	495	54.74747	0.70810	0	633	1718	36.84517	0.5523	0	436	2783	15.666547	0.5647	0	258	5110	5.048924	0.08961	0	505	10.876588	0.3849	0	1390	29.93754	0.8755	1	646	3590	17.99443	0.6562	0	292	1181	24.72481	0.7632	1	20	4310	0.4640371	0.3134	0	1780	4643	38.33728	0.5942	0	1865	91	4.879357	0.5508	0	252	13.512064	0.7236	0	52	1718	3.0267753	0.7465	0	197	1718	11.466822	0.7913	1	0	4643	0.000000	0.3814	0	2.13141	0.3754	0	2.9932	0.7618	2	0.5942	0.5863	0	3.1936	0.8133	1	8.91241	0.6347	3
10001040501	10	001	040501	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	5172	2061	1721	2008	5121	39.211092	0.8425	1	134	1988	6.740443	0.4302	0	443	1191	37.19563	0.7145	0	312	530	58.86792	0.78710	1	755	1721	43.86984	0.7444	0	486	3108	15.637066	0.5640	0	493	4902	10.057120	0.26220	0	700	13.534416	0.5573	0	1681	32.50193	0.9414	1	518	3508	14.76625	0.4887	0	580	1392	41.66667	0.9424	1	12	4692	0.2557545	0.2451	0	3222	5172	62.29698	0.7880	1	2061	281	13.634158	0.7133	0	223	10.819990	0.6938	0	139	1721	8.0766996	0.9538	1	63	1721	3.660662	0.3972	0	0	5172	0.000000	0.3814	0	2.84330	0.5967	1	3.1749	0.8372	2	0.7880	0.7776	1	3.1395	0.7936	1	9.94570	0.7681	5
10001040502	10	001	040502	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	2087	921	921	192	2087	9.199808	0.1738	0	35	722	4.847645	0.2495	0	281	700	40.14286	0.7819	1	64	221	28.95928	0.17110	0	345	921	37.45928	0.5710	0	284	1546	18.369987	0.6484	0	119	2121	5.610561	0.10710	0	518	24.820316	0.9068	1	480	22.99952	0.4910	0	328	1527	21.48003	0.7959	1	173	680	25.44118	0.7769	1	100	1998	5.0050050	0.7960	1	560	2087	26.83277	0.4524	0	921	0	0.000000	0.1428	0	273	29.641694	0.8785	1	0	921	0.0000000	0.1488	0	30	921	3.257329	0.3600	0	0	2087	0.000000	0.3814	0	1.74980	0.2670	0	3.7666	0.9666	4	0.4524	0.4464	0	1.9115	0.2071	1	7.88030	0.4785	5

# National 2020 Data
svi_2020_national <- load_svi_data(svi_2020, percentile=.75)
svi_2020_national %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	FIPS_st	FIPS_county	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	F_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	F_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	F_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	F_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	F_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	F_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	F_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	F_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	F_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	F_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	F_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	F_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	F_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	F_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	F_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	F_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	F_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20	F_GROUPQ_20	SPL_THEME1	RPL_THEME1	F_THEME1	SPL_THEME2	RPL_THEME2	F_THEME2	SPL_THEME3	RPL_THEME3	F_THEME3	SPL_THEME4	RPL_THEME4	F_THEME4	SPL_THEMES	RPL_THEMES	F_TOTAL
01001020100	01	001	020100	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	1941	710	693	352	1941	18.13498	0.4630	0	18	852	2.112676	0.15070	0	81	507	15.976331	0.26320	0	63	186	33.87097	0.2913	0	144	693	20.77922	0.2230	0	187	1309	14.285714	0.6928	0	187	1941	9.634209	0.6617	0	295	15.19835	0.4601	0	415	21.38073	0.4681	0	391	1526	25.62254	0.9011	1	58	555	10.45045	0.3451	0	0	1843	0.0000000	0.09479	0	437	1941	22.51417	0.3902	0	710	0	0.0000000	0.1079	0	88	12.3943662	0.8263	1	0	693	0.0000000	0.09796	0	10	693	1.443001	0.1643	0	0	1941	0.000000	0.1831	0	2.19120	0.4084	0	2.26919	0.3503	1	0.3902	0.3869	0	1.37956	0.07216	1	6.23015	0.2314	2
01001020200	01	001	020200	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	1757	720	573	384	1511	25.41363	0.6427	0	29	717	4.044630	0.41320	0	33	392	8.418367	0.03542	0	116	181	64.08840	0.9086	1	149	573	26.00349	0.4041	0	139	1313	10.586443	0.5601	0	91	1533	5.936073	0.4343	0	284	16.16392	0.5169	0	325	18.49744	0.2851	0	164	1208	13.57616	0.4127	0	42	359	11.69916	0.3998	0	0	1651	0.0000000	0.09479	0	1116	1757	63.51736	0.7591	1	720	3	0.4166667	0.2470	0	5	0.6944444	0.5106	0	9	573	1.5706806	0.46880	0	57	573	9.947644	0.7317	0	212	1757	12.066022	0.9549	1	2.45440	0.4888	0	1.70929	0.1025	0	0.7591	0.7527	1	2.91300	0.68620	1	7.83579	0.4802	2
01001020300	01	001	020300	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	3694	1464	1351	842	3694	22.79372	0.5833	0	53	1994	2.657974	0.22050	0	117	967	12.099276	0.11370	0	147	384	38.28125	0.3856	0	264	1351	19.54108	0.1827	0	317	2477	12.797739	0.6460	0	127	3673	3.457664	0.2308	0	464	12.56091	0.3088	0	929	25.14889	0.7080	0	473	2744	17.23761	0.6211	0	263	975	26.97436	0.8234	1	128	3586	3.5694367	0.70770	0	1331	3694	36.03140	0.5515	0	1464	26	1.7759563	0.3675	0	14	0.9562842	0.5389	0	35	1351	2.5906736	0.60550	0	42	1351	3.108808	0.3415	0	0	3694	0.000000	0.1831	0	1.86330	0.3063	0	3.16900	0.8380	1	0.5515	0.5468	0	2.03650	0.26830	0	7.62030	0.4460	1
01001020400	01	001	020400	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	3539	1741	1636	503	3539	14.21305	0.3472	0	39	1658	2.352232	0.17990	0	219	1290	16.976744	0.30880	0	74	346	21.38728	0.1037	0	293	1636	17.90954	0.1333	0	173	2775	6.234234	0.3351	0	169	3529	4.788892	0.3448	0	969	27.38062	0.9225	1	510	14.41085	0.1208	0	670	3019	22.19278	0.8194	1	148	1137	13.01671	0.4541	0	89	3409	2.6107363	0.64690	0	454	3539	12.82848	0.2364	0	1741	143	8.2136703	0.6028	0	0	0.0000000	0.2186	0	10	1636	0.6112469	0.28340	0	72	1636	4.400978	0.4538	0	0	3539	0.000000	0.1831	0	1.34030	0.1575	0	2.96370	0.7496	2	0.2364	0.2344	0	1.74170	0.16270	0	6.28210	0.2389	2
01001020500	01	001	020500	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	10674	4504	4424	1626	10509	15.47245	0.3851	0	81	5048	1.604596	0.09431	0	321	2299	13.962592	0.17970	0	711	2125	33.45882	0.2836	0	1032	4424	23.32731	0.3109	0	531	6816	7.790493	0.4251	0	301	10046	2.996217	0.1894	0	1613	15.11149	0.4553	0	2765	25.90407	0.7494	0	1124	7281	15.43744	0.5253	0	342	2912	11.74451	0.4019	0	52	9920	0.5241935	0.35230	0	2603	10674	24.38636	0.4160	0	4504	703	15.6083481	0.7378	0	29	0.6438721	0.5037	0	37	4424	0.8363472	0.33420	0	207	4424	4.679023	0.4754	0	176	10674	1.648866	0.7598	1	1.40481	0.1743	0	2.48420	0.4802	0	0.4160	0.4125	0	2.81090	0.63730	1	7.11591	0.3654	1
01001020600	01	001	020600	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	3536	1464	1330	1279	3523	36.30429	0.8215	1	34	1223	2.780049	0.23780	0	321	1111	28.892889	0.75870	1	67	219	30.59361	0.2305	0	388	1330	29.17293	0.5075	0	306	2380	12.857143	0.6480	0	415	3496	11.870709	0.7535	1	547	15.46946	0.4760	0	982	27.77149	0.8327	1	729	2514	28.99761	0.9488	1	95	880	10.79545	0.3601	0	0	3394	0.0000000	0.09479	0	985	3536	27.85633	0.4608	0	1464	0	0.0000000	0.1079	0	364	24.8633880	0.9300	1	0	1330	0.0000000	0.09796	0	17	1330	1.278196	0.1463	0	0	3536	0.000000	0.1831	0	2.96830	0.6434	2	2.71239	0.6156	2	0.4608	0.4569	0	1.46526	0.08976	1	7.60675	0.4440	5

# Divisional 2020 Data
svi_2020_divisional <- load_svi_data(svi_2020, rank_by = "divisional", location =  census_division, percentile=.75)
svi_2020_divisional %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	FIPS_st	FIPS_county	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	F_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	F_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	F_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	F_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	F_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	F_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	F_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	F_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	F_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	F_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	F_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	F_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	F_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	F_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	F_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	F_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	F_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20	F_GROUPQ_20	SPL_THEME1	RPL_THEME1	F_THEME1	SPL_THEME2	RPL_THEME2	F_THEME2	SPL_THEME3	RPL_THEME3	F_THEME3	SPL_THEME4	RPL_THEME4	F_THEME4	SPL_THEMES	RPL_THEMES	F_TOTAL
10001040100	10	001	040100	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	7531	2850	2587	2226	7519	29.60500	0.7109	0	392	3820	10.261780	0.8786	1	527	2067	25.49589	0.6863	0	180	520	34.61538	0.2805	0	707	2587	27.32895	0.4528	0	765	4950	15.454546	0.7234	0	353	7523	4.692277	0.2050	0	1007	13.37140	0.3312	0	2035	27.02164	0.8398	1	1227	5488.000	22.35787	0.8093	1	239	1893.0000	12.62546	0.4261	0	276	7262	3.8006059	0.75930	1	1507	7531.000	20.01062	0.2789	0	2850	1	0.0350877	0.2526	0	697	24.456140	0.8585	1	93	2587	3.5948976	0.7607	1	55	2587.0000	2.126015	0.2641	0	0	7531	0.000000	0.2111	0	2.9707	0.6413	1	3.16570	0.8440	3	0.2789	0.2755	0	2.3470	0.4035	2	8.76230	0.6215	6
10001040201	10	001	040201	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	4770	1906	1732	755	4692	16.09122	0.3758	0	92	2500	3.680000	0.3633	0	197	1184	16.63851	0.2804	0	235	548	42.88321	0.4609	0	432	1732	24.94226	0.3622	0	251	3100	8.096774	0.4085	0	228	4770	4.779874	0.2116	0	549	11.50943	0.2329	0	1352	28.34382	0.8865	1	490	3418.125	14.33535	0.4309	0	328	1263.2064	25.96567	0.8111	1	0	4526	0.0000000	0.09987	0	1875	4769.908	39.30893	0.5372	0	1906	72	3.7775446	0.4876	0	128	6.715635	0.6610	0	10	1732	0.5773672	0.3165	0	32	1731.7111	1.847883	0.2303	0	0	4770	0.000000	0.2111	0	1.7214	0.2531	0	2.46127	0.4594	2	0.5372	0.5306	0	1.9065	0.2183	0	6.62637	0.2829	2
10001040202	10	001	040202	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	16537	5776	5768	2288	16141	14.17508	0.3194	0	237	8403	2.820421	0.2461	0	1493	4973	30.02212	0.8197	1	315	795	39.62264	0.3868	0	1808	5768	31.34535	0.5823	0	986	11516	8.562001	0.4338	0	1326	15886	8.346972	0.4327	0	2472	14.94830	0.4225	0	3814	23.06343	0.6287	0	1742	12086.000	14.41337	0.4347	0	742	4396.0000	16.87898	0.5844	0	185	15466	1.1961722	0.50680	0	7164	16537.000	43.32104	0.5828	0	5776	138	2.3891967	0.4352	0	385	6.665513	0.6600	0	0	5768	0.0000000	0.1168	0	165	5768.0000	2.860610	0.3442	0	373	16537	2.255548	0.8244	1	2.0143	0.3437	0	2.57710	0.5357	0	0.5828	0.5757	0	2.3806	0.4199	1	7.55480	0.4282	1
10001040203	10	001	040203	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	5310	2259	2097	1163	5283	22.01401	0.5368	0	69	2413	2.859511	0.2514	0	418	1516	27.57256	0.7526	1	382	581	65.74871	0.9074	1	800	2097	38.14974	0.7609	1	162	3597	4.503753	0.2087	0	704	5297	13.290542	0.7059	0	1320	24.85876	0.8299	1	1429	26.91149	0.8347	1	513	3868.000	13.26267	0.3691	0	290	1430.0000	20.27972	0.6849	0	34	5062	0.6716713	0.39280	0	2582	5310.000	48.62524	0.6381	0	2259	153	6.7729084	0.5732	0	291	12.881806	0.7430	0	45	2097	2.1459227	0.6153	0	71	2097.0000	3.385789	0.3952	0	5	5310	0.094162	0.4618	0	2.4637	0.4806	1	3.11140	0.8223	2	0.6381	0.6303	0	2.7885	0.6181	0	9.00170	0.6555	3
10001040501	10	001	040501	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	4731	2061	1979	1016	4703	21.60323	0.5269	0	208	2511	8.283552	0.8001	1	402	1423	28.25018	0.7714	1	300	556	53.95683	0.7197	0	702	1979	35.47246	0.6995	0	412	3336	12.350120	0.6094	0	230	4731	4.861552	0.2173	0	964	20.37624	0.6876	0	926	19.57303	0.4025	0	959	3805.000	25.20368	0.8859	1	278	1180.0000	23.55932	0.7634	1	244	4465	5.4647256	0.82700	1	2404	4731.000	50.81378	0.6589	0	2061	260	12.6152353	0.6783	0	299	14.507521	0.7619	1	39	1979	1.9706923	0.5899	0	133	1979.0000	6.720566	0.6323	0	0	4731	0.000000	0.2111	0	2.8532	0.6053	1	3.56640	0.9479	3	0.6589	0.6509	0	2.8735	0.6632	1	9.95200	0.7755	5
10001040502	10	001	040502	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	2555	1030	954	565	2555	22.11350	0.5385	0	135	1154	11.698440	0.9175	1	144	691	20.83936	0.4865	0	168	262	64.12214	0.8894	1	312	953	32.73872	0.6259	0	192	1782	10.774411	0.5377	0	198	2519	7.860262	0.4016	0	519	20.31311	0.6851	0	664	25.98826	0.7939	1	341	1854.295	18.38974	0.6427	0	195	614.6519	31.72527	0.8870	1	75	2351	3.1901319	0.72360	0	1215	2555.353	47.54725	0.6272	0	1030	61	5.9223301	0.5514	0	170	16.504854	0.7844	1	58	954	6.0796646	0.8947	1	83	953.5886	8.703963	0.7220	0	0	2555	0.000000	0.2111	0	3.0212	0.6565	1	3.73230	0.9680	2	0.6272	0.6195	0	3.1636	0.7893	2	10.54430	0.8433	5

Merge 2010 & 2020 data

# Find tracts with divisional data in both 2010 and 2020
svi_divisional <- merge_svi_data(svi_2010_divisional, svi_2020_divisional)
svi_divisional %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	FIPS_st	FIPS_county	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_10	E_HU_10	E_HH_10	E_POV150_10	ET_POVSTATUS_10	EP_POV150_10	EPL_POV150_10	F_POV150_10	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	F_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	F_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	F_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	F_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	F_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	F_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	F_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	F_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	F_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	F_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	F_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	F_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	F_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	F_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	F_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	F_NOVEH_10	E_GROUPQ_10	ET_HHTYPE_10	EP_GROUPQ_10	EPL_GROUPQ_10	F_GROUPQ_10	SPL_THEME1_10	RPL_THEME1_10	F_THEME1_10	SPL_THEME2_10	RPL_THEME2_10	F_THEME2_10	SPL_THEME3_10	RPL_THEME3_10	F_THEME3_10	SPL_THEME4_10	RPL_THEME4_10	F_THEME4_10	SPL_THEMES_10	RPL_THEMES_10	F_TOTAL_10	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	F_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	F_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	F_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	F_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	F_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	F_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	F_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	F_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	F_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	F_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	F_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	F_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	F_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	F_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	F_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	F_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	F_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20	F_GROUPQ_20	SPL_THEME1_20	RPL_THEME1_20	F_THEME1_20	SPL_THEME2_20	RPL_THEME2_20	F_THEME2_20	SPL_THEME3_20	RPL_THEME3_20	F_THEME3_20	SPL_THEME4_20	RPL_THEME4_20	F_THEME4_20	SPL_THEMES_20	RPL_THEMES_20	F_TOTAL_20
10001040100	10	001	040100	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	6468	2388	2272	868	6455	13.446940	0.2879	0	201	3230	6.222910	0.3819	0	708	2036	34.77407	0.6489	0	47	236	19.91525	0.07811	0	755	2272	33.23063	0.4315	0	691	4369	15.815976	0.5692	0	400	6594	6.066121	0.12090	0	688	10.636982	0.3706	0	1689	26.11317	0.6923	0	725	5107	14.19620	0.4566	0	209	1742	11.99770	0.3673	0	0	5993	0.0000000	0.1022	0	845	6468	13.06432	0.2341	0	2388	0	0.000000	0.1428	0	601	25.167504	0.8423	1	14	2272	0.6161972	0.3716	0	92	2272	4.049296	0.4329	0	0	6468	0.000000	0.3814	0	1.79140	0.2784	0	1.9890	0.2036	0	0.2341	0.2310	0	2.1710	0.3144	1	6.18550	0.2310	1	7531	2850	2587	2226	7519	29.60500	0.7109	0	392	3820	10.261780	0.8786	1	527	2067	25.49589	0.6863	0	180	520	34.61538	0.2805	0	707	2587	27.32895	0.4528	0	765	4950	15.454546	0.7234	0	353	7523	4.692277	0.2050	0	1007	13.37140	0.3312	0	2035	27.02164	0.8398	1	1227	5488.000	22.35787	0.8093	1	239	1893.0000	12.62546	0.4261	0	276	7262	3.8006059	0.75930	1	1507	7531.000	20.01062	0.2789	0	2850	1	0.0350877	0.2526	0	697	24.456140	0.8585	1	93	2587	3.5948976	0.7607	1	55	2587.0000	2.126015	0.2641	0	0	7531	0.000000	0.2111	0	2.9707	0.6413	1	3.16570	0.8440	3	0.2789	0.2755	0	2.3470	0.4035	2	8.76230	0.6215	6
10001040201	10	001	040201	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	5208	1953	1809	850	5183	16.399769	0.3672	0	147	2550	5.764706	0.3364	0	385	1323	29.10053	0.4581	0	222	486	45.67901	0.49650	0	607	1809	33.55445	0.4431	0	459	3090	14.854369	0.5386	0	435	5283	8.233958	0.19610	0	454	8.717358	0.2561	0	1588	30.49155	0.8927	1	537	3716	14.45102	0.4708	0	417	1343	31.04989	0.8599	1	69	4835	1.4270941	0.5240	0	1881	5208	36.11751	0.5689	0	1953	87	4.454685	0.5392	0	148	7.578085	0.6495	0	39	1809	2.1558872	0.6471	0	121	1809	6.688778	0.6124	0	0	5208	0.000000	0.3814	0	1.88140	0.3053	0	3.0035	0.7667	2	0.5689	0.5614	0	2.8296	0.6516	0	8.28340	0.5452	2	4770	1906	1732	755	4692	16.09122	0.3758	0	92	2500	3.680000	0.3633	0	197	1184	16.63851	0.2804	0	235	548	42.88321	0.4609	0	432	1732	24.94226	0.3622	0	251	3100	8.096774	0.4085	0	228	4770	4.779874	0.2116	0	549	11.50943	0.2329	0	1352	28.34382	0.8865	1	490	3418.125	14.33535	0.4309	0	328	1263.2064	25.96567	0.8111	1	0	4526	0.0000000	0.09987	0	1875	4769.908	39.30893	0.5372	0	1906	72	3.7775446	0.4876	0	128	6.715635	0.6610	0	10	1732	0.5773672	0.3165	0	32	1731.7111	1.847883	0.2303	0	0	4770	0.000000	0.2111	0	1.7214	0.2531	0	2.46127	0.4594	2	0.5372	0.5306	0	1.9065	0.2183	0	6.62637	0.2829	2
10001040202	10	001	040202	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	11385	4350	4041	1680	10992	15.283843	0.3360	0	475	5262	9.026986	0.6217	0	1237	3491	35.43397	0.6683	0	255	550	46.36364	0.51190	0	1492	4041	36.92155	0.5546	0	751	7545	9.953612	0.3559	0	803	12478	6.435326	0.13310	0	1756	15.423803	0.6665	0	3042	26.71937	0.7280	0	1556	9021	17.24864	0.6195	0	336	3110	10.80386	0.3143	0	62	10616	0.5840241	0.3482	0	3295	11385	28.94159	0.4817	0	4350	100	2.298851	0.4559	0	478	10.988506	0.6962	0	20	4041	0.4949270	0.3450	0	192	4041	4.751299	0.4900	0	387	11385	3.399209	0.8606	1	2.00130	0.3396	0	2.6765	0.5915	0	0.4817	0.4753	0	2.8477	0.6611	1	8.00720	0.5007	1	16537	5776	5768	2288	16141	14.17508	0.3194	0	237	8403	2.820421	0.2461	0	1493	4973	30.02212	0.8197	1	315	795	39.62264	0.3868	0	1808	5768	31.34535	0.5823	0	986	11516	8.562001	0.4338	0	1326	15886	8.346972	0.4327	0	2472	14.94830	0.4225	0	3814	23.06343	0.6287	0	1742	12086.000	14.41337	0.4347	0	742	4396.0000	16.87898	0.5844	0	185	15466	1.1961722	0.50680	0	7164	16537.000	43.32104	0.5828	0	5776	138	2.3891967	0.4352	0	385	6.665513	0.6600	0	0	5768	0.0000000	0.1168	0	165	5768.0000	2.860610	0.3442	0	373	16537	2.255548	0.8244	1	2.0143	0.3437	0	2.57710	0.5357	0	0.5828	0.5757	0	2.3806	0.4199	1	7.55480	0.4282	1
10001040203	10	001	040203	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	4643	1865	1718	1441	4597	31.346530	0.7241	0	99	2296	4.311847	0.2007	0	362	1223	29.59935	0.4754	0	271	495	54.74747	0.70810	0	633	1718	36.84517	0.5523	0	436	2783	15.666547	0.5647	0	258	5110	5.048924	0.08961	0	505	10.876588	0.3849	0	1390	29.93754	0.8755	1	646	3590	17.99443	0.6562	0	292	1181	24.72481	0.7632	1	20	4310	0.4640371	0.3134	0	1780	4643	38.33728	0.5942	0	1865	91	4.879357	0.5508	0	252	13.512064	0.7236	0	52	1718	3.0267753	0.7465	0	197	1718	11.466822	0.7913	1	0	4643	0.000000	0.3814	0	2.13141	0.3754	0	2.9932	0.7618	2	0.5942	0.5863	0	3.1936	0.8133	1	8.91241	0.6347	3	5310	2259	2097	1163	5283	22.01401	0.5368	0	69	2413	2.859511	0.2514	0	418	1516	27.57256	0.7526	1	382	581	65.74871	0.9074	1	800	2097	38.14974	0.7609	1	162	3597	4.503753	0.2087	0	704	5297	13.290542	0.7059	0	1320	24.85876	0.8299	1	1429	26.91149	0.8347	1	513	3868.000	13.26267	0.3691	0	290	1430.0000	20.27972	0.6849	0	34	5062	0.6716713	0.39280	0	2582	5310.000	48.62524	0.6381	0	2259	153	6.7729084	0.5732	0	291	12.881806	0.7430	0	45	2097	2.1459227	0.6153	0	71	2097.0000	3.385789	0.3952	0	5	5310	0.094162	0.4618	0	2.4637	0.4806	1	3.11140	0.8223	2	0.6381	0.6303	0	2.7885	0.6181	0	9.00170	0.6555	3
10001040501	10	001	040501	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	5172	2061	1721	2008	5121	39.211092	0.8425	1	134	1988	6.740443	0.4302	0	443	1191	37.19563	0.7145	0	312	530	58.86792	0.78710	1	755	1721	43.86984	0.7444	0	486	3108	15.637066	0.5640	0	493	4902	10.057120	0.26220	0	700	13.534416	0.5573	0	1681	32.50193	0.9414	1	518	3508	14.76625	0.4887	0	580	1392	41.66667	0.9424	1	12	4692	0.2557545	0.2451	0	3222	5172	62.29698	0.7880	1	2061	281	13.634158	0.7133	0	223	10.819990	0.6938	0	139	1721	8.0766996	0.9538	1	63	1721	3.660662	0.3972	0	0	5172	0.000000	0.3814	0	2.84330	0.5967	1	3.1749	0.8372	2	0.7880	0.7776	1	3.1395	0.7936	1	9.94570	0.7681	5	4731	2061	1979	1016	4703	21.60323	0.5269	0	208	2511	8.283552	0.8001	1	402	1423	28.25018	0.7714	1	300	556	53.95683	0.7197	0	702	1979	35.47246	0.6995	0	412	3336	12.350120	0.6094	0	230	4731	4.861552	0.2173	0	964	20.37624	0.6876	0	926	19.57303	0.4025	0	959	3805.000	25.20368	0.8859	1	278	1180.0000	23.55932	0.7634	1	244	4465	5.4647256	0.82700	1	2404	4731.000	50.81378	0.6589	0	2061	260	12.6152353	0.6783	0	299	14.507521	0.7619	1	39	1979	1.9706923	0.5899	0	133	1979.0000	6.720566	0.6323	0	0	4731	0.000000	0.2111	0	2.8532	0.6053	1	3.56640	0.9479	3	0.6589	0.6509	0	2.8735	0.6632	1	9.95200	0.7755	5
10001040502	10	001	040502	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	2087	921	921	192	2087	9.199808	0.1738	0	35	722	4.847645	0.2495	0	281	700	40.14286	0.7819	1	64	221	28.95928	0.17110	0	345	921	37.45928	0.5710	0	284	1546	18.369987	0.6484	0	119	2121	5.610561	0.10710	0	518	24.820316	0.9068	1	480	22.99952	0.4910	0	328	1527	21.48003	0.7959	1	173	680	25.44118	0.7769	1	100	1998	5.0050050	0.7960	1	560	2087	26.83277	0.4524	0	921	0	0.000000	0.1428	0	273	29.641694	0.8785	1	0	921	0.0000000	0.1488	0	30	921	3.257329	0.3600	0	0	2087	0.000000	0.3814	0	1.74980	0.2670	0	3.7666	0.9666	4	0.4524	0.4464	0	1.9115	0.2071	1	7.88030	0.4785	5	2555	1030	954	565	2555	22.11350	0.5385	0	135	1154	11.698440	0.9175	1	144	691	20.83936	0.4865	0	168	262	64.12214	0.8894	1	312	953	32.73872	0.6259	0	192	1782	10.774411	0.5377	0	198	2519	7.860262	0.4016	0	519	20.31311	0.6851	0	664	25.98826	0.7939	1	341	1854.295	18.38974	0.6427	0	195	614.6519	31.72527	0.8870	1	75	2351	3.1901319	0.72360	0	1215	2555.353	47.54725	0.6272	0	1030	61	5.9223301	0.5514	0	170	16.504854	0.7844	1	58	954	6.0796646	0.8947	1	83	953.5886	8.703963	0.7220	0	0	2555	0.000000	0.2111	0	3.0212	0.6565	1	3.73230	0.9680	2	0.6272	0.6195	0	3.1636	0.7893	2	10.54430	0.8433	5

# Find tracts with divisional data in both 2010 and 2020
svi_national <- merge_svi_data(svi_2010_national, svi_2020_national)
svi_national %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	FIPS_st	FIPS_county	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_10	E_HU_10	E_HH_10	E_POV150_10	ET_POVSTATUS_10	EP_POV150_10	EPL_POV150_10	F_POV150_10	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	F_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	F_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	F_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	F_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	F_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	F_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	F_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	F_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	F_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	F_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	F_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	F_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	F_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	F_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	F_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	F_NOVEH_10	E_GROUPQ_10	ET_HHTYPE_10	EP_GROUPQ_10	EPL_GROUPQ_10	F_GROUPQ_10	SPL_THEME1_10	RPL_THEME1_10	F_THEME1_10	SPL_THEME2_10	RPL_THEME2_10	F_THEME2_10	SPL_THEME3_10	RPL_THEME3_10	F_THEME3_10	SPL_THEME4_10	RPL_THEME4_10	F_THEME4_10	SPL_THEMES_10	RPL_THEMES_10	F_TOTAL_10	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	F_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	F_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	F_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	F_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	F_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	F_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	F_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	F_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	F_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	F_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	F_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	F_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	F_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	F_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	F_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	F_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	F_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20	F_GROUPQ_20	SPL_THEME1_20	RPL_THEME1_20	F_THEME1_20	SPL_THEME2_20	RPL_THEME2_20	F_THEME2_20	SPL_THEME3_20	RPL_THEME3_20	F_THEME3_20	SPL_THEME4_20	RPL_THEME4_20	F_THEME4_20	SPL_THEMES_20	RPL_THEMES_20	F_TOTAL_20
01001020100	01	001	020100	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	1809	771	696	297	1809	16.41791	0.3871	0	36	889	4.049494	0.1790	0	127	598	21.23746	0.20770	0	47	98	47.95918	0.5767	0	174	696	25.00000	0.18790	0	196	1242	15.780998	0.6093	0	186	1759	10.574190	0.3790	0	222	12.271973	0.4876	0	445	24.59923	0.5473	0	298	1335	22.32210	0.8454	1	27	545	4.954128	0.09275	0	36	1705	2.1114370	0.59040	0	385	1809	21.282477	0.4524	0	771	0	0.0000000	0.1224	0	92	11.9325551	0.8005	1	0	696	0.0000000	0.1238	0	50	696	7.183908	0.6134	0	0	1809	0	0.364	0	1.74230	0.28200	0	2.56345	0.5296	1	0.4524	0.4482	0	2.0241	0.2519	1	6.78225	0.3278	2	1941	710	693	352	1941	18.13498	0.4630	0	18	852	2.112676	0.15070	0	81	507	15.976331	0.26320	0	63	186	33.87097	0.2913	0	144	693	20.77922	0.2230	0	187	1309	14.285714	0.6928	0	187	1941	9.634209	0.6617	0	295	15.19835	0.4601	0	415	21.38073	0.4681	0	391	1526	25.62254	0.9011	1	58	555	10.45045	0.3451	0	0	1843	0.0000000	0.09479	0	437	1941	22.51417	0.3902	0	710	0	0.0000000	0.1079	0	88	12.3943662	0.8263	1	0	693	0.0000000	0.09796	0	10	693	1.443001	0.1643	0	0	1941	0.000000	0.1831	0	2.19120	0.4084	0	2.26919	0.3503	1	0.3902	0.3869	0	1.37956	0.07216	1	6.23015	0.2314	2
01001020200	01	001	020200	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	2020	816	730	495	1992	24.84940	0.5954	0	68	834	8.153477	0.5754	0	49	439	11.16173	0.02067	0	105	291	36.08247	0.3019	0	154	730	21.09589	0.09312	0	339	1265	26.798419	0.8392	1	313	2012	15.556660	0.6000	0	204	10.099010	0.3419	0	597	29.55446	0.8192	1	359	1515	23.69637	0.8791	1	132	456	28.947368	0.83510	1	15	1890	0.7936508	0.40130	0	1243	2020	61.534653	0.7781	1	816	0	0.0000000	0.1224	0	34	4.1666667	0.6664	0	13	730	1.7808219	0.5406	0	115	730	15.753425	0.8382	1	0	2020	0	0.364	0	2.70312	0.56650	1	3.27660	0.8614	3	0.7781	0.7709	1	2.5316	0.5047	1	9.28942	0.6832	6	1757	720	573	384	1511	25.41363	0.6427	0	29	717	4.044630	0.41320	0	33	392	8.418367	0.03542	0	116	181	64.08840	0.9086	1	149	573	26.00349	0.4041	0	139	1313	10.586443	0.5601	0	91	1533	5.936073	0.4343	0	284	16.16392	0.5169	0	325	18.49744	0.2851	0	164	1208	13.57616	0.4127	0	42	359	11.69916	0.3998	0	0	1651	0.0000000	0.09479	0	1116	1757	63.51736	0.7591	1	720	3	0.4166667	0.2470	0	5	0.6944444	0.5106	0	9	573	1.5706806	0.46880	0	57	573	9.947644	0.7317	0	212	1757	12.066022	0.9549	1	2.45440	0.4888	0	1.70929	0.1025	0	0.7591	0.7527	1	2.91300	0.68620	1	7.83579	0.4802	2
01001020300	01	001	020300	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	3543	1403	1287	656	3533	18.56779	0.4443	0	93	1552	5.992268	0.3724	0	273	957	28.52665	0.45780	0	178	330	53.93939	0.7152	0	451	1287	35.04274	0.49930	0	346	2260	15.309734	0.5950	0	252	3102	8.123791	0.2596	0	487	13.745413	0.5868	0	998	28.16822	0.7606	1	371	2224	16.68165	0.6266	0	126	913	13.800657	0.46350	0	0	3365	0.0000000	0.09298	0	637	3543	17.979114	0.4049	0	1403	10	0.7127584	0.3015	0	2	0.1425517	0.4407	0	0	1287	0.0000000	0.1238	0	101	1287	7.847708	0.6443	0	0	3543	0	0.364	0	2.17060	0.41010	0	2.53048	0.5116	1	0.4049	0.4011	0	1.8743	0.1942	0	6.98028	0.3576	1	3694	1464	1351	842	3694	22.79372	0.5833	0	53	1994	2.657974	0.22050	0	117	967	12.099276	0.11370	0	147	384	38.28125	0.3856	0	264	1351	19.54108	0.1827	0	317	2477	12.797739	0.6460	0	127	3673	3.457664	0.2308	0	464	12.56091	0.3088	0	929	25.14889	0.7080	0	473	2744	17.23761	0.6211	0	263	975	26.97436	0.8234	1	128	3586	3.5694367	0.70770	0	1331	3694	36.03140	0.5515	0	1464	26	1.7759563	0.3675	0	14	0.9562842	0.5389	0	35	1351	2.5906736	0.60550	0	42	1351	3.108808	0.3415	0	0	3694	0.000000	0.1831	0	1.86330	0.3063	0	3.16900	0.8380	1	0.5515	0.5468	0	2.03650	0.26830	0	7.62030	0.4460	1
01001020400	01	001	020400	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	4840	1957	1839	501	4840	10.35124	0.2177	0	101	2129	4.744011	0.2447	0	310	1549	20.01291	0.17080	0	89	290	30.68966	0.2044	0	399	1839	21.69657	0.10540	0	274	3280	8.353658	0.3205	0	399	4293	9.294200	0.3171	0	955	19.731405	0.8643	1	1195	24.69008	0.5530	0	625	3328	18.78005	0.7233	0	152	1374	11.062591	0.34710	0	10	4537	0.2204100	0.22560	0	297	4840	6.136364	0.1647	0	1957	33	1.6862545	0.3843	0	25	1.2774655	0.5516	0	14	1839	0.7612833	0.3564	0	19	1839	1.033170	0.1127	0	0	4840	0	0.364	0	1.20540	0.13470	0	2.71330	0.6129	1	0.1647	0.1632	0	1.7690	0.1591	0	5.85240	0.1954	1	3539	1741	1636	503	3539	14.21305	0.3472	0	39	1658	2.352232	0.17990	0	219	1290	16.976744	0.30880	0	74	346	21.38728	0.1037	0	293	1636	17.90954	0.1333	0	173	2775	6.234234	0.3351	0	169	3529	4.788892	0.3448	0	969	27.38062	0.9225	1	510	14.41085	0.1208	0	670	3019	22.19278	0.8194	1	148	1137	13.01671	0.4541	0	89	3409	2.6107363	0.64690	0	454	3539	12.82848	0.2364	0	1741	143	8.2136703	0.6028	0	0	0.0000000	0.2186	0	10	1636	0.6112469	0.28340	0	72	1636	4.400978	0.4538	0	0	3539	0.000000	0.1831	0	1.34030	0.1575	0	2.96370	0.7496	2	0.2364	0.2344	0	1.74170	0.16270	0	6.28210	0.2389	2
01001020500	01	001	020500	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	9938	3969	3741	1096	9938	11.02838	0.2364	0	188	4937	3.807981	0.1577	0	426	2406	17.70574	0.11050	0	528	1335	39.55056	0.3753	0	954	3741	25.50120	0.20140	0	293	5983	4.897209	0.1655	0	740	10110	7.319486	0.2211	0	837	8.422218	0.2408	0	3012	30.30791	0.8455	1	759	7155	10.60797	0.2668	0	476	2529	18.821669	0.63540	0	78	9297	0.8389803	0.41110	0	1970	9938	19.822902	0.4330	0	3969	306	7.7097506	0.6153	0	0	0.0000000	0.2198	0	7	3741	0.1871157	0.2535	0	223	3741	5.960973	0.5483	0	0	9938	0	0.364	0	0.98210	0.08468	0	2.39960	0.4381	1	0.4330	0.4290	0	2.0009	0.2430	0	5.81560	0.1905	1	10674	4504	4424	1626	10509	15.47245	0.3851	0	81	5048	1.604596	0.09431	0	321	2299	13.962592	0.17970	0	711	2125	33.45882	0.2836	0	1032	4424	23.32731	0.3109	0	531	6816	7.790493	0.4251	0	301	10046	2.996217	0.1894	0	1613	15.11149	0.4553	0	2765	25.90407	0.7494	0	1124	7281	15.43744	0.5253	0	342	2912	11.74451	0.4019	0	52	9920	0.5241935	0.35230	0	2603	10674	24.38636	0.4160	0	4504	703	15.6083481	0.7378	0	29	0.6438721	0.5037	0	37	4424	0.8363472	0.33420	0	207	4424	4.679023	0.4754	0	176	10674	1.648866	0.7598	1	1.40481	0.1743	0	2.48420	0.4802	0	0.4160	0.4125	0	2.81090	0.63730	1	7.11591	0.3654	1
01001020600	01	001	020600	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	3402	1456	1308	735	3402	21.60494	0.5199	0	134	1720	7.790698	0.5436	0	242	1032	23.44961	0.28010	0	62	276	22.46377	0.1035	0	304	1308	23.24159	0.14070	0	301	2151	13.993491	0.5510	0	355	3445	10.304790	0.3656	0	386	11.346267	0.4232	0	931	27.36626	0.7200	0	440	2439	18.04018	0.6912	0	143	924	15.476190	0.52900	0	4	3254	0.1229256	0.19840	0	723	3402	21.252205	0.4519	0	1456	18	1.2362637	0.3507	0	433	29.7390110	0.9468	1	16	1308	1.2232416	0.4493	0	28	1308	2.140673	0.2298	0	0	3402	0	0.364	0	2.12080	0.39510	0	2.56180	0.5288	0	0.4519	0.4477	0	2.3406	0.4048	1	7.47510	0.4314	1	3536	1464	1330	1279	3523	36.30429	0.8215	1	34	1223	2.780049	0.23780	0	321	1111	28.892889	0.75870	1	67	219	30.59361	0.2305	0	388	1330	29.17293	0.5075	0	306	2380	12.857143	0.6480	0	415	3496	11.870709	0.7535	1	547	15.46946	0.4760	0	982	27.77149	0.8327	1	729	2514	28.99761	0.9488	1	95	880	10.79545	0.3601	0	0	3394	0.0000000	0.09479	0	985	3536	27.85633	0.4608	0	1464	0	0.0000000	0.1079	0	364	24.8633880	0.9300	1	0	1330	0.0000000	0.09796	0	17	1330	1.278196	0.1463	0	0	3536	0.000000	0.1831	0	2.96830	0.6434	2	2.71239	0.6156	2	0.4608	0.4569	0	1.46526	0.08976	1	7.60675	0.4440	5

NMTC Data Wrangling

orig_nmtc_df <- orig_nmtc %>% 
  rename("GEOID10" = "2010 Census Tract Number FIPS code. GEOID",
         "nmtc_eligibility_orig" = "Does Census Tract Qualify For NMTC Low-Income Community (LIC) on Poverty or Income Criteria?")

orig_nmtc_df %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID10	OMB Metro/Non-metro Designation, July 2015 (OMB 15-01)	nmtc_eligibility_orig	Census Tract Poverty Rate % (2011-2015 ACS)	Does Census Tract Qualify on Poverty Criteria\>=20%?	Census Tract Percent of Benchmarked Median Family Income (%) 2011-2015 ACS	Does Census Tract Qualify on Median Family Income Criteria\<=80%?	Census Tract Unemployment Rate (%) 2011-2015	County Code	State Abbreviation	State Name	County Name	Census Tract Unemployment to National Unemployment Ratio	Is Tract Unemployment to National Unemployment Ratio \>1.5?	Population for whom poverty status is determined 2011-2015 ACS
01001020100	Metropolitan	No	8.1	No	122.930646878856	No	5.4	01001	AL	Alabama	Autauga	0.6506024096385542	No	1948
01001020200	Metropolitan	Yes	25.5	Yes	82.402258244451573	No	13.3	01001	AL	Alabama	Autauga	1.6024096385542168	Yes	1983
01001020300	Metropolitan	No	12.7	No	94.261422220719723	No	6.2	01001	AL	Alabama	Autauga	0.74698795180722888	No	2968
01001020400	Metropolitan	No	2.1	No	116.82358310373388	No	10.8	01001	AL	Alabama	Autauga	1.3012048192771084	No	4423
01001020500	Metropolitan	No	11.4	No	127.74293876033198	No	4.2	01001	AL	Alabama	Autauga	0.50602409638554213	No	10563
01001020600	Metropolitan	No	14.4	No	111.98255607579317	No	10.9	01001	AL	Alabama	Autauga	1.3132530120481927	No	3851

high_migration_nmtc_df <- high_migration_nmtc %>% rename("GEOID10" = "2010 Census Tract Number FIPS code GEOID")

high_migration_nmtc_df %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID10	20-year County population loss 1990-2010 census	% Median Family Income (MFI) / Area Income 2011-2015 (between 80%-85% MFI)
01087231601	-0.1394416	82.06754
05039970300	-0.1558144	84.78236
08017960600	-0.2340426	84.36239
17067953800	-0.1061620	80.36788
17067954200	-0.1061620	84.48551
17067954300	-0.1061620	84.44497

# Add column to label tracts as high migration
high_migration_nmtc_df <- high_migration_nmtc_df %>% mutate(high_migration = "Yes")

# Join to original column
orig_nmtc_df <- left_join(orig_nmtc_df, high_migration_nmtc_df, join_by(GEOID10 == GEOID10))

# Update eligibility column with coalesce()
nmtc_df <- orig_nmtc_df %>% 
  mutate(nmtc_eligibility = coalesce(high_migration, nmtc_eligibility_orig))

nmtc_df %>% filter(GEOID10 == "01087231601") %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID10	OMB Metro/Non-metro Designation, July 2015 (OMB 15-01)	nmtc_eligibility_orig	Census Tract Poverty Rate % (2011-2015 ACS)	Does Census Tract Qualify on Poverty Criteria\>=20%?	Census Tract Percent of Benchmarked Median Family Income (%) 2011-2015 ACS	Does Census Tract Qualify on Median Family Income Criteria\<=80%?	Census Tract Unemployment Rate (%) 2011-2015	County Code	State Abbreviation	State Name	County Name	Census Tract Unemployment to National Unemployment Ratio	Is Tract Unemployment to National Unemployment Ratio \>1.5?	Population for whom poverty status is determined 2011-2015 ACS	20-year County population loss 1990-2010 census	% Median Family Income (MFI) / Area Income 2011-2015 (between 80%-85% MFI)	high_migration	nmtc_eligibility
01087231601	Non-Metropolitan	No	16.2	No	82.067544858242542	No	11.3	01087	AL	Alabama	Macon	1.3614457831325302	No	888	-0.1394416	82.06754	Yes	Yes

nmtc_eligible <- nmtc_df %>% 
  select(GEOID10, nmtc_eligibility, `County Code`, `County Name`, `State Abbreviation`, `State Name`) %>% 
  filter(tolower(nmtc_eligibility) == "yes")

nmtc_eligible %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID10	nmtc_eligibility	County Code	County Name	State Abbreviation	State Name
01001020200	Yes	01001	Autauga	AL	Alabama
01001020700	Yes	01001	Autauga	AL	Alabama
01001021100	Yes	01001	Autauga	AL	Alabama
01003010200	Yes	01003	Baldwin	AL	Alabama
01003010500	Yes	01003	Baldwin	AL	Alabama
01003010600	Yes	01003	Baldwin	AL	Alabama

# Save just tract ID and eligibility
nmtc_eligible_df <- nmtc_eligible %>% select(GEOID10, nmtc_eligibility)
nmtc_eligible_df %>% head()

## # A tibble: 6 × 2
##   GEOID10     nmtc_eligibility
##   <chr>       <chr>           
## 1 01001020200 Yes             
## 2 01001020700 Yes             
## 3 01001021100 Yes             
## 4 01003010200 Yes             
## 5 01003010500 Yes             
## 6 01003010600 Yes

nmtc_awards <- nmtc_awards_data %>% 
  mutate(`2010 Census Tract` = str_pad(`2010 Census Tract`, 11, "left", pad=0)) %>%
  rename("GEOID10" =`2010 Census Tract`)

nmtc_awards %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

Project ID	GEOID10	Metro/Non-Metro, 2010 Census	Origination Year	Community Development Entity (CDE) Name	Project QLICI Amount	Estimated Total Project Cost	City	State	Zip Code	QALICB Type	Multi-CDE	Multi-Tract Project
AK0001	02070000100	Non-Metropolitan	2008	Alaska Growth Capital BIDCO, Inc.	300000	300000	Aleknagik	Alaska	99555	NRE	NO	NO
AK0002	02020001000	Metropolitan	2006	Alaska Growth Capital BIDCO, Inc.	1008750	1345000	Anchorage	Alaska	99501	NRE	NO	NO
AK0003	02020000600	Metropolitan	2006	HEDC New Markets, Inc	5061506	8694457	Anchorage	Alaska	99508	NRE	NO	NO
AK0004	02020001000	Metropolitan	2006	Alaska Growth Capital BIDCO, Inc.	187500	250000	Anchorage	Alaska	99501	NRE	NO	NO
AK0006	02020001802	Metropolitan	2006	Alaska Growth Capital BIDCO, Inc.	750000	1180000	Anchorage	Alaska	99507	NRE	NO	NO
AK0007	02020001900	Metropolitan	2006	Alaska Growth Capital BIDCO, Inc.	127500	150000	Anchorage	Alaska	99503	NRE	NO	NO

# Create character zip_code column:
nmtc_awards <- nmtc_awards %>% 
  mutate(zip_code = str_pad(`Zip Code`, 5, "left", pad=0))

nmtc_awards %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

Project ID	GEOID10	Metro/Non-Metro, 2010 Census	Origination Year	Community Development Entity (CDE) Name	Project QLICI Amount	Estimated Total Project Cost	City	State	Zip Code	QALICB Type	Multi-CDE	Multi-Tract Project	zip_code
AK0001	02070000100	Non-Metropolitan	2008	Alaska Growth Capital BIDCO, Inc.	300000	300000	Aleknagik	Alaska	99555	NRE	NO	NO	99555
AK0002	02020001000	Metropolitan	2006	Alaska Growth Capital BIDCO, Inc.	1008750	1345000	Anchorage	Alaska	99501	NRE	NO	NO	99501
AK0003	02020000600	Metropolitan	2006	HEDC New Markets, Inc	5061506	8694457	Anchorage	Alaska	99508	NRE	NO	NO	99508
AK0004	02020001000	Metropolitan	2006	Alaska Growth Capital BIDCO, Inc.	187500	250000	Anchorage	Alaska	99501	NRE	NO	NO	99501
AK0006	02020001802	Metropolitan	2006	Alaska Growth Capital BIDCO, Inc.	750000	1180000	Anchorage	Alaska	99507	NRE	NO	NO	99507
AK0007	02020001900	Metropolitan	2006	Alaska Growth Capital BIDCO, Inc.	127500	150000	Anchorage	Alaska	99503	NRE	NO	NO	99503

# View tracts
nmtc_awards_pre2010 <- nmtc_awards %>% 
  filter(`Origination Year` <= 2010) %>% 
  count(GEOID10) %>% 
  rename("pre10_nmtc_project_cnt" = "n")

nmtc_awards_dollars_pre2010 <- nmtc_awards %>% 
  filter(`Origination Year` <= 2010) %>% 
  group_by(GEOID10) %>% 
  summarise(pre10_nmtc_dollars = sum(`Project QLICI Amount`, na.rm = TRUE))

nmtc_awards_pre2010 <- left_join(nmtc_awards_pre2010, 
                                 nmtc_awards_dollars_pre2010, 
                                 join_by(GEOID10 == GEOID10))

nmtc_awards_pre2010$pre10_nmtc_dollars_formatted <- scales::dollar_format()(nmtc_awards_pre2010$pre10_nmtc_dollars)

nmtc_awards_pre2010 %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID10	pre10_nmtc_project_cnt	pre10_nmtc_dollars	pre10_nmtc_dollars_formatted
01059973500	1	5000000	\$5,000,000
01069041400	1	2500000	\$2,500,000
01073001902	1	14400000	\$14,400,000
01073002700	1	1000000	\$1,000,000
01073004200	1	5908129	\$5,908,129
01073004500	3	37950000	\$37,950,000

nmtc_awards_post2010 <- nmtc_awards %>% 
  filter(`Origination Year` > 2010 & `Origination Year` <= 2020) %>% 
  count(GEOID10) %>% 
  rename("post10_nmtc_project_cnt" = "n")

nmtc_awards_dollars_post2010 <- nmtc_awards %>% 
  filter(`Origination Year` > 2010 & `Origination Year` <= 2020) %>% 
  group_by(GEOID10) %>% 
  summarise(post10_nmtc_dollars = sum(`Project QLICI Amount`, na.rm = TRUE))

nmtc_awards_post2010 <- left_join(nmtc_awards_post2010, 
                                  nmtc_awards_dollars_post2010, 
                                  join_by(GEOID10 == GEOID10))

nmtc_awards_post2010$post10_nmtc_dollars_formatted <- scales::dollar_format()(nmtc_awards_post2010$post10_nmtc_dollars)

nmtc_awards_post2010 %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID10	post10_nmtc_project_cnt	post10_nmtc_dollars	post10_nmtc_dollars_formatted
0.	3	24200000	\$24,200,000
01003010200	1	408000	\$408,000
01003010300	1	9880000	\$9,880,000
01003010600	1	8000000	\$8,000,000
01003010904	1	22460000	\$22,460,000
01003011501	6	37147460	\$37,147,460

Join Divisional & National 2010 Eligible Data

# Divisional data
svi_divisional_nmtc_eligible <- left_join(svi_divisional, nmtc_eligible_df, join_by("GEOID_2010_trt" == "GEOID10")) %>% filter(tolower(nmtc_eligibility) == "yes")

svi_divisional_nmtc_eligible %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	FIPS_st	FIPS_county	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_10	E_HU_10	E_HH_10	E_POV150_10	ET_POVSTATUS_10	EP_POV150_10	EPL_POV150_10	F_POV150_10	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	F_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	F_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	F_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	F_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	F_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	F_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	F_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	F_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	F_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	F_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	F_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	F_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	F_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	F_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	F_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	F_NOVEH_10	E_GROUPQ_10	ET_HHTYPE_10	EP_GROUPQ_10	EPL_GROUPQ_10	F_GROUPQ_10	SPL_THEME1_10	RPL_THEME1_10	F_THEME1_10	SPL_THEME2_10	RPL_THEME2_10	F_THEME2_10	SPL_THEME3_10	RPL_THEME3_10	F_THEME3_10	SPL_THEME4_10	RPL_THEME4_10	F_THEME4_10	SPL_THEMES_10	RPL_THEMES_10	F_TOTAL_10	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	F_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	F_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	F_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	F_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	F_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	F_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	F_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	F_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	F_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	F_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	F_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	F_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	F_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	F_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	F_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	F_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	F_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20	F_GROUPQ_20	SPL_THEME1_20	RPL_THEME1_20	F_THEME1_20	SPL_THEME2_20	RPL_THEME2_20	F_THEME2_20	SPL_THEME3_20	RPL_THEME3_20	F_THEME3_20	SPL_THEME4_20	RPL_THEME4_20	F_THEME4_20	SPL_THEMES_20	RPL_THEMES_20	F_TOTAL_20	nmtc_eligibility
10001040201	10	001	040201	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	5208	1953	1809	850	5183	16.399769	0.3672	0	147	2550	5.764706	0.3364	0	385	1323	29.10053	0.45810	0	222	486	45.67901	0.49650	0	607	1809	33.55445	0.4431	0	459	3090	14.8543689	0.5386	0	435	5283	8.233958	0.19610	0	454	8.717358	0.256100	0	1588	30.491551	0.8927	1	537	3716	14.451023	0.4708	0	417	1343	31.04989	0.8599	1	69	4835	1.427094	0.5240	0	1881	5208	36.11751	0.5689	0	1953	87	4.454685	0.5392	0	148	7.578085	0.6495	0	39	1809	2.155887	0.6471	0	121	1809	6.688778	0.6124	0	0	5208	0.0000000	0.3814	0	1.88140	0.3053	0	3.003500	0.7667	2	0.5689	0.5614	0	2.8296	0.6516	0	8.283400	0.5452	2	4770	1906	1732	755	4692	16.09122	0.3758	0	92	2500	3.680000	0.3633	0	197	1184	16.638513	0.280400	0	235	548	42.88321	0.4609	0	432	1732	24.94226	0.3622	0	251	3100	8.096774	0.4085	0	228	4770	4.779874	0.21160	0	549	11.509434	0.23290	0	1352	28.343816	0.88650	1	490	3418.125	14.335346	0.4309	0	328	1263.2064	25.965669	0.8111	1	0	4526	0.0000000	0.09987	0	1875	4769.908	39.30893	0.5372	0	1906	72	3.7775446	0.4876	0	128	6.7156348	0.6610	0	10	1732	0.5773672	0.3165	0	32	1731.7111	1.847883	0.2303	0	0	4770	0.0000000	0.2111	0	1.72140	0.2531	0	2.46127	0.45940	2	0.5372	0.5306	0	1.9065	0.2183	0	6.62637	0.2829	2	Yes
10001040502	10	001	040502	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	2087	921	921	192	2087	9.199808	0.1738	0	35	722	4.847645	0.2495	0	281	700	40.14286	0.78190	1	64	221	28.95928	0.17110	0	345	921	37.45928	0.5710	0	284	1546	18.3699871	0.6484	0	119	2121	5.610561	0.10710	0	518	24.820316	0.906800	1	480	22.999521	0.4910	0	328	1527	21.480026	0.7959	1	173	680	25.44118	0.7769	1	100	1998	5.005005	0.7960	1	560	2087	26.83277	0.4524	0	921	0	0.000000	0.1428	0	273	29.641694	0.8785	1	0	921	0.000000	0.1488	0	30	921	3.257329	0.3600	0	0	2087	0.0000000	0.3814	0	1.74980	0.2670	0	3.766600	0.9666	4	0.4524	0.4464	0	1.9115	0.2071	1	7.880300	0.4785	5	2555	1030	954	565	2555	22.11350	0.5385	0	135	1154	11.698440	0.9175	1	144	691	20.839363	0.486500	0	168	262	64.12214	0.8894	1	312	953	32.73872	0.6259	0	192	1782	10.774411	0.5377	0	198	2519	7.860262	0.40160	0	519	20.313112	0.68510	0	664	25.988258	0.79390	1	341	1854.295	18.389741	0.6427	0	195	614.6519	31.725274	0.8870	1	75	2351	3.1901319	0.72360	0	1215	2555.353	47.54725	0.6272	0	1030	61	5.9223301	0.5514	0	170	16.5048544	0.7844	1	58	954	6.0796646	0.8947	1	83	953.5886	8.703963	0.7220	0	0	2555	0.0000000	0.2111	0	3.02120	0.6565	1	3.73230	0.96800	2	0.6272	0.6195	0	3.1636	0.7893	2	10.54430	0.8433	5	Yes
10001040900	10	001	040900	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	2363	1205	1007	526	1741	30.212522	0.7006	0	171	1089	15.702479	0.9032	1	98	362	27.07182	0.37770	0	258	645	40.00000	0.36590	0	356	1007	35.35253	0.5041	0	248	1416	17.5141243	0.6235	0	118	2479	4.759984	0.08169	0	509	21.540415	0.863300	1	168	7.109606	0.0386	0	428	1427	29.992992	0.9611	1	44	387	11.36951	0.3400	0	50	2349	2.128565	0.6157	0	727	2363	30.76598	0.5048	0	1205	378	31.369295	0.8688	1	0	0.000000	0.1809	0	0	1007	0.000000	0.1488	0	256	1007	25.422046	0.9457	1	622	2363	26.3224714	0.9741	1	2.81309	0.5851	1	2.818700	0.6717	2	0.5048	0.4981	0	3.1183	0.7840	3	9.254890	0.6833	6	2373	1114	1028	574	1679	34.18702	0.7904	1	19	1034	1.837524	0.1211	0	26	313	8.306709	0.029920	0	335	715	46.85315	0.5540	0	361	1028	35.11673	0.6908	0	224	1292	17.337461	0.7807	1	78	2250	3.466667	0.13250	0	501	21.112516	0.71690	0	208	8.765276	0.05851	0	391	1505.000	25.980066	0.9018	1	29	220.0000	13.181818	0.4476	0	7	2268	0.3086420	0.28740	0	974	2373.000	41.04509	0.5571	0	1114	476	42.7289048	0.9104	1	0	0.0000000	0.1800	0	5	1028	0.4863813	0.2927	0	248	1028.0000	24.124514	0.9466	1	678	2373	28.5714286	0.9778	1	2.51550	0.4976	2	2.41221	0.42860	1	0.5571	0.5503	0	3.3075	0.8411	3	8.79231	0.6264	6	Yes
10001041000	10	001	041000	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	5577	2570	2369	1291	5577	23.148646	0.5435	0	144	2702	5.329386	0.2938	0	468	1312	35.67073	0.67500	0	135	1057	12.77200	0.04274	0	603	2369	25.45378	0.1769	0	759	3504	21.6609589	0.7311	0	655	5999	10.918486	0.29800	0	594	10.650888	0.371200	0	1487	26.663080	0.7247	0	683	4587	14.889906	0.4947	0	364	1466	24.82947	0.7646	1	188	5105	3.682664	0.7342	0	3384	5577	60.67778	0.7785	1	2570	479	18.638132	0.7741	1	567	22.062257	0.8159	1	91	2369	3.841283	0.8132	1	221	2369	9.328831	0.7266	0	9	5577	0.1613771	0.7632	1	2.04330	0.3511	0	3.089400	0.8041	1	0.7785	0.7682	1	3.8930	0.9705	4	9.804200	0.7522	6	6719	3107	2804	2006	6656	30.13822	0.7207	0	436	3058	14.257685	0.9556	1	299	1387	21.557318	0.521400	0	583	1417	41.14326	0.4216	0	882	2804	31.45506	0.5861	0	953	4915	19.389624	0.8333	1	509	6603	7.708617	0.39290	0	1221	18.172347	0.58640	0	1389	20.672719	0.47220	0	1393	5214.000	26.716532	0.9150	1	661	1752.0000	37.728310	0.9336	1	340	6411	5.3033848	0.82180	1	4068	6719.048	60.54429	0.7409	0	3107	469	15.0949469	0.7136	0	586	18.8606373	0.8099	1	54	2804	1.9258203	0.5840	0	253	2804.0000	9.022825	0.7342	0	70	6719	1.0418217	0.7345	0	3.48860	0.7870	2	3.72900	0.96760	3	0.7409	0.7319	0	3.5762	0.9178	1	11.53470	0.9313	6	Yes
10001041100	10	001	041100	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	2957	800	738	499	2555	19.530333	0.4511	0	44	845	5.207101	0.2813	0	0	8	0.00000	0.00257	0	395	730	54.10959	0.69280	0	395	738	53.52304	0.9155	1	11	1118	0.9838998	0.0213	0	65	2559	2.540055	0.02694	0	0	0.000000	0.003549	0	1198	40.514035	0.9944	1	117	1192	9.815436	0.2221	0	133	693	19.19192	0.6322	0	42	2551	1.646413	0.5567	0	720	2957	24.34900	0.4177	0	800	0	0.000000	0.1428	0	0	0.000000	0.1809	0	0	738	0.000000	0.1488	0	10	738	1.355014	0.1640	0	402	2957	13.5948597	0.9492	1	1.69614	0.2527	1	2.408949	0.4377	1	0.4177	0.4122	0	1.5857	0.1097	1	6.108489	0.2207	3	3881	1350	1322	1031	3618	28.49641	0.6874	0	58	877	6.613455	0.6891	0	0	3	0.000000	0.002567	0	758	1319	57.46778	0.7890	1	758	1322	57.33737	0.9772	1	41	1801	2.276513	0.0857	0	33	2762	1.194786	0.02847	0	64	1.649059	0.01027	0	1291	33.264623	0.97230	1	129	1470.497	8.772546	0.1457	0	113	1156.7160	9.769036	0.3067	0	4	3373	0.1185888	0.22310	0	1672	3881.437	43.07683	0.5797	0	1350	9	0.6666667	0.3180	0	10	0.7407407	0.4343	0	1	1322	0.0756430	0.2359	0	18	1321.8616	1.361716	0.1744	0	263	3881	6.7766040	0.9251	1	2.46787	0.4818	1	1.65807	0.08121	1	0.5797	0.5726	0	2.0877	0.2866	1	6.79334	0.3059	3	Yes
10001041200	10	001	041200	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	4723	1880	1742	778	4710	16.518047	0.3700	0	193	2264	8.524735	0.5848	0	481	1168	41.18151	0.80360	1	257	574	44.77352	0.47360	0	738	1742	42.36510	0.7087	0	573	3071	18.6584175	0.6563	0	471	3937	11.963424	0.34310	0	600	12.703790	0.504200	0	1257	26.614440	0.7223	0	583	3036	19.202899	0.7085	0	240	1183	20.28740	0.6645	0	190	4378	4.339881	0.7670	1	2933	4723	62.10036	0.7871	1	1880	327	17.393617	0.7606	1	353	18.776596	0.7841	1	41	1742	2.353616	0.6746	0	92	1742	5.281286	0.5288	0	0	4723	0.0000000	0.3814	0	2.66290	0.5376	0	3.366500	0.8969	1	0.7871	0.7767	1	3.1295	0.7889	2	9.946000	0.7681	4	4135	1851	1712	870	4076	21.34446	0.5206	0	180	1879	9.579564	0.8567	1	384	1230	31.219512	0.844800	1	226	482	46.88797	0.5550	0	610	1712	35.63084	0.7027	0	286	2785	10.269300	0.5146	0	204	4124	4.946654	0.22200	0	755	18.258767	0.59080	0	1067	25.804111	0.78410	1	571	3057.653	18.674456	0.6593	0	375	1138.2043	32.946633	0.8989	1	26	3953	0.6577283	0.38940	0	2299	4134.641	55.60337	0.6982	0	1851	175	9.4543490	0.6291	0	438	23.6628849	0.8514	1	5	1712	0.2920561	0.2552	0	143	1712.2269	8.351697	0.7088	0	20	4135	0.4836759	0.6479	0	2.81660	0.5950	1	3.32250	0.89620	2	0.6982	0.6897	0	3.0924	0.7625	1	9.92970	0.7729	4	Yes

# National data
svi_national_nmtc_eligible <- left_join(svi_national, nmtc_eligible_df, join_by("GEOID_2010_trt" == "GEOID10")) %>% filter(tolower(nmtc_eligibility) == "yes")

svi_national_nmtc_eligible %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	FIPS_st	FIPS_county	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_10	E_HU_10	E_HH_10	E_POV150_10	ET_POVSTATUS_10	EP_POV150_10	EPL_POV150_10	F_POV150_10	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	F_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	F_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	F_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	F_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	F_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	F_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	F_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	F_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	F_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	F_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	F_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	F_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	F_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	F_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	F_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	F_NOVEH_10	E_GROUPQ_10	ET_HHTYPE_10	EP_GROUPQ_10	EPL_GROUPQ_10	F_GROUPQ_10	SPL_THEME1_10	RPL_THEME1_10	F_THEME1_10	SPL_THEME2_10	RPL_THEME2_10	F_THEME2_10	SPL_THEME3_10	RPL_THEME3_10	F_THEME3_10	SPL_THEME4_10	RPL_THEME4_10	F_THEME4_10	SPL_THEMES_10	RPL_THEMES_10	F_TOTAL_10	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	F_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	F_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	F_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	F_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	F_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	F_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	F_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	F_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	F_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	F_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	F_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	F_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	F_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	F_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	F_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	F_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	F_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20	F_GROUPQ_20	SPL_THEME1_20	RPL_THEME1_20	F_THEME1_20	SPL_THEME2_20	RPL_THEME2_20	F_THEME2_20	SPL_THEME3_20	RPL_THEME3_20	F_THEME3_20	SPL_THEME4_20	RPL_THEME4_20	F_THEME4_20	SPL_THEMES_20	RPL_THEMES_20	F_TOTAL_20	nmtc_eligibility
01001020200	01	001	020200	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	2020	816	730	495	1992	24.84940	0.5954	0	68	834	8.153477	0.57540	0	49	439	11.16173	0.02067	0	105	291	36.08247	0.30190	0	154	730	21.09589	0.09312	0	339	1265	26.79842	0.8392	1	313	2012	15.55666	0.6000	0	204	10.09901	0.3419	0	597	29.55446	0.8192	1	359	1515	23.69637	0.8791	1	132	456	28.947368	0.8351	1	15	1890	0.7936508	0.40130	0	1243	2020	61.53465	0.7781	1	816	0	0.0000000	0.1224	0	34	4.1666667	0.6664	0	13	730	1.780822	0.5406	0	115	730	15.753425	0.8382	1	0	2020	0.0000	0.3640	0	2.70312	0.5665	1	3.27660	0.8614	3	0.7781	0.7709	1	2.5316	0.5047	1	9.28942	0.6832	6	1757	720	573	384	1511	25.41363	0.6427	0	29	717	4.044630	0.4132	0	33	392	8.418367	0.03542	0	116	181	64.08840	0.9086	1	149	573	26.00349	0.40410	0	139	1313	10.58644	0.5601	0	91	1533	5.936073	0.4343	0	284	16.163916	0.5169	0	325	18.49744	0.2851	0	164	1208.000	13.57616	0.4127	0	42	359.0000	11.699164	0.3998	0	0	1651	0.0000000	0.09479	0	1116	1757.000	63.51736	0.7591	1	720	3	0.4166667	0.2470	0	5	0.6944444	0.5106	0	9	573	1.5706806	0.4688	0	57	573.000	9.947644	0.7317	0	212	1757	12.0660216	0.9549	1	2.45440	0.4888	0	1.70929	0.1025	0	0.7591	0.7527	1	2.9130	0.6862	1	7.83579	0.4802	2	Yes
01001020700	01	001	020700	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	2664	1254	1139	710	2664	26.65165	0.6328	0	29	1310	2.213741	0.05255	0	134	710	18.87324	0.13890	0	187	429	43.58974	0.47090	0	321	1139	28.18262	0.28130	0	396	1852	21.38229	0.7478	0	345	2878	11.98749	0.4459	0	389	14.60210	0.6417	0	599	22.48499	0.4007	0	510	2168	23.52399	0.8752	1	228	712	32.022472	0.8712	1	0	2480	0.0000000	0.09298	0	694	2664	26.05105	0.5138	0	1254	8	0.6379585	0.2931	0	460	36.6826156	0.9714	1	0	1139	0.000000	0.1238	0	125	1139	10.974539	0.7477	0	0	2664	0.0000	0.3640	0	2.16035	0.4069	0	2.88178	0.6997	2	0.5138	0.5090	0	2.5000	0.4882	1	8.05593	0.5185	3	3562	1313	1248	1370	3528	38.83220	0.8512	1	128	1562	8.194622	0.7935	1	168	844	19.905213	0.44510	0	237	404	58.66337	0.8359	1	405	1248	32.45192	0.60420	0	396	2211	17.91045	0.7857	1	444	3547	12.517620	0.7758	1	355	9.966311	0.1800	0	954	26.78271	0.7923	1	629	2593.000	24.25762	0.8730	1	171	797.0000	21.455458	0.7186	0	0	3211	0.0000000	0.09479	0	1009	3562.000	28.32678	0.4668	0	1313	14	1.0662605	0.3165	0	443	33.7395278	0.9663	1	73	1248	5.8493590	0.8211	1	17	1248.000	1.362180	0.1554	0	112	3562	3.1443010	0.8514	1	3.81040	0.8569	4	2.65869	0.5847	2	0.4668	0.4629	0	3.1107	0.7714	3	10.04659	0.7851	9	Yes
01001021100	01	001	021100	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	3298	1502	1323	860	3298	26.07641	0.6211	0	297	1605	18.504673	0.94340	1	250	1016	24.60630	0.32070	0	74	307	24.10423	0.11920	0	324	1323	24.48980	0.17380	0	710	2231	31.82429	0.8976	1	654	3565	18.34502	0.7018	0	411	12.46210	0.5001	0	738	22.37720	0.3934	0	936	2861	32.71583	0.9807	1	138	825	16.727273	0.5715	0	9	3155	0.2852615	0.25010	0	1979	3298	60.00606	0.7703	1	1502	14	0.9320905	0.3234	0	659	43.8748336	0.9849	1	44	1323	3.325775	0.7062	0	137	1323	10.355253	0.7313	0	0	3298	0.0000	0.3640	0	3.33770	0.7351	2	2.69580	0.6028	1	0.7703	0.7631	1	3.1098	0.7827	1	9.91360	0.7557	5	3499	1825	1462	1760	3499	50.30009	0.9396	1	42	966	4.347826	0.4539	0	426	1274	33.437991	0.85200	1	52	188	27.65957	0.1824	0	478	1462	32.69494	0.61110	0	422	2488	16.96141	0.7638	1	497	3499	14.204058	0.8246	1	853	24.378394	0.8688	1	808	23.09231	0.5829	0	908	2691.100	33.74084	0.9808	1	179	811.6985	22.052524	0.7323	0	8	3248	0.2463054	0.26220	0	1986	3498.713	56.76373	0.7175	0	1825	29	1.5890411	0.3551	0	576	31.5616438	0.9594	1	88	1462	6.0191518	0.8269	1	148	1461.993	10.123166	0.7364	0	38	3499	1.0860246	0.7013	0	3.59300	0.8073	3	3.42700	0.9156	2	0.7175	0.7114	0	3.5791	0.9216	2	11.31660	0.9150	7	Yes
01003010200	01	003	010200	AL	Alabama	Baldwin County	3	South Region	6	East South Central Division	2612	1220	1074	338	2605	12.97505	0.2907	0	44	1193	3.688181	0.14720	0	172	928	18.53448	0.13090	0	31	146	21.23288	0.09299	0	203	1074	18.90130	0.05657	0	455	1872	24.30556	0.8016	1	456	2730	16.70330	0.6445	0	401	15.35222	0.6847	0	563	21.55436	0.3406	0	410	2038	20.11776	0.7755	1	64	779	8.215661	0.2181	0	0	2510	0.0000000	0.09298	0	329	2612	12.59571	0.3113	0	1220	38	3.1147541	0.4648	0	385	31.5573770	0.9545	1	20	1074	1.862197	0.5509	0	43	1074	4.003724	0.4088	0	0	2612	0.0000	0.3640	0	1.94057	0.3398	1	2.11188	0.2802	1	0.3113	0.3084	0	2.7430	0.6129	1	7.10675	0.3771	3	2928	1312	1176	884	2928	30.19126	0.7334	0	29	1459	1.987663	0.1356	0	71	830	8.554217	0.03726	0	134	346	38.72832	0.3964	0	205	1176	17.43197	0.12010	0	294	2052	14.32749	0.6940	0	219	2925	7.487179	0.5423	0	556	18.989071	0.6705	0	699	23.87295	0.6339	0	489	2226.455	21.96317	0.8122	1	191	783.8820	24.365914	0.7799	1	0	2710	0.0000000	0.09479	0	398	2927.519	13.59513	0.2511	0	1312	13	0.9908537	0.3111	0	400	30.4878049	0.9557	1	6	1176	0.5102041	0.2590	0	81	1176.202	6.886570	0.6115	0	7	2928	0.2390710	0.4961	0	2.22540	0.4183	0	2.99129	0.7634	2	0.2511	0.2490	0	2.6334	0.5496	1	8.10119	0.5207	3	Yes
01003010500	01	003	010500	AL	Alabama	Baldwin County	3	South Region	6	East South Central Division	4230	1779	1425	498	3443	14.46413	0.3337	0	166	1625	10.215385	0.71790	0	151	1069	14.12535	0.04638	0	196	356	55.05618	0.73830	0	347	1425	24.35088	0.17010	0	707	2945	24.00679	0.7967	1	528	4001	13.19670	0.5005	0	619	14.63357	0.6436	0	790	18.67612	0.1937	0	536	3096	17.31266	0.6572	0	165	920	17.934783	0.6102	0	20	4021	0.4973887	0.32320	0	754	4230	17.82506	0.4023	0	1779	97	5.4525014	0.5525	0	8	0.4496908	0.4600	0	63	1425	4.421053	0.7762	1	90	1425	6.315790	0.5691	0	787	4230	18.6052	0.9649	1	2.51890	0.5121	1	2.42790	0.4539	0	0.4023	0.3986	0	3.3227	0.8628	2	8.67180	0.6054	3	5877	1975	1836	820	5244	15.63692	0.3902	0	90	2583	3.484321	0.3361	0	159	1345	11.821561	0.10530	0	139	491	28.30957	0.1924	0	298	1836	16.23094	0.09053	0	570	4248	13.41808	0.6669	0	353	5247	6.727654	0.4924	0	1109	18.870172	0.6645	0	1144	19.46571	0.3411	0	717	4102.545	17.47696	0.6332	0	103	1286.1180	8.008596	0.2341	0	0	5639	0.0000000	0.09479	0	868	5877.481	14.76823	0.2709	0	1975	26	1.3164557	0.3359	0	45	2.2784810	0.6271	0	9	1836	0.4901961	0.2540	0	116	1835.798	6.318779	0.5811	0	633	5877	10.7708014	0.9507	1	1.97613	0.3410	0	1.96769	0.1961	0	0.2709	0.2686	0	2.7488	0.6077	1	6.96352	0.3406	1	Yes
01003010600	01	003	010600	AL	Alabama	Baldwin County	3	South Region	6	East South Central Division	3724	1440	1147	1973	3724	52.98067	0.9342	1	142	1439	9.867964	0.69680	0	235	688	34.15698	0.62950	0	187	459	40.74074	0.40290	0	422	1147	36.79163	0.55150	0	497	1876	26.49254	0.8354	1	511	3661	13.95794	0.5334	0	246	6.60580	0.1481	0	1256	33.72718	0.9305	1	496	2522	19.66693	0.7587	1	274	838	32.696897	0.8779	1	32	3479	0.9198045	0.42810	0	2606	3724	69.97852	0.8184	1	1440	21	1.4583333	0.3683	0	321	22.2916667	0.9036	1	97	1147	8.456844	0.8956	1	167	1147	14.559721	0.8209	1	0	3724	0.0000	0.3640	0	3.55130	0.7859	2	3.14330	0.8145	3	0.8184	0.8108	1	3.3524	0.8725	3	10.86540	0.8550	9	4115	1534	1268	1676	3997	41.93145	0.8814	1	294	1809	16.252073	0.9674	1	341	814	41.891892	0.94320	1	204	454	44.93392	0.5438	0	545	1268	42.98107	0.83620	1	624	2425	25.73196	0.9002	1	994	4115	24.155529	0.9602	1	642	15.601458	0.4841	0	1126	27.36331	0.8175	1	568	2989.000	19.00301	0.7045	0	212	715.0000	29.650350	0.8592	1	56	3825	1.4640523	0.53120	0	2715	4115.000	65.97813	0.7732	1	1534	0	0.0000000	0.1079	0	529	34.4850065	0.9685	1	101	1268	7.9652997	0.8795	1	89	1268.000	7.018927	0.6184	0	17	4115	0.4131227	0.5707	0	4.54540	0.9754	5	3.39650	0.9081	2	0.7732	0.7667	1	3.1450	0.7858	2	11.86010	0.9520	10	Yes

# Join divisional data to nmtc_awards_pre2010, set count to 0 if no data
svi_divisional_nmtc_eligible <- 
  left_join(svi_divisional_nmtc_eligible, nmtc_awards_pre2010, join_by("GEOID_2010_trt" == "GEOID10")) %>%
  mutate(pre10_nmtc_project_cnt = if_else(is.na(pre10_nmtc_project_cnt), 0, pre10_nmtc_project_cnt)) %>%
    mutate(pre10_nmtc_dollars = if_else(is.na(pre10_nmtc_dollars), 0, pre10_nmtc_dollars)) %>%
    mutate(pre10_nmtc_dollars_formatted = if_else(is.na(pre10_nmtc_dollars_formatted), "$0", pre10_nmtc_dollars_formatted))

# View table
svi_divisional_nmtc_eligible %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	FIPS_st	FIPS_county	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_10	E_HU_10	E_HH_10	E_POV150_10	ET_POVSTATUS_10	EP_POV150_10	EPL_POV150_10	F_POV150_10	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	F_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	F_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	F_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	F_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	F_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	F_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	F_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	F_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	F_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	F_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	F_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	F_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	F_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	F_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	F_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	F_NOVEH_10	E_GROUPQ_10	ET_HHTYPE_10	EP_GROUPQ_10	EPL_GROUPQ_10	F_GROUPQ_10	SPL_THEME1_10	RPL_THEME1_10	F_THEME1_10	SPL_THEME2_10	RPL_THEME2_10	F_THEME2_10	SPL_THEME3_10	RPL_THEME3_10	F_THEME3_10	SPL_THEME4_10	RPL_THEME4_10	F_THEME4_10	SPL_THEMES_10	RPL_THEMES_10	F_TOTAL_10	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	F_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	F_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	F_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	F_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	F_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	F_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	F_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	F_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	F_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	F_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	F_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	F_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	F_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	F_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	F_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	F_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	F_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20	F_GROUPQ_20	SPL_THEME1_20	RPL_THEME1_20	F_THEME1_20	SPL_THEME2_20	RPL_THEME2_20	F_THEME2_20	SPL_THEME3_20	RPL_THEME3_20	F_THEME3_20	SPL_THEME4_20	RPL_THEME4_20	F_THEME4_20	SPL_THEMES_20	RPL_THEMES_20	F_TOTAL_20	nmtc_eligibility	pre10_nmtc_project_cnt	pre10_nmtc_dollars	pre10_nmtc_dollars_formatted
10001040201	10	001	040201	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	5208	1953	1809	850	5183	16.399769	0.3672	0	147	2550	5.764706	0.3364	0	385	1323	29.10053	0.45810	0	222	486	45.67901	0.49650	0	607	1809	33.55445	0.4431	0	459	3090	14.8543689	0.5386	0	435	5283	8.233958	0.19610	0	454	8.717358	0.256100	0	1588	30.491551	0.8927	1	537	3716	14.451023	0.4708	0	417	1343	31.04989	0.8599	1	69	4835	1.427094	0.5240	0	1881	5208	36.11751	0.5689	0	1953	87	4.454685	0.5392	0	148	7.578085	0.6495	0	39	1809	2.155887	0.6471	0	121	1809	6.688778	0.6124	0	0	5208	0.0000000	0.3814	0	1.88140	0.3053	0	3.003500	0.7667	2	0.5689	0.5614	0	2.8296	0.6516	0	8.283400	0.5452	2	4770	1906	1732	755	4692	16.09122	0.3758	0	92	2500	3.680000	0.3633	0	197	1184	16.638513	0.280400	0	235	548	42.88321	0.4609	0	432	1732	24.94226	0.3622	0	251	3100	8.096774	0.4085	0	228	4770	4.779874	0.21160	0	549	11.509434	0.23290	0	1352	28.343816	0.88650	1	490	3418.125	14.335346	0.4309	0	328	1263.2064	25.965669	0.8111	1	0	4526	0.0000000	0.09987	0	1875	4769.908	39.30893	0.5372	0	1906	72	3.7775446	0.4876	0	128	6.7156348	0.6610	0	10	1732	0.5773672	0.3165	0	32	1731.7111	1.847883	0.2303	0	0	4770	0.0000000	0.2111	0	1.72140	0.2531	0	2.46127	0.45940	2	0.5372	0.5306	0	1.9065	0.2183	0	6.62637	0.2829	2	Yes	0	0	\$0
10001040502	10	001	040502	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	2087	921	921	192	2087	9.199808	0.1738	0	35	722	4.847645	0.2495	0	281	700	40.14286	0.78190	1	64	221	28.95928	0.17110	0	345	921	37.45928	0.5710	0	284	1546	18.3699871	0.6484	0	119	2121	5.610561	0.10710	0	518	24.820316	0.906800	1	480	22.999521	0.4910	0	328	1527	21.480026	0.7959	1	173	680	25.44118	0.7769	1	100	1998	5.005005	0.7960	1	560	2087	26.83277	0.4524	0	921	0	0.000000	0.1428	0	273	29.641694	0.8785	1	0	921	0.000000	0.1488	0	30	921	3.257329	0.3600	0	0	2087	0.0000000	0.3814	0	1.74980	0.2670	0	3.766600	0.9666	4	0.4524	0.4464	0	1.9115	0.2071	1	7.880300	0.4785	5	2555	1030	954	565	2555	22.11350	0.5385	0	135	1154	11.698440	0.9175	1	144	691	20.839363	0.486500	0	168	262	64.12214	0.8894	1	312	953	32.73872	0.6259	0	192	1782	10.774411	0.5377	0	198	2519	7.860262	0.40160	0	519	20.313112	0.68510	0	664	25.988258	0.79390	1	341	1854.295	18.389741	0.6427	0	195	614.6519	31.725274	0.8870	1	75	2351	3.1901319	0.72360	0	1215	2555.353	47.54725	0.6272	0	1030	61	5.9223301	0.5514	0	170	16.5048544	0.7844	1	58	954	6.0796646	0.8947	1	83	953.5886	8.703963	0.7220	0	0	2555	0.0000000	0.2111	0	3.02120	0.6565	1	3.73230	0.96800	2	0.6272	0.6195	0	3.1636	0.7893	2	10.54430	0.8433	5	Yes	0	0	\$0
10001040900	10	001	040900	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	2363	1205	1007	526	1741	30.212522	0.7006	0	171	1089	15.702479	0.9032	1	98	362	27.07182	0.37770	0	258	645	40.00000	0.36590	0	356	1007	35.35253	0.5041	0	248	1416	17.5141243	0.6235	0	118	2479	4.759984	0.08169	0	509	21.540415	0.863300	1	168	7.109606	0.0386	0	428	1427	29.992992	0.9611	1	44	387	11.36951	0.3400	0	50	2349	2.128565	0.6157	0	727	2363	30.76598	0.5048	0	1205	378	31.369295	0.8688	1	0	0.000000	0.1809	0	0	1007	0.000000	0.1488	0	256	1007	25.422046	0.9457	1	622	2363	26.3224714	0.9741	1	2.81309	0.5851	1	2.818700	0.6717	2	0.5048	0.4981	0	3.1183	0.7840	3	9.254890	0.6833	6	2373	1114	1028	574	1679	34.18702	0.7904	1	19	1034	1.837524	0.1211	0	26	313	8.306709	0.029920	0	335	715	46.85315	0.5540	0	361	1028	35.11673	0.6908	0	224	1292	17.337461	0.7807	1	78	2250	3.466667	0.13250	0	501	21.112516	0.71690	0	208	8.765276	0.05851	0	391	1505.000	25.980066	0.9018	1	29	220.0000	13.181818	0.4476	0	7	2268	0.3086420	0.28740	0	974	2373.000	41.04509	0.5571	0	1114	476	42.7289048	0.9104	1	0	0.0000000	0.1800	0	5	1028	0.4863813	0.2927	0	248	1028.0000	24.124514	0.9466	1	678	2373	28.5714286	0.9778	1	2.51550	0.4976	2	2.41221	0.42860	1	0.5571	0.5503	0	3.3075	0.8411	3	8.79231	0.6264	6	Yes	0	0	\$0
10001041000	10	001	041000	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	5577	2570	2369	1291	5577	23.148646	0.5435	0	144	2702	5.329386	0.2938	0	468	1312	35.67073	0.67500	0	135	1057	12.77200	0.04274	0	603	2369	25.45378	0.1769	0	759	3504	21.6609589	0.7311	0	655	5999	10.918486	0.29800	0	594	10.650888	0.371200	0	1487	26.663080	0.7247	0	683	4587	14.889906	0.4947	0	364	1466	24.82947	0.7646	1	188	5105	3.682664	0.7342	0	3384	5577	60.67778	0.7785	1	2570	479	18.638132	0.7741	1	567	22.062257	0.8159	1	91	2369	3.841283	0.8132	1	221	2369	9.328831	0.7266	0	9	5577	0.1613771	0.7632	1	2.04330	0.3511	0	3.089400	0.8041	1	0.7785	0.7682	1	3.8930	0.9705	4	9.804200	0.7522	6	6719	3107	2804	2006	6656	30.13822	0.7207	0	436	3058	14.257685	0.9556	1	299	1387	21.557318	0.521400	0	583	1417	41.14326	0.4216	0	882	2804	31.45506	0.5861	0	953	4915	19.389624	0.8333	1	509	6603	7.708617	0.39290	0	1221	18.172347	0.58640	0	1389	20.672719	0.47220	0	1393	5214.000	26.716532	0.9150	1	661	1752.0000	37.728310	0.9336	1	340	6411	5.3033848	0.82180	1	4068	6719.048	60.54429	0.7409	0	3107	469	15.0949469	0.7136	0	586	18.8606373	0.8099	1	54	2804	1.9258203	0.5840	0	253	2804.0000	9.022825	0.7342	0	70	6719	1.0418217	0.7345	0	3.48860	0.7870	2	3.72900	0.96760	3	0.7409	0.7319	0	3.5762	0.9178	1	11.53470	0.9313	6	Yes	0	0	\$0
10001041100	10	001	041100	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	2957	800	738	499	2555	19.530333	0.4511	0	44	845	5.207101	0.2813	0	0	8	0.00000	0.00257	0	395	730	54.10959	0.69280	0	395	738	53.52304	0.9155	1	11	1118	0.9838998	0.0213	0	65	2559	2.540055	0.02694	0	0	0.000000	0.003549	0	1198	40.514035	0.9944	1	117	1192	9.815436	0.2221	0	133	693	19.19192	0.6322	0	42	2551	1.646413	0.5567	0	720	2957	24.34900	0.4177	0	800	0	0.000000	0.1428	0	0	0.000000	0.1809	0	0	738	0.000000	0.1488	0	10	738	1.355014	0.1640	0	402	2957	13.5948597	0.9492	1	1.69614	0.2527	1	2.408949	0.4377	1	0.4177	0.4122	0	1.5857	0.1097	1	6.108489	0.2207	3	3881	1350	1322	1031	3618	28.49641	0.6874	0	58	877	6.613455	0.6891	0	0	3	0.000000	0.002567	0	758	1319	57.46778	0.7890	1	758	1322	57.33737	0.9772	1	41	1801	2.276513	0.0857	0	33	2762	1.194786	0.02847	0	64	1.649059	0.01027	0	1291	33.264623	0.97230	1	129	1470.497	8.772546	0.1457	0	113	1156.7160	9.769036	0.3067	0	4	3373	0.1185888	0.22310	0	1672	3881.437	43.07683	0.5797	0	1350	9	0.6666667	0.3180	0	10	0.7407407	0.4343	0	1	1322	0.0756430	0.2359	0	18	1321.8616	1.361716	0.1744	0	263	3881	6.7766040	0.9251	1	2.46787	0.4818	1	1.65807	0.08121	1	0.5797	0.5726	0	2.0877	0.2866	1	6.79334	0.3059	3	Yes	0	0	\$0
10001041200	10	001	041200	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	4723	1880	1742	778	4710	16.518047	0.3700	0	193	2264	8.524735	0.5848	0	481	1168	41.18151	0.80360	1	257	574	44.77352	0.47360	0	738	1742	42.36510	0.7087	0	573	3071	18.6584175	0.6563	0	471	3937	11.963424	0.34310	0	600	12.703790	0.504200	0	1257	26.614440	0.7223	0	583	3036	19.202899	0.7085	0	240	1183	20.28740	0.6645	0	190	4378	4.339881	0.7670	1	2933	4723	62.10036	0.7871	1	1880	327	17.393617	0.7606	1	353	18.776596	0.7841	1	41	1742	2.353616	0.6746	0	92	1742	5.281286	0.5288	0	0	4723	0.0000000	0.3814	0	2.66290	0.5376	0	3.366500	0.8969	1	0.7871	0.7767	1	3.1295	0.7889	2	9.946000	0.7681	4	4135	1851	1712	870	4076	21.34446	0.5206	0	180	1879	9.579564	0.8567	1	384	1230	31.219512	0.844800	1	226	482	46.88797	0.5550	0	610	1712	35.63084	0.7027	0	286	2785	10.269300	0.5146	0	204	4124	4.946654	0.22200	0	755	18.258767	0.59080	0	1067	25.804111	0.78410	1	571	3057.653	18.674456	0.6593	0	375	1138.2043	32.946633	0.8989	1	26	3953	0.6577283	0.38940	0	2299	4134.641	55.60337	0.6982	0	1851	175	9.4543490	0.6291	0	438	23.6628849	0.8514	1	5	1712	0.2920561	0.2552	0	143	1712.2269	8.351697	0.7088	0	20	4135	0.4836759	0.6479	0	2.81660	0.5950	1	3.32250	0.89620	2	0.6982	0.6897	0	3.0924	0.7625	1	9.92970	0.7729	4	Yes	0	0	\$0

# Join national data to nmtc_awards_pre2010, set count to 0 if no data
svi_national_nmtc_eligible <- 
  left_join(svi_national_nmtc_eligible, nmtc_awards_pre2010, join_by("GEOID_2010_trt" == "GEOID10")) %>%
  mutate(pre10_nmtc_project_cnt = if_else(is.na(pre10_nmtc_project_cnt), 0, pre10_nmtc_project_cnt)) %>%
    mutate(pre10_nmtc_dollars = if_else(is.na(pre10_nmtc_dollars), 0, pre10_nmtc_dollars))%>%
    mutate(pre10_nmtc_dollars_formatted = if_else(is.na(pre10_nmtc_dollars_formatted), "$0", pre10_nmtc_dollars_formatted))

# View table
svi_national_nmtc_eligible %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	FIPS_st	FIPS_county	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_10	E_HU_10	E_HH_10	E_POV150_10	ET_POVSTATUS_10	EP_POV150_10	EPL_POV150_10	F_POV150_10	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	F_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	F_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	F_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	F_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	F_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	F_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	F_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	F_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	F_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	F_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	F_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	F_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	F_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	F_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	F_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	F_NOVEH_10	E_GROUPQ_10	ET_HHTYPE_10	EP_GROUPQ_10	EPL_GROUPQ_10	F_GROUPQ_10	SPL_THEME1_10	RPL_THEME1_10	F_THEME1_10	SPL_THEME2_10	RPL_THEME2_10	F_THEME2_10	SPL_THEME3_10	RPL_THEME3_10	F_THEME3_10	SPL_THEME4_10	RPL_THEME4_10	F_THEME4_10	SPL_THEMES_10	RPL_THEMES_10	F_TOTAL_10	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	F_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	F_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	F_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	F_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	F_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	F_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	F_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	F_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	F_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	F_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	F_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	F_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	F_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	F_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	F_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	F_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	F_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20	F_GROUPQ_20	SPL_THEME1_20	RPL_THEME1_20	F_THEME1_20	SPL_THEME2_20	RPL_THEME2_20	F_THEME2_20	SPL_THEME3_20	RPL_THEME3_20	F_THEME3_20	SPL_THEME4_20	RPL_THEME4_20	F_THEME4_20	SPL_THEMES_20	RPL_THEMES_20	F_TOTAL_20	nmtc_eligibility	pre10_nmtc_project_cnt	pre10_nmtc_dollars	pre10_nmtc_dollars_formatted
01001020200	01	001	020200	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	2020	816	730	495	1992	24.84940	0.5954	0	68	834	8.153477	0.57540	0	49	439	11.16173	0.02067	0	105	291	36.08247	0.30190	0	154	730	21.09589	0.09312	0	339	1265	26.79842	0.8392	1	313	2012	15.55666	0.6000	0	204	10.09901	0.3419	0	597	29.55446	0.8192	1	359	1515	23.69637	0.8791	1	132	456	28.947368	0.8351	1	15	1890	0.7936508	0.40130	0	1243	2020	61.53465	0.7781	1	816	0	0.0000000	0.1224	0	34	4.1666667	0.6664	0	13	730	1.780822	0.5406	0	115	730	15.753425	0.8382	1	0	2020	0.0000	0.3640	0	2.70312	0.5665	1	3.27660	0.8614	3	0.7781	0.7709	1	2.5316	0.5047	1	9.28942	0.6832	6	1757	720	573	384	1511	25.41363	0.6427	0	29	717	4.044630	0.4132	0	33	392	8.418367	0.03542	0	116	181	64.08840	0.9086	1	149	573	26.00349	0.40410	0	139	1313	10.58644	0.5601	0	91	1533	5.936073	0.4343	0	284	16.163916	0.5169	0	325	18.49744	0.2851	0	164	1208.000	13.57616	0.4127	0	42	359.0000	11.699164	0.3998	0	0	1651	0.0000000	0.09479	0	1116	1757.000	63.51736	0.7591	1	720	3	0.4166667	0.2470	0	5	0.6944444	0.5106	0	9	573	1.5706806	0.4688	0	57	573.000	9.947644	0.7317	0	212	1757	12.0660216	0.9549	1	2.45440	0.4888	0	1.70929	0.1025	0	0.7591	0.7527	1	2.9130	0.6862	1	7.83579	0.4802	2	Yes	0	0	\$0
01001020700	01	001	020700	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	2664	1254	1139	710	2664	26.65165	0.6328	0	29	1310	2.213741	0.05255	0	134	710	18.87324	0.13890	0	187	429	43.58974	0.47090	0	321	1139	28.18262	0.28130	0	396	1852	21.38229	0.7478	0	345	2878	11.98749	0.4459	0	389	14.60210	0.6417	0	599	22.48499	0.4007	0	510	2168	23.52399	0.8752	1	228	712	32.022472	0.8712	1	0	2480	0.0000000	0.09298	0	694	2664	26.05105	0.5138	0	1254	8	0.6379585	0.2931	0	460	36.6826156	0.9714	1	0	1139	0.000000	0.1238	0	125	1139	10.974539	0.7477	0	0	2664	0.0000	0.3640	0	2.16035	0.4069	0	2.88178	0.6997	2	0.5138	0.5090	0	2.5000	0.4882	1	8.05593	0.5185	3	3562	1313	1248	1370	3528	38.83220	0.8512	1	128	1562	8.194622	0.7935	1	168	844	19.905213	0.44510	0	237	404	58.66337	0.8359	1	405	1248	32.45192	0.60420	0	396	2211	17.91045	0.7857	1	444	3547	12.517620	0.7758	1	355	9.966311	0.1800	0	954	26.78271	0.7923	1	629	2593.000	24.25762	0.8730	1	171	797.0000	21.455458	0.7186	0	0	3211	0.0000000	0.09479	0	1009	3562.000	28.32678	0.4668	0	1313	14	1.0662605	0.3165	0	443	33.7395278	0.9663	1	73	1248	5.8493590	0.8211	1	17	1248.000	1.362180	0.1554	0	112	3562	3.1443010	0.8514	1	3.81040	0.8569	4	2.65869	0.5847	2	0.4668	0.4629	0	3.1107	0.7714	3	10.04659	0.7851	9	Yes	0	0	\$0
01001021100	01	001	021100	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	3298	1502	1323	860	3298	26.07641	0.6211	0	297	1605	18.504673	0.94340	1	250	1016	24.60630	0.32070	0	74	307	24.10423	0.11920	0	324	1323	24.48980	0.17380	0	710	2231	31.82429	0.8976	1	654	3565	18.34502	0.7018	0	411	12.46210	0.5001	0	738	22.37720	0.3934	0	936	2861	32.71583	0.9807	1	138	825	16.727273	0.5715	0	9	3155	0.2852615	0.25010	0	1979	3298	60.00606	0.7703	1	1502	14	0.9320905	0.3234	0	659	43.8748336	0.9849	1	44	1323	3.325775	0.7062	0	137	1323	10.355253	0.7313	0	0	3298	0.0000	0.3640	0	3.33770	0.7351	2	2.69580	0.6028	1	0.7703	0.7631	1	3.1098	0.7827	1	9.91360	0.7557	5	3499	1825	1462	1760	3499	50.30009	0.9396	1	42	966	4.347826	0.4539	0	426	1274	33.437991	0.85200	1	52	188	27.65957	0.1824	0	478	1462	32.69494	0.61110	0	422	2488	16.96141	0.7638	1	497	3499	14.204058	0.8246	1	853	24.378394	0.8688	1	808	23.09231	0.5829	0	908	2691.100	33.74084	0.9808	1	179	811.6985	22.052524	0.7323	0	8	3248	0.2463054	0.26220	0	1986	3498.713	56.76373	0.7175	0	1825	29	1.5890411	0.3551	0	576	31.5616438	0.9594	1	88	1462	6.0191518	0.8269	1	148	1461.993	10.123166	0.7364	0	38	3499	1.0860246	0.7013	0	3.59300	0.8073	3	3.42700	0.9156	2	0.7175	0.7114	0	3.5791	0.9216	2	11.31660	0.9150	7	Yes	0	0	\$0
01003010200	01	003	010200	AL	Alabama	Baldwin County	3	South Region	6	East South Central Division	2612	1220	1074	338	2605	12.97505	0.2907	0	44	1193	3.688181	0.14720	0	172	928	18.53448	0.13090	0	31	146	21.23288	0.09299	0	203	1074	18.90130	0.05657	0	455	1872	24.30556	0.8016	1	456	2730	16.70330	0.6445	0	401	15.35222	0.6847	0	563	21.55436	0.3406	0	410	2038	20.11776	0.7755	1	64	779	8.215661	0.2181	0	0	2510	0.0000000	0.09298	0	329	2612	12.59571	0.3113	0	1220	38	3.1147541	0.4648	0	385	31.5573770	0.9545	1	20	1074	1.862197	0.5509	0	43	1074	4.003724	0.4088	0	0	2612	0.0000	0.3640	0	1.94057	0.3398	1	2.11188	0.2802	1	0.3113	0.3084	0	2.7430	0.6129	1	7.10675	0.3771	3	2928	1312	1176	884	2928	30.19126	0.7334	0	29	1459	1.987663	0.1356	0	71	830	8.554217	0.03726	0	134	346	38.72832	0.3964	0	205	1176	17.43197	0.12010	0	294	2052	14.32749	0.6940	0	219	2925	7.487179	0.5423	0	556	18.989071	0.6705	0	699	23.87295	0.6339	0	489	2226.455	21.96317	0.8122	1	191	783.8820	24.365914	0.7799	1	0	2710	0.0000000	0.09479	0	398	2927.519	13.59513	0.2511	0	1312	13	0.9908537	0.3111	0	400	30.4878049	0.9557	1	6	1176	0.5102041	0.2590	0	81	1176.202	6.886570	0.6115	0	7	2928	0.2390710	0.4961	0	2.22540	0.4183	0	2.99129	0.7634	2	0.2511	0.2490	0	2.6334	0.5496	1	8.10119	0.5207	3	Yes	0	0	\$0
01003010500	01	003	010500	AL	Alabama	Baldwin County	3	South Region	6	East South Central Division	4230	1779	1425	498	3443	14.46413	0.3337	0	166	1625	10.215385	0.71790	0	151	1069	14.12535	0.04638	0	196	356	55.05618	0.73830	0	347	1425	24.35088	0.17010	0	707	2945	24.00679	0.7967	1	528	4001	13.19670	0.5005	0	619	14.63357	0.6436	0	790	18.67612	0.1937	0	536	3096	17.31266	0.6572	0	165	920	17.934783	0.6102	0	20	4021	0.4973887	0.32320	0	754	4230	17.82506	0.4023	0	1779	97	5.4525014	0.5525	0	8	0.4496908	0.4600	0	63	1425	4.421053	0.7762	1	90	1425	6.315790	0.5691	0	787	4230	18.6052	0.9649	1	2.51890	0.5121	1	2.42790	0.4539	0	0.4023	0.3986	0	3.3227	0.8628	2	8.67180	0.6054	3	5877	1975	1836	820	5244	15.63692	0.3902	0	90	2583	3.484321	0.3361	0	159	1345	11.821561	0.10530	0	139	491	28.30957	0.1924	0	298	1836	16.23094	0.09053	0	570	4248	13.41808	0.6669	0	353	5247	6.727654	0.4924	0	1109	18.870172	0.6645	0	1144	19.46571	0.3411	0	717	4102.545	17.47696	0.6332	0	103	1286.1180	8.008596	0.2341	0	0	5639	0.0000000	0.09479	0	868	5877.481	14.76823	0.2709	0	1975	26	1.3164557	0.3359	0	45	2.2784810	0.6271	0	9	1836	0.4901961	0.2540	0	116	1835.798	6.318779	0.5811	0	633	5877	10.7708014	0.9507	1	1.97613	0.3410	0	1.96769	0.1961	0	0.2709	0.2686	0	2.7488	0.6077	1	6.96352	0.3406	1	Yes	0	0	\$0
01003010600	01	003	010600	AL	Alabama	Baldwin County	3	South Region	6	East South Central Division	3724	1440	1147	1973	3724	52.98067	0.9342	1	142	1439	9.867964	0.69680	0	235	688	34.15698	0.62950	0	187	459	40.74074	0.40290	0	422	1147	36.79163	0.55150	0	497	1876	26.49254	0.8354	1	511	3661	13.95794	0.5334	0	246	6.60580	0.1481	0	1256	33.72718	0.9305	1	496	2522	19.66693	0.7587	1	274	838	32.696897	0.8779	1	32	3479	0.9198045	0.42810	0	2606	3724	69.97852	0.8184	1	1440	21	1.4583333	0.3683	0	321	22.2916667	0.9036	1	97	1147	8.456844	0.8956	1	167	1147	14.559721	0.8209	1	0	3724	0.0000	0.3640	0	3.55130	0.7859	2	3.14330	0.8145	3	0.8184	0.8108	1	3.3524	0.8725	3	10.86540	0.8550	9	4115	1534	1268	1676	3997	41.93145	0.8814	1	294	1809	16.252073	0.9674	1	341	814	41.891892	0.94320	1	204	454	44.93392	0.5438	0	545	1268	42.98107	0.83620	1	624	2425	25.73196	0.9002	1	994	4115	24.155529	0.9602	1	642	15.601458	0.4841	0	1126	27.36331	0.8175	1	568	2989.000	19.00301	0.7045	0	212	715.0000	29.650350	0.8592	1	56	3825	1.4640523	0.53120	0	2715	4115.000	65.97813	0.7732	1	1534	0	0.0000000	0.1079	0	529	34.4850065	0.9685	1	101	1268	7.9652997	0.8795	1	89	1268.000	7.018927	0.6184	0	17	4115	0.4131227	0.5707	0	4.54540	0.9754	5	3.39650	0.9081	2	0.7732	0.7667	1	3.1450	0.7858	2	11.86010	0.9520	10	Yes	0	0	\$0

Filter Data to Revevant Time-Frame

# Find count of NMTC projects after 2010
# Remove all tracts that do not have SVI flag counts for 2010
# Remove all tracts that do not have SVI flag counts for 2020
# Remove all tracts that had an NMTC project before 2010
svi_divisional_nmtc <- 
  left_join(svi_divisional_nmtc_eligible, nmtc_awards_post2010, join_by("GEOID_2010_trt" == "GEOID10")) %>%
  mutate(post10_nmtc_project_cnt = if_else(is.na(post10_nmtc_project_cnt), 0, post10_nmtc_project_cnt)) %>%
  mutate(post10_nmtc_dollars = if_else(is.na(post10_nmtc_dollars), 0, post10_nmtc_dollars))%>%
  mutate(post10_nmtc_dollars_formatted = if_else(is.na(post10_nmtc_dollars_formatted), "$0", post10_nmtc_dollars_formatted)) %>%
  mutate(nmtc_flag = if_else(post10_nmtc_project_cnt > 0, 1, 0)) %>% 
  filter(!is.na(F_TOTAL_10)) %>% 
  filter(!is.na(F_TOTAL_20)) %>% 
  filter(pre10_nmtc_project_cnt < 1)

svi_divisional_nmtc %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	FIPS_st	FIPS_county	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_10	E_HU_10	E_HH_10	E_POV150_10	ET_POVSTATUS_10	EP_POV150_10	EPL_POV150_10	F_POV150_10	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	F_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	F_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	F_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	F_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	F_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	F_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	F_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	F_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	F_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	F_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	F_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	F_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	F_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	F_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	F_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	F_NOVEH_10	E_GROUPQ_10	ET_HHTYPE_10	EP_GROUPQ_10	EPL_GROUPQ_10	F_GROUPQ_10	SPL_THEME1_10	RPL_THEME1_10	F_THEME1_10	SPL_THEME2_10	RPL_THEME2_10	F_THEME2_10	SPL_THEME3_10	RPL_THEME3_10	F_THEME3_10	SPL_THEME4_10	RPL_THEME4_10	F_THEME4_10	SPL_THEMES_10	RPL_THEMES_10	F_TOTAL_10	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	F_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	F_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	F_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	F_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	F_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	F_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	F_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	F_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	F_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	F_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	F_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	F_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	F_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	F_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	F_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	F_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	F_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20	F_GROUPQ_20	SPL_THEME1_20	RPL_THEME1_20	F_THEME1_20	SPL_THEME2_20	RPL_THEME2_20	F_THEME2_20	SPL_THEME3_20	RPL_THEME3_20	F_THEME3_20	SPL_THEME4_20	RPL_THEME4_20	F_THEME4_20	SPL_THEMES_20	RPL_THEMES_20	F_TOTAL_20	nmtc_eligibility	pre10_nmtc_project_cnt	pre10_nmtc_dollars	pre10_nmtc_dollars_formatted	post10_nmtc_project_cnt	post10_nmtc_dollars	post10_nmtc_dollars_formatted	nmtc_flag
10001040201	10	001	040201	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	5208	1953	1809	850	5183	16.399769	0.3672	0	147	2550	5.764706	0.3364	0	385	1323	29.10053	0.45810	0	222	486	45.67901	0.49650	0	607	1809	33.55445	0.4431	0	459	3090	14.8543689	0.5386	0	435	5283	8.233958	0.19610	0	454	8.717358	0.256100	0	1588	30.491551	0.8927	1	537	3716	14.451023	0.4708	0	417	1343	31.04989	0.8599	1	69	4835	1.427094	0.5240	0	1881	5208	36.11751	0.5689	0	1953	87	4.454685	0.5392	0	148	7.578085	0.6495	0	39	1809	2.155887	0.6471	0	121	1809	6.688778	0.6124	0	0	5208	0.0000000	0.3814	0	1.88140	0.3053	0	3.003500	0.7667	2	0.5689	0.5614	0	2.8296	0.6516	0	8.283400	0.5452	2	4770	1906	1732	755	4692	16.09122	0.3758	0	92	2500	3.680000	0.3633	0	197	1184	16.638513	0.280400	0	235	548	42.88321	0.4609	0	432	1732	24.94226	0.3622	0	251	3100	8.096774	0.4085	0	228	4770	4.779874	0.21160	0	549	11.509434	0.23290	0	1352	28.343816	0.88650	1	490	3418.125	14.335346	0.4309	0	328	1263.2064	25.965669	0.8111	1	0	4526	0.0000000	0.09987	0	1875	4769.908	39.30893	0.5372	0	1906	72	3.7775446	0.4876	0	128	6.7156348	0.6610	0	10	1732	0.5773672	0.3165	0	32	1731.7111	1.847883	0.2303	0	0	4770	0.0000000	0.2111	0	1.72140	0.2531	0	2.46127	0.45940	2	0.5372	0.5306	0	1.9065	0.2183	0	6.62637	0.2829	2	Yes	0	0	\$0	0	0	\$0	0
10001040502	10	001	040502	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	2087	921	921	192	2087	9.199808	0.1738	0	35	722	4.847645	0.2495	0	281	700	40.14286	0.78190	1	64	221	28.95928	0.17110	0	345	921	37.45928	0.5710	0	284	1546	18.3699871	0.6484	0	119	2121	5.610561	0.10710	0	518	24.820316	0.906800	1	480	22.999521	0.4910	0	328	1527	21.480026	0.7959	1	173	680	25.44118	0.7769	1	100	1998	5.005005	0.7960	1	560	2087	26.83277	0.4524	0	921	0	0.000000	0.1428	0	273	29.641694	0.8785	1	0	921	0.000000	0.1488	0	30	921	3.257329	0.3600	0	0	2087	0.0000000	0.3814	0	1.74980	0.2670	0	3.766600	0.9666	4	0.4524	0.4464	0	1.9115	0.2071	1	7.880300	0.4785	5	2555	1030	954	565	2555	22.11350	0.5385	0	135	1154	11.698440	0.9175	1	144	691	20.839363	0.486500	0	168	262	64.12214	0.8894	1	312	953	32.73872	0.6259	0	192	1782	10.774411	0.5377	0	198	2519	7.860262	0.40160	0	519	20.313112	0.68510	0	664	25.988258	0.79390	1	341	1854.295	18.389741	0.6427	0	195	614.6519	31.725274	0.8870	1	75	2351	3.1901319	0.72360	0	1215	2555.353	47.54725	0.6272	0	1030	61	5.9223301	0.5514	0	170	16.5048544	0.7844	1	58	954	6.0796646	0.8947	1	83	953.5886	8.703963	0.7220	0	0	2555	0.0000000	0.2111	0	3.02120	0.6565	1	3.73230	0.96800	2	0.6272	0.6195	0	3.1636	0.7893	2	10.54430	0.8433	5	Yes	0	0	\$0	0	0	\$0	0
10001040900	10	001	040900	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	2363	1205	1007	526	1741	30.212522	0.7006	0	171	1089	15.702479	0.9032	1	98	362	27.07182	0.37770	0	258	645	40.00000	0.36590	0	356	1007	35.35253	0.5041	0	248	1416	17.5141243	0.6235	0	118	2479	4.759984	0.08169	0	509	21.540415	0.863300	1	168	7.109606	0.0386	0	428	1427	29.992992	0.9611	1	44	387	11.36951	0.3400	0	50	2349	2.128565	0.6157	0	727	2363	30.76598	0.5048	0	1205	378	31.369295	0.8688	1	0	0.000000	0.1809	0	0	1007	0.000000	0.1488	0	256	1007	25.422046	0.9457	1	622	2363	26.3224714	0.9741	1	2.81309	0.5851	1	2.818700	0.6717	2	0.5048	0.4981	0	3.1183	0.7840	3	9.254890	0.6833	6	2373	1114	1028	574	1679	34.18702	0.7904	1	19	1034	1.837524	0.1211	0	26	313	8.306709	0.029920	0	335	715	46.85315	0.5540	0	361	1028	35.11673	0.6908	0	224	1292	17.337461	0.7807	1	78	2250	3.466667	0.13250	0	501	21.112516	0.71690	0	208	8.765276	0.05851	0	391	1505.000	25.980066	0.9018	1	29	220.0000	13.181818	0.4476	0	7	2268	0.3086420	0.28740	0	974	2373.000	41.04509	0.5571	0	1114	476	42.7289048	0.9104	1	0	0.0000000	0.1800	0	5	1028	0.4863813	0.2927	0	248	1028.0000	24.124514	0.9466	1	678	2373	28.5714286	0.9778	1	2.51550	0.4976	2	2.41221	0.42860	1	0.5571	0.5503	0	3.3075	0.8411	3	8.79231	0.6264	6	Yes	0	0	\$0	0	0	\$0	0
10001041000	10	001	041000	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	5577	2570	2369	1291	5577	23.148646	0.5435	0	144	2702	5.329386	0.2938	0	468	1312	35.67073	0.67500	0	135	1057	12.77200	0.04274	0	603	2369	25.45378	0.1769	0	759	3504	21.6609589	0.7311	0	655	5999	10.918486	0.29800	0	594	10.650888	0.371200	0	1487	26.663080	0.7247	0	683	4587	14.889906	0.4947	0	364	1466	24.82947	0.7646	1	188	5105	3.682664	0.7342	0	3384	5577	60.67778	0.7785	1	2570	479	18.638132	0.7741	1	567	22.062257	0.8159	1	91	2369	3.841283	0.8132	1	221	2369	9.328831	0.7266	0	9	5577	0.1613771	0.7632	1	2.04330	0.3511	0	3.089400	0.8041	1	0.7785	0.7682	1	3.8930	0.9705	4	9.804200	0.7522	6	6719	3107	2804	2006	6656	30.13822	0.7207	0	436	3058	14.257685	0.9556	1	299	1387	21.557318	0.521400	0	583	1417	41.14326	0.4216	0	882	2804	31.45506	0.5861	0	953	4915	19.389624	0.8333	1	509	6603	7.708617	0.39290	0	1221	18.172347	0.58640	0	1389	20.672719	0.47220	0	1393	5214.000	26.716532	0.9150	1	661	1752.0000	37.728310	0.9336	1	340	6411	5.3033848	0.82180	1	4068	6719.048	60.54429	0.7409	0	3107	469	15.0949469	0.7136	0	586	18.8606373	0.8099	1	54	2804	1.9258203	0.5840	0	253	2804.0000	9.022825	0.7342	0	70	6719	1.0418217	0.7345	0	3.48860	0.7870	2	3.72900	0.96760	3	0.7409	0.7319	0	3.5762	0.9178	1	11.53470	0.9313	6	Yes	0	0	\$0	0	0	\$0	0
10001041100	10	001	041100	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	2957	800	738	499	2555	19.530333	0.4511	0	44	845	5.207101	0.2813	0	0	8	0.00000	0.00257	0	395	730	54.10959	0.69280	0	395	738	53.52304	0.9155	1	11	1118	0.9838998	0.0213	0	65	2559	2.540055	0.02694	0	0	0.000000	0.003549	0	1198	40.514035	0.9944	1	117	1192	9.815436	0.2221	0	133	693	19.19192	0.6322	0	42	2551	1.646413	0.5567	0	720	2957	24.34900	0.4177	0	800	0	0.000000	0.1428	0	0	0.000000	0.1809	0	0	738	0.000000	0.1488	0	10	738	1.355014	0.1640	0	402	2957	13.5948597	0.9492	1	1.69614	0.2527	1	2.408949	0.4377	1	0.4177	0.4122	0	1.5857	0.1097	1	6.108489	0.2207	3	3881	1350	1322	1031	3618	28.49641	0.6874	0	58	877	6.613455	0.6891	0	0	3	0.000000	0.002567	0	758	1319	57.46778	0.7890	1	758	1322	57.33737	0.9772	1	41	1801	2.276513	0.0857	0	33	2762	1.194786	0.02847	0	64	1.649059	0.01027	0	1291	33.264623	0.97230	1	129	1470.497	8.772546	0.1457	0	113	1156.7160	9.769036	0.3067	0	4	3373	0.1185888	0.22310	0	1672	3881.437	43.07683	0.5797	0	1350	9	0.6666667	0.3180	0	10	0.7407407	0.4343	0	1	1322	0.0756430	0.2359	0	18	1321.8616	1.361716	0.1744	0	263	3881	6.7766040	0.9251	1	2.46787	0.4818	1	1.65807	0.08121	1	0.5797	0.5726	0	2.0877	0.2866	1	6.79334	0.3059	3	Yes	0	0	\$0	0	0	\$0	0
10001041200	10	001	041200	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	4723	1880	1742	778	4710	16.518047	0.3700	0	193	2264	8.524735	0.5848	0	481	1168	41.18151	0.80360	1	257	574	44.77352	0.47360	0	738	1742	42.36510	0.7087	0	573	3071	18.6584175	0.6563	0	471	3937	11.963424	0.34310	0	600	12.703790	0.504200	0	1257	26.614440	0.7223	0	583	3036	19.202899	0.7085	0	240	1183	20.28740	0.6645	0	190	4378	4.339881	0.7670	1	2933	4723	62.10036	0.7871	1	1880	327	17.393617	0.7606	1	353	18.776596	0.7841	1	41	1742	2.353616	0.6746	0	92	1742	5.281286	0.5288	0	0	4723	0.0000000	0.3814	0	2.66290	0.5376	0	3.366500	0.8969	1	0.7871	0.7767	1	3.1295	0.7889	2	9.946000	0.7681	4	4135	1851	1712	870	4076	21.34446	0.5206	0	180	1879	9.579564	0.8567	1	384	1230	31.219512	0.844800	1	226	482	46.88797	0.5550	0	610	1712	35.63084	0.7027	0	286	2785	10.269300	0.5146	0	204	4124	4.946654	0.22200	0	755	18.258767	0.59080	0	1067	25.804111	0.78410	1	571	3057.653	18.674456	0.6593	0	375	1138.2043	32.946633	0.8989	1	26	3953	0.6577283	0.38940	0	2299	4134.641	55.60337	0.6982	0	1851	175	9.4543490	0.6291	0	438	23.6628849	0.8514	1	5	1712	0.2920561	0.2552	0	143	1712.2269	8.351697	0.7088	0	20	4135	0.4836759	0.6479	0	2.81660	0.5950	1	3.32250	0.89620	2	0.6982	0.6897	0	3.0924	0.7625	1	9.92970	0.7729	4	Yes	0	0	\$0	0	0	\$0	0

# Find count of NMTC projects after 2010
# Remove all tracts that do not have SVI flag counts for 2010
# Remove all tracts that do not have SVI flag counts for 2020
# Remove all tracts that had an NMTC project before 2010
svi_national_nmtc <- 
  left_join(svi_national_nmtc_eligible, nmtc_awards_post2010, join_by("GEOID_2010_trt" == "GEOID10")) %>%
  mutate(post10_nmtc_project_cnt = if_else(is.na(post10_nmtc_project_cnt), 0, post10_nmtc_project_cnt)) %>%
  mutate(post10_nmtc_dollars = if_else(is.na(post10_nmtc_dollars), 0, post10_nmtc_dollars))%>%
  mutate(post10_nmtc_dollars_formatted = if_else(is.na(post10_nmtc_dollars_formatted), "$0", post10_nmtc_dollars_formatted)) %>%
  mutate(nmtc_flag = if_else(post10_nmtc_project_cnt > 0, 1, 0)) %>% 
  filter(!is.na(F_TOTAL_10)) %>% 
  filter(!is.na(F_TOTAL_20)) %>% 
  filter(pre10_nmtc_project_cnt < 1)

svi_national_nmtc %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	FIPS_st	FIPS_county	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_10	E_HU_10	E_HH_10	E_POV150_10	ET_POVSTATUS_10	EP_POV150_10	EPL_POV150_10	F_POV150_10	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	F_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	F_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	F_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	F_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	F_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	F_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	F_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	F_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	F_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	F_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	F_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	F_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	F_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	F_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	F_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	F_NOVEH_10	E_GROUPQ_10	ET_HHTYPE_10	EP_GROUPQ_10	EPL_GROUPQ_10	F_GROUPQ_10	SPL_THEME1_10	RPL_THEME1_10	F_THEME1_10	SPL_THEME2_10	RPL_THEME2_10	F_THEME2_10	SPL_THEME3_10	RPL_THEME3_10	F_THEME3_10	SPL_THEME4_10	RPL_THEME4_10	F_THEME4_10	SPL_THEMES_10	RPL_THEMES_10	F_TOTAL_10	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	F_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	F_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	F_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	F_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	F_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	F_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	F_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	F_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	F_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	F_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	F_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	F_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	F_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	F_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	F_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	F_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	F_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20	F_GROUPQ_20	SPL_THEME1_20	RPL_THEME1_20	F_THEME1_20	SPL_THEME2_20	RPL_THEME2_20	F_THEME2_20	SPL_THEME3_20	RPL_THEME3_20	F_THEME3_20	SPL_THEME4_20	RPL_THEME4_20	F_THEME4_20	SPL_THEMES_20	RPL_THEMES_20	F_TOTAL_20	nmtc_eligibility	pre10_nmtc_project_cnt	pre10_nmtc_dollars	pre10_nmtc_dollars_formatted	post10_nmtc_project_cnt	post10_nmtc_dollars	post10_nmtc_dollars_formatted	nmtc_flag
01001020200	01	001	020200	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	2020	816	730	495	1992	24.84940	0.5954	0	68	834	8.153477	0.57540	0	49	439	11.16173	0.02067	0	105	291	36.08247	0.30190	0	154	730	21.09589	0.09312	0	339	1265	26.79842	0.8392	1	313	2012	15.55666	0.6000	0	204	10.09901	0.3419	0	597	29.55446	0.8192	1	359	1515	23.69637	0.8791	1	132	456	28.947368	0.8351	1	15	1890	0.7936508	0.40130	0	1243	2020	61.53465	0.7781	1	816	0	0.0000000	0.1224	0	34	4.1666667	0.6664	0	13	730	1.780822	0.5406	0	115	730	15.753425	0.8382	1	0	2020	0.0000	0.3640	0	2.70312	0.5665	1	3.27660	0.8614	3	0.7781	0.7709	1	2.5316	0.5047	1	9.28942	0.6832	6	1757	720	573	384	1511	25.41363	0.6427	0	29	717	4.044630	0.4132	0	33	392	8.418367	0.03542	0	116	181	64.08840	0.9086	1	149	573	26.00349	0.40410	0	139	1313	10.58644	0.5601	0	91	1533	5.936073	0.4343	0	284	16.163916	0.5169	0	325	18.49744	0.2851	0	164	1208.000	13.57616	0.4127	0	42	359.0000	11.699164	0.3998	0	0	1651	0.0000000	0.09479	0	1116	1757.000	63.51736	0.7591	1	720	3	0.4166667	0.2470	0	5	0.6944444	0.5106	0	9	573	1.5706806	0.4688	0	57	573.000	9.947644	0.7317	0	212	1757	12.0660216	0.9549	1	2.45440	0.4888	0	1.70929	0.1025	0	0.7591	0.7527	1	2.9130	0.6862	1	7.83579	0.4802	2	Yes	0	0	\$0	0	0	\$0	0
01001020700	01	001	020700	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	2664	1254	1139	710	2664	26.65165	0.6328	0	29	1310	2.213741	0.05255	0	134	710	18.87324	0.13890	0	187	429	43.58974	0.47090	0	321	1139	28.18262	0.28130	0	396	1852	21.38229	0.7478	0	345	2878	11.98749	0.4459	0	389	14.60210	0.6417	0	599	22.48499	0.4007	0	510	2168	23.52399	0.8752	1	228	712	32.022472	0.8712	1	0	2480	0.0000000	0.09298	0	694	2664	26.05105	0.5138	0	1254	8	0.6379585	0.2931	0	460	36.6826156	0.9714	1	0	1139	0.000000	0.1238	0	125	1139	10.974539	0.7477	0	0	2664	0.0000	0.3640	0	2.16035	0.4069	0	2.88178	0.6997	2	0.5138	0.5090	0	2.5000	0.4882	1	8.05593	0.5185	3	3562	1313	1248	1370	3528	38.83220	0.8512	1	128	1562	8.194622	0.7935	1	168	844	19.905213	0.44510	0	237	404	58.66337	0.8359	1	405	1248	32.45192	0.60420	0	396	2211	17.91045	0.7857	1	444	3547	12.517620	0.7758	1	355	9.966311	0.1800	0	954	26.78271	0.7923	1	629	2593.000	24.25762	0.8730	1	171	797.0000	21.455458	0.7186	0	0	3211	0.0000000	0.09479	0	1009	3562.000	28.32678	0.4668	0	1313	14	1.0662605	0.3165	0	443	33.7395278	0.9663	1	73	1248	5.8493590	0.8211	1	17	1248.000	1.362180	0.1554	0	112	3562	3.1443010	0.8514	1	3.81040	0.8569	4	2.65869	0.5847	2	0.4668	0.4629	0	3.1107	0.7714	3	10.04659	0.7851	9	Yes	0	0	\$0	0	0	\$0	0
01001021100	01	001	021100	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	3298	1502	1323	860	3298	26.07641	0.6211	0	297	1605	18.504673	0.94340	1	250	1016	24.60630	0.32070	0	74	307	24.10423	0.11920	0	324	1323	24.48980	0.17380	0	710	2231	31.82429	0.8976	1	654	3565	18.34502	0.7018	0	411	12.46210	0.5001	0	738	22.37720	0.3934	0	936	2861	32.71583	0.9807	1	138	825	16.727273	0.5715	0	9	3155	0.2852615	0.25010	0	1979	3298	60.00606	0.7703	1	1502	14	0.9320905	0.3234	0	659	43.8748336	0.9849	1	44	1323	3.325775	0.7062	0	137	1323	10.355253	0.7313	0	0	3298	0.0000	0.3640	0	3.33770	0.7351	2	2.69580	0.6028	1	0.7703	0.7631	1	3.1098	0.7827	1	9.91360	0.7557	5	3499	1825	1462	1760	3499	50.30009	0.9396	1	42	966	4.347826	0.4539	0	426	1274	33.437991	0.85200	1	52	188	27.65957	0.1824	0	478	1462	32.69494	0.61110	0	422	2488	16.96141	0.7638	1	497	3499	14.204058	0.8246	1	853	24.378394	0.8688	1	808	23.09231	0.5829	0	908	2691.100	33.74084	0.9808	1	179	811.6985	22.052524	0.7323	0	8	3248	0.2463054	0.26220	0	1986	3498.713	56.76373	0.7175	0	1825	29	1.5890411	0.3551	0	576	31.5616438	0.9594	1	88	1462	6.0191518	0.8269	1	148	1461.993	10.123166	0.7364	0	38	3499	1.0860246	0.7013	0	3.59300	0.8073	3	3.42700	0.9156	2	0.7175	0.7114	0	3.5791	0.9216	2	11.31660	0.9150	7	Yes	0	0	\$0	0	0	\$0	0
01003010200	01	003	010200	AL	Alabama	Baldwin County	3	South Region	6	East South Central Division	2612	1220	1074	338	2605	12.97505	0.2907	0	44	1193	3.688181	0.14720	0	172	928	18.53448	0.13090	0	31	146	21.23288	0.09299	0	203	1074	18.90130	0.05657	0	455	1872	24.30556	0.8016	1	456	2730	16.70330	0.6445	0	401	15.35222	0.6847	0	563	21.55436	0.3406	0	410	2038	20.11776	0.7755	1	64	779	8.215661	0.2181	0	0	2510	0.0000000	0.09298	0	329	2612	12.59571	0.3113	0	1220	38	3.1147541	0.4648	0	385	31.5573770	0.9545	1	20	1074	1.862197	0.5509	0	43	1074	4.003724	0.4088	0	0	2612	0.0000	0.3640	0	1.94057	0.3398	1	2.11188	0.2802	1	0.3113	0.3084	0	2.7430	0.6129	1	7.10675	0.3771	3	2928	1312	1176	884	2928	30.19126	0.7334	0	29	1459	1.987663	0.1356	0	71	830	8.554217	0.03726	0	134	346	38.72832	0.3964	0	205	1176	17.43197	0.12010	0	294	2052	14.32749	0.6940	0	219	2925	7.487179	0.5423	0	556	18.989071	0.6705	0	699	23.87295	0.6339	0	489	2226.455	21.96317	0.8122	1	191	783.8820	24.365914	0.7799	1	0	2710	0.0000000	0.09479	0	398	2927.519	13.59513	0.2511	0	1312	13	0.9908537	0.3111	0	400	30.4878049	0.9557	1	6	1176	0.5102041	0.2590	0	81	1176.202	6.886570	0.6115	0	7	2928	0.2390710	0.4961	0	2.22540	0.4183	0	2.99129	0.7634	2	0.2511	0.2490	0	2.6334	0.5496	1	8.10119	0.5207	3	Yes	0	0	\$0	1	408000	\$408,000	1
01003010500	01	003	010500	AL	Alabama	Baldwin County	3	South Region	6	East South Central Division	4230	1779	1425	498	3443	14.46413	0.3337	0	166	1625	10.215385	0.71790	0	151	1069	14.12535	0.04638	0	196	356	55.05618	0.73830	0	347	1425	24.35088	0.17010	0	707	2945	24.00679	0.7967	1	528	4001	13.19670	0.5005	0	619	14.63357	0.6436	0	790	18.67612	0.1937	0	536	3096	17.31266	0.6572	0	165	920	17.934783	0.6102	0	20	4021	0.4973887	0.32320	0	754	4230	17.82506	0.4023	0	1779	97	5.4525014	0.5525	0	8	0.4496908	0.4600	0	63	1425	4.421053	0.7762	1	90	1425	6.315790	0.5691	0	787	4230	18.6052	0.9649	1	2.51890	0.5121	1	2.42790	0.4539	0	0.4023	0.3986	0	3.3227	0.8628	2	8.67180	0.6054	3	5877	1975	1836	820	5244	15.63692	0.3902	0	90	2583	3.484321	0.3361	0	159	1345	11.821561	0.10530	0	139	491	28.30957	0.1924	0	298	1836	16.23094	0.09053	0	570	4248	13.41808	0.6669	0	353	5247	6.727654	0.4924	0	1109	18.870172	0.6645	0	1144	19.46571	0.3411	0	717	4102.545	17.47696	0.6332	0	103	1286.1180	8.008596	0.2341	0	0	5639	0.0000000	0.09479	0	868	5877.481	14.76823	0.2709	0	1975	26	1.3164557	0.3359	0	45	2.2784810	0.6271	0	9	1836	0.4901961	0.2540	0	116	1835.798	6.318779	0.5811	0	633	5877	10.7708014	0.9507	1	1.97613	0.3410	0	1.96769	0.1961	0	0.2709	0.2686	0	2.7488	0.6077	1	6.96352	0.3406	1	Yes	0	0	\$0	0	0	\$0	0
01003010600	01	003	010600	AL	Alabama	Baldwin County	3	South Region	6	East South Central Division	3724	1440	1147	1973	3724	52.98067	0.9342	1	142	1439	9.867964	0.69680	0	235	688	34.15698	0.62950	0	187	459	40.74074	0.40290	0	422	1147	36.79163	0.55150	0	497	1876	26.49254	0.8354	1	511	3661	13.95794	0.5334	0	246	6.60580	0.1481	0	1256	33.72718	0.9305	1	496	2522	19.66693	0.7587	1	274	838	32.696897	0.8779	1	32	3479	0.9198045	0.42810	0	2606	3724	69.97852	0.8184	1	1440	21	1.4583333	0.3683	0	321	22.2916667	0.9036	1	97	1147	8.456844	0.8956	1	167	1147	14.559721	0.8209	1	0	3724	0.0000	0.3640	0	3.55130	0.7859	2	3.14330	0.8145	3	0.8184	0.8108	1	3.3524	0.8725	3	10.86540	0.8550	9	4115	1534	1268	1676	3997	41.93145	0.8814	1	294	1809	16.252073	0.9674	1	341	814	41.891892	0.94320	1	204	454	44.93392	0.5438	0	545	1268	42.98107	0.83620	1	624	2425	25.73196	0.9002	1	994	4115	24.155529	0.9602	1	642	15.601458	0.4841	0	1126	27.36331	0.8175	1	568	2989.000	19.00301	0.7045	0	212	715.0000	29.650350	0.8592	1	56	3825	1.4640523	0.53120	0	2715	4115.000	65.97813	0.7732	1	1534	0	0.0000000	0.1079	0	529	34.4850065	0.9685	1	101	1268	7.9652997	0.8795	1	89	1268.000	7.018927	0.6184	0	17	4115	0.4131227	0.5707	0	4.54540	0.9754	5	3.39650	0.9081	2	0.7732	0.7667	1	3.1450	0.7858	2	11.86010	0.9520	10	Yes	0	0	\$0	1	8000000	\$8,000,000	1

svi_national_nmtc_county_sum <- summarize_county_nmtc(svi_national_nmtc)

svi_national_nmtc_county_sum %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

State	County	Division	post10_nmtc_project_cnt	tract_cnt	post10_nmtc_project_dollars	post10_nmtc_dollars_formatted
AK	Aleutians East Borough	Pacific Division	1	1	15762500	\$15,762,500
AK	Aleutians West Census Area	Pacific Division	0	1	0	\$0
AK	Anchorage Municipality	Pacific Division	1	13	9800000	\$9,800,000
AK	Bethel Census Area	Pacific Division	0	1	0	\$0
AK	Fairbanks North Star Borough	Pacific Division	0	4	0	\$0
AK	Hoonah-Angoon Census Area	Pacific Division	0	1	0	\$0

svi_divisional_nmtc_county_sum <- summarize_county_nmtc(svi_divisional_nmtc)
svi_divisional_nmtc_county_sum %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

State	County	Division	post10_nmtc_project_cnt	tract_cnt	post10_nmtc_project_dollars	post10_nmtc_dollars_formatted
DC	District of Columbia	South Atlantic Division	22	78	377155570	\$377,155,570
DE	Kent County	South Atlantic Division	0	14	0	\$0
DE	New Castle County	South Atlantic Division	2	44	27912271	\$27,912,271
DE	Sussex County	South Atlantic Division	0	16	0	\$0
FL	Alachua County	South Atlantic Division	2	28	16360000	\$16,360,000
FL	Baker County	South Atlantic Division	0	2	0	\$0

Create data frame of NMTC eligible tracts 2010 nationally

# Create data frame of NMTC eligible tracts 2010 nationally
svi_national_nmtc10 <- svi_national_nmtc %>% select(GEOID_2010_trt, FIPS_st, FIPS_county, 
    state, state_name, county, region_number, region, division_number, 
    division, F_TOTAL_10, E_TOTPOP_10) %>% rename("F_TOTAL" = "F_TOTAL_10")

# Count national-level SVI flags for 2010, create unified fips column
svi_2010_national_county_flags_nmtc <- flag_summarize(svi_national_nmtc10, "E_TOTPOP_10") %>% unite("fips_county_st", FIPS_st:FIPS_county, remove = FALSE, sep="")

# Add suffix to flag columns 2010
colnames(svi_2010_national_county_flags_nmtc)[11:15] <- paste0(colnames(svi_2010_national_county_flags_nmtc)[11:15], 10)

# Create data frame of NMTC eligible tracts 2020 nationally
svi_national_nmtc20 <- svi_national_nmtc %>% select(GEOID_2010_trt, FIPS_st, FIPS_county, 
    state, state_name, county, region_number, region, division_number, 
    division, F_TOTAL_20, E_TOTPOP_20) %>% rename("F_TOTAL" = "F_TOTAL_20")

# Count national-level SVI flags for 2020, create unified fips column
svi_2020_national_county_flags_nmtc <- flag_summarize(svi_national_nmtc20, "E_TOTPOP_20") %>% unite("fips_county_st", FIPS_st:FIPS_county, remove = FALSE, sep="")

# Identify needed columns for 2020, add suffix
colnames(svi_2020_national_county_flags_nmtc)[11:15] <- paste0(colnames(svi_2020_national_county_flags_nmtc)[11:15], "20")

# Filter to needed columns for 2020 to avoid duplicate column column names
svi_2020_national_county_flags_join_nmtc <- svi_2020_national_county_flags_nmtc %>% ungroup() %>% select("fips_county_st", all_of(colnames(svi_2020_national_county_flags_nmtc)[11:15]))
 
# Join 2010 and 2020 data
svi_national_county_flags_nmtc <- left_join(svi_2010_national_county_flags_nmtc, svi_2020_national_county_flags_join_nmtc, join_by("fips_county_st" == "fips_county_st")) 

svi_national_county_flags_nmtc %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

fips_county_st	FIPS_st	FIPS_county	state	state_name	county	region_number	region	division_number	division	flag_count10	pop10	flag_by_pop10	flag_count_quantile10	flag_pop_quantile10	flag_count20	pop20	flag_by_pop20	flag_count_quantile20	flag_pop_quantile20
01001	01	001	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	14	7982	0.0017539	0.6	0.8	18	8818	0.0020413	0.6	1.0
01003	01	003	AL	Alabama	Baldwin County	3	South Region	6	East South Central Division	34	38458	0.0008841	0.8	0.4	34	46255	0.0007351	0.8	0.2
01005	01	005	AL	Alabama	Barbour County	3	South Region	6	East South Central Division	43	21287	0.0020200	0.8	1.0	44	18811	0.0023391	0.8	1.0
01007	01	007	AL	Alabama	Bibb County	3	South Region	6	East South Central Division	11	17570	0.0006261	0.4	0.2	16	17663	0.0009058	0.6	0.4
01009	01	009	AL	Alabama	Blount County	3	South Region	6	East South Central Division	12	16995	0.0007061	0.4	0.2	8	16546	0.0004835	0.4	0.2
01011	01	011	AL	Alabama	Bullock County	3	South Region	6	East South Central Division	21	10923	0.0019225	0.6	1.0	18	10173	0.0017694	0.6	0.8

svi_national_county_nmtc <- left_join(svi_national_nmtc_county_sum,
                                      svi_national_county_flags_nmtc,
                                    join_by("State" == "state", "County" == "county",
                                            "Division" == "division"))

svi_national_county_nmtc$post10_nmtc_project_cnt[is.na(svi_national_county_nmtc$post10_nmtc_project_cnt)] <- 0

svi_national_county_nmtc$county_name <- paste0(svi_national_county_nmtc$County, ", ", svi_national_county_nmtc$State)

svi_national_county_nmtc %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

State	County	Division	post10_nmtc_project_cnt	tract_cnt	post10_nmtc_project_dollars	post10_nmtc_dollars_formatted	fips_county_st	FIPS_st	FIPS_county	state_name	region_number	region	division_number	flag_count10	pop10	flag_by_pop10	flag_count_quantile10	flag_pop_quantile10	flag_count20	pop20	flag_by_pop20	flag_count_quantile20	flag_pop_quantile20	county_name
AK	Aleutians East Borough	Pacific Division	1	1	15762500	\$15,762,500	02013	02	013	Alaska	4	West Region	9	8	3703	0.0021604	0.4	1.0	5	3389	0.0014754	0.2	0.8	Aleutians East Borough, AK
AK	Aleutians West Census Area	Pacific Division	0	1	0	\$0	02016	02	016	Alaska	4	West Region	9	6	1774	0.0033822	0.2	1.0	6	950	0.0063158	0.2	1.0	Aleutians West Census Area, AK
AK	Anchorage Municipality	Pacific Division	1	13	9800000	\$9,800,000	02020	02	020	Alaska	4	West Region	9	72	64432	0.0011175	1.0	0.4	87	69679	0.0012486	1.0	0.6	Anchorage Municipality, AK
AK	Bethel Census Area	Pacific Division	0	1	0	\$0	02050	02	050	Alaska	4	West Region	9	8	1386	0.0057720	0.4	1.0	10	1404	0.0071225	0.4	1.0	Bethel Census Area, AK
AK	Fairbanks North Star Borough	Pacific Division	0	4	0	\$0	02090	02	090	Alaska	4	West Region	9	13	17281	0.0007523	0.4	0.2	17	20094	0.0008460	0.6	0.4	Fairbanks North Star Borough, AK
AK	Hoonah-Angoon Census Area	Pacific Division	0	1	0	\$0	02105	02	105	Alaska	4	West Region	9	4	1888	0.0021186	0.2	1.0	5	2073	0.0024120	0.2	1.0	Hoonah-Angoon Census Area, AK

Create data frame of NMTC eligible tracts 2020 nationally

# Create data frame of NMTC eligible tracts 2020 nationally
svi_divisional_nmtc10 <- svi_divisional_nmtc %>% select(GEOID_2010_trt, FIPS_st, FIPS_county, 
    state, state_name, county, region_number, region, division_number, 
    division, F_TOTAL_10, E_TOTPOP_10) %>% rename("F_TOTAL" = "F_TOTAL_10")

# Count divisional-level SVI flags for 2010, create unified fips column
svi_2010_divisional_county_flags_nmtc <- flag_summarize(svi_divisional_nmtc10, "E_TOTPOP_10") %>% unite("fips_county_st", FIPS_st:FIPS_county, remove = FALSE, sep="")

# Add suffix to flag columns 2010
colnames(svi_2010_divisional_county_flags_nmtc)[11:15] <- paste0(colnames(svi_2010_divisional_county_flags_nmtc)[11:15], "10")

# Create data frame of NMTC eligible tracts 2020 nationally
svi_divisional_nmtc20 <- svi_divisional_nmtc %>% select(GEOID_2010_trt, FIPS_st, FIPS_county, 
    state, state_name, county, region_number, region, division_number, 
    division, F_TOTAL_20, E_TOTPOP_20) %>% rename("F_TOTAL" = "F_TOTAL_20")

# Count divisional-level SVI flags for 2020, create unified fips column
svi_2020_divisional_county_flags_nmtc <- flag_summarize(svi_divisional_nmtc20, "E_TOTPOP_20") %>% unite("fips_county_st", FIPS_st:FIPS_county, remove = FALSE, sep="")

# Identify needed columns for 2020
colnames(svi_2020_divisional_county_flags_nmtc)[11:15] <- paste0(colnames(svi_2020_divisional_county_flags_nmtc)[11:15], "20")

# Filter to needed columns for 2020 to avoid duplicate column column names
svi_2020_divisional_county_flags_join_nmtc <- svi_2020_divisional_county_flags_nmtc %>% ungroup() %>% select("fips_county_st", all_of(colnames(svi_2020_divisional_county_flags_nmtc)[11:15]))
 
# Join 2010 and 2020 data
svi_divisional_county_flags_nmtc <- left_join(svi_2010_divisional_county_flags_nmtc, svi_2020_divisional_county_flags_join_nmtc, join_by("fips_county_st" == "fips_county_st")) 

svi_divisional_county_flags_nmtc %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

fips_county_st	FIPS_st	FIPS_county	state	state_name	county	region_number	region	division_number	division	flag_count10	pop10	flag_by_pop10	flag_count_quantile10	flag_pop_quantile10	flag_count20	pop20	flag_by_pop20	flag_count_quantile20	flag_pop_quantile20
10001	10	001	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	57	54435	0.0010471	0.8	0.4	75	58746	0.0012767	1.0	0.6
10003	10	003	DE	Delaware	New Castle County	3	South Region	5	South Atlantic Division	230	157982	0.0014559	1.0	0.8	215	157528	0.0013648	1.0	0.8
10005	10	005	DE	Delaware	Sussex County	3	South Region	5	South Atlantic Division	65	69409	0.0009365	1.0	0.4	61	77024	0.0007920	0.8	0.4
11001	11	001	DC	District of Columbia	District of Columbia	3	South Region	5	South Atlantic Division	605	234370	0.0025814	1.0	1.0	576	290905	0.0019800	1.0	1.0
12001	12	001	FL	Florida	Alachua County	3	South Region	5	South Atlantic Division	160	107400	0.0014898	1.0	0.8	156	115817	0.0013470	1.0	0.8
12003	12	003	FL	Florida	Baker County	3	South Region	5	South Atlantic Division	9	15229	0.0005910	0.2	0.2	8	15148	0.0005281	0.2	0.2

svi_divisional_county_nmtc <- left_join(svi_divisional_nmtc_county_sum, 
                                        svi_divisional_county_flags_nmtc,
                                    join_by("State" == "state", "County" == "county",
                                            "Division" == "division"))

svi_divisional_county_nmtc$post10_nmtc_project_cnt[is.na(svi_divisional_county_nmtc $post10_nmtc_project_cnt)] <- 0

svi_divisional_county_nmtc$county_name <- paste0(svi_divisional_county_nmtc$County, ", ", svi_divisional_county_nmtc$State)

svi_divisional_county_nmtc %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

State	County	Division	post10_nmtc_project_cnt	tract_cnt	post10_nmtc_project_dollars	post10_nmtc_dollars_formatted	fips_county_st	FIPS_st	FIPS_county	state_name	region_number	region	division_number	flag_count10	pop10	flag_by_pop10	flag_count_quantile10	flag_pop_quantile10	flag_count20	pop20	flag_by_pop20	flag_count_quantile20	flag_pop_quantile20	county_name
DC	District of Columbia	South Atlantic Division	22	78	377155570	\$377,155,570	11001	11	001	District of Columbia	3	South Region	5	605	234370	0.0025814	1.0	1.0	576	290905	0.0019800	1.0	1.0	District of Columbia, DC
DE	Kent County	South Atlantic Division	0	14	0	\$0	10001	10	001	Delaware	3	South Region	5	57	54435	0.0010471	0.8	0.4	75	58746	0.0012767	1.0	0.6	Kent County, DE
DE	New Castle County	South Atlantic Division	2	44	27912271	\$27,912,271	10003	10	003	Delaware	3	South Region	5	230	157982	0.0014559	1.0	0.8	215	157528	0.0013648	1.0	0.8	New Castle County, DE
DE	Sussex County	South Atlantic Division	0	16	0	\$0	10005	10	005	Delaware	3	South Region	5	65	69409	0.0009365	1.0	0.4	61	77024	0.0007920	0.8	0.4	Sussex County, DE
FL	Alachua County	South Atlantic Division	2	28	16360000	\$16,360,000	12001	12	001	Florida	3	South Region	5	160	107400	0.0014898	1.0	0.8	156	115817	0.0013470	1.0	0.8	Alachua County, FL
FL	Baker County	South Atlantic Division	0	2	0	\$0	12003	12	003	Florida	3	South Region	5	9	15229	0.0005910	0.2	0.2	8	15148	0.0005281	0.2	0.2	Baker County, FL

LIHTC Data Wrangling

lihtc_eligible_flag <- lihtc_eligible %>% 
  select("fips", "state", "county", "stcnty", "tract", "metro", "cbsa", "qct_2010") %>% 
  rename("GEOID10" = "fips") %>% 
  mutate(lihtc_eligibility = if_else(qct_2010 == 1, "Yes", "No")) %>% 
  filter(tolower(lihtc_eligibility) == "yes") %>% 
  select(GEOID10, lihtc_eligibility)

lihtc_eligible_flag %>% head() 

## # A tibble: 6 × 2
##   GEOID10     lihtc_eligibility
##   <chr>       <chr>            
## 1 01003010600 Yes              
## 2 01005950200 Yes              
## 3 01005950300 Yes              
## 4 01005950400 Yes              
## 5 01005950600 Yes              
## 6 01005950700 Yes

lihtc_projects10 <- lihtc_projects %>% 
  filter(yr_alloc < 8000) %>% 
  filter(yr_alloc <= 2010) %>% 
  count(fips2010) %>% 
  rename("pre10_lihtc_project_cnt" = "n")

lihtc_projects10 %>% head() 

##      fips2010 pre10_lihtc_project_cnt
## 1 01001020300                       2
## 2 01001020500                       5
## 3 01001021100                       1
## 4 01003010200                       1
## 5 01003010600                       1
## 6 01003010703                       1

lihtc_dollars10 <- lihtc_projects %>% 
  filter(yr_alloc < 8000) %>% 
  filter(yr_alloc <= 2010) %>%
  select(fips2010, allocamt)

lihtc_dollars10$allocamt[is.na(lihtc_dollars10$allocamt)] <- 0

lihtc_dollars10 <- lihtc_dollars10 %>% 
  group_by(fips2010) %>% 
  summarise(pre10_lihtc_project_dollars = sum(allocamt, na.rm = TRUE))

lihtc_dollars10 %>% head() 

## # A tibble: 6 × 2
##   fips2010    pre10_lihtc_project_dollars
##   <chr>                             <dbl>
## 1 01001020300                      216593
## 2 01001020500                     2250459
## 3 01001021100                       53109
## 4 01003010200                           0
## 5 01003010600                      376889
## 6 01003010703                      717113

lihtc_projects10 <- left_join(lihtc_projects10, lihtc_dollars10, join_by(fips2010 == fips2010))

lihtc_projects10 %>% head()

##      fips2010 pre10_lihtc_project_cnt pre10_lihtc_project_dollars
## 1 01001020300                       2                      216593
## 2 01001020500                       5                     2250459
## 3 01001021100                       1                       53109
## 4 01003010200                       1                           0
## 5 01003010600                       1                      376889
## 6 01003010703                       1                      717113

lihtc_projects20 <- lihtc_projects %>% 
  filter(yr_alloc < 8000) %>% 
  filter(yr_alloc > 2010) %>% 
  filter(yr_alloc < 2021) %>% 
  count(fips2010) %>% 
  rename("post10_lihtc_project_cnt" = "n")

lihtc_projects20 %>% head() 

##      fips2010 post10_lihtc_project_cnt
## 1 01003010500                        1
## 2 01003011403                        1
## 3 01003011601                        1
## 4 01005950900                        1
## 5 01009050102                        1
## 6 01017954600                        2

lihtc_dollars20 <- lihtc_projects %>% 
  filter(yr_alloc < 8000) %>% 
  filter(yr_alloc > 2010) %>% 
  filter(yr_alloc < 2021) %>% 
  select(fips2010, allocamt)

lihtc_dollars20$allocamt[is.na(lihtc_dollars20$allocamt)] <- 0

lihtc_dollars20 <- lihtc_dollars20 %>% 
  group_by(fips2010) %>% 
  summarise(post10_lihtc_project_dollars = sum(allocamt, na.rm = TRUE))

lihtc_dollars20 %>% head() 

## # A tibble: 6 × 2
##   fips2010    post10_lihtc_project_dollars
##   <chr>                              <dbl>
## 1 01003010500                       481325
## 2 01003011403                       828342
## 3 01003011601                       887856
## 4 01005950900                       400758
## 5 01009050102                       463000
## 6 01017954600                       950192

Join National & Divisional Data

lihtc_projects20 <- left_join(lihtc_projects20, lihtc_dollars20, join_by(fips2010 == fips2010))

lihtc_projects20 %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

fips2010	post10_lihtc_project_cnt	post10_lihtc_project_dollars
01003010500	1	481325
01003011403	1	828342
01003011601	1	887856
01005950900	1	400758
01009050102	1	463000
01017954600	2	950192

svi_divisional_lihtc10 <- left_join(svi_divisional, lihtc_projects10, join_by("GEOID_2010_trt" == "fips2010"))

svi_divisional_lihtc10 %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	FIPS_st	FIPS_county	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_10	E_HU_10	E_HH_10	E_POV150_10	ET_POVSTATUS_10	EP_POV150_10	EPL_POV150_10	F_POV150_10	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	F_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	F_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	F_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	F_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	F_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	F_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	F_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	F_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	F_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	F_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	F_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	F_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	F_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	F_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	F_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	F_NOVEH_10	E_GROUPQ_10	ET_HHTYPE_10	EP_GROUPQ_10	EPL_GROUPQ_10	F_GROUPQ_10	SPL_THEME1_10	RPL_THEME1_10	F_THEME1_10	SPL_THEME2_10	RPL_THEME2_10	F_THEME2_10	SPL_THEME3_10	RPL_THEME3_10	F_THEME3_10	SPL_THEME4_10	RPL_THEME4_10	F_THEME4_10	SPL_THEMES_10	RPL_THEMES_10	F_TOTAL_10	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	F_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	F_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	F_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	F_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	F_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	F_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	F_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	F_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	F_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	F_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	F_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	F_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	F_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	F_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	F_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	F_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	F_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20	F_GROUPQ_20	SPL_THEME1_20	RPL_THEME1_20	F_THEME1_20	SPL_THEME2_20	RPL_THEME2_20	F_THEME2_20	SPL_THEME3_20	RPL_THEME3_20	F_THEME3_20	SPL_THEME4_20	RPL_THEME4_20	F_THEME4_20	SPL_THEMES_20	RPL_THEMES_20	F_TOTAL_20	pre10_lihtc_project_cnt	pre10_lihtc_project_dollars
10001040100	10	001	040100	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	6468	2388	2272	868	6455	13.446940	0.2879	0	201	3230	6.222910	0.3819	0	708	2036	34.77407	0.6489	0	47	236	19.91525	0.07811	0	755	2272	33.23063	0.4315	0	691	4369	15.815976	0.5692	0	400	6594	6.066121	0.12090	0	688	10.636982	0.3706	0	1689	26.11317	0.6923	0	725	5107	14.19620	0.4566	0	209	1742	11.99770	0.3673	0	0	5993	0.0000000	0.1022	0	845	6468	13.06432	0.2341	0	2388	0	0.000000	0.1428	0	601	25.167504	0.8423	1	14	2272	0.6161972	0.3716	0	92	2272	4.049296	0.4329	0	0	6468	0.000000	0.3814	0	1.79140	0.2784	0	1.9890	0.2036	0	0.2341	0.2310	0	2.1710	0.3144	1	6.18550	0.2310	1	7531	2850	2587	2226	7519	29.60500	0.7109	0	392	3820	10.261780	0.8786	1	527	2067	25.49589	0.6863	0	180	520	34.61538	0.2805	0	707	2587	27.32895	0.4528	0	765	4950	15.454546	0.7234	0	353	7523	4.692277	0.2050	0	1007	13.37140	0.3312	0	2035	27.02164	0.8398	1	1227	5488.000	22.35787	0.8093	1	239	1893.0000	12.62546	0.4261	0	276	7262	3.8006059	0.75930	1	1507	7531.000	20.01062	0.2789	0	2850	1	0.0350877	0.2526	0	697	24.456140	0.8585	1	93	2587	3.5948976	0.7607	1	55	2587.0000	2.126015	0.2641	0	0	7531	0.000000	0.2111	0	2.9707	0.6413	1	3.16570	0.8440	3	0.2789	0.2755	0	2.3470	0.4035	2	8.76230	0.6215	6	NA	NA
10001040201	10	001	040201	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	5208	1953	1809	850	5183	16.399769	0.3672	0	147	2550	5.764706	0.3364	0	385	1323	29.10053	0.4581	0	222	486	45.67901	0.49650	0	607	1809	33.55445	0.4431	0	459	3090	14.854369	0.5386	0	435	5283	8.233958	0.19610	0	454	8.717358	0.2561	0	1588	30.49155	0.8927	1	537	3716	14.45102	0.4708	0	417	1343	31.04989	0.8599	1	69	4835	1.4270941	0.5240	0	1881	5208	36.11751	0.5689	0	1953	87	4.454685	0.5392	0	148	7.578085	0.6495	0	39	1809	2.1558872	0.6471	0	121	1809	6.688778	0.6124	0	0	5208	0.000000	0.3814	0	1.88140	0.3053	0	3.0035	0.7667	2	0.5689	0.5614	0	2.8296	0.6516	0	8.28340	0.5452	2	4770	1906	1732	755	4692	16.09122	0.3758	0	92	2500	3.680000	0.3633	0	197	1184	16.63851	0.2804	0	235	548	42.88321	0.4609	0	432	1732	24.94226	0.3622	0	251	3100	8.096774	0.4085	0	228	4770	4.779874	0.2116	0	549	11.50943	0.2329	0	1352	28.34382	0.8865	1	490	3418.125	14.33535	0.4309	0	328	1263.2064	25.96567	0.8111	1	0	4526	0.0000000	0.09987	0	1875	4769.908	39.30893	0.5372	0	1906	72	3.7775446	0.4876	0	128	6.715635	0.6610	0	10	1732	0.5773672	0.3165	0	32	1731.7111	1.847883	0.2303	0	0	4770	0.000000	0.2111	0	1.7214	0.2531	0	2.46127	0.4594	2	0.5372	0.5306	0	1.9065	0.2183	0	6.62637	0.2829	2	NA	NA
10001040202	10	001	040202	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	11385	4350	4041	1680	10992	15.283843	0.3360	0	475	5262	9.026986	0.6217	0	1237	3491	35.43397	0.6683	0	255	550	46.36364	0.51190	0	1492	4041	36.92155	0.5546	0	751	7545	9.953612	0.3559	0	803	12478	6.435326	0.13310	0	1756	15.423803	0.6665	0	3042	26.71937	0.7280	0	1556	9021	17.24864	0.6195	0	336	3110	10.80386	0.3143	0	62	10616	0.5840241	0.3482	0	3295	11385	28.94159	0.4817	0	4350	100	2.298851	0.4559	0	478	10.988506	0.6962	0	20	4041	0.4949270	0.3450	0	192	4041	4.751299	0.4900	0	387	11385	3.399209	0.8606	1	2.00130	0.3396	0	2.6765	0.5915	0	0.4817	0.4753	0	2.8477	0.6611	1	8.00720	0.5007	1	16537	5776	5768	2288	16141	14.17508	0.3194	0	237	8403	2.820421	0.2461	0	1493	4973	30.02212	0.8197	1	315	795	39.62264	0.3868	0	1808	5768	31.34535	0.5823	0	986	11516	8.562001	0.4338	0	1326	15886	8.346972	0.4327	0	2472	14.94830	0.4225	0	3814	23.06343	0.6287	0	1742	12086.000	14.41337	0.4347	0	742	4396.0000	16.87898	0.5844	0	185	15466	1.1961722	0.50680	0	7164	16537.000	43.32104	0.5828	0	5776	138	2.3891967	0.4352	0	385	6.665513	0.6600	0	0	5768	0.0000000	0.1168	0	165	5768.0000	2.860610	0.3442	0	373	16537	2.255548	0.8244	1	2.0143	0.3437	0	2.57710	0.5357	0	0.5828	0.5757	0	2.3806	0.4199	1	7.55480	0.4282	1	NA	NA
10001040203	10	001	040203	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	4643	1865	1718	1441	4597	31.346530	0.7241	0	99	2296	4.311847	0.2007	0	362	1223	29.59935	0.4754	0	271	495	54.74747	0.70810	0	633	1718	36.84517	0.5523	0	436	2783	15.666547	0.5647	0	258	5110	5.048924	0.08961	0	505	10.876588	0.3849	0	1390	29.93754	0.8755	1	646	3590	17.99443	0.6562	0	292	1181	24.72481	0.7632	1	20	4310	0.4640371	0.3134	0	1780	4643	38.33728	0.5942	0	1865	91	4.879357	0.5508	0	252	13.512064	0.7236	0	52	1718	3.0267753	0.7465	0	197	1718	11.466822	0.7913	1	0	4643	0.000000	0.3814	0	2.13141	0.3754	0	2.9932	0.7618	2	0.5942	0.5863	0	3.1936	0.8133	1	8.91241	0.6347	3	5310	2259	2097	1163	5283	22.01401	0.5368	0	69	2413	2.859511	0.2514	0	418	1516	27.57256	0.7526	1	382	581	65.74871	0.9074	1	800	2097	38.14974	0.7609	1	162	3597	4.503753	0.2087	0	704	5297	13.290542	0.7059	0	1320	24.85876	0.8299	1	1429	26.91149	0.8347	1	513	3868.000	13.26267	0.3691	0	290	1430.0000	20.27972	0.6849	0	34	5062	0.6716713	0.39280	0	2582	5310.000	48.62524	0.6381	0	2259	153	6.7729084	0.5732	0	291	12.881806	0.7430	0	45	2097	2.1459227	0.6153	0	71	2097.0000	3.385789	0.3952	0	5	5310	0.094162	0.4618	0	2.4637	0.4806	1	3.11140	0.8223	2	0.6381	0.6303	0	2.7885	0.6181	0	9.00170	0.6555	3	4	537986
10001040501	10	001	040501	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	5172	2061	1721	2008	5121	39.211092	0.8425	1	134	1988	6.740443	0.4302	0	443	1191	37.19563	0.7145	0	312	530	58.86792	0.78710	1	755	1721	43.86984	0.7444	0	486	3108	15.637066	0.5640	0	493	4902	10.057120	0.26220	0	700	13.534416	0.5573	0	1681	32.50193	0.9414	1	518	3508	14.76625	0.4887	0	580	1392	41.66667	0.9424	1	12	4692	0.2557545	0.2451	0	3222	5172	62.29698	0.7880	1	2061	281	13.634158	0.7133	0	223	10.819990	0.6938	0	139	1721	8.0766996	0.9538	1	63	1721	3.660662	0.3972	0	0	5172	0.000000	0.3814	0	2.84330	0.5967	1	3.1749	0.8372	2	0.7880	0.7776	1	3.1395	0.7936	1	9.94570	0.7681	5	4731	2061	1979	1016	4703	21.60323	0.5269	0	208	2511	8.283552	0.8001	1	402	1423	28.25018	0.7714	1	300	556	53.95683	0.7197	0	702	1979	35.47246	0.6995	0	412	3336	12.350120	0.6094	0	230	4731	4.861552	0.2173	0	964	20.37624	0.6876	0	926	19.57303	0.4025	0	959	3805.000	25.20368	0.8859	1	278	1180.0000	23.55932	0.7634	1	244	4465	5.4647256	0.82700	1	2404	4731.000	50.81378	0.6589	0	2061	260	12.6152353	0.6783	0	299	14.507521	0.7619	1	39	1979	1.9706923	0.5899	0	133	1979.0000	6.720566	0.6323	0	0	4731	0.000000	0.2111	0	2.8532	0.6053	1	3.56640	0.9479	3	0.6589	0.6509	0	2.8735	0.6632	1	9.95200	0.7755	5	NA	NA
10001040502	10	001	040502	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	2087	921	921	192	2087	9.199808	0.1738	0	35	722	4.847645	0.2495	0	281	700	40.14286	0.7819	1	64	221	28.95928	0.17110	0	345	921	37.45928	0.5710	0	284	1546	18.369987	0.6484	0	119	2121	5.610561	0.10710	0	518	24.820316	0.9068	1	480	22.99952	0.4910	0	328	1527	21.48003	0.7959	1	173	680	25.44118	0.7769	1	100	1998	5.0050050	0.7960	1	560	2087	26.83277	0.4524	0	921	0	0.000000	0.1428	0	273	29.641694	0.8785	1	0	921	0.0000000	0.1488	0	30	921	3.257329	0.3600	0	0	2087	0.000000	0.3814	0	1.74980	0.2670	0	3.7666	0.9666	4	0.4524	0.4464	0	1.9115	0.2071	1	7.88030	0.4785	5	2555	1030	954	565	2555	22.11350	0.5385	0	135	1154	11.698440	0.9175	1	144	691	20.83936	0.4865	0	168	262	64.12214	0.8894	1	312	953	32.73872	0.6259	0	192	1782	10.774411	0.5377	0	198	2519	7.860262	0.4016	0	519	20.31311	0.6851	0	664	25.98826	0.7939	1	341	1854.295	18.38974	0.6427	0	195	614.6519	31.72527	0.8870	1	75	2351	3.1901319	0.72360	0	1215	2555.353	47.54725	0.6272	0	1030	61	5.9223301	0.5514	0	170	16.504854	0.7844	1	58	954	6.0796646	0.8947	1	83	953.5886	8.703963	0.7220	0	0	2555	0.000000	0.2111	0	3.0212	0.6565	1	3.73230	0.9680	2	0.6272	0.6195	0	3.1636	0.7893	2	10.54430	0.8433	5	2	245978

svi_national_lihtc10 <- left_join(svi_national, lihtc_projects10, join_by("GEOID_2010_trt" == "fips2010"))

svi_national_lihtc10 %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	FIPS_st	FIPS_county	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_10	E_HU_10	E_HH_10	E_POV150_10	ET_POVSTATUS_10	EP_POV150_10	EPL_POV150_10	F_POV150_10	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	F_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	F_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	F_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	F_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	F_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	F_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	F_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	F_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	F_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	F_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	F_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	F_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	F_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	F_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	F_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	F_NOVEH_10	E_GROUPQ_10	ET_HHTYPE_10	EP_GROUPQ_10	EPL_GROUPQ_10	F_GROUPQ_10	SPL_THEME1_10	RPL_THEME1_10	F_THEME1_10	SPL_THEME2_10	RPL_THEME2_10	F_THEME2_10	SPL_THEME3_10	RPL_THEME3_10	F_THEME3_10	SPL_THEME4_10	RPL_THEME4_10	F_THEME4_10	SPL_THEMES_10	RPL_THEMES_10	F_TOTAL_10	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	F_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	F_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	F_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	F_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	F_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	F_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	F_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	F_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	F_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	F_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	F_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	F_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	F_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	F_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	F_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	F_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	F_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20	F_GROUPQ_20	SPL_THEME1_20	RPL_THEME1_20	F_THEME1_20	SPL_THEME2_20	RPL_THEME2_20	F_THEME2_20	SPL_THEME3_20	RPL_THEME3_20	F_THEME3_20	SPL_THEME4_20	RPL_THEME4_20	F_THEME4_20	SPL_THEMES_20	RPL_THEMES_20	F_TOTAL_20	pre10_lihtc_project_cnt	pre10_lihtc_project_dollars
01001020100	01	001	020100	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	1809	771	696	297	1809	16.41791	0.3871	0	36	889	4.049494	0.1790	0	127	598	21.23746	0.20770	0	47	98	47.95918	0.5767	0	174	696	25.00000	0.18790	0	196	1242	15.780998	0.6093	0	186	1759	10.574190	0.3790	0	222	12.271973	0.4876	0	445	24.59923	0.5473	0	298	1335	22.32210	0.8454	1	27	545	4.954128	0.09275	0	36	1705	2.1114370	0.59040	0	385	1809	21.282477	0.4524	0	771	0	0.0000000	0.1224	0	92	11.9325551	0.8005	1	0	696	0.0000000	0.1238	0	50	696	7.183908	0.6134	0	0	1809	0	0.364	0	1.74230	0.28200	0	2.56345	0.5296	1	0.4524	0.4482	0	2.0241	0.2519	1	6.78225	0.3278	2	1941	710	693	352	1941	18.13498	0.4630	0	18	852	2.112676	0.15070	0	81	507	15.976331	0.26320	0	63	186	33.87097	0.2913	0	144	693	20.77922	0.2230	0	187	1309	14.285714	0.6928	0	187	1941	9.634209	0.6617	0	295	15.19835	0.4601	0	415	21.38073	0.4681	0	391	1526	25.62254	0.9011	1	58	555	10.45045	0.3451	0	0	1843	0.0000000	0.09479	0	437	1941	22.51417	0.3902	0	710	0	0.0000000	0.1079	0	88	12.3943662	0.8263	1	0	693	0.0000000	0.09796	0	10	693	1.443001	0.1643	0	0	1941	0.000000	0.1831	0	2.19120	0.4084	0	2.26919	0.3503	1	0.3902	0.3869	0	1.37956	0.07216	1	6.23015	0.2314	2	NA	NA
01001020200	01	001	020200	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	2020	816	730	495	1992	24.84940	0.5954	0	68	834	8.153477	0.5754	0	49	439	11.16173	0.02067	0	105	291	36.08247	0.3019	0	154	730	21.09589	0.09312	0	339	1265	26.798419	0.8392	1	313	2012	15.556660	0.6000	0	204	10.099010	0.3419	0	597	29.55446	0.8192	1	359	1515	23.69637	0.8791	1	132	456	28.947368	0.83510	1	15	1890	0.7936508	0.40130	0	1243	2020	61.534653	0.7781	1	816	0	0.0000000	0.1224	0	34	4.1666667	0.6664	0	13	730	1.7808219	0.5406	0	115	730	15.753425	0.8382	1	0	2020	0	0.364	0	2.70312	0.56650	1	3.27660	0.8614	3	0.7781	0.7709	1	2.5316	0.5047	1	9.28942	0.6832	6	1757	720	573	384	1511	25.41363	0.6427	0	29	717	4.044630	0.41320	0	33	392	8.418367	0.03542	0	116	181	64.08840	0.9086	1	149	573	26.00349	0.4041	0	139	1313	10.586443	0.5601	0	91	1533	5.936073	0.4343	0	284	16.16392	0.5169	0	325	18.49744	0.2851	0	164	1208	13.57616	0.4127	0	42	359	11.69916	0.3998	0	0	1651	0.0000000	0.09479	0	1116	1757	63.51736	0.7591	1	720	3	0.4166667	0.2470	0	5	0.6944444	0.5106	0	9	573	1.5706806	0.46880	0	57	573	9.947644	0.7317	0	212	1757	12.066022	0.9549	1	2.45440	0.4888	0	1.70929	0.1025	0	0.7591	0.7527	1	2.91300	0.68620	1	7.83579	0.4802	2	NA	NA
01001020300	01	001	020300	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	3543	1403	1287	656	3533	18.56779	0.4443	0	93	1552	5.992268	0.3724	0	273	957	28.52665	0.45780	0	178	330	53.93939	0.7152	0	451	1287	35.04274	0.49930	0	346	2260	15.309734	0.5950	0	252	3102	8.123791	0.2596	0	487	13.745413	0.5868	0	998	28.16822	0.7606	1	371	2224	16.68165	0.6266	0	126	913	13.800657	0.46350	0	0	3365	0.0000000	0.09298	0	637	3543	17.979114	0.4049	0	1403	10	0.7127584	0.3015	0	2	0.1425517	0.4407	0	0	1287	0.0000000	0.1238	0	101	1287	7.847708	0.6443	0	0	3543	0	0.364	0	2.17060	0.41010	0	2.53048	0.5116	1	0.4049	0.4011	0	1.8743	0.1942	0	6.98028	0.3576	1	3694	1464	1351	842	3694	22.79372	0.5833	0	53	1994	2.657974	0.22050	0	117	967	12.099276	0.11370	0	147	384	38.28125	0.3856	0	264	1351	19.54108	0.1827	0	317	2477	12.797739	0.6460	0	127	3673	3.457664	0.2308	0	464	12.56091	0.3088	0	929	25.14889	0.7080	0	473	2744	17.23761	0.6211	0	263	975	26.97436	0.8234	1	128	3586	3.5694367	0.70770	0	1331	3694	36.03140	0.5515	0	1464	26	1.7759563	0.3675	0	14	0.9562842	0.5389	0	35	1351	2.5906736	0.60550	0	42	1351	3.108808	0.3415	0	0	3694	0.000000	0.1831	0	1.86330	0.3063	0	3.16900	0.8380	1	0.5515	0.5468	0	2.03650	0.26830	0	7.62030	0.4460	1	2	216593
01001020400	01	001	020400	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	4840	1957	1839	501	4840	10.35124	0.2177	0	101	2129	4.744011	0.2447	0	310	1549	20.01291	0.17080	0	89	290	30.68966	0.2044	0	399	1839	21.69657	0.10540	0	274	3280	8.353658	0.3205	0	399	4293	9.294200	0.3171	0	955	19.731405	0.8643	1	1195	24.69008	0.5530	0	625	3328	18.78005	0.7233	0	152	1374	11.062591	0.34710	0	10	4537	0.2204100	0.22560	0	297	4840	6.136364	0.1647	0	1957	33	1.6862545	0.3843	0	25	1.2774655	0.5516	0	14	1839	0.7612833	0.3564	0	19	1839	1.033170	0.1127	0	0	4840	0	0.364	0	1.20540	0.13470	0	2.71330	0.6129	1	0.1647	0.1632	0	1.7690	0.1591	0	5.85240	0.1954	1	3539	1741	1636	503	3539	14.21305	0.3472	0	39	1658	2.352232	0.17990	0	219	1290	16.976744	0.30880	0	74	346	21.38728	0.1037	0	293	1636	17.90954	0.1333	0	173	2775	6.234234	0.3351	0	169	3529	4.788892	0.3448	0	969	27.38062	0.9225	1	510	14.41085	0.1208	0	670	3019	22.19278	0.8194	1	148	1137	13.01671	0.4541	0	89	3409	2.6107363	0.64690	0	454	3539	12.82848	0.2364	0	1741	143	8.2136703	0.6028	0	0	0.0000000	0.2186	0	10	1636	0.6112469	0.28340	0	72	1636	4.400978	0.4538	0	0	3539	0.000000	0.1831	0	1.34030	0.1575	0	2.96370	0.7496	2	0.2364	0.2344	0	1.74170	0.16270	0	6.28210	0.2389	2	NA	NA
01001020500	01	001	020500	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	9938	3969	3741	1096	9938	11.02838	0.2364	0	188	4937	3.807981	0.1577	0	426	2406	17.70574	0.11050	0	528	1335	39.55056	0.3753	0	954	3741	25.50120	0.20140	0	293	5983	4.897209	0.1655	0	740	10110	7.319486	0.2211	0	837	8.422218	0.2408	0	3012	30.30791	0.8455	1	759	7155	10.60797	0.2668	0	476	2529	18.821669	0.63540	0	78	9297	0.8389803	0.41110	0	1970	9938	19.822902	0.4330	0	3969	306	7.7097506	0.6153	0	0	0.0000000	0.2198	0	7	3741	0.1871157	0.2535	0	223	3741	5.960973	0.5483	0	0	9938	0	0.364	0	0.98210	0.08468	0	2.39960	0.4381	1	0.4330	0.4290	0	2.0009	0.2430	0	5.81560	0.1905	1	10674	4504	4424	1626	10509	15.47245	0.3851	0	81	5048	1.604596	0.09431	0	321	2299	13.962592	0.17970	0	711	2125	33.45882	0.2836	0	1032	4424	23.32731	0.3109	0	531	6816	7.790493	0.4251	0	301	10046	2.996217	0.1894	0	1613	15.11149	0.4553	0	2765	25.90407	0.7494	0	1124	7281	15.43744	0.5253	0	342	2912	11.74451	0.4019	0	52	9920	0.5241935	0.35230	0	2603	10674	24.38636	0.4160	0	4504	703	15.6083481	0.7378	0	29	0.6438721	0.5037	0	37	4424	0.8363472	0.33420	0	207	4424	4.679023	0.4754	0	176	10674	1.648866	0.7598	1	1.40481	0.1743	0	2.48420	0.4802	0	0.4160	0.4125	0	2.81090	0.63730	1	7.11591	0.3654	1	5	2250459
01001020600	01	001	020600	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	3402	1456	1308	735	3402	21.60494	0.5199	0	134	1720	7.790698	0.5436	0	242	1032	23.44961	0.28010	0	62	276	22.46377	0.1035	0	304	1308	23.24159	0.14070	0	301	2151	13.993491	0.5510	0	355	3445	10.304790	0.3656	0	386	11.346267	0.4232	0	931	27.36626	0.7200	0	440	2439	18.04018	0.6912	0	143	924	15.476190	0.52900	0	4	3254	0.1229256	0.19840	0	723	3402	21.252205	0.4519	0	1456	18	1.2362637	0.3507	0	433	29.7390110	0.9468	1	16	1308	1.2232416	0.4493	0	28	1308	2.140673	0.2298	0	0	3402	0	0.364	0	2.12080	0.39510	0	2.56180	0.5288	0	0.4519	0.4477	0	2.3406	0.4048	1	7.47510	0.4314	1	3536	1464	1330	1279	3523	36.30429	0.8215	1	34	1223	2.780049	0.23780	0	321	1111	28.892889	0.75870	1	67	219	30.59361	0.2305	0	388	1330	29.17293	0.5075	0	306	2380	12.857143	0.6480	0	415	3496	11.870709	0.7535	1	547	15.46946	0.4760	0	982	27.77149	0.8327	1	729	2514	28.99761	0.9488	1	95	880	10.79545	0.3601	0	0	3394	0.0000000	0.09479	0	985	3536	27.85633	0.4608	0	1464	0	0.0000000	0.1079	0	364	24.8633880	0.9300	1	0	1330	0.0000000	0.09796	0	17	1330	1.278196	0.1463	0	0	3536	0.000000	0.1831	0	2.96830	0.6434	2	2.71239	0.6156	2	0.4608	0.4569	0	1.46526	0.08976	1	7.60675	0.4440	5	NA	NA

svi_divisional_lihtc20 <- left_join(svi_divisional_lihtc10, lihtc_projects20, join_by("GEOID_2010_trt" == "fips2010"))

svi_divisional_lihtc20 %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	FIPS_st	FIPS_county	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_10	E_HU_10	E_HH_10	E_POV150_10	ET_POVSTATUS_10	EP_POV150_10	EPL_POV150_10	F_POV150_10	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	F_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	F_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	F_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	F_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	F_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	F_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	F_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	F_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	F_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	F_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	F_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	F_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	F_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	F_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	F_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	F_NOVEH_10	E_GROUPQ_10	ET_HHTYPE_10	EP_GROUPQ_10	EPL_GROUPQ_10	F_GROUPQ_10	SPL_THEME1_10	RPL_THEME1_10	F_THEME1_10	SPL_THEME2_10	RPL_THEME2_10	F_THEME2_10	SPL_THEME3_10	RPL_THEME3_10	F_THEME3_10	SPL_THEME4_10	RPL_THEME4_10	F_THEME4_10	SPL_THEMES_10	RPL_THEMES_10	F_TOTAL_10	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	F_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	F_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	F_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	F_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	F_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	F_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	F_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	F_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	F_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	F_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	F_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	F_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	F_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	F_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	F_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	F_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	F_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20	F_GROUPQ_20	SPL_THEME1_20	RPL_THEME1_20	F_THEME1_20	SPL_THEME2_20	RPL_THEME2_20	F_THEME2_20	SPL_THEME3_20	RPL_THEME3_20	F_THEME3_20	SPL_THEME4_20	RPL_THEME4_20	F_THEME4_20	SPL_THEMES_20	RPL_THEMES_20	F_TOTAL_20	pre10_lihtc_project_cnt	pre10_lihtc_project_dollars	post10_lihtc_project_cnt	post10_lihtc_project_dollars
10001040100	10	001	040100	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	6468	2388	2272	868	6455	13.446940	0.2879	0	201	3230	6.222910	0.3819	0	708	2036	34.77407	0.6489	0	47	236	19.91525	0.07811	0	755	2272	33.23063	0.4315	0	691	4369	15.815976	0.5692	0	400	6594	6.066121	0.12090	0	688	10.636982	0.3706	0	1689	26.11317	0.6923	0	725	5107	14.19620	0.4566	0	209	1742	11.99770	0.3673	0	0	5993	0.0000000	0.1022	0	845	6468	13.06432	0.2341	0	2388	0	0.000000	0.1428	0	601	25.167504	0.8423	1	14	2272	0.6161972	0.3716	0	92	2272	4.049296	0.4329	0	0	6468	0.000000	0.3814	0	1.79140	0.2784	0	1.9890	0.2036	0	0.2341	0.2310	0	2.1710	0.3144	1	6.18550	0.2310	1	7531	2850	2587	2226	7519	29.60500	0.7109	0	392	3820	10.261780	0.8786	1	527	2067	25.49589	0.6863	0	180	520	34.61538	0.2805	0	707	2587	27.32895	0.4528	0	765	4950	15.454546	0.7234	0	353	7523	4.692277	0.2050	0	1007	13.37140	0.3312	0	2035	27.02164	0.8398	1	1227	5488.000	22.35787	0.8093	1	239	1893.0000	12.62546	0.4261	0	276	7262	3.8006059	0.75930	1	1507	7531.000	20.01062	0.2789	0	2850	1	0.0350877	0.2526	0	697	24.456140	0.8585	1	93	2587	3.5948976	0.7607	1	55	2587.0000	2.126015	0.2641	0	0	7531	0.000000	0.2111	0	2.9707	0.6413	1	3.16570	0.8440	3	0.2789	0.2755	0	2.3470	0.4035	2	8.76230	0.6215	6	NA	NA	NA	NA
10001040201	10	001	040201	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	5208	1953	1809	850	5183	16.399769	0.3672	0	147	2550	5.764706	0.3364	0	385	1323	29.10053	0.4581	0	222	486	45.67901	0.49650	0	607	1809	33.55445	0.4431	0	459	3090	14.854369	0.5386	0	435	5283	8.233958	0.19610	0	454	8.717358	0.2561	0	1588	30.49155	0.8927	1	537	3716	14.45102	0.4708	0	417	1343	31.04989	0.8599	1	69	4835	1.4270941	0.5240	0	1881	5208	36.11751	0.5689	0	1953	87	4.454685	0.5392	0	148	7.578085	0.6495	0	39	1809	2.1558872	0.6471	0	121	1809	6.688778	0.6124	0	0	5208	0.000000	0.3814	0	1.88140	0.3053	0	3.0035	0.7667	2	0.5689	0.5614	0	2.8296	0.6516	0	8.28340	0.5452	2	4770	1906	1732	755	4692	16.09122	0.3758	0	92	2500	3.680000	0.3633	0	197	1184	16.63851	0.2804	0	235	548	42.88321	0.4609	0	432	1732	24.94226	0.3622	0	251	3100	8.096774	0.4085	0	228	4770	4.779874	0.2116	0	549	11.50943	0.2329	0	1352	28.34382	0.8865	1	490	3418.125	14.33535	0.4309	0	328	1263.2064	25.96567	0.8111	1	0	4526	0.0000000	0.09987	0	1875	4769.908	39.30893	0.5372	0	1906	72	3.7775446	0.4876	0	128	6.715635	0.6610	0	10	1732	0.5773672	0.3165	0	32	1731.7111	1.847883	0.2303	0	0	4770	0.000000	0.2111	0	1.7214	0.2531	0	2.46127	0.4594	2	0.5372	0.5306	0	1.9065	0.2183	0	6.62637	0.2829	2	NA	NA	NA	NA
10001040202	10	001	040202	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	11385	4350	4041	1680	10992	15.283843	0.3360	0	475	5262	9.026986	0.6217	0	1237	3491	35.43397	0.6683	0	255	550	46.36364	0.51190	0	1492	4041	36.92155	0.5546	0	751	7545	9.953612	0.3559	0	803	12478	6.435326	0.13310	0	1756	15.423803	0.6665	0	3042	26.71937	0.7280	0	1556	9021	17.24864	0.6195	0	336	3110	10.80386	0.3143	0	62	10616	0.5840241	0.3482	0	3295	11385	28.94159	0.4817	0	4350	100	2.298851	0.4559	0	478	10.988506	0.6962	0	20	4041	0.4949270	0.3450	0	192	4041	4.751299	0.4900	0	387	11385	3.399209	0.8606	1	2.00130	0.3396	0	2.6765	0.5915	0	0.4817	0.4753	0	2.8477	0.6611	1	8.00720	0.5007	1	16537	5776	5768	2288	16141	14.17508	0.3194	0	237	8403	2.820421	0.2461	0	1493	4973	30.02212	0.8197	1	315	795	39.62264	0.3868	0	1808	5768	31.34535	0.5823	0	986	11516	8.562001	0.4338	0	1326	15886	8.346972	0.4327	0	2472	14.94830	0.4225	0	3814	23.06343	0.6287	0	1742	12086.000	14.41337	0.4347	0	742	4396.0000	16.87898	0.5844	0	185	15466	1.1961722	0.50680	0	7164	16537.000	43.32104	0.5828	0	5776	138	2.3891967	0.4352	0	385	6.665513	0.6600	0	0	5768	0.0000000	0.1168	0	165	5768.0000	2.860610	0.3442	0	373	16537	2.255548	0.8244	1	2.0143	0.3437	0	2.57710	0.5357	0	0.5828	0.5757	0	2.3806	0.4199	1	7.55480	0.4282	1	NA	NA	NA	NA
10001040203	10	001	040203	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	4643	1865	1718	1441	4597	31.346530	0.7241	0	99	2296	4.311847	0.2007	0	362	1223	29.59935	0.4754	0	271	495	54.74747	0.70810	0	633	1718	36.84517	0.5523	0	436	2783	15.666547	0.5647	0	258	5110	5.048924	0.08961	0	505	10.876588	0.3849	0	1390	29.93754	0.8755	1	646	3590	17.99443	0.6562	0	292	1181	24.72481	0.7632	1	20	4310	0.4640371	0.3134	0	1780	4643	38.33728	0.5942	0	1865	91	4.879357	0.5508	0	252	13.512064	0.7236	0	52	1718	3.0267753	0.7465	0	197	1718	11.466822	0.7913	1	0	4643	0.000000	0.3814	0	2.13141	0.3754	0	2.9932	0.7618	2	0.5942	0.5863	0	3.1936	0.8133	1	8.91241	0.6347	3	5310	2259	2097	1163	5283	22.01401	0.5368	0	69	2413	2.859511	0.2514	0	418	1516	27.57256	0.7526	1	382	581	65.74871	0.9074	1	800	2097	38.14974	0.7609	1	162	3597	4.503753	0.2087	0	704	5297	13.290542	0.7059	0	1320	24.85876	0.8299	1	1429	26.91149	0.8347	1	513	3868.000	13.26267	0.3691	0	290	1430.0000	20.27972	0.6849	0	34	5062	0.6716713	0.39280	0	2582	5310.000	48.62524	0.6381	0	2259	153	6.7729084	0.5732	0	291	12.881806	0.7430	0	45	2097	2.1459227	0.6153	0	71	2097.0000	3.385789	0.3952	0	5	5310	0.094162	0.4618	0	2.4637	0.4806	1	3.11140	0.8223	2	0.6381	0.6303	0	2.7885	0.6181	0	9.00170	0.6555	3	4	537986	1	540738
10001040501	10	001	040501	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	5172	2061	1721	2008	5121	39.211092	0.8425	1	134	1988	6.740443	0.4302	0	443	1191	37.19563	0.7145	0	312	530	58.86792	0.78710	1	755	1721	43.86984	0.7444	0	486	3108	15.637066	0.5640	0	493	4902	10.057120	0.26220	0	700	13.534416	0.5573	0	1681	32.50193	0.9414	1	518	3508	14.76625	0.4887	0	580	1392	41.66667	0.9424	1	12	4692	0.2557545	0.2451	0	3222	5172	62.29698	0.7880	1	2061	281	13.634158	0.7133	0	223	10.819990	0.6938	0	139	1721	8.0766996	0.9538	1	63	1721	3.660662	0.3972	0	0	5172	0.000000	0.3814	0	2.84330	0.5967	1	3.1749	0.8372	2	0.7880	0.7776	1	3.1395	0.7936	1	9.94570	0.7681	5	4731	2061	1979	1016	4703	21.60323	0.5269	0	208	2511	8.283552	0.8001	1	402	1423	28.25018	0.7714	1	300	556	53.95683	0.7197	0	702	1979	35.47246	0.6995	0	412	3336	12.350120	0.6094	0	230	4731	4.861552	0.2173	0	964	20.37624	0.6876	0	926	19.57303	0.4025	0	959	3805.000	25.20368	0.8859	1	278	1180.0000	23.55932	0.7634	1	244	4465	5.4647256	0.82700	1	2404	4731.000	50.81378	0.6589	0	2061	260	12.6152353	0.6783	0	299	14.507521	0.7619	1	39	1979	1.9706923	0.5899	0	133	1979.0000	6.720566	0.6323	0	0	4731	0.000000	0.2111	0	2.8532	0.6053	1	3.56640	0.9479	3	0.6589	0.6509	0	2.8735	0.6632	1	9.95200	0.7755	5	NA	NA	NA	NA
10001040502	10	001	040502	DE	Delaware	Kent County	3	South Region	5	South Atlantic Division	2087	921	921	192	2087	9.199808	0.1738	0	35	722	4.847645	0.2495	0	281	700	40.14286	0.7819	1	64	221	28.95928	0.17110	0	345	921	37.45928	0.5710	0	284	1546	18.369987	0.6484	0	119	2121	5.610561	0.10710	0	518	24.820316	0.9068	1	480	22.99952	0.4910	0	328	1527	21.48003	0.7959	1	173	680	25.44118	0.7769	1	100	1998	5.0050050	0.7960	1	560	2087	26.83277	0.4524	0	921	0	0.000000	0.1428	0	273	29.641694	0.8785	1	0	921	0.0000000	0.1488	0	30	921	3.257329	0.3600	0	0	2087	0.000000	0.3814	0	1.74980	0.2670	0	3.7666	0.9666	4	0.4524	0.4464	0	1.9115	0.2071	1	7.88030	0.4785	5	2555	1030	954	565	2555	22.11350	0.5385	0	135	1154	11.698440	0.9175	1	144	691	20.83936	0.4865	0	168	262	64.12214	0.8894	1	312	953	32.73872	0.6259	0	192	1782	10.774411	0.5377	0	198	2519	7.860262	0.4016	0	519	20.31311	0.6851	0	664	25.98826	0.7939	1	341	1854.295	18.38974	0.6427	0	195	614.6519	31.72527	0.8870	1	75	2351	3.1901319	0.72360	0	1215	2555.353	47.54725	0.6272	0	1030	61	5.9223301	0.5514	0	170	16.504854	0.7844	1	58	954	6.0796646	0.8947	1	83	953.5886	8.703963	0.7220	0	0	2555	0.000000	0.2111	0	3.0212	0.6565	1	3.73230	0.9680	2	0.6272	0.6195	0	3.1636	0.7893	2	10.54430	0.8433	5	2	245978	NA	NA

svi_national_lihtc20 <- left_join(svi_national_lihtc10, lihtc_projects20, join_by("GEOID_2010_trt" == "fips2010"))

svi_national_lihtc20 %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	FIPS_st	FIPS_county	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_10	E_HU_10	E_HH_10	E_POV150_10	ET_POVSTATUS_10	EP_POV150_10	EPL_POV150_10	F_POV150_10	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	F_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	F_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	F_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	F_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	F_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	F_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	F_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	F_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	F_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	F_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	F_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	F_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	F_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	F_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	F_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	F_NOVEH_10	E_GROUPQ_10	ET_HHTYPE_10	EP_GROUPQ_10	EPL_GROUPQ_10	F_GROUPQ_10	SPL_THEME1_10	RPL_THEME1_10	F_THEME1_10	SPL_THEME2_10	RPL_THEME2_10	F_THEME2_10	SPL_THEME3_10	RPL_THEME3_10	F_THEME3_10	SPL_THEME4_10	RPL_THEME4_10	F_THEME4_10	SPL_THEMES_10	RPL_THEMES_10	F_TOTAL_10	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	F_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	F_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	F_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	F_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	F_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	F_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	F_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	F_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	F_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	F_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	F_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	F_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	F_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	F_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	F_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	F_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	F_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20	F_GROUPQ_20	SPL_THEME1_20	RPL_THEME1_20	F_THEME1_20	SPL_THEME2_20	RPL_THEME2_20	F_THEME2_20	SPL_THEME3_20	RPL_THEME3_20	F_THEME3_20	SPL_THEME4_20	RPL_THEME4_20	F_THEME4_20	SPL_THEMES_20	RPL_THEMES_20	F_TOTAL_20	pre10_lihtc_project_cnt	pre10_lihtc_project_dollars	post10_lihtc_project_cnt	post10_lihtc_project_dollars
01001020100	01	001	020100	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	1809	771	696	297	1809	16.41791	0.3871	0	36	889	4.049494	0.1790	0	127	598	21.23746	0.20770	0	47	98	47.95918	0.5767	0	174	696	25.00000	0.18790	0	196	1242	15.780998	0.6093	0	186	1759	10.574190	0.3790	0	222	12.271973	0.4876	0	445	24.59923	0.5473	0	298	1335	22.32210	0.8454	1	27	545	4.954128	0.09275	0	36	1705	2.1114370	0.59040	0	385	1809	21.282477	0.4524	0	771	0	0.0000000	0.1224	0	92	11.9325551	0.8005	1	0	696	0.0000000	0.1238	0	50	696	7.183908	0.6134	0	0	1809	0	0.364	0	1.74230	0.28200	0	2.56345	0.5296	1	0.4524	0.4482	0	2.0241	0.2519	1	6.78225	0.3278	2	1941	710	693	352	1941	18.13498	0.4630	0	18	852	2.112676	0.15070	0	81	507	15.976331	0.26320	0	63	186	33.87097	0.2913	0	144	693	20.77922	0.2230	0	187	1309	14.285714	0.6928	0	187	1941	9.634209	0.6617	0	295	15.19835	0.4601	0	415	21.38073	0.4681	0	391	1526	25.62254	0.9011	1	58	555	10.45045	0.3451	0	0	1843	0.0000000	0.09479	0	437	1941	22.51417	0.3902	0	710	0	0.0000000	0.1079	0	88	12.3943662	0.8263	1	0	693	0.0000000	0.09796	0	10	693	1.443001	0.1643	0	0	1941	0.000000	0.1831	0	2.19120	0.4084	0	2.26919	0.3503	1	0.3902	0.3869	0	1.37956	0.07216	1	6.23015	0.2314	2	NA	NA	NA	NA
01001020200	01	001	020200	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	2020	816	730	495	1992	24.84940	0.5954	0	68	834	8.153477	0.5754	0	49	439	11.16173	0.02067	0	105	291	36.08247	0.3019	0	154	730	21.09589	0.09312	0	339	1265	26.798419	0.8392	1	313	2012	15.556660	0.6000	0	204	10.099010	0.3419	0	597	29.55446	0.8192	1	359	1515	23.69637	0.8791	1	132	456	28.947368	0.83510	1	15	1890	0.7936508	0.40130	0	1243	2020	61.534653	0.7781	1	816	0	0.0000000	0.1224	0	34	4.1666667	0.6664	0	13	730	1.7808219	0.5406	0	115	730	15.753425	0.8382	1	0	2020	0	0.364	0	2.70312	0.56650	1	3.27660	0.8614	3	0.7781	0.7709	1	2.5316	0.5047	1	9.28942	0.6832	6	1757	720	573	384	1511	25.41363	0.6427	0	29	717	4.044630	0.41320	0	33	392	8.418367	0.03542	0	116	181	64.08840	0.9086	1	149	573	26.00349	0.4041	0	139	1313	10.586443	0.5601	0	91	1533	5.936073	0.4343	0	284	16.16392	0.5169	0	325	18.49744	0.2851	0	164	1208	13.57616	0.4127	0	42	359	11.69916	0.3998	0	0	1651	0.0000000	0.09479	0	1116	1757	63.51736	0.7591	1	720	3	0.4166667	0.2470	0	5	0.6944444	0.5106	0	9	573	1.5706806	0.46880	0	57	573	9.947644	0.7317	0	212	1757	12.066022	0.9549	1	2.45440	0.4888	0	1.70929	0.1025	0	0.7591	0.7527	1	2.91300	0.68620	1	7.83579	0.4802	2	NA	NA	NA	NA
01001020300	01	001	020300	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	3543	1403	1287	656	3533	18.56779	0.4443	0	93	1552	5.992268	0.3724	0	273	957	28.52665	0.45780	0	178	330	53.93939	0.7152	0	451	1287	35.04274	0.49930	0	346	2260	15.309734	0.5950	0	252	3102	8.123791	0.2596	0	487	13.745413	0.5868	0	998	28.16822	0.7606	1	371	2224	16.68165	0.6266	0	126	913	13.800657	0.46350	0	0	3365	0.0000000	0.09298	0	637	3543	17.979114	0.4049	0	1403	10	0.7127584	0.3015	0	2	0.1425517	0.4407	0	0	1287	0.0000000	0.1238	0	101	1287	7.847708	0.6443	0	0	3543	0	0.364	0	2.17060	0.41010	0	2.53048	0.5116	1	0.4049	0.4011	0	1.8743	0.1942	0	6.98028	0.3576	1	3694	1464	1351	842	3694	22.79372	0.5833	0	53	1994	2.657974	0.22050	0	117	967	12.099276	0.11370	0	147	384	38.28125	0.3856	0	264	1351	19.54108	0.1827	0	317	2477	12.797739	0.6460	0	127	3673	3.457664	0.2308	0	464	12.56091	0.3088	0	929	25.14889	0.7080	0	473	2744	17.23761	0.6211	0	263	975	26.97436	0.8234	1	128	3586	3.5694367	0.70770	0	1331	3694	36.03140	0.5515	0	1464	26	1.7759563	0.3675	0	14	0.9562842	0.5389	0	35	1351	2.5906736	0.60550	0	42	1351	3.108808	0.3415	0	0	3694	0.000000	0.1831	0	1.86330	0.3063	0	3.16900	0.8380	1	0.5515	0.5468	0	2.03650	0.26830	0	7.62030	0.4460	1	2	216593	NA	NA
01001020400	01	001	020400	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	4840	1957	1839	501	4840	10.35124	0.2177	0	101	2129	4.744011	0.2447	0	310	1549	20.01291	0.17080	0	89	290	30.68966	0.2044	0	399	1839	21.69657	0.10540	0	274	3280	8.353658	0.3205	0	399	4293	9.294200	0.3171	0	955	19.731405	0.8643	1	1195	24.69008	0.5530	0	625	3328	18.78005	0.7233	0	152	1374	11.062591	0.34710	0	10	4537	0.2204100	0.22560	0	297	4840	6.136364	0.1647	0	1957	33	1.6862545	0.3843	0	25	1.2774655	0.5516	0	14	1839	0.7612833	0.3564	0	19	1839	1.033170	0.1127	0	0	4840	0	0.364	0	1.20540	0.13470	0	2.71330	0.6129	1	0.1647	0.1632	0	1.7690	0.1591	0	5.85240	0.1954	1	3539	1741	1636	503	3539	14.21305	0.3472	0	39	1658	2.352232	0.17990	0	219	1290	16.976744	0.30880	0	74	346	21.38728	0.1037	0	293	1636	17.90954	0.1333	0	173	2775	6.234234	0.3351	0	169	3529	4.788892	0.3448	0	969	27.38062	0.9225	1	510	14.41085	0.1208	0	670	3019	22.19278	0.8194	1	148	1137	13.01671	0.4541	0	89	3409	2.6107363	0.64690	0	454	3539	12.82848	0.2364	0	1741	143	8.2136703	0.6028	0	0	0.0000000	0.2186	0	10	1636	0.6112469	0.28340	0	72	1636	4.400978	0.4538	0	0	3539	0.000000	0.1831	0	1.34030	0.1575	0	2.96370	0.7496	2	0.2364	0.2344	0	1.74170	0.16270	0	6.28210	0.2389	2	NA	NA	NA	NA
01001020500	01	001	020500	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	9938	3969	3741	1096	9938	11.02838	0.2364	0	188	4937	3.807981	0.1577	0	426	2406	17.70574	0.11050	0	528	1335	39.55056	0.3753	0	954	3741	25.50120	0.20140	0	293	5983	4.897209	0.1655	0	740	10110	7.319486	0.2211	0	837	8.422218	0.2408	0	3012	30.30791	0.8455	1	759	7155	10.60797	0.2668	0	476	2529	18.821669	0.63540	0	78	9297	0.8389803	0.41110	0	1970	9938	19.822902	0.4330	0	3969	306	7.7097506	0.6153	0	0	0.0000000	0.2198	0	7	3741	0.1871157	0.2535	0	223	3741	5.960973	0.5483	0	0	9938	0	0.364	0	0.98210	0.08468	0	2.39960	0.4381	1	0.4330	0.4290	0	2.0009	0.2430	0	5.81560	0.1905	1	10674	4504	4424	1626	10509	15.47245	0.3851	0	81	5048	1.604596	0.09431	0	321	2299	13.962592	0.17970	0	711	2125	33.45882	0.2836	0	1032	4424	23.32731	0.3109	0	531	6816	7.790493	0.4251	0	301	10046	2.996217	0.1894	0	1613	15.11149	0.4553	0	2765	25.90407	0.7494	0	1124	7281	15.43744	0.5253	0	342	2912	11.74451	0.4019	0	52	9920	0.5241935	0.35230	0	2603	10674	24.38636	0.4160	0	4504	703	15.6083481	0.7378	0	29	0.6438721	0.5037	0	37	4424	0.8363472	0.33420	0	207	4424	4.679023	0.4754	0	176	10674	1.648866	0.7598	1	1.40481	0.1743	0	2.48420	0.4802	0	0.4160	0.4125	0	2.81090	0.63730	1	7.11591	0.3654	1	5	2250459	NA	NA
01001020600	01	001	020600	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	3402	1456	1308	735	3402	21.60494	0.5199	0	134	1720	7.790698	0.5436	0	242	1032	23.44961	0.28010	0	62	276	22.46377	0.1035	0	304	1308	23.24159	0.14070	0	301	2151	13.993491	0.5510	0	355	3445	10.304790	0.3656	0	386	11.346267	0.4232	0	931	27.36626	0.7200	0	440	2439	18.04018	0.6912	0	143	924	15.476190	0.52900	0	4	3254	0.1229256	0.19840	0	723	3402	21.252205	0.4519	0	1456	18	1.2362637	0.3507	0	433	29.7390110	0.9468	1	16	1308	1.2232416	0.4493	0	28	1308	2.140673	0.2298	0	0	3402	0	0.364	0	2.12080	0.39510	0	2.56180	0.5288	0	0.4519	0.4477	0	2.3406	0.4048	1	7.47510	0.4314	1	3536	1464	1330	1279	3523	36.30429	0.8215	1	34	1223	2.780049	0.23780	0	321	1111	28.892889	0.75870	1	67	219	30.59361	0.2305	0	388	1330	29.17293	0.5075	0	306	2380	12.857143	0.6480	0	415	3496	11.870709	0.7535	1	547	15.46946	0.4760	0	982	27.77149	0.8327	1	729	2514	28.99761	0.9488	1	95	880	10.79545	0.3601	0	0	3394	0.0000000	0.09479	0	985	3536	27.85633	0.4608	0	1464	0	0.0000000	0.1079	0	364	24.8633880	0.9300	1	0	1330	0.0000000	0.09796	0	17	1330	1.278196	0.1463	0	0	3536	0.000000	0.1831	0	2.96830	0.6434	2	2.71239	0.6156	2	0.4608	0.4569	0	1.46526	0.08976	1	7.60675	0.4440	5	NA	NA	NA	NA

svi_divisional_lihtc20 <- svi_divisional_lihtc20 %>% 
  filter(is.na(pre10_lihtc_project_cnt)) %>% 
  filter(post10_lihtc_project_cnt >= 1) %>% 
  select(GEOID_2010_trt, pre10_lihtc_project_cnt, pre10_lihtc_project_dollars, post10_lihtc_project_cnt, post10_lihtc_project_dollars)

# View data
svi_divisional_lihtc20 %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	pre10_lihtc_project_cnt	pre10_lihtc_project_dollars	post10_lihtc_project_cnt	post10_lihtc_project_dollars
10003001902	NA	NA	1	0
10003010300	NA	NA	1	366252
10003012700	NA	NA	1	495592
10003014100	NA	NA	1	0
10003014501	NA	NA	2	0
10003014705	NA	NA	1	0

svi_national_lihtc20 <- svi_national_lihtc20 %>% 
  filter(is.na(pre10_lihtc_project_cnt)) %>% 
  filter(post10_lihtc_project_cnt >= 1) %>% 
  select(GEOID_2010_trt, pre10_lihtc_project_cnt, pre10_lihtc_project_dollars, post10_lihtc_project_cnt, post10_lihtc_project_dollars)

# View data
svi_national_lihtc20 %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	pre10_lihtc_project_cnt	pre10_lihtc_project_dollars	post10_lihtc_project_cnt	post10_lihtc_project_dollars
01003010500	NA	NA	1	481325
01003011601	NA	NA	1	887856
01017954600	NA	NA	2	950192
01021060101	NA	NA	1	812048
01039962600	NA	NA	1	434742
01043964900	NA	NA	1	1046201

Filter to Relevant Time-Frame

# Filter SVI divisional data to remove all tracts that had a project in 2010 or before:
svi_divisional_lihtc <-  svi_divisional %>% 
  filter(! GEOID_2010_trt %in% lihtc_projects10$fips2010)

# Merge SVI divisional data with post 2010 project data, create flag for projects (1 for tracts that have LIHTC project, 0 for those that do not):
svi_divisional_lihtc <- left_join(svi_divisional_lihtc, 
                                  svi_divisional_lihtc20, 
                                  join_by("GEOID_2010_trt" == "GEOID_2010_trt")) %>% 
                        mutate(pre10_lihtc_project_cnt = replace_na(pre10_lihtc_project_cnt, 0),
                               post10_lihtc_project_cnt = replace_na(post10_lihtc_project_cnt, 0),
                               pre10_lihtc_project_dollars = replace_na(pre10_lihtc_project_dollars, 0),
                               post10_lihtc_project_dollars = replace_na(post10_lihtc_project_dollars, 0),
                               lihtc_flag = if_else(post10_lihtc_project_cnt >= 1, 1, 0))

# Finally, we want to filter our dataset to only have tracts that are eligible for the LIHTC program and that have SVI data:
svi_divisional_lihtc <- left_join(svi_divisional_lihtc, lihtc_eligible_flag, 
                                  join_by("GEOID_2010_trt" == "GEOID10")) %>%
                        filter(tolower(lihtc_eligibility) == "yes") %>%
                        filter(!is.na(F_TOTAL_10)) %>% 
                        filter(!is.na(F_TOTAL_20)) 


# View data
svi_divisional_lihtc %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	FIPS_st	FIPS_county	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_10	E_HU_10	E_HH_10	E_POV150_10	ET_POVSTATUS_10	EP_POV150_10	EPL_POV150_10	F_POV150_10	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	F_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	F_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	F_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	F_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	F_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	F_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	F_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	F_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	F_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	F_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	F_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	F_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	F_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	F_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	F_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	F_NOVEH_10	E_GROUPQ_10	ET_HHTYPE_10	EP_GROUPQ_10	EPL_GROUPQ_10	F_GROUPQ_10	SPL_THEME1_10	RPL_THEME1_10	F_THEME1_10	SPL_THEME2_10	RPL_THEME2_10	F_THEME2_10	SPL_THEME3_10	RPL_THEME3_10	F_THEME3_10	SPL_THEME4_10	RPL_THEME4_10	F_THEME4_10	SPL_THEMES_10	RPL_THEMES_10	F_TOTAL_10	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	F_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	F_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	F_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	F_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	F_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	F_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	F_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	F_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	F_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	F_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	F_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	F_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	F_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	F_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	F_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	F_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	F_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20	F_GROUPQ_20	SPL_THEME1_20	RPL_THEME1_20	F_THEME1_20	SPL_THEME2_20	RPL_THEME2_20	F_THEME2_20	SPL_THEME3_20	RPL_THEME3_20	F_THEME3_20	SPL_THEME4_20	RPL_THEME4_20	F_THEME4_20	SPL_THEMES_20	RPL_THEMES_20	F_TOTAL_20	pre10_lihtc_project_cnt	pre10_lihtc_project_dollars	post10_lihtc_project_cnt	post10_lihtc_project_dollars	lihtc_flag	lihtc_eligibility
10003002200	10	003	002200	DE	Delaware	New Castle County	3	South Region	5	South Atlantic Division	3765	1162	957	2101	3738	56.20653	0.9604	1	191	1458	13.1001372	0.83340	1	194	329	58.96657	0.97690	1	377	628	60.03185	0.8081	1	571	957	59.66562	0.9664	1	779	1798	43.325918	0.9854	1	493	3339	14.764900	0.46710	0	225	5.976096	0.11800	0	1429	37.9548473	0.989700	1	417	1970	21.167513	0.78440	1	314	714	43.97759	0.95280	1	500	3388	14.7579693	0.9426	1	3668	3765	97.423639	0.9678	1	1162	23	1.979346	0.4385	0	0	0	0.1809	0	134	957	14.0020899	0.9922	1	308	957	32.18391	0.9657	1	7	3765	0.185923	0.7635	1	4.21270	0.9244	4	3.787500	0.968900	4	0.9678	0.9550	1	3.3408	0.8640	3	12.308800	0.9629	12	2815	994	737	1430	2784	51.36494	0.9554	1	106	1114	9.515260	0.8545	1	143	340	42.05882	0.9621	1	155	397	39.04282	0.3733	0	298	737	40.43419	0.8083	1	606	1745	34.727794	0.9837	1	402	2815	14.280639	0.7435	0	503	17.868561	0.57060	0	846	30.053286	0.92830	1	491	1969	24.936516	0.88050	1	151	565	26.72566	0.825600	1	560	2625	21.3333333	0.9754	1	2646	2815	93.99645	0.9462	1	994	11	1.10664	0.3592	0	0	0	0.18	0	6	737	0.8141113	0.3774	0	108	737	14.65400	0.8690	1	31	2815	1.1012433	0.7405	0	4.3454	0.9499	4	4.180400	0.984000	4	0.9462	0.9347	1	2.5261	0.4921	1	11.998100	0.9568	10	0	0	0	0	0	Yes
10003014501	10	003	014501	DE	Delaware	New Castle County	3	South Region	5	South Atlantic Division	1955	939	777	1386	1955	70.89514	0.9895	1	48	1122	4.2780749	0.19780	0	0	60	0.00000	0.00257	0	541	717	75.45328	0.9611	1	541	777	69.62677	0.9944	1	147	618	23.786408	0.7845	1	52	2056	2.529183	0.02664	0	131	6.700767	0.15180	0	6	0.3069054	0.005545	0	176	2056	8.560311	0.16120	0	0	80	0.00000	0.01071	0	8	1955	0.4092072	0.2950	0	184	1955	9.411765	0.1643	0	939	370	39.403621	0.9074	1	0	0	0.1809	0	0	777	0.0000000	0.1488	0	146	777	18.79022	0.9044	1	0	1955	0.000000	0.3814	0	2.99284	0.6440	3	0.624255	0.003867	0	0.1643	0.1621	0	2.5229	0.4936	2	6.304295	0.2474	5	2126	1068	956	1531	2126	72.01317	0.9948	1	136	1067	12.746017	0.9370	1	17	19	89.47368	0.9990	1	638	937	68.08965	0.9311	1	655	956	68.51464	0.9967	1	36	615	5.853658	0.2880	0	175	2126	8.231421	0.4254	0	174	8.184384	0.10240	0	140	6.585136	0.03945	0	121	1986	6.092649	0.05007	0	37	102	36.27451	0.923300	1	50	2074	2.4108004	0.6655	0	796	2126	37.44120	0.5142	0	1068	676	63.29588	0.9619	1	0	0	0.18	0	95	956	9.9372385	0.9670	1	322	956	33.68201	0.9751	1	0	2126	0.0000000	0.2111	0	3.6419	0.8250	3	1.780720	0.113400	1	0.5142	0.5080	0	3.2951	0.8374	3	9.231920	0.6857	7	0	0	2	0	1	Yes
10003014502	10	003	014502	DE	Delaware	New Castle County	3	South Region	5	South Atlantic Division	5783	1441	1105	2275	2996	75.93458	0.9934	1	132	2389	5.5253244	0.31400	0	50	229	21.83406	0.19290	0	652	876	74.42922	0.9567	1	702	1105	63.52941	0.9830	1	33	587	5.621806	0.1831	0	180	6088	2.956636	0.03618	0	118	2.040463	0.02041	0	233	4.0290507	0.020260	0	151	3410	4.428153	0.02916	0	115	199	57.78894	0.98670	1	39	5711	0.6828927	0.3758	0	491	5783	8.490403	0.1472	0	1441	472	32.755031	0.8757	1	0	0	0.1809	0	52	1105	4.7058824	0.8627	1	242	1105	21.90045	0.9274	1	2787	5783	48.192979	0.9885	1	2.50968	0.4907	2	1.432330	0.040710	1	0.1472	0.1453	0	3.8352	0.9660	4	7.924410	0.4875	7	6752	1338	1064	2027	2989	67.81532	0.9920	1	353	2685	13.147114	0.9437	1	45	160	28.12500	0.7682	1	620	904	68.58407	0.9350	1	665	1064	62.50000	0.9903	1	52	1141	4.557406	0.2116	0	305	6727	4.533968	0.1964	0	201	2.976896	0.01566	0	103	1.525474	0.01056	0	182	2931	6.209485	0.05207	0	0	196	0.00000	0.008258	0	33	6752	0.4887441	0.3436	0	1244	6752	18.42417	0.2554	0	1338	386	28.84903	0.8433	1	0	0	0.18	0	0	1064	0.0000000	0.1168	0	117	1064	10.99624	0.7959	1	3772	6752	55.8649289	0.9908	1	3.3340	0.7465	3	0.430148	0.002408	0	0.2554	0.2523	0	2.9268	0.6874	3	6.946348	0.3282	6	0	0	0	0	0	Yes
11001001803	11	001	001803	DC	District of Columbia	District of Columbia	3	South Region	5	South Atlantic Division	2965	1716	1585	712	2965	24.01349	0.5660	0	249	1742	14.2939150	0.86780	1	85	352	24.14773	0.27170	0	654	1233	53.04136	0.6691	0	739	1585	46.62461	0.8084	1	530	2290	23.144105	0.7689	1	621	3307	18.778349	0.64490	0	442	14.907251	0.64050	0	433	14.6037099	0.130700	0	397	2619	15.158457	0.50950	0	128	666	19.21922	0.63310	0	323	2875	11.2347826	0.9153	1	2807	2965	94.671164	0.9471	1	1716	1178	68.648019	0.9715	1	0	0	0.1809	0	92	1585	5.8044164	0.9056	1	605	1585	38.17035	0.9779	1	0	2965	0.000000	0.3814	0	3.65600	0.8220	3	2.829100	0.677100	1	0.9471	0.9346	1	3.4173	0.8900	3	10.849500	0.8633	8	4161	1765	1623	1067	4161	25.64287	0.6257	0	166	2245	7.394209	0.7467	0	60	216	27.77778	0.7579	1	682	1407	48.47193	0.5905	0	742	1623	45.71781	0.8878	1	624	2995	20.834725	0.8628	1	563	4161	13.530401	0.7154	0	400	9.613074	0.14970	0	918	22.062004	0.56160	0	717	3243	22.109158	0.80240	1	200	730	27.39726	0.836500	1	533	3914	13.6177823	0.9433	1	3689	4161	88.65657	0.9109	1	1765	1317	74.61756	0.9757	1	0	0	0.18	0	199	1623	12.2612446	0.9830	1	690	1623	42.51386	0.9878	1	5	4161	0.1201634	0.4852	0	3.8384	0.8685	2	3.293500	0.887500	3	0.9109	0.8998	1	3.6117	0.9260	3	11.654500	0.9370	9	0	0	0	0	0	Yes
11001002001	11	001	002001	DC	District of Columbia	District of Columbia	3	South Region	5	South Atlantic Division	2668	1096	1004	345	2668	12.93103	0.2723	0	13	1550	0.8387097	0.02102	0	78	406	19.21182	0.12550	0	398	598	66.55518	0.8962	1	476	1004	47.41036	0.8256	1	327	2055	15.912409	0.5720	0	219	2427	9.023486	0.22340	0	308	11.544228	0.42830	0	457	17.1289355	0.196800	0	299	1964	15.224033	0.51180	0	221	606	36.46865	0.91070	1	127	2521	5.0376835	0.7970	1	2082	2668	78.035982	0.8691	1	1096	471	42.974453	0.9187	1	0	0	0.1809	0	3	1004	0.2988048	0.3128	0	342	1004	34.06374	0.9705	1	183	2668	6.859070	0.9133	1	1.91432	0.3153	1	2.844600	0.685900	2	0.8691	0.8576	1	3.2962	0.8507	3	8.924220	0.6368	7	3578	1241	1181	1230	3571	34.44413	0.7939	1	88	1925	4.571429	0.4798	0	96	340	28.23529	0.7710	1	534	841	63.49584	0.8822	1	630	1181	53.34462	0.9577	1	828	2392	34.615385	0.9832	1	137	3572	3.835386	0.1527	0	570	15.930687	0.47320	0	988	27.613192	0.86160	1	358	2588	13.833076	0.40220	0	188	855	21.98830	0.727700	0	698	3258	21.4241866	0.9755	1	3296	3578	92.11850	0.9329	1	1241	838	67.52619	0.9678	1	0	0	0.18	0	216	1181	18.2895851	0.9962	1	490	1181	41.49026	0.9866	1	96	3578	2.6830632	0.8444	1	3.3673	0.7557	3	3.440200	0.924000	2	0.9329	0.9215	1	3.9750	0.9729	4	11.715400	0.9405	10	0	0	0	0	0	Yes
11001002101	11	001	002101	DC	District of Columbia	District of Columbia	3	South Region	5	South Atlantic Division	4735	2173	1797	1273	4735	26.88490	0.6333	0	380	2545	14.9312377	0.88620	1	413	910	45.38462	0.87540	1	528	887	59.52649	0.7988	1	941	1797	52.36505	0.9021	1	974	3332	29.231693	0.8912	1	1104	5530	19.963834	0.68980	0	511	10.791975	0.37930	0	962	20.3167899	0.326700	0	473	4252	11.124177	0.29400	0	318	997	31.89569	0.86880	1	523	4484	11.6636931	0.9194	1	4604	4735	97.233368	0.9664	1	2173	861	39.622642	0.9084	1	0	0	0.1809	0	60	1797	3.3388982	0.7732	1	427	1797	23.76183	0.9390	1	116	4735	2.449842	0.8331	1	4.00260	0.8910	3	2.788200	0.656100	2	0.9664	0.9536	1	3.6346	0.9380	4	11.391800	0.9101	10	5693	2360	2143	1011	5693	17.75865	0.4264	0	82	3360	2.440476	0.1930	0	390	1070	36.44860	0.9206	1	344	1073	32.05965	0.2338	0	734	2143	34.25105	0.6685	0	518	3999	12.953238	0.6323	0	320	5693	5.620938	0.2628	0	684	12.014755	0.25940	0	1440	25.294221	0.75820	1	387	4253	9.099459	0.15780	0	438	1260	34.76190	0.914000	1	281	5250	5.3523810	0.8235	1	5097	5693	89.53100	0.9159	1	2360	1021	43.26271	0.9125	1	0	0	0.18	0	70	2143	3.2664489	0.7350	0	490	2143	22.86514	0.9409	1	12	5693	0.2107852	0.5502	0	2.1830	0.3950	0	2.912900	0.728400	3	0.9159	0.9047	1	3.3186	0.8452	2	9.330400	0.6994	6	0	0	2	301689	1	Yes

# Filter SVI national data to remove all tracts that had a project in 2010 or before:
svi_national_lihtc <-  svi_national %>% 
  filter(! GEOID_2010_trt %in% lihtc_projects10$fips2010)

# Merge SVI national data with post 2010 project data, create flag for projects (1 for tracts that have LIHTC project, 0 for those that do not):
svi_national_lihtc <- left_join(svi_national_lihtc, 
                                  svi_national_lihtc20, 
                                  join_by("GEOID_2010_trt" == "GEOID_2010_trt")) %>% 
                        mutate(pre10_lihtc_project_cnt = replace_na(pre10_lihtc_project_cnt, 0),
                               post10_lihtc_project_cnt = replace_na(post10_lihtc_project_cnt, 0),
                                pre10_lihtc_project_dollars = replace_na(pre10_lihtc_project_dollars, 0),
                               post10_lihtc_project_dollars = replace_na(post10_lihtc_project_dollars, 0),
                               lihtc_flag = if_else(post10_lihtc_project_cnt >= 1, 1, 0))

# Finally, we want to filter our dataset to only have tracts that are eligible for the LIHTC program and that have SVI data:
svi_national_lihtc <- left_join(svi_national_lihtc, lihtc_eligible_flag, 
                                  join_by("GEOID_2010_trt" == "GEOID10")) %>%
                        filter(tolower(lihtc_eligibility) == "yes") %>%
                        filter(!is.na(F_TOTAL_10)) %>% 
                        filter(!is.na(F_TOTAL_20)) 


# View data
svi_national_lihtc %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	FIPS_st	FIPS_county	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_10	E_HU_10	E_HH_10	E_POV150_10	ET_POVSTATUS_10	EP_POV150_10	EPL_POV150_10	F_POV150_10	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	F_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	F_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	F_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	F_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	F_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	F_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	F_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	F_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	F_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	F_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	F_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	F_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	F_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	F_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	F_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	F_NOVEH_10	E_GROUPQ_10	ET_HHTYPE_10	EP_GROUPQ_10	EPL_GROUPQ_10	F_GROUPQ_10	SPL_THEME1_10	RPL_THEME1_10	F_THEME1_10	SPL_THEME2_10	RPL_THEME2_10	F_THEME2_10	SPL_THEME3_10	RPL_THEME3_10	F_THEME3_10	SPL_THEME4_10	RPL_THEME4_10	F_THEME4_10	SPL_THEMES_10	RPL_THEMES_10	F_TOTAL_10	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	F_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	F_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	F_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	F_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	F_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	F_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	F_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	F_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	F_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	F_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	F_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	F_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	F_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	F_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	F_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	F_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	F_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20	F_GROUPQ_20	SPL_THEME1_20	RPL_THEME1_20	F_THEME1_20	SPL_THEME2_20	RPL_THEME2_20	F_THEME2_20	SPL_THEME3_20	RPL_THEME3_20	F_THEME3_20	SPL_THEME4_20	RPL_THEME4_20	F_THEME4_20	SPL_THEMES_20	RPL_THEMES_20	F_TOTAL_20	pre10_lihtc_project_cnt	pre10_lihtc_project_dollars	post10_lihtc_project_cnt	post10_lihtc_project_dollars	lihtc_flag	lihtc_eligibility
01005950700	01	005	950700	AL	Alabama	Barbour County	3	South Region	6	East South Central Division	1753	687	563	615	1628	37.77641	0.8088	1	17	667	2.548726	0.06941	0	41	376	10.90426	0.01945	0	62	187	33.15508	0.2464	0	103	563	18.29485	0.04875	0	264	1208	21.85430	0.7570	1	201	1527	13.163065	0.4991	0	368	20.992584	0.89510	1	462	26.354820	0.66130	0	211	1085	19.44700	0.7505	1	107	399	26.81704	0.8048	1	0	1628	0.000000	0.09298	0	861	1753	49.11580	0.7101	0	687	17	2.474527	0.4324	0	38	5.5312955	0.6970	0	3	563	0.5328597	0.3037	0	19	563	3.374778	0.3529	0	233	1753	13.29150	0.9517	1	2.18306	0.4137	2	3.20468	0.8377	3	0.7101	0.7035	0	2.7377	0.6100	1	8.83554	0.6264	6	1527	691	595	565	1365	41.39194	0.8765	1	37	572	6.468532	0.6776	0	70	376	18.617021	0.38590	0	92	219	42.00913	0.4736	0	162	595	27.22689	0.4454	0	280	1114	25.13465	0.8942	1	105	1378	7.619739	0.5505	0	383	25.081860	0.88450	1	337	22.069417	0.51380	0	237	1041.0000	22.76657	0.8360	1	144	413.0000	34.86683	0.9114	1	11	1466	0.7503411	0.40700	0	711	1527.0000	46.56189	0.6441	0	691	13	1.881331	0.3740	0	37	5.3545586	0.7152	0	0	595	0.0000000	0.09796	0	115	595.0000	19.327731	0.8859	1	149	1527	9.757695	0.9470	1	3.4442	0.7707	2	3.55270	0.9403	3	0.6441	0.6387	0	3.02006	0.7337	2	10.66106	0.8537	7	0	0	0	0	0	Yes
01011952100	01	011	952100	AL	Alabama	Bullock County	3	South Region	6	East South Central Division	1652	796	554	564	1652	34.14044	0.7613	1	46	816	5.637255	0.33630	0	96	458	20.96070	0.19930	0	62	96	64.58333	0.8917	1	158	554	28.51986	0.29220	0	271	1076	25.18587	0.8163	1	155	1663	9.320505	0.3183	0	199	12.046005	0.47180	0	420	25.423729	0.60240	0	327	1279	25.56685	0.9151	1	137	375	36.53333	0.9108	1	0	1590	0.000000	0.09298	0	1428	1652	86.44068	0.8939	1	796	0	0.000000	0.1224	0	384	48.2412060	0.9897	1	19	554	3.4296029	0.7145	0	45	554	8.122744	0.6556	0	0	1652	0.00000	0.3640	0	2.52440	0.5138	2	2.99308	0.7515	2	0.8939	0.8856	1	2.8462	0.6637	1	9.25758	0.6790	6	1382	748	549	742	1382	53.69030	0.9560	1	40	511	7.827789	0.7730	1	110	402	27.363184	0.71780	0	45	147	30.61224	0.2307	0	155	549	28.23315	0.4773	0	181	905	20.00000	0.8253	1	232	1382	16.787265	0.8813	1	164	11.866860	0.27170	0	250	18.089725	0.26290	0	258	1132.0000	22.79152	0.8368	1	99	279.0000	35.48387	0.9162	1	33	1275	2.5882353	0.64520	0	1347	1382.0000	97.46744	0.9681	1	748	0	0.000000	0.1079	0	375	50.1336898	0.9922	1	0	549	0.0000000	0.09796	0	37	549.0000	6.739526	0.6039	0	0	1382	0.000000	0.1831	0	3.9129	0.8785	4	2.93280	0.7342	2	0.9681	0.9599	1	1.98506	0.2471	1	9.79886	0.7570	8	0	0	0	0	0	Yes
01015000300	01	015	000300	AL	Alabama	Calhoun County	3	South Region	6	East South Central Division	3074	1635	1330	1904	3067	62.08021	0.9710	1	293	1362	21.512482	0.96630	1	180	513	35.08772	0.65450	0	383	817	46.87882	0.5504	0	563	1330	42.33083	0.70280	0	720	2127	33.85049	0.9148	1	628	2835	22.151675	0.8076	1	380	12.361744	0.49340	0	713	23.194535	0.45030	0	647	2111	30.64898	0.9708	1	298	773	38.55110	0.9247	1	0	2878	0.000000	0.09298	0	2623	3074	85.32856	0.8883	1	1635	148	9.051988	0.6465	0	6	0.3669725	0.4502	0	68	1330	5.1127820	0.8082	1	303	1330	22.781955	0.9029	1	0	3074	0.00000	0.3640	0	4.36250	0.9430	4	2.93218	0.7233	2	0.8883	0.8800	1	3.1718	0.8070	2	11.35478	0.9009	9	2390	1702	1282	1287	2390	53.84937	0.9566	1	102	1066	9.568480	0.8541	1	158	609	25.944171	0.67520	0	286	673	42.49629	0.4856	0	444	1282	34.63339	0.6634	0	467	1685	27.71513	0.9180	1	369	2379	15.510719	0.8562	1	342	14.309623	0.40850	0	548	22.928870	0.57100	0	647	1831.0000	35.33588	0.9862	1	202	576.0000	35.06944	0.9130	1	16	2134	0.7497657	0.40690	0	1896	2390.0000	79.33054	0.8451	1	1702	96	5.640423	0.5329	0	0	0.0000000	0.2186	0	0	1282	0.0000000	0.09796	0	186	1282.0000	14.508580	0.8308	1	43	2390	1.799163	0.7727	1	4.2483	0.9395	4	3.28560	0.8773	2	0.8451	0.8379	1	2.45296	0.4602	2	10.83196	0.8718	9	0	0	0	0	0	Yes
01015000500	01	015	000500	AL	Alabama	Calhoun County	3	South Region	6	East South Central Division	1731	1175	743	1042	1619	64.36072	0.9767	1	124	472	26.271186	0.98460	1	136	461	29.50108	0.48970	0	163	282	57.80142	0.7919	1	299	743	40.24226	0.64910	0	340	1270	26.77165	0.8389	1	460	1794	25.641026	0.8722	1	271	15.655690	0.70190	0	368	21.259388	0.32190	0	507	1449	34.98965	0.9885	1	150	386	38.86010	0.9269	1	0	1677	0.000000	0.09298	0	1559	1731	90.06355	0.9123	1	1175	50	4.255319	0.5128	0	4	0.3404255	0.4480	0	0	743	0.0000000	0.1238	0	122	743	16.419919	0.8473	1	0	1731	0.00000	0.3640	0	4.32150	0.9362	4	3.03218	0.7679	2	0.9123	0.9038	1	2.2959	0.3818	1	10.56188	0.8244	8	940	907	488	586	940	62.34043	0.9815	1	59	297	19.865320	0.9833	1	100	330	30.303030	0.79220	1	58	158	36.70886	0.3497	0	158	488	32.37705	0.6020	0	199	795	25.03145	0.8930	1	118	940	12.553192	0.7770	1	246	26.170213	0.90530	1	118	12.553192	0.08233	0	383	822.5089	46.56484	0.9984	1	30	197.8892	15.16000	0.5363	0	0	889	0.0000000	0.09479	0	898	940.3866	95.49264	0.9489	1	907	0	0.000000	0.1079	0	2	0.2205072	0.4456	0	2	488	0.4098361	0.23670	0	146	487.6463	29.939736	0.9404	1	0	940	0.000000	0.1831	0	4.2368	0.9379	4	2.61712	0.5593	2	0.9489	0.9409	1	1.91370	0.2196	1	9.71652	0.7468	8	0	0	0	0	0	Yes
01015000600	01	015	000600	AL	Alabama	Calhoun County	3	South Region	6	East South Central Division	2571	992	796	1394	2133	65.35396	0.9789	1	263	905	29.060773	0.98990	1	121	306	39.54248	0.75940	1	209	490	42.65306	0.4481	0	330	796	41.45729	0.68030	0	641	1556	41.19537	0.9554	1	416	1760	23.636364	0.8383	1	220	8.556982	0.24910	0	584	22.714897	0.41610	0	539	1353	39.83740	0.9955	1	243	466	52.14592	0.9783	1	30	2366	1.267963	0.48990	0	1944	2571	75.61260	0.8440	1	992	164	16.532258	0.7673	1	8	0.8064516	0.5110	0	46	796	5.7788945	0.8329	1	184	796	23.115578	0.9049	1	614	2571	23.88176	0.9734	1	4.44280	0.9548	4	3.12890	0.8088	2	0.8440	0.8362	1	3.9895	0.9792	4	12.40520	0.9696	11	1950	964	719	837	1621	51.63479	0.9467	1	157	652	24.079755	0.9922	1	22	364	6.043956	0.01547	0	129	355	36.33803	0.3420	0	151	719	21.00139	0.2303	0	363	1387	26.17159	0.9048	1	351	1613	21.760694	0.9435	1	249	12.769231	0.32090	0	356	18.256410	0.27140	0	332	1259.7041	26.35540	0.9135	1	136	435.6156	31.22018	0.8775	1	0	1891	0.0000000	0.09479	0	1463	1949.9821	75.02633	0.8219	1	964	14	1.452282	0.3459	0	8	0.8298755	0.5269	0	19	719	2.6425591	0.61120	0	197	719.0542	27.397100	0.9316	1	329	1950	16.871795	0.9655	1	4.0175	0.9001	4	2.47809	0.4764	2	0.8219	0.8149	1	3.38110	0.8712	2	10.69859	0.8583	9	0	0	0	0	0	Yes
01015002101	01	015	002101	AL	Alabama	Calhoun County	3	South Region	6	East South Central Division	3872	1454	1207	1729	2356	73.38710	0.9916	1	489	2020	24.207921	0.97860	1	20	168	11.90476	0.02541	0	718	1039	69.10491	0.9332	1	738	1207	61.14333	0.96900	1	113	725	15.58621	0.6035	0	664	3943	16.839970	0.6495	0	167	4.313016	0.05978	0	238	6.146694	0.02255	0	264	2359	11.19118	0.3027	0	94	263	35.74144	0.9050	1	46	3769	1.220483	0.48250	0	1601	3872	41.34814	0.6572	0	1454	761	52.338377	0.9504	1	65	4.4704264	0.6738	0	5	1207	0.4142502	0.2791	0	113	1207	9.362055	0.7004	0	1516	3872	39.15289	0.9860	1	4.19220	0.9133	3	1.77253	0.1304	1	0.6572	0.6511	0	3.5897	0.9337	2	10.21163	0.7885	6	3238	1459	1014	1082	1836	58.93246	0.9735	1	251	1403	17.890235	0.9767	1	31	155	20.000000	0.44920	0	515	859	59.95343	0.8554	1	546	1014	53.84615	0.9535	1	134	916	14.62882	0.7033	0	251	3238	7.751699	0.5588	0	167	5.157505	0.03597	0	169	5.219271	0.02111	0	323	1667.0000	19.37612	0.7205	0	94	277.0000	33.93502	0.9040	1	0	3164	0.0000000	0.09479	0	1045	3238.0000	32.27301	0.5125	0	1459	607	41.603838	0.9185	1	65	4.4551062	0.6949	0	24	1014	2.3668639	0.57900	0	85	1014.0000	8.382643	0.6775	0	1402	3238	43.298332	0.9876	1	4.1658	0.9263	3	1.77637	0.1225	1	0.5125	0.5082	0	3.85750	0.9661	2	10.31217	0.8160	6	0	0	0	0	0	Yes

svi_national_lihtc_county_sum <- summarize_county_lihtc(svi_national_lihtc)

svi_national_lihtc_county_sum %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

State	County	Division	post10_lihtc_project_cnt	tract_cnt	post10_lihtc_project_dollars	post10_lihtc_dollars_formatted
AK	Bethel Census Area	Pacific Division	0	2	0	\$0
AK	Dillingham Census Area	Pacific Division	0	1	0	\$0
AK	Kenai Peninsula Borough	Pacific Division	0	1	0	\$0
AK	Nome Census Area	Pacific Division	0	1	0	\$0
AK	Yukon-Koyukuk Census Area	Pacific Division	0	2	0	\$0
AL	Barbour County	East South Central Division	0	1	0	\$0

svi_divisional_lihtc_county_sum <- summarize_county_lihtc(svi_divisional_lihtc)
svi_divisional_lihtc_county_sum %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

State	County	Division	post10_lihtc_project_cnt	tract_cnt	post10_lihtc_project_dollars	post10_lihtc_dollars_formatted
DC	District of Columbia	South Atlantic Division	21	53	11695555	\$11,695,555
DE	New Castle County	South Atlantic Division	2	3	0	\$0
FL	Alachua County	South Atlantic Division	0	1	0	\$0
FL	Bay County	South Atlantic Division	1	2	0	\$0
FL	Brevard County	South Atlantic Division	0	1	0	\$0
FL	Broward County	South Atlantic Division	3	17	2561000	\$2,561,000

Create data frame of LIHTC eligible tracts 2010 nationally

# Create data frame of LIHTC eligible tracts 2010 nationally
svi_national_lihtc10 <- svi_national_lihtc %>% select(GEOID_2010_trt, FIPS_st, FIPS_county, 
    state, state_name, county, region_number, region, division_number, 
    division, F_TOTAL_10, E_TOTPOP_10) %>% rename("F_TOTAL" = "F_TOTAL_10")

# Count national-level SVI flags for 2010, create unified fips column
svi_2010_national_county_flags_lihtc <- flag_summarize(svi_national_lihtc10, "E_TOTPOP_10") %>% unite("fips_county_st", FIPS_st:FIPS_county, remove = FALSE, sep="")

# Add suffix to flag columns 2010
colnames(svi_2010_national_county_flags_lihtc)[11:15] <- paste0(colnames(svi_2010_national_county_flags_lihtc)[11:15], 10)

# Create data frame of LIHTC eligible tracts 2020 nationally
svi_national_lihtc20 <- svi_national_lihtc %>% select(GEOID_2010_trt, FIPS_st, FIPS_county, 
    state, state_name, county, region_number, region, division_number, 
    division, F_TOTAL_20, E_TOTPOP_20) %>% rename("F_TOTAL" = "F_TOTAL_20")

# Count national-level SVI flags for 2020, create unified fips column
svi_2020_national_county_flags_lihtc <- flag_summarize(svi_national_lihtc20, "E_TOTPOP_20") %>% unite("fips_county_st", FIPS_st:FIPS_county, remove = FALSE, sep="")

# Identify needed columns for 2020, add suffix
colnames(svi_2020_national_county_flags_lihtc)[11:15] <- paste0(colnames(svi_2020_national_county_flags_lihtc)[11:15], "20")

# Filter to needed columns for 2020 to avoid duplicate column column names
svi_2020_national_county_flags_join_lihtc <- svi_2020_national_county_flags_lihtc %>% ungroup() %>% select("fips_county_st", all_of(colnames(svi_2020_national_county_flags_lihtc)[11:15]))
 
# Join 2010 and 2020 data
svi_national_county_flags_lihtc <- left_join(svi_2010_national_county_flags_lihtc, svi_2020_national_county_flags_join_lihtc, join_by("fips_county_st" == "fips_county_st")) 

svi_national_county_flags_lihtc %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

fips_county_st	FIPS_st	FIPS_county	state	state_name	county	region_number	region	division_number	division	flag_count10	pop10	flag_by_pop10	flag_count_quantile10	flag_pop_quantile10	flag_count20	pop20	flag_by_pop20	flag_count_quantile20	flag_pop_quantile20
01005	01	005	AL	Alabama	Barbour County	3	South Region	6	East South Central Division	6	1753	0.0034227	0.2	0.8	7	1527	0.0045842	0.2	1.0
01011	01	011	AL	Alabama	Bullock County	3	South Region	6	East South Central Division	6	1652	0.0036320	0.2	0.8	8	1382	0.0057887	0.4	1.0
01015	01	015	AL	Alabama	Calhoun County	3	South Region	6	East South Central Division	40	15130	0.0026438	0.8	0.6	37	11783	0.0031401	0.8	0.8
01023	01	023	AL	Alabama	Choctaw County	3	South Region	6	East South Central Division	12	5578	0.0021513	0.6	0.4	15	5412	0.0027716	0.6	0.8
01031	01	031	AL	Alabama	Coffee County	3	South Region	6	East South Central Division	12	8139	0.0014744	0.6	0.2	13	8517	0.0015264	0.6	0.2
01033	01	033	AL	Alabama	Colbert County	3	South Region	6	East South Central Division	10	1983	0.0050429	0.4	1.0	8	1931	0.0041429	0.4	1.0

svi_national_county_lihtc <- left_join(svi_national_lihtc_county_sum,
                                      svi_national_county_flags_lihtc,
                                    join_by("State" == "state", "County" == "county",
                                            "Division" == "division"))

svi_national_county_lihtc$post10_lihtc_project_cnt[is.na(svi_national_county_lihtc$post10_lihtc_project_cnt)] <- 0

svi_national_county_lihtc$county_name <- paste0(svi_national_county_lihtc$County, ", ", svi_national_county_lihtc$State)

svi_national_county_lihtc %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

State	County	Division	post10_lihtc_project_cnt	tract_cnt	post10_lihtc_project_dollars	post10_lihtc_dollars_formatted	fips_county_st	FIPS_st	FIPS_county	state_name	region_number	region	division_number	flag_count10	pop10	flag_by_pop10	flag_count_quantile10	flag_pop_quantile10	flag_count20	pop20	flag_by_pop20	flag_count_quantile20	flag_pop_quantile20	county_name
AK	Bethel Census Area	Pacific Division	0	2	0	\$0	02050	02	050	Alaska	4	West Region	9	18	10867	0.0016564	0.6	0.4	20	11715	0.0017072	0.8	0.4	Bethel Census Area, AK
AK	Dillingham Census Area	Pacific Division	0	1	0	\$0	02070	02	070	Alaska	4	West Region	9	9	2569	0.0035033	0.4	0.8	10	2801	0.0035702	0.4	0.8	Dillingham Census Area, AK
AK	Kenai Peninsula Borough	Pacific Division	0	1	0	\$0	02122	02	122	Alaska	4	West Region	9	7	251	0.0278884	0.2	1.0	8	531	0.0150659	0.4	1.0	Kenai Peninsula Borough, AK
AK	Nome Census Area	Pacific Division	0	1	0	\$0	02180	02	180	Alaska	4	West Region	9	9	5766	0.0015609	0.4	0.2	10	5901	0.0016946	0.4	0.4	Nome Census Area, AK
AK	Yukon-Koyukuk Census Area	Pacific Division	0	2	0	\$0	02290	02	290	Alaska	4	West Region	9	18	2300	0.0078261	0.6	1.0	21	2153	0.0097538	0.8	1.0	Yukon-Koyukuk Census Area, AK
AL	Barbour County	East South Central Division	0	1	0	\$0	01005	01	005	Alabama	3	South Region	6	6	1753	0.0034227	0.2	0.8	7	1527	0.0045842	0.2	1.0	Barbour County, AL

Create data frame of LIHTC eligible tracts 2020 nationally

# Create data frame of LIHTC eligible tracts 2020 nationally
svi_divisional_lihtc10 <- svi_divisional_lihtc %>% select(GEOID_2010_trt, FIPS_st, FIPS_county, 
    state, state_name, county, region_number, region, division_number, 
    division, F_TOTAL_10, E_TOTPOP_10) %>% rename("F_TOTAL" = "F_TOTAL_10")

# Count divisional-level SVI flags for 2010, create unified fips column
svi_2010_divisional_county_flags_lihtc <- flag_summarize(svi_divisional_lihtc10, "E_TOTPOP_10") %>% unite("fips_county_st", FIPS_st:FIPS_county, remove = FALSE, sep="")

# Add suffix to flag columns 2010
colnames(svi_2010_divisional_county_flags_lihtc)[11:15] <- paste0(colnames(svi_2010_divisional_county_flags_lihtc)[11:15], "10")

# Create data frame of LIHTC eligible tracts 2020 nationally
svi_divisional_lihtc20 <- svi_divisional_lihtc %>% select(GEOID_2010_trt, FIPS_st, FIPS_county, 
    state, state_name, county, region_number, region, division_number, 
    division, F_TOTAL_20, E_TOTPOP_20) %>% rename("F_TOTAL" = "F_TOTAL_20")

# Count divisional-level SVI flags for 2020, create unified fips column
svi_2020_divisional_county_flags_lihtc <- flag_summarize(svi_divisional_lihtc20, "E_TOTPOP_20") %>% unite("fips_county_st", FIPS_st:FIPS_county, remove = FALSE, sep="")

# Identify needed columns for 2020
colnames(svi_2020_divisional_county_flags_lihtc)[11:15] <- paste0(colnames(svi_2020_divisional_county_flags_lihtc)[11:15], "20")

# Filter to needed columns for 2020 to avoid duplicate column column names
svi_2020_divisional_county_flags_join_lihtc <- svi_2020_divisional_county_flags_lihtc %>% ungroup() %>% select("fips_county_st", all_of(colnames(svi_2020_divisional_county_flags_lihtc)[11:15]))
 
# Join 2010 and 2020 data
svi_divisional_county_flags_lihtc <- left_join(svi_2010_divisional_county_flags_lihtc, svi_2020_divisional_county_flags_join_lihtc, join_by("fips_county_st" == "fips_county_st")) 

svi_divisional_county_flags_lihtc %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

fips_county_st	FIPS_st	FIPS_county	state	state_name	county	region_number	region	division_number	division	flag_count10	pop10	flag_by_pop10	flag_count_quantile10	flag_pop_quantile10	flag_count20	pop20	flag_by_pop20	flag_count_quantile20	flag_pop_quantile20
10003	10	003	DE	Delaware	New Castle County	3	South Region	5	South Atlantic Division	24	11503	0.0020864	0.8	0.4	23	11693	0.0019670	0.8	0.4
11001	11	001	DC	District of Columbia	District of Columbia	3	South Region	5	South Atlantic Division	407	157923	0.0025772	1.0	0.6	387	192201	0.0020135	1.0	0.4
12001	12	001	FL	Florida	Alachua County	3	South Region	5	South Atlantic Division	7	6610	0.0010590	0.4	0.2	5	7702	0.0006492	0.2	0.2
12005	12	005	FL	Florida	Bay County	3	South Region	5	South Atlantic Division	17	5145	0.0033042	0.8	0.8	12	4297	0.0027926	0.6	0.8
12009	12	009	FL	Florida	Brevard County	3	South Region	5	South Atlantic Division	9	2367	0.0038023	0.4	1.0	8	3293	0.0024294	0.4	0.6
12011	12	011	FL	Florida	Broward County	3	South Region	5	South Atlantic Division	166	71933	0.0023077	1.0	0.6	160	84636	0.0018904	1.0	0.4

svi_divisional_county_lihtc <- left_join(svi_divisional_lihtc_county_sum, 
                                        svi_divisional_county_flags_lihtc,
                                    join_by("State" == "state", "County" == "county",
                                            "Division" == "division"))

svi_divisional_county_lihtc$post10_lihtc_project_cnt[is.na(svi_divisional_county_lihtc $post10_lihtc_project_cnt)] <- 0

svi_divisional_county_lihtc$county_name <- paste0(svi_divisional_county_lihtc$County, ", ", svi_divisional_county_lihtc$State)

svi_divisional_county_lihtc %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

State	County	Division	post10_lihtc_project_cnt	tract_cnt	post10_lihtc_project_dollars	post10_lihtc_dollars_formatted	fips_county_st	FIPS_st	FIPS_county	state_name	region_number	region	division_number	flag_count10	pop10	flag_by_pop10	flag_count_quantile10	flag_pop_quantile10	flag_count20	pop20	flag_by_pop20	flag_count_quantile20	flag_pop_quantile20	county_name
DC	District of Columbia	South Atlantic Division	21	53	11695555	\$11,695,555	11001	11	001	District of Columbia	3	South Region	5	407	157923	0.0025772	1.0	0.6	387	192201	0.0020135	1.0	0.4	District of Columbia, DC
DE	New Castle County	South Atlantic Division	2	3	0	\$0	10003	10	003	Delaware	3	South Region	5	24	11503	0.0020864	0.8	0.4	23	11693	0.0019670	0.8	0.4	New Castle County, DE
FL	Alachua County	South Atlantic Division	0	1	0	\$0	12001	12	001	Florida	3	South Region	5	7	6610	0.0010590	0.4	0.2	5	7702	0.0006492	0.2	0.2	Alachua County, FL
FL	Bay County	South Atlantic Division	1	2	0	\$0	12005	12	005	Florida	3	South Region	5	17	5145	0.0033042	0.8	0.8	12	4297	0.0027926	0.6	0.8	Bay County, FL
FL	Brevard County	South Atlantic Division	0	1	0	\$0	12009	12	009	Florida	3	South Region	5	9	2367	0.0038023	0.4	1.0	8	3293	0.0024294	0.4	0.6	Brevard County, FL
FL	Broward County	South Atlantic Division	3	17	2561000	\$2,561,000	12011	12	011	Florida	3	South Region	5	166	71933	0.0023077	1.0	0.6	160	84636	0.0018904	1.0	0.4	Broward County, FL

Exporatory Data Analysis

National Data Summary

svi_national_county_nmtc_projects <- svi_national_county_nmtc %>% filter(post10_nmtc_project_cnt > 0)

summary(svi_national_county_nmtc_projects$flag_count10)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##       0      19      40     177     132    9936

summary(svi_national_county_nmtc_projects$post10_nmtc_project_dollars)

##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
##      5154   5970000  12760000  30332743  28505000 987407086

Looking into our data, we see a large variety between minimum and maximums for both flags as well as dollars. Our means however, are much closer to our first and third quartials than to the maximium values. This could indicate that we have outliers skewing our results.

# Scatterplot
# y is our independent variable (NMTC Project Dollars),  
# x is our dependent variable (SVI flag count)
ggplot2::ggplot(svi_national_county_nmtc_projects,
                aes(x=flag_count10,
                    y=post10_nmtc_project_dollars)) +
        geom_point() +
        geom_smooth(method="lm") +
        scale_y_continuous(labels = scales::dollar_format()) 

# Pearson's r calculation
cor(svi_national_county_nmtc_projects$flag_count10, svi_national_county_nmtc_projects$post10_nmtc_project_dollars, method = "pearson")

## [1] 0.8197007

While we do see a strong correlation with out Pearson’s R Calculation, looking at our data in a scatterplot confirms that our data is likely skewed.

boxplot(svi_national_county_nmtc_projects$flag_count10)

boxplot.stats(svi_national_county_nmtc_projects$flag_count10)$out %>% sort(decreasing = TRUE)

##   [1] 9936 5111 3732 3561 2941 2537 2508 2453 2296 2279 1906 1731 1569 1489 1486
##  [16] 1436 1110 1106 1096 1057 1013 1004  995  991  984  977  960  942  919  871
##  [31]  854  850  836  822  795  774  759  746  731  728  718  714  705  701  691
##  [46]  682  651  650  641  629  595  572  571  569  564  546  541  531  517  499
##  [61]  491  487  484  468  467  463  461  458  458  455  451  442  429  423  422
##  [76]  413  413  408  401  399  389  381  378  378  377  374  369  363  362  358
##  [91]  356  355  352  350  349  349  342  341  338  328  326  318  316  313

svi_national_nmtc_cluster <- svi_national_county_nmtc_projects %>% 
                            select(county_name, post10_nmtc_project_dollars, 
                                   flag_count10) %>% 
                            remove_rownames %>% 
                            column_to_rownames(var="county_name")

# Remove nulls, if in dataset
svi_national_nmtc_cluster <- na.omit(svi_national_nmtc_cluster)


# Scale numeric variables
svi_national_nmtc_cluster <- scale(svi_national_nmtc_cluster)


svi_national_nmtc_cluster %>% head(5)

##                               post10_nmtc_project_dollars flag_count10
## Aleutians East Borough, AK                     -0.2125961   -0.3189664
## Anchorage Municipality, AK                     -0.2995956   -0.1981871
## Wade Hampton Census Area, AK                   -0.1300468   -0.3170792
## Yukon-Koyukuk Census Area, AK                  -0.3310540   -0.2831100
## Baldwin County, AL                             -0.1906291   -0.2698998

set.seed(123)
k2_nmtc_nat <- kmeans(svi_national_nmtc_cluster, centers = 2, nstart = 25)
set.seed(123)
k3_nmtc_nat <- kmeans(svi_national_nmtc_cluster, centers = 3, nstart = 25)
set.seed(123)
k4_nmtc_nat <- kmeans(svi_national_nmtc_cluster, centers = 4, nstart = 25)
set.seed(123)
k5_nmtc_nat <- kmeans(svi_national_nmtc_cluster, centers = 5, nstart = 25)

# plots to compare
p_k2_nmtc_nat <- factoextra::fviz_cluster(k2_nmtc_nat, geom = "point", data = svi_national_nmtc_cluster) + ggtitle("k = 2")

p_k3_nmtc_nat <- factoextra::fviz_cluster(k3_nmtc_nat, geom = "point", data = svi_national_nmtc_cluster) + ggtitle("k = 3")

p_k4_nmtc_nat <- factoextra::fviz_cluster(k4_nmtc_nat, geom = "point",  data = svi_national_nmtc_cluster) + ggtitle("k = 4")

p_k5_nmtc_nat <- factoextra::fviz_cluster(k5_nmtc_nat, geom = "point",  data = svi_national_nmtc_cluster) + ggtitle("k = 5")

grid.arrange(p_k2_nmtc_nat, p_k3_nmtc_nat, p_k4_nmtc_nat, p_k5_nmtc_nat, nrow = 2)

elbow_plot(svi_national_nmtc_cluster)

##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15
## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
## [1] 6
## [1] 7
## [1] 8
## [1] 9
## [1] 10
## [1] 11
## [1] 12
## [1] 13
## [1] 14
## [1] 15

It appears that it would be best to view our data in clusters of three, so we can look at different groupings and see how the data correlates without as much bias.

p_k3_nmtc_nat <- factoextra::fviz_cluster(k3_nmtc_nat, geom = "point", data = svi_national_nmtc_cluster) + ggtitle("k = 3")

p_k3_nmtc_nat

svi_national_nmtc_cluster_label <- as.data.frame(svi_national_nmtc_cluster) %>%
                                  rownames_to_column(var = "county_name") %>%
                                  as_tibble() %>%
                                  mutate(cluster = k3_nmtc_nat$cluster) %>%
                                  select(county_name, cluster)

svi_national_county_nmtc_projects2 <- left_join(svi_national_county_nmtc_projects, svi_national_nmtc_cluster_label, join_by(county_name == county_name))

# View county counts in each cluster
table(svi_national_county_nmtc_projects2$cluster)

## 
##   1   2   3 
##   2  27 758

# Cluster 1 Scatterplot
# y is our independent variable (NMTC Project Dollars),  
# x is our dependent variable (SVI flag count)

svi_national_county_nmtc_projects2 %>% 
  filter(cluster == 1) %>%
  ggplot2::ggplot(aes(x=flag_count10,
                    y=post10_nmtc_project_dollars)) +
        geom_point() +
        geom_smooth(method="lm") +
        scale_y_continuous(labels = scales::dollar_format()) 

svi_national_county_nmtc_projects2 %>% 
  filter(cluster == 1) %>%
  select(flag_count10,post10_nmtc_project_dollars) %>%
  cor(method = "pearson")

##                             flag_count10 post10_nmtc_project_dollars
## flag_count10                           1                           1
## post10_nmtc_project_dollars            1                           1

Looking at our fisrt cluster, we can see that there is a direct correlation between money spent and total flags. However, with only two groups in the cluster, it does not show enough data to show that this trend is true and repeatable.

svi_national_county_nmtc_projects2 %>% 
  filter(cluster == 1) %>%
  select(county_name, flag_count10, post10_nmtc_dollars_formatted) %>% arrange(flag_count10) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

county_name	flag_count10	post10_nmtc_dollars_formatted
Cook County, IL	5111	\$790,158,215
Los Angeles County, CA	9936	\$987,407,086

# Cluster 2 Scatterplot
# y is our independent variable (NMTC Project Dollars),  
# x is our dependent variable (SVI flag count)

svi_national_county_nmtc_projects2 %>% 
  filter(cluster == 2) %>%
  ggplot2::ggplot(aes(x=flag_count10,
                    y=post10_nmtc_project_dollars)) +
        geom_point() +
        geom_smooth(method="lm") +
        scale_y_continuous(labels = scales::dollar_format()) 

Looking at our group 2 on the other hand, we see no strong correlation between the amount of money and the amount of flags.

svi_national_county_nmtc_projects2 %>% 
  filter(cluster == 2) %>%
  select(flag_count10,post10_nmtc_project_dollars) %>%
  cor(method = "pearson")

##                             flag_count10 post10_nmtc_project_dollars
## flag_count10                  1.00000000                 -0.05722041
## post10_nmtc_project_dollars  -0.05722041                  1.00000000

svi_national_county_nmtc_projects2 %>% 
  filter(cluster == 2) %>%
  select(county_name, flag_count10, post10_nmtc_dollars_formatted) %>% arrange(flag_count10) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

county_name	flag_count10	post10_nmtc_dollars_formatted
Hennepin County, MN	564	\$173,393,000
District of Columbia, DC	595	\$377,155,570
Hamilton County, OH	629	\$397,543,856
Jackson County, MO	651	\$201,937,281
Suffolk County, MA	701	\$192,010,188
Orleans Parish, LA	718	\$233,891,078
Allegheny County, PA	731	\$244,342,400
Fulton County, GA	795	\$437,737,000
New York County, NY	850	\$255,545,686
Marion County, IN	919	\$138,426,520
Baltimore city, MD	960	\$333,109,232
Milwaukee County, WI	984	\$315,495,493
Shelby County, TN	1004	\$289,352,388
Fresno County, CA	1110	\$115,258,480
Bexar County, TX	1436	\$199,964,269
Clark County, NV	1486	\$225,315,967
Cuyahoga County, OH	1489	\$306,121,487
San Diego County, CA	1569	\$221,738,411
Philadelphia County, PA	1906	\$550,969,964
Queens County, NY	2279	\$85,225,100
Miami-Dade County, FL	2296	\$233,661,603
Maricopa County, AZ	2453	\$84,923,464
Dallas County, TX	2508	\$302,019,013
Bronx County, NY	2537	\$306,355,715
Wayne County, MI	2941	\$360,949,480
Harris County, TX	3561	\$262,456,477
Kings County, NY	3732	\$216,348,600

# Cluster 3 Scatterplot
# y is our independent variable (NMTC Project Dollars),  
# x is our dependent variable (SVI flag count)

svi_national_county_nmtc_projects2 %>% 
  filter(cluster == 3) %>%
  ggplot2::ggplot(aes(x=flag_count10,
                    y=post10_nmtc_project_dollars)) +
        geom_point() +
        geom_smooth(method="lm") +
        scale_y_continuous(labels = scales::dollar_format()) 

svi_national_county_nmtc_projects2 %>% 
  filter(cluster == 3) %>%
  select(flag_count10,post10_nmtc_project_dollars) %>%
  cor(method = "pearson")

##                             flag_count10 post10_nmtc_project_dollars
## flag_count10                   1.0000000                   0.4736348
## post10_nmtc_project_dollars    0.4736348                   1.0000000

Finally, when we look at our third and largest cluster, we do see a positive trend. While it is nota very strong trend, it still does suggest some positive correlation.

# Recall that we are working with 2010 census tracts, thus we need to pull the 2010 shapefiles
county_sf = tigris::counties(year = 2010, cb = TRUE)

##   |                                                                              |                                                                      |   0%  |                                                                              |                                                                      |   1%  |                                                                              |=                                                                     |   1%  |                                                                              |=                                                                     |   2%  |                                                                              |==                                                                    |   2%  |                                                                              |==                                                                    |   3%  |                                                                              |===                                                                   |   4%  |                                                                              |===                                                                   |   5%  |                                                                              |====                                                                  |   5%  |                                                                              |====                                                                  |   6%  |                                                                              |=====                                                                 |   7%  |                                                                              |=====                                                                 |   8%  |                                                                              |======                                                                |   8%  |                                                                              |======                                                                |   9%  |                                                                              |=======                                                               |   9%  |                                                                              |=======                                                               |  10%  |                                                                              |=======                                                               |  11%  |                                                                              |========                                                              |  11%  |                                                                              |========                                                              |  12%  |                                                                              |=========                                                             |  12%  |                                                                              |=========                                                             |  13%  |                                                                              |==========                                                            |  14%  |                                                                              |==========                                                            |  15%  |                                                                              |===========                                                           |  15%  |                                                                              |===========                                                           |  16%  |                                                                              |============                                                          |  17%  |                                                                              |=============                                                         |  18%  |                                                                              |=============                                                         |  19%  |                                                                              |==============                                                        |  19%  |                                                                              |==============                                                        |  20%  |                                                                              |===============                                                       |  21%  |                                                                              |===============                                                       |  22%  |                                                                              |================                                                      |  22%  |                                                                              |================                                                      |  23%  |                                                                              |=================                                                     |  24%  |                                                                              |=================                                                     |  25%  |                                                                              |==================                                                    |  25%  |                                                                              |===================                                                   |  27%  |                                                                              |===================                                                   |  28%  |                                                                              |====================                                                  |  28%  |                                                                              |====================                                                  |  29%  |                                                                              |=====================                                                 |  29%  |                                                                              |=====================                                                 |  30%  |                                                                              |======================                                                |  32%  |                                                                              |=======================                                               |  32%  |                                                                              |=======================                                               |  33%  |                                                                              |========================                                              |  34%  |                                                                              |========================                                              |  35%  |                                                                              |==========================                                            |  37%  |                                                                              |==========================                                            |  38%  |                                                                              |===========================                                           |  38%  |                                                                              |===========================                                           |  39%  |                                                                              |============================                                          |  39%  |                                                                              |============================                                          |  40%  |                                                                              |============================                                          |  41%  |                                                                              |=============================                                         |  41%  |                                                                              |=============================                                         |  42%  |                                                                              |==============================                                        |  42%  |                                                                              |==============================                                        |  43%  |                                                                              |==============================                                        |  44%  |                                                                              |===============================                                       |  44%  |                                                                              |===============================                                       |  45%  |                                                                              |================================                                      |  45%  |                                                                              |================================                                      |  46%  |                                                                              |=================================                                     |  47%  |                                                                              |=================================                                     |  48%  |                                                                              |==================================                                    |  48%  |                                                                              |==================================                                    |  49%  |                                                                              |===================================                                   |  49%  |                                                                              |===================================                                   |  50%  |                                                                              |====================================                                  |  51%  |                                                                              |====================================                                  |  52%  |                                                                              |=====================================                                 |  52%  |                                                                              |=====================================                                 |  53%  |                                                                              |======================================                                |  54%  |                                                                              |======================================                                |  55%  |                                                                              |=======================================                               |  55%  |                                                                              |=======================================                               |  56%  |                                                                              |========================================                              |  56%  |                                                                              |========================================                              |  57%  |                                                                              |========================================                              |  58%  |                                                                              |=========================================                             |  58%  |                                                                              |=========================================                             |  59%  |                                                                              |==========================================                            |  59%  |                                                                              |==========================================                            |  60%  |                                                                              |==========================================                            |  61%  |                                                                              |===========================================                           |  61%  |                                                                              |============================================                          |  63%  |                                                                              |==============================================                        |  65%  |                                                                              |===============================================                       |  67%  |                                                                              |================================================                      |  69%  |                                                                              |==================================================                    |  71%  |                                                                              |===================================================                   |  73%  |                                                                              |=====================================================                 |  75%  |                                                                              |======================================================                |  77%  |                                                                              |=======================================================               |  79%  |                                                                              |=========================================================             |  81%  |                                                                              |==========================================================            |  83%  |                                                                              |===========================================================           |  84%  |                                                                              |============================================================          |  86%  |                                                                              |=============================================================         |  87%  |                                                                              |=============================================================         |  88%  |                                                                              |==============================================================        |  88%  |                                                                              |==============================================================        |  89%  |                                                                              |===============================================================       |  89%  |                                                                              |===============================================================       |  90%  |                                                                              |===============================================================       |  91%  |                                                                              |================================================================      |  91%  |                                                                              |================================================================      |  92%  |                                                                              |=================================================================     |  92%  |                                                                              |=================================================================     |  93%  |                                                                              |==================================================================    |  94%  |                                                                              |==================================================================    |  95%  |                                                                              |===================================================================   |  95%  |                                                                              |===================================================================   |  96%  |                                                                              |====================================================================  |  97%  |                                                                              |====================================================================  |  98%  |                                                                              |======================================================================|  99%  |                                                                              |======================================================================| 100%

# Shift geometric locations of AK and HI
county_sf <- shift_geometry(
              county_sf,
              geoid_column = NULL,
              preserve_area = FALSE,
              position = c("below", "outside")
            )

st_sf <- tigris::states(year = 2010, cb = TRUE) %>% filter(STATE != "72")

##   |                                                                              |                                                                      |   0%  |                                                                              |=                                                                     |   1%  |                                                                              |===                                                                   |   4%  |                                                                              |====                                                                  |   6%  |                                                                              |=====                                                                 |   8%  |                                                                              |======                                                                |   9%  |                                                                              |===========                                                           |  15%  |                                                                              |=============                                                         |  18%  |                                                                              |==============                                                        |  20%  |                                                                              |================                                                      |  23%  |                                                                              |=================                                                     |  24%  |                                                                              |==================                                                    |  26%  |                                                                              |====================                                                  |  29%  |                                                                              |=====================                                                 |  30%  |                                                                              |=======================                                               |  33%  |                                                                              |========================                                              |  35%  |                                                                              |==========================                                            |  36%  |                                                                              |============================                                          |  40%  |                                                                              |=============================                                         |  41%  |                                                                              |==============================                                        |  43%  |                                                                              |===============================                                       |  44%  |                                                                              |================================                                      |  46%  |                                                                              |==================================                                    |  49%  |                                                                              |===================================                                   |  50%  |                                                                              |====================================                                  |  52%  |                                                                              |======================================                                |  55%  |                                                                              |========================================                              |  58%  |                                                                              |==========================================                            |  59%  |                                                                              |===========================================                           |  61%  |                                                                              |============================================                          |  62%  |                                                                              |=============================================                         |  64%  |                                                                              |===============================================                       |  67%  |                                                                              |================================================                      |  69%  |                                                                              |=================================================                     |  70%  |                                                                              |==================================================                    |  72%  |                                                                              |===================================================                   |  73%  |                                                                              |=====================================================                 |  76%  |                                                                              |======================================================                |  78%  |                                                                              |=========================================================             |  81%  |                                                                              |==========================================================            |  82%  |                                                                              |===========================================================           |  84%  |                                                                              |============================================================          |  85%  |                                                                              |=============================================================         |  87%  |                                                                              |===============================================================       |  90%  |                                                                              |================================================================      |  91%  |                                                                              |==================================================================    |  94%  |                                                                              |====================================================================  |  98%  |                                                                              |======================================================================| 100%

# Shift geometric locations of AK and HI
st_sf <- shift_geometry(
              st_sf,
              geoid_column = NULL,
              preserve_area = FALSE,
              position = c("below", "outside")
            )

# Join our NMTC projects data with our shapefile geocoordinates
svi_national_county_nmtc_sf <- left_join(svi_national_county_nmtc_projects, county_sf, join_by("FIPS_st" == "STATEFP", "FIPS_county" == "COUNTYFP"))

svi_national_county_nmtc_sf %>% head(5) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

State	County	Division	post10_nmtc_project_cnt	tract_cnt	post10_nmtc_project_dollars	post10_nmtc_dollars_formatted	fips_county_st	FIPS_st	FIPS_county	state_name	region_number	region	division_number	flag_count10	pop10	flag_by_pop10	flag_count_quantile10	flag_pop_quantile10	flag_count20	pop20	flag_by_pop20	flag_count_quantile20	flag_pop_quantile20	county_name	GEO_ID	STATE	COUNTY	NAME	LSAD	CENSUSAREA	geometry
AK	Aleutians East Borough	Pacific Division	1	1	15762500	\$15,762,500	02013	02	013	Alaska	4	West Region	9	8	3703	0.0021604	0.4	1.0	5	3389	0.0014754	0.2	0.8	Aleutians East Borough, AK	0500000US02013	02	013	Aleutians East	Borough	6981.943	MULTIPOLYGON (((-2385249 -1…
AK	Anchorage Municipality	Pacific Division	1	13	9800000	\$9,800,000	02020	02	020	Alaska	4	West Region	9	72	64432	0.0011175	1.0	0.4	87	69679	0.0012486	1.0	0.6	Anchorage Municipality, AK	0500000US02020	02	020	Anchorage	Muny	1704.683	MULTIPOLYGON (((-1927463 -1…
AK	Wade Hampton Census Area	Pacific Division	1	1	21420000	\$21,420,000	02270	02	270	Alaska	4	West Region	9	9	7398	0.0012165	0.4	0.6	10	8298	0.0012051	0.4	0.6	Wade Hampton Census Area, AK	0500000US02270	02	270	Wade Hampton	CA	17081.433	MULTIPOLYGON (((-2310112 -1…
AK	Yukon-Koyukuk Census Area	Pacific Division	1	3	7644000	\$7,644,000	02290	02	290	Alaska	4	West Region	9	27	4027	0.0067047	0.8	1.0	31	3979	0.0077909	0.8	1.0	Yukon-Koyukuk Census Area, AK	0500000US02290	02	290	Yukon-Koyukuk	CA	145504.789	MULTIPOLYGON (((-1736112 -9…
AL	Baldwin County	East South Central Division	4	8	17268000	\$17,268,000	01003	01	003	Alabama	3	South Region	6	34	38458	0.0008841	0.8	0.4	34	46255	0.0007351	0.8	0.2	Baldwin County, AL	0500000US01003	01	003	Baldwin	County	1589.784	MULTIPOLYGON (((820813.4 -7…

# Create classes for bivariate mapping 
svi_national_county_nmtc_sf <- bi_class(svi_national_county_nmtc_sf, x = flag_count10, y = post10_nmtc_project_dollars, style = "quantile", dim = 3)

# View data
svi_national_county_nmtc_sf %>% head(5) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

State	County	Division	post10_nmtc_project_cnt	tract_cnt	post10_nmtc_project_dollars	post10_nmtc_dollars_formatted	fips_county_st	FIPS_st	FIPS_county	state_name	region_number	region	division_number	flag_count10	pop10	flag_by_pop10	flag_count_quantile10	flag_pop_quantile10	flag_count20	pop20	flag_by_pop20	flag_count_quantile20	flag_pop_quantile20	county_name	GEO_ID	STATE	COUNTY	NAME	LSAD	CENSUSAREA	geometry	bi_class
AK	Aleutians East Borough	Pacific Division	1	1	15762500	\$15,762,500	02013	02	013	Alaska	4	West Region	9	8	3703	0.0021604	0.4	1.0	5	3389	0.0014754	0.2	0.8	Aleutians East Borough, AK	0500000US02013	02	013	Aleutians East	Borough	6981.943	MULTIPOLYGON (((-2385249 -1…	1-2
AK	Anchorage Municipality	Pacific Division	1	13	9800000	\$9,800,000	02020	02	020	Alaska	4	West Region	9	72	64432	0.0011175	1.0	0.4	87	69679	0.0012486	1.0	0.6	Anchorage Municipality, AK	0500000US02020	02	020	Anchorage	Muny	1704.683	MULTIPOLYGON (((-1927463 -1…	2-2
AK	Wade Hampton Census Area	Pacific Division	1	1	21420000	\$21,420,000	02270	02	270	Alaska	4	West Region	9	9	7398	0.0012165	0.4	0.6	10	8298	0.0012051	0.4	0.6	Wade Hampton Census Area, AK	0500000US02270	02	270	Wade Hampton	CA	17081.433	MULTIPOLYGON (((-2310112 -1…	1-3
AK	Yukon-Koyukuk Census Area	Pacific Division	1	3	7644000	\$7,644,000	02290	02	290	Alaska	4	West Region	9	27	4027	0.0067047	0.8	1.0	31	3979	0.0077909	0.8	1.0	Yukon-Koyukuk Census Area, AK	0500000US02290	02	290	Yukon-Koyukuk	CA	145504.789	MULTIPOLYGON (((-1736112 -9…	2-1
AL	Baldwin County	East South Central Division	4	8	17268000	\$17,268,000	01003	01	003	Alabama	3	South Region	6	34	38458	0.0008841	0.8	0.4	34	46255	0.0007351	0.8	0.2	Baldwin County, AL	0500000US01003	01	003	Baldwin	County	1589.784	MULTIPOLYGON (((820813.4 -7…	2-2

# Create map with ggplot
svi_national_county_nmtc_map <- ggplot() +
  # Map county shapefile, fill with bi_class categories
  geom_sf(data = svi_national_county_nmtc_sf, mapping = aes(geometry=geometry, fill = bi_class), color = "white", size = 0.1, show.legend = FALSE) +
  # Set to biscale palette
  bi_scale_fill(pal = "GrPink", dim = 3) +
  # Add state shapefiles for outline
  geom_sf(data=st_sf, color="black", fill=NA, linewidth=.5, aes(geometry=geometry)) +
  labs(
    title = "Correlation of 2010 National SVI Flag Count and 2011 - 2020 NMTC Tax Dollars",
  ) +
  # Set them to biscale
  bi_theme(base_size = 10)

# Create biscale legend
svi_national_county_nmtc_legend <- bi_legend(pal = "GrPink",
                    dim = 3,
                    xlab = "SVI Flag Count",
                    ylab = "NMTC Dollars",
                    size = 8)

# Combine map with legend using cowplot
svi_national_county_nmtc_bivarmap <- ggdraw() +
  draw_plot(svi_national_county_nmtc_map) +
  # Set legend location
  draw_plot(svi_national_county_nmtc_legend, x= -.02,  y = -.05,
 width=.20)


# View map
svi_national_county_nmtc_bivarmap

We can see a largest amount of flags in FL, Az, and CA. We can also see a higher amount of spending accros the Atlantic divisions, without as high of flags.

svi_national_county_lihtc_projects <- svi_national_county_lihtc %>% filter(post10_lihtc_project_cnt > 0)

summary(svi_national_county_lihtc_projects$flag_count10)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     0.0    17.0    33.0   104.6   113.5  2457.0

summary(svi_national_county_lihtc_projects$post10_lihtc_project_dollars)

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##        0   368649   833390  2747385  2597486 50547731

svi_national_county_lihtc_projects <- svi_national_county_lihtc_projects %>% filter(post10_lihtc_project_dollars > 0)

summary(svi_national_county_lihtc_projects$flag_count10)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     0.0    17.0    34.0   109.8   112.5  2457.0

summary(svi_national_county_lihtc_projects$post10_lihtc_project_dollars)

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##    56314   605483  1275416  3233901  3424308 50547731

# Scatterplot
# y is our independent variable (LIHTC Project Dollars),  
# x is our dependent variable (SVI flag count)
ggplot2::ggplot(svi_national_county_lihtc_projects,
                aes(x=flag_count10,
                    y=post10_lihtc_project_dollars)) +
        geom_point() +
        geom_smooth(method="lm") +
        scale_y_continuous(labels = scales::dollar_format()) 

# Pearson's r calculation
cor(svi_national_county_lihtc_projects$flag_count10, svi_national_county_lihtc_projects$post10_lihtc_project_dollars, method = "pearson")

## [1] 0.7368838

Similar to our NMTC, we can see that there is a strong positive correlation between flags and amount of spending. However, we once again see that outliers are potentially swaying our data.

boxplot(svi_national_county_lihtc_projects$flag_count10)

boxplot.stats(svi_national_county_lihtc_projects$flag_count10)$out %>% sort(decreasing = TRUE)

##  [1] 2457 1254 1135 1002  835  560  475  440  418  403  402  340  327  323  303
## [16]  296  293  276  271  263

svi_national_county_lihtc_projects %>% filter(flag_count10 == 2457)

##   State             County         Division post10_lihtc_project_cnt tract_cnt
## 1    CA Los Angeles County Pacific Division                       58       238
##   post10_lihtc_project_dollars post10_lihtc_dollars_formatted fips_county_st
## 1                     50547731                    $50,547,731          06037
##   FIPS_st FIPS_county state_name region_number      region division_number
## 1      06         037 California             4 West Region               9
##   flag_count10  pop10 flag_by_pop10 flag_count_quantile10 flag_pop_quantile10
## 1         2457 988225   0.002486276                     1                 0.6
##   flag_count20  pop20 flag_by_pop20 flag_count_quantile20 flag_pop_quantile20
## 1         2272 991036    0.00229255                     1                 0.6
##              county_name
## 1 Los Angeles County, CA

svi_national_lihtc %>% filter(county == "Los Angeles County") %>% select(GEOID_2010_trt, F_TOTAL_10, post10_lihtc_project_dollars) %>% summary()

##  GEOID_2010_trt       F_TOTAL_10    post10_lihtc_project_dollars
##  Length:238         Min.   : 6.00   Min.   :      0             
##  Class :character   1st Qu.:10.00   1st Qu.:      0             
##  Mode  :character   Median :10.00   Median :      0             
##                     Mean   :10.32   Mean   : 212385             
##                     3rd Qu.:11.00   3rd Qu.:      0             
##                     Max.   :13.00   Max.   :4945372

svi_national_lihtc_cluster <- svi_national_county_lihtc_projects %>% 
                            select(county_name, post10_lihtc_project_dollars, 
                                   flag_count10) %>% 
                            remove_rownames %>% 
                            column_to_rownames(var="county_name")

# Remove nulls, if in dataset
svi_national_lihtc_cluster <- na.omit(svi_national_lihtc_cluster)


# Scale numeric variables
svi_national_lihtc_cluster <- scale(svi_national_lihtc_cluster)


svi_national_lihtc_cluster %>% head(5)

##                      post10_lihtc_project_dollars flag_count10
## Dale County, AL                        -0.4426510   -0.4100636
## Mobile County, AL                      -0.4215575    0.1570056
## Craighead County, AR                   -0.1126573   -0.4100636
## Jefferson County, AR                   -0.4806216   -0.2744601
## Pope County, AR                        -0.4860654   -0.4265004

set.seed(123)
k2_lihtc_nat <- kmeans(svi_national_lihtc_cluster, centers = 2, nstart = 25)
set.seed(123)
k3_lihtc_nat <- kmeans(svi_national_lihtc_cluster, centers = 3, nstart = 25)
set.seed(123)
k4_lihtc_nat <- kmeans(svi_national_lihtc_cluster, centers = 4, nstart = 25)
set.seed(123)
k5_lihtc_nat <- kmeans(svi_national_lihtc_cluster, centers = 5, nstart = 25)

# plots to compare
p_k2_lihtc_nat <- factoextra::fviz_cluster(k2_lihtc_nat, geom = "point", data = svi_national_lihtc_cluster) + ggtitle("k = 2")

p_k3_lihtc_nat <- factoextra::fviz_cluster(k3_lihtc_nat, geom = "point", data = svi_national_lihtc_cluster) + ggtitle("k = 3")

p_k4_lihtc_nat <- factoextra::fviz_cluster(k4_lihtc_nat, geom = "point",  data = svi_national_lihtc_cluster) + ggtitle("k = 4")

p_k5_lihtc_nat <- factoextra::fviz_cluster(k5_lihtc_nat, geom = "point",  data = svi_national_lihtc_cluster) + ggtitle("k = 5")

grid.arrange(p_k2_lihtc_nat, p_k3_lihtc_nat, p_k4_lihtc_nat, p_k5_lihtc_nat, nrow = 2)

elbow_plot(svi_national_lihtc_cluster)

##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15
## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
## [1] 6
## [1] 7
## [1] 8
## [1] 9
## [1] 10
## [1] 11
## [1] 12
## [1] 13
## [1] 14
## [1] 15

p_k3_lihtc_nat <- factoextra::fviz_cluster(k3_lihtc_nat, geom = "point", data = svi_national_lihtc_cluster) + ggtitle("k = 3")

p_k3_lihtc_nat

svi_national_lihtc_cluster_label <- as.data.frame(svi_national_lihtc_cluster) %>%
                                  rownames_to_column(var = "county_name") %>%
                                  as_tibble() %>%
                                  mutate(cluster = k3_lihtc_nat$cluster) %>%
                                  select(county_name, cluster)

svi_national_county_lihtc_projects2 <- left_join(svi_national_county_lihtc_projects, svi_national_lihtc_cluster_label, join_by(county_name == county_name))

# View county counts in each cluster
table(svi_national_county_lihtc_projects2$cluster)

## 
##   1   2   3 
## 164  27   1

It appears once again that three cluster are best to view our data

svi_national_lihtc_cluster_label <- as.data.frame(svi_national_lihtc_cluster) %>%
                                  rownames_to_column(var = "county_name") %>%
                                  as_tibble() %>%
                                  mutate(cluster = k3_lihtc_nat$cluster) %>%
                                  select(county_name, cluster)

svi_national_county_lihtc_projects2 <- left_join(svi_national_county_lihtc_projects, svi_national_lihtc_cluster_label, join_by(county_name == county_name))

# View county counts in each cluster
table(svi_national_county_lihtc_projects2$cluster)

## 
##   1   2   3 
## 164  27   1

# Cluster 1 Scatterplot
# y is our independent variable (LIHTC Project Dollars),  
# x is our dependent variable (SVI flag count)

svi_national_county_lihtc_projects2 %>% 
  filter(cluster == 1) %>%
  ggplot2::ggplot(aes(x=flag_count10,
                    y=post10_lihtc_project_dollars)) +
        geom_point() +
        geom_smooth(method="lm") +
        scale_y_continuous(labels = scales::dollar_format()) 

svi_national_county_lihtc_projects2 %>% 
  filter(cluster == 1) %>%
  select(flag_count10,post10_lihtc_project_dollars) %>%
  cor(method = "pearson")

##                              flag_count10 post10_lihtc_project_dollars
## flag_count10                    1.0000000                    0.2812405
## post10_lihtc_project_dollars    0.2812405                    1.0000000

svi_national_county_lihtc_projects2 %>% 
  filter(cluster == 1) %>%
  select(county_name, flag_count10, post10_lihtc_dollars_formatted) %>%
  arrange(desc(flag_count10)) %>% head(10) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

county_name	flag_count10	post10_lihtc_dollars_formatted
Queens County, NY	296	\$4,094,899
Oklahoma County, OK	293	\$590,000
Orleans Parish, LA	276	\$3,558,074
Sacramento County, CA	207	\$4,065,883
Passaic County, NJ	206	\$4,564,679
Hartford County, CT	203	\$256,961
Alameda County, CA	202	\$1,590,984
Erie County, NY	167	\$2,220,667
Shelby County, TN	167	\$201,735
Monroe County, NY	165	\$4,944,903

Our first cluster, which is also our largest cluster, does not show a strong correlation between flags and dollars spent.

# Cluster 2 Scatterplot
# y is our independent variable (LIHTC Project Dollars),  
# x is our dependent variable (SVI flag count)

svi_national_county_lihtc_projects2 %>% 
  filter(cluster == 2) %>%
  ggplot2::ggplot(aes(x=flag_count10,
                    y=post10_lihtc_project_dollars)) +
        geom_point() +
        geom_smooth(method="lm") +
        scale_y_continuous(labels = scales::dollar_format()) 

svi_national_county_lihtc_projects2 %>% 
  filter(cluster == 2) %>%
  select(flag_count10,post10_lihtc_project_dollars) %>%
  cor(method = "pearson")

##                              flag_count10 post10_lihtc_project_dollars
## flag_count10                   1.00000000                  -0.02464747
## post10_lihtc_project_dollars  -0.02464747                   1.00000000

svi_national_county_lihtc_projects2 %>% 
  filter(cluster == 2) %>%
  select(county_name, flag_count10, post10_lihtc_dollars_formatted) %>%
  arrange(desc(flag_count10)) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

county_name	flag_count10	post10_lihtc_dollars_formatted
Cook County, IL	1254	\$6,872,376
Kings County, NY	1135	\$26,054,976
Wayne County, MI	1002	\$11,226,253
Harris County, TX	835	\$179,539
Essex County, NJ	560	\$7,410,996
Maricopa County, AZ	475	\$13,115,292
Bronx County, NY	440	\$19,909,800
Cuyahoga County, OH	418	\$4,515,742
District of Columbia, DC	403	\$11,695,555
San Diego County, CA	402	\$20,961,962
Milwaukee County, WI	340	\$11,454,416
Baltimore city, MD	327	\$6,501,415
Orange County, CA	323	\$10,245,926
Miami-Dade County, FL	303	\$12,039,478
Riverside County, CA	271	\$6,335,483
San Bernardino County, CA	263	\$5,757,810
Philadelphia County, PA	253	\$6,805,843
New York County, NY	202	\$9,337,368
Allegheny County, PA	191	\$8,889,633
Hennepin County, MN	178	\$8,327,899
Onondaga County, NY	152	\$18,599,402
Tulsa County, OK	146	\$8,175,983
Hillsborough County, FL	143	\$10,116,734
San Francisco County, CA	79	\$11,194,380
Delaware County, IN	31	\$17,000,000
Vigo County, IN	22	\$18,646,385
Mesa County, CO	13	\$15,437,500

Our second cluster, which as the largest spread, shows a slightly negative correlation.

# Cluster 3 Scatterplot
# y is our independent variable (LIHTC Project Dollars),  
# x is our dependent variable (SVI flag count)

svi_national_county_lihtc_projects2 %>% 
  filter(cluster == 3) %>%
  ggplot2::ggplot(aes(x=flag_count10,
                    y=post10_lihtc_project_dollars)) +
        geom_point() +
        geom_smooth(method="lm") +
        scale_y_continuous(labels = scales::dollar_format()) 

svi_national_county_lihtc_projects2 %>% 
  filter(cluster == 3) %>%
  select(flag_count10,post10_lihtc_project_dollars) %>%
  cor(method = "pearson")

##                              flag_count10 post10_lihtc_project_dollars
## flag_count10                           NA                           NA
## post10_lihtc_project_dollars           NA                           NA

svi_national_county_lihtc_projects2 %>% 
  filter(cluster == 3) %>%
  select(county_name, flag_count10, post10_lihtc_dollars_formatted) %>% 
  arrange(desc(flag_count10)) %>% head(10) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

county_name	flag_count10	post10_lihtc_dollars_formatted
Los Angeles County, CA	2457	\$50,547,731

Unfortionately, our third cluster only has one instance, so we cannot tell if there is a trend or if it is repeatable.

# Join our LIHTC projects data with our shapefile geocoordinates
svi_national_county_lihtc_sf <- left_join(svi_national_county_lihtc_projects, county_sf, join_by("FIPS_st" == "STATEFP", "FIPS_county" == "COUNTYFP"))

svi_national_county_lihtc_sf %>% head(5) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

State	County	Division	post10_lihtc_project_cnt	tract_cnt	post10_lihtc_project_dollars	post10_lihtc_dollars_formatted	fips_county_st	FIPS_st	FIPS_county	state_name	region_number	region	division_number	flag_count10	pop10	flag_by_pop10	flag_count_quantile10	flag_pop_quantile10	flag_count20	pop20	flag_by_pop20	flag_count_quantile20	flag_pop_quantile20	county_name	GEO_ID	STATE	COUNTY	NAME	LSAD	CENSUSAREA	geometry
AL	Dale County	East South Central Division	1	2	801670	\$801,670	01045	01	045	Alabama	3	South Region	6	10	4943	0.0020231	0.4	0.4	15	4853	0.0030909	0.6	0.8	Dale County, AL	0500000US01045	01	045	Dale	County	561.150	MULTIPOLYGON (((966402.4 -6…
AL	Mobile County	East South Central Division	1	18	917572	\$917,572	01097	01	097	Alabama	3	South Region	6	148	41977	0.0035257	1.0	0.8	126	34483	0.0036540	1.0	0.8	Mobile County, AL	0500000US01097	01	097	Mobile	County	1229.435	MULTIPOLYGON (((763284.2 -7…
AR	Craighead County	West South Central Division	6	1	2614884	\$2,614,884	05031	05	031	Arkansas	3	South Region	7	10	5701	0.0017541	0.4	0.4	9	6088	0.0014783	0.4	0.2	Craighead County, AR	0500000US05031	05	031	Craighead	County	707.206	MULTIPOLYGON (((509938.5 -1…
AR	Jefferson County	West South Central Division	1	5	593033	\$593,033	05069	05	069	Arkansas	3	South Region	7	43	14174	0.0030337	1.0	0.8	39	11370	0.0034301	0.8	0.8	Jefferson County, AR	0500000US05069	05	069	Jefferson	County	870.746	MULTIPOLYGON (((391413.8 -3…
AR	Pope County	West South Central Division	1	1	563121	\$563,121	05115	05	115	Arkansas	3	South Region	7	6	4220	0.0014218	0.2	0.2	9	4890	0.0018405	0.4	0.4	Pope County, AR	0500000US05115	05	115	Pope	County	812.548	MULTIPOLYGON (((243262.7 -2…

# Create classes for bivariate mapping 
svi_national_county_lihtc_sf <- bi_class(svi_national_county_lihtc_sf, x = flag_count10, y = post10_lihtc_project_dollars, style = "quantile", dim = 3)

# View data
svi_national_county_lihtc_sf %>% head(5) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

State	County	Division	post10_lihtc_project_cnt	tract_cnt	post10_lihtc_project_dollars	post10_lihtc_dollars_formatted	fips_county_st	FIPS_st	FIPS_county	state_name	region_number	region	division_number	flag_count10	pop10	flag_by_pop10	flag_count_quantile10	flag_pop_quantile10	flag_count20	pop20	flag_by_pop20	flag_count_quantile20	flag_pop_quantile20	county_name	GEO_ID	STATE	COUNTY	NAME	LSAD	CENSUSAREA	geometry	bi_class
AL	Dale County	East South Central Division	1	2	801670	\$801,670	01045	01	045	Alabama	3	South Region	6	10	4943	0.0020231	0.4	0.4	15	4853	0.0030909	0.6	0.8	Dale County, AL	0500000US01045	01	045	Dale	County	561.150	MULTIPOLYGON (((966402.4 -6…	1-2
AL	Mobile County	East South Central Division	1	18	917572	\$917,572	01097	01	097	Alabama	3	South Region	6	148	41977	0.0035257	1.0	0.8	126	34483	0.0036540	1.0	0.8	Mobile County, AL	0500000US01097	01	097	Mobile	County	1229.435	MULTIPOLYGON (((763284.2 -7…	3-2
AR	Craighead County	West South Central Division	6	1	2614884	\$2,614,884	05031	05	031	Arkansas	3	South Region	7	10	5701	0.0017541	0.4	0.4	9	6088	0.0014783	0.4	0.2	Craighead County, AR	0500000US05031	05	031	Craighead	County	707.206	MULTIPOLYGON (((509938.5 -1…	1-3
AR	Jefferson County	West South Central Division	1	5	593033	\$593,033	05069	05	069	Arkansas	3	South Region	7	43	14174	0.0030337	1.0	0.8	39	11370	0.0034301	0.8	0.8	Jefferson County, AR	0500000US05069	05	069	Jefferson	County	870.746	MULTIPOLYGON (((391413.8 -3…	2-1
AR	Pope County	West South Central Division	1	1	563121	\$563,121	05115	05	115	Arkansas	3	South Region	7	6	4220	0.0014218	0.2	0.2	9	4890	0.0018405	0.4	0.4	Pope County, AR	0500000US05115	05	115	Pope	County	812.548	MULTIPOLYGON (((243262.7 -2…	1-1

# Create map with ggplot
svi_national_county_lihtc_map <- ggplot() +
  # Map county shapefile, fill with bi_class categories
  geom_sf(data = svi_national_county_lihtc_sf, mapping = aes(geometry=geometry, fill = bi_class), color = "white", size = 0.1, show.legend = FALSE) +
  # Set to biscale palette
  bi_scale_fill(pal = "GrPink", dim = 3) +
  # Add state shapefiles for outline
  geom_sf(data=st_sf, color="black", fill=NA, linewidth=.5, aes(geometry=geometry)) +
  labs(
    title = "Correlation of 2010 National SVI Flag Count and 2011 - 2020 LIHTC Tax Dollars",
  ) +
  # Set them to biscale
  bi_theme(base_size = 10)

# Create biscale legend
svi_national_county_lihtc_legend <- bi_legend(pal = "GrPink",
                    dim = 3,
                    xlab = "SVI Flag Count",
                    ylab = "LIHTC Dollars",
                    size = 8)

# Combine map with legend using cowplot
svi_national_county_lihtc_bivarmap <- ggdraw() +
  draw_plot(svi_national_county_lihtc_map) +
  # Set legend location
  draw_plot(svi_national_county_lihtc_legend, x= -.02,  y = -.05,
 width=.20)


# View map
svi_national_county_lihtc_bivarmap

Here we once again see the larges amounts of flags in both Florida and California.

svi_national_county_lihtc_sf %>% filter(State == "CO") %>% select(State, County, flag_count10, post10_lihtc_dollars_formatted) %>% arrange(desc(flag_count10)) %>% head(6)

##   State         County flag_count10 post10_lihtc_dollars_formatted
## 1    CO  Denver County           69                     $5,226,128
## 2    CO El Paso County           18                     $1,011,891
## 3    CO Larimer County           17                     $2,609,648
## 4    CO    Mesa County           13                    $15,437,500

svi_national_county_lihtc_sf %>% filter(State == "CA") %>% select(State, County, flag_count10, post10_lihtc_dollars_formatted) %>% arrange(desc(flag_count10)) %>% head(6)

##   State                County flag_count10 post10_lihtc_dollars_formatted
## 1    CA    Los Angeles County         2457                    $50,547,731
## 2    CA      San Diego County          402                    $20,961,962
## 3    CA         Orange County          323                    $10,245,926
## 4    CA      Riverside County          271                     $6,335,483
## 5    CA San Bernardino County          263                     $5,757,810
## 6    CA     Sacramento County          207                     $4,065,883

Divisional Data Summary

svi_divisional_county_nmtc_projects <- svi_divisional_county_nmtc %>% filter(post10_nmtc_project_cnt > 0)

summary(svi_divisional_county_nmtc_projects$flag_count10)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     2.0    25.0    52.0   156.4   170.2  2301.0

summary(svi_divisional_county_nmtc_projects$post10_nmtc_project_dollars)

##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
##      6235   7063450  13963750  29058223  31893750 437737000

Looking into our summary statistics for the South Atlantic division, we can see a large spread in both flags and in money spent. Flags rangs from 2 to 2301, a very large gap. Money spent also ranges from 6235 to over 400,000,000. Interestingly, the average amount of flags is 156.4, which is far below the maximum of 2301. This suggests that which there are outliers pulling the data to 2301, most divisions are actually much smaller. Similarly, the average amount of money is just over 29,000,000, which is significantly different than the maximum of over 400,000,000

# Scatterplot
# y is our independent variable (NMTC Project Dollars),  
# x is our dependent variable (SVI flag count)
ggplot2::ggplot(svi_divisional_county_nmtc_projects,
                aes(x=flag_count10,
                    y=post10_nmtc_project_dollars)) +
        geom_point() +
        geom_smooth(method="lm") +
        scale_y_continuous(labels = scales::dollar_format()) 

# Scatterplot
# y is our independent variable (NMTC Project Dollars),  
# x is our dependent variable (SVI flag count)
ggplot2::ggplot(svi_divisional_county_nmtc_projects,
                aes(x=flag_count10,
                    y=post10_nmtc_project_dollars)) +
        geom_point() +
        geom_smooth(method="lm") +
        scale_y_continuous(labels = scales::dollar_format()) 

# Pearson's r calculation
cor(svi_divisional_county_nmtc_projects$flag_count10, svi_divisional_county_nmtc_projects$post10_nmtc_project_dollars, method = "pearson")

## [1] 0.5999762

If we view the data in a scatter plot, we can more clearly see how the outliers are pulling our data. While there are instances that reach a spending of over $100,000,000, there are only 5 in total. In fact, there is only one instance that reaches over the $400,000,000 threshhold.

This stark difference is examined by looking at our Pearson’s R Calculation. While still showing that flag count has a strong impact on money spent, the pattern is much less strong than how it initially looks. This is because the majority of our data is in the bottom left corner, with less than $100,000,000 spent, and less than 500 flags.

boxplot(svi_divisional_county_nmtc_projects$flag_count10)

boxplot.stats(svi_divisional_county_nmtc_projects$flag_count10)$out %>% sort(decreasing = TRUE)

##  [1] 2301  993  962  946  846  805  756  717  660  605  572  514  459

svi_divisional_county_nmtc_projects %>% 
  select(county_name, flag_count10, post10_nmtc_dollars_formatted) %>% 
  arrange(desc(flag_count10)) %>% 
  head(7) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

county_name	flag_count10	post10_nmtc_dollars_formatted
Miami-Dade County, FL	2301	\$233,661,603
Broward County, FL	993	\$48,975,143
Baltimore city, MD	962	\$333,109,232
Hillsborough County, FL	946	\$117,152,400
Palm Beach County, FL	846	\$12,202,000
Fulton County, GA	805	\$437,737,000
Prince George’s County, MD	756	\$4,900,000

If we sort by our largest amount of flags, there is a very large difference between number 7 and number 1. However, if just look at the flags between 2 and 7, they are much more comparable. Yet, the money spent does not seem to increase in any particular trend. In fact, the most amount of money spent is number 6, which is almost double that of number 1, even though number 1 has almost 3 times the amount of flags as number 6.

svi_divisional_nmtc_cluster <- svi_divisional_county_nmtc_projects %>% 
                            select(county_name, post10_nmtc_project_dollars, 
                                   flag_count10) %>% 
                            remove_rownames %>% 
                            column_to_rownames(var="county_name")

# Remove nulls, if in dataset
svi_divisional_nmtc_cluster <- na.omit(svi_divisional_nmtc_cluster)


# Scale numeric variables
svi_divisional_nmtc_cluster <- scale(svi_divisional_nmtc_cluster)


svi_divisional_nmtc_cluster %>% head(5)

##                          post10_nmtc_project_dollars flag_count10
## District of Columbia, DC                  5.95900625   1.63887937
## New Castle County, DE                    -0.01961731   0.26881224
## Alachua County, FL                       -0.21737824   0.01306638
## Brevard County, FL                       -0.37714793   0.04594799
## Broward County, FL                        0.34095362   3.05644216

K-Means Clustering

set.seed(123)
k2_nmtc_div <- kmeans(svi_divisional_nmtc_cluster, centers = 2, nstart = 25)
set.seed(123)
k3_nmtc_div <- kmeans(svi_divisional_nmtc_cluster, centers = 3, nstart = 25)
set.seed(123)
k4_nmtc_div <- kmeans(svi_divisional_nmtc_cluster, centers = 4, nstart = 25)
set.seed(123)
k5_nmtc_div <- kmeans(svi_divisional_nmtc_cluster, centers = 5, nstart = 25)

# plots to compare
p_k2_nmtc_div <- factoextra::fviz_cluster(k2_nmtc_div, geom = "point", data = svi_divisional_nmtc_cluster) + ggtitle("k = 2")

p_k3_nmtc_div <- factoextra::fviz_cluster(k3_nmtc_div, geom = "point", data = svi_divisional_nmtc_cluster) + ggtitle("k = 3")

p_k4_nmtc_div <- factoextra::fviz_cluster(k4_nmtc_div, geom = "point",  data = svi_divisional_nmtc_cluster) + ggtitle("k = 4")

p_k5_nmtc_div <- factoextra::fviz_cluster(k5_nmtc_div, geom = "point",  data = svi_divisional_nmtc_cluster) + ggtitle("k = 5")

grid.arrange(p_k2_nmtc_div, p_k3_nmtc_div, p_k4_nmtc_div, p_k5_nmtc_div, nrow = 2)

elbow_plot(svi_divisional_nmtc_cluster)

##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15
## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
## [1] 6
## [1] 7
## [1] 8
## [1] 9
## [1] 10
## [1] 11
## [1] 12
## [1] 13
## [1] 14
## [1] 15

p_k3_nmtc_div <- factoextra::fviz_cluster(k3_nmtc_div, geom = "point", data = svi_divisional_nmtc_cluster) + ggtitle("k = 3")

p_k3_nmtc_div

Since there is such a large difference in both flag counts and project dollars spent, we can divide our groups into clusters. Our group is best divided into three different clusters, which may show different flag or money trends.

svi_divisional_nmtc_cluster_label <- as.data.frame(svi_divisional_nmtc_cluster) %>%
                                  rownames_to_column(var = "county_name") %>%
                                  as_tibble() %>%
                                  mutate(cluster = k3_nmtc_div$cluster) %>%
                                  select(county_name, cluster)

svi_divisional_county_nmtc_projects2 <- left_join(svi_divisional_county_nmtc_projects, svi_divisional_nmtc_cluster_label, join_by(county_name == county_name))

# View county counts in each cluster
table(svi_divisional_county_nmtc_projects2$cluster)

## 
##   1   2   3 
##  12 128   4

# Cluster 1 Scatterplot
# y is our independent variable (NMTC Project Dollars),  
# x is our dependent variable (SVI flag count)

svi_divisional_county_nmtc_projects2 %>% 
  filter(cluster == 1) %>%
  ggplot2::ggplot(aes(x=flag_count10,
                    y=post10_nmtc_project_dollars)) +
        geom_point() +
        geom_smooth(method="lm") +
        scale_y_continuous(labels = scales::dollar_format()) 

svi_divisional_county_nmtc_projects2 %>% 
  filter(cluster == 1) %>%
  select(flag_count10,post10_nmtc_project_dollars) %>%
  cor(method = "pearson")

##                             flag_count10 post10_nmtc_project_dollars
## flag_count10                   1.0000000                   0.2395816
## post10_nmtc_project_dollars    0.2395816                   1.0000000

svi_divisional_county_nmtc_projects2 %>% 
  filter(cluster == 1) %>%
  select(county_name, flag_count10, post10_nmtc_dollars_formatted) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

county_name	flag_count10	post10_nmtc_dollars_formatted
Broward County, FL	993	\$48,975,143
Duval County, FL	459	\$66,878,307
Hillsborough County, FL	946	\$117,152,400
Lee County, FL	361	\$34,965,000
Orange County, FL	717	\$64,320,444
Palm Beach County, FL	846	\$12,202,000
Pinellas County, FL	381	\$21,235,000
Polk County, FL	514	\$23,450,000
DeKalb County, GA	572	\$45,575,000
Prince George’s County, MD	756	\$4,900,000
Guilford County, NC	370	\$56,800,000
Mecklenburg County, NC	660	\$37,629,278

If we look at our cluster 1, we can see that they do not have a strong coorelation between money spent and flags.

# Cluster 2 Scatterplot
# y is our independent variable (NMTC Project Dollars),  
# x is our dependent variable (SVI flag count)

svi_divisional_county_nmtc_projects2 %>% 
  filter(cluster == 2) %>%
  ggplot2::ggplot(aes(x=flag_count10,
                    y=post10_nmtc_project_dollars)) +
        geom_point() +
        geom_smooth(method="lm") +
        scale_y_continuous(labels = scales::dollar_format()) 

svi_divisional_county_nmtc_projects2 %>% 
  filter(cluster == 2) %>%
  select(flag_count10,post10_nmtc_project_dollars) %>%
  cor(method = "pearson")

##                             flag_count10 post10_nmtc_project_dollars
## flag_count10                    1.000000                    0.289368
## post10_nmtc_project_dollars     0.289368                    1.000000

svi_divisional_county_nmtc_projects2 %>% 
  filter(cluster == 2) %>%
  select(county_name, flag_count10, post10_nmtc_dollars_formatted) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

county_name	flag_count10	post10_nmtc_dollars_formatted
New Castle County, DE	230	\$27,912,271
Alachua County, FL	160	\$16,360,000
Brevard County, FL	169	\$7,027,000
Clay County, FL	34	\$8,941,080
Collier County, FL	169	\$15,775,000
Escambia County, FL	194	\$8,071,834
Glades County, FL	16	\$10,000,000
Hendry County, FL	37	\$9,500,000
Highlands County, FL	106	\$9,997,500
Holmes County, FL	17	\$1,400,000
Lake County, FL	116	\$14,340,000
Leon County, FL	184	\$31,875,000
Manatee County, FL	171	\$13,927,500
Okaloosa County, FL	19	\$1,000,000
Pasco County, FL	326	\$9,800,000
Santa Rosa County, FL	21	\$2,884,750
Seminole County, FL	88	\$500,000
Taylor County, FL	21	\$32,880,000
Volusia County, FL	229	\$945,000
Washington County, FL	21	\$9,000,000
Baldwin County, GA	40	\$2,000,000
Bibb County, GA	237	\$2,000,000
Bulloch County, GA	47	\$23,635,000
Carroll County, GA	59	\$11,700,000
Chatham County, GA	235	\$18,663,750
Clarke County, GA	122	\$48,125,000
Clayton County, GA	283	\$55,708,000
Coffee County, GA	46	\$37,260,000
Colquitt County, GA	58	\$11,682,000
Decatur County, GA	31	\$68,230,000
Echols County, GA	17	\$20,000,000
Emanuel County, GA	30	\$1,660,000
Franklin County, GA	18	\$59,786,701
Glynn County, GA	51	\$13,823,750
Gordon County, GA	21	\$70,484,594
Gwinnett County, GA	320	\$16,890,000
Irwin County, GA	12	\$1,391,250
Jeff Davis County, GA	13	\$14,613,500
Muscogee County, GA	244	\$6,825,000
Peach County, GA	22	\$29,310,000
Rabun County, GA	21	\$7,350,000
Richmond County, GA	234	\$34,750,000
Screven County, GA	23	\$14,920,000
Spalding County, GA	48	\$8,320,000
Telfair County, GA	21	\$14,000,000
Tift County, GA	43	\$31,950,000
Troup County, GA	58	\$11,520,000
Upson County, GA	18	\$40,210,000
Wilkinson County, GA	8	\$18,100,000
Talbot County, MD	9	\$7,550,000
Washington County, MD	78	\$7,075,600
Anson County, NC	33	\$15,000,000
Beaufort County, NC	25	\$582,500
Cabarrus County, NC	83	\$9,500,000
Chatham County, NC	26	\$1,381,291
Cleveland County, NC	44	\$11,600,000
Columbus County, NC	53	\$3,000,000
Duplin County, NC	74	\$12,460,000
Durham County, NC	200	\$58,719,117
Halifax County, NC	73	\$22,072,000
Harnett County, NC	84	\$29,800,000
Hertford County, NC	26	\$2,060,000
Johnston County, NC	83	\$3,249,239
Jones County, NC	13	\$15,100,000
Lenoir County, NC	65	\$11,270,000
Mitchell County, NC	12	\$10,185,000
Montgomery County, NC	38	\$32,576,600
New Hanover County, NC	93	\$19,886,415
Northampton County, NC	30	\$21,264,000
Robeson County, NC	233	\$52,367,992
Rutherford County, NC	41	\$1,240,101
Sampson County, NC	57	\$12,000,000
Vance County, NC	44	\$3,638,400
Wake County, NC	280	\$49,684,621
Warren County, NC	33	\$1,750,000
Wayne County, NC	100	\$8,650,000
Wilkes County, NC	62	\$15,757,000
Wilson County, NC	76	\$3,000,000
Allendale County, SC	25	\$46,260,000
Charleston County, SC	241	\$33,400,000
Cherokee County, SC	49	\$22,069,493
Colleton County, SC	44	\$12,870,000
Darlington County, SC	47	\$37,026,000
Fairfield County, SC	29	\$12,000,000
Florence County, SC	119	\$14,700,000
Georgetown County, SC	57	\$27,403,451
Greenville County, SC	266	\$68,000,000
Horry County, SC	142	\$6,235
Jasper County, SC	23	\$5,280,000
Marlboro County, SC	50	\$31,950,000
Newberry County, SC	28	\$25,200,000
Oconee County, SC	29	\$23,220,000
Orangeburg County, SC	112	\$10,051,000
Pickens County, SC	51	\$11,800,000
Richland County, SC	247	\$20,500,000
Spartanburg County, SC	170	\$42,080,000
Sumter County, SC	92	\$35,060,000
Williamsburg County, SC	82	\$14,000,000
York County, SC	90	\$5,000,000
Alleghany County, VA	10	\$10,478,000
Bath County, VA	2	\$16,155,350
Bristol city, VA	23	\$19,982,000
Brunswick County, VA	22	\$13,095,000
Buchanan County, VA	39	\$35,640,000
Charlottesville city, VA	35	\$2,116,510
Culpeper County, VA	10	\$11,504,473
Henry County, VA	67	\$54,630,000
Nelson County, VA	8	\$8,191,650
Newport News city, VA	152	\$10,249,514
Norfolk city, VA	258	\$2,000,000
Patrick County, VA	18	\$10,738,175
Petersburg city, VA	82	\$19,633,250
Portsmouth city, VA	108	\$9,500,000
Richmond city, VA	291	\$71,688,438
Smyth County, VA	39	\$25,088,000
Berkeley County, WV	13	\$2,000,000
Braxton County, WV	11	\$4,000,000
Fayette County, WV	38	\$4,600,000
Greenbrier County, WV	16	\$23,386,235
Harrison County, WV	36	\$19,700,000
Kanawha County, WV	84	\$11,700,000
Marion County, WV	24	\$500,000
Monongalia County, WV	33	\$1,250,000
Nicholas County, WV	9	\$4,000,000
Ohio County, WV	27	\$1,550,000
Upshur County, WV	9	\$3,120,000
Wetzel County, WV	5	\$4,000,000
Wood County, WV	39	\$800,000

Looking at our second cluster, which contains the majority of our instances, we can see that there is still no strong coorelation.

# Cluster 2 Scatterplot
# y is our independent variable (NMTC Project Dollars),  
# x is our dependent variable (SVI flag count)

svi_divisional_county_nmtc_projects2 %>% 
  filter(cluster == 3) %>%
  ggplot2::ggplot(aes(x=flag_count10,
                    y=post10_nmtc_project_dollars)) +
        geom_point() +
        geom_smooth(method="lm") +
        scale_y_continuous(labels = scales::dollar_format()) 

svi_divisional_county_nmtc_projects2 %>% 
  filter(cluster == 3) %>%
  select(flag_count10,post10_nmtc_project_dollars) %>%
  cor(method = "pearson")

##                             flag_count10 post10_nmtc_project_dollars
## flag_count10                   1.0000000                  -0.8845317
## post10_nmtc_project_dollars   -0.8845317                   1.0000000

svi_divisional_county_nmtc_projects2 %>% 
  filter(cluster == 3) %>%
  select(county_name, flag_count10, post10_nmtc_dollars_formatted) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

county_name	flag_count10	post10_nmtc_dollars_formatted
District of Columbia, DC	605	\$377,155,570
Miami-Dade County, FL	2301	\$233,661,603
Fulton County, GA	805	\$437,737,000
Baltimore city, MD	962	\$333,109,232

Finally, when we look at our third cluster, which contains our most extreme outliers, we can see that there is in fact a negative coorelation.

divisional_county_sf <- svi_county_map2010 %>% select(COUNTYFP, STATEFP, geometry)

divisional_county_sf %>% head(5)

## Simple feature collection with 5 features and 2 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -85.38976 ymin: 30.19998 xmax: -75.04894 ymax: 39.83901
## Geodetic CRS:  NAD83
##   COUNTYFP STATEFP                       geometry
## 1      001      10 MULTIPOLYGON (((-75.7601 39...
## 2      003      10 MULTIPOLYGON (((-75.5655 39...
## 3      005      10 MULTIPOLYGON (((-75.69878 3...
## 4      001      11 MULTIPOLYGON (((-76.95772 3...
## 5      013      12 MULTIPOLYGON (((-85.02994 3...

Bivariate Mapping

# Join our NMTC projects data with our shapefile geocoordinates
svi_divisional_county_nmtc_sf <- left_join(svi_divisional_county_nmtc_projects, divisional_county_sf, join_by("FIPS_st" == "STATEFP", "FIPS_county" == "COUNTYFP"))

svi_divisional_county_nmtc_sf %>% head(5) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

State	County	Division	post10_nmtc_project_cnt	tract_cnt	post10_nmtc_project_dollars	post10_nmtc_dollars_formatted	fips_county_st	FIPS_st	FIPS_county	state_name	region_number	region	division_number	flag_count10	pop10	flag_by_pop10	flag_count_quantile10	flag_pop_quantile10	flag_count20	pop20	flag_by_pop20	flag_count_quantile20	flag_pop_quantile20	county_name	geometry
DC	District of Columbia	South Atlantic Division	22	78	377155570	\$377,155,570	11001	11	001	District of Columbia	3	South Region	5	605	234370	0.0025814	1	1.0	576	290905	0.0019800	1	1.0	District of Columbia, DC	MULTIPOLYGON (((-76.95772 3…
DE	New Castle County	South Atlantic Division	2	44	27912271	\$27,912,271	10003	10	003	Delaware	3	South Region	5	230	157982	0.0014559	1	0.8	215	157528	0.0013648	1	0.8	New Castle County, DE	MULTIPOLYGON (((-75.5655 39…
FL	Alachua County	South Atlantic Division	2	28	16360000	\$16,360,000	12001	12	001	Florida	3	South Region	5	160	107400	0.0014898	1	0.8	156	115817	0.0013470	1	0.8	Alachua County, FL	MULTIPOLYGON (((-82.24401 2…
FL	Brevard County	South Atlantic Division	1	37	7027000	\$7,027,000	12009	12	009	Florida	3	South Region	5	169	171842	0.0009835	1	0.4	166	185164	0.0008965	1	0.4	Brevard County, FL	MULTIPOLYGON (((-80.57159 2…
FL	Broward County	South Atlantic Division	7	119	48975143	\$48,975,143	12011	12	011	Florida	3	South Region	5	993	571520	0.0017375	1	1.0	1027	648697	0.0015832	1	0.8	Broward County, FL	MULTIPOLYGON (((-80.08484 2…

# Create classes for bivariate mapping 
svi_divisional_county_nmtc_sf <- bi_class(svi_divisional_county_nmtc_sf, x = flag_count10, y = post10_nmtc_project_dollars, style = "quantile", dim = 3)

# View data
svi_divisional_county_nmtc_sf %>% head(5) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

State	County	Division	post10_nmtc_project_cnt	tract_cnt	post10_nmtc_project_dollars	post10_nmtc_dollars_formatted	fips_county_st	FIPS_st	FIPS_county	state_name	region_number	region	division_number	flag_count10	pop10	flag_by_pop10	flag_count_quantile10	flag_pop_quantile10	flag_count20	pop20	flag_by_pop20	flag_count_quantile20	flag_pop_quantile20	county_name	geometry	bi_class
DC	District of Columbia	South Atlantic Division	22	78	377155570	\$377,155,570	11001	11	001	District of Columbia	3	South Region	5	605	234370	0.0025814	1	1.0	576	290905	0.0019800	1	1.0	District of Columbia, DC	MULTIPOLYGON (((-76.95772 3…	3-3
DE	New Castle County	South Atlantic Division	2	44	27912271	\$27,912,271	10003	10	003	Delaware	3	South Region	5	230	157982	0.0014559	1	0.8	215	157528	0.0013648	1	0.8	New Castle County, DE	MULTIPOLYGON (((-75.5655 39…	3-3
FL	Alachua County	South Atlantic Division	2	28	16360000	\$16,360,000	12001	12	001	Florida	3	South Region	5	160	107400	0.0014898	1	0.8	156	115817	0.0013470	1	0.8	Alachua County, FL	MULTIPOLYGON (((-82.24401 2…	3-2
FL	Brevard County	South Atlantic Division	1	37	7027000	\$7,027,000	12009	12	009	Florida	3	South Region	5	169	171842	0.0009835	1	0.4	166	185164	0.0008965	1	0.4	Brevard County, FL	MULTIPOLYGON (((-80.57159 2…	3-1
FL	Broward County	South Atlantic Division	7	119	48975143	\$48,975,143	12011	12	011	Florida	3	South Region	5	993	571520	0.0017375	1	1.0	1027	648697	0.0015832	1	0.8	Broward County, FL	MULTIPOLYGON (((-80.08484 2…	3-3

# Create map with ggplot
svi_divisional_county_nmtc_map <- ggplot() +
  # Map county shapefile, fill with bi_class categories
  geom_sf(data = svi_divisional_county_nmtc_sf, mapping = aes(geometry=geometry, fill = bi_class), color = "white", size = 0.1, show.legend = FALSE) +
  # Set to biscale palette
  bi_scale_fill(pal = "GrPink", dim = 3) +
  # Add state shapefiles for outline
  geom_sf(data=divisional_st_sf, color="black", fill=NA, linewidth=.5, aes(geometry=geometry)) +
  labs(
    title = paste0("Correlation of 2010 ", census_division, " SVI Flag Count \n and 2011 - 2020 NMTC Tax Dollars"),
  ) +
  # Set them to biscale
  bi_theme(base_size = 10)

# Create biscale legend
svi_divisional_county_nmtc_legend <- bi_legend(pal = "GrPink",
                    dim = 3,
                    xlab = "SVI Flag Count",
                    ylab = "NMTC Dollars",
                    size = 8)

# Combine map with legend using cowplot
svi_divisional_county_nmtc_bivarmap <- ggdraw() +
  draw_plot(svi_divisional_county_nmtc_map) +
  # Set legend location
  draw_plot(svi_divisional_county_nmtc_legend, x= -.02,  y = -.05,
 width=.20)


# View map
svi_divisional_county_nmtc_bivarmap

Here we can see the distribution of our counties with NMTC projects based on correlation between 2010 SVI Flag Count and NMTC dollars for the South Atlantic Division.

We can see that the majority of our high flag counts are in Florida, with some moderate flag counts and higher NMTC spending between Georgia and North Carolina.

svi_divisional_county_nmtc_sf %>% filter(State %in% c("FL", "GE", "NC")) %>% select(State, County, flag_count10, post10_nmtc_dollars_formatted) %>% arrange(desc(flag_count10)) %>% head(6) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

State	County	flag_count10	post10_nmtc_dollars_formatted
FL	Miami-Dade County	2301	\$233,661,603
FL	Broward County	993	\$48,975,143
FL	Hillsborough County	946	\$117,152,400
FL	Palm Beach County	846	\$12,202,000
FL	Orange County	717	\$64,320,444
NC	Mecklenburg County	660	\$37,629,278

svi_divisional_county_nmtc_sf %>% filter(State == "FL") %>%
  arrange(desc(post10_nmtc_project_dollars), flag_count10) %>% select(State, County, flag_count10, post10_nmtc_dollars_formatted) %>% head(10) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

State	County	flag_count10	post10_nmtc_dollars_formatted
FL	Miami-Dade County	2301	\$233,661,603
FL	Hillsborough County	946	\$117,152,400
FL	Duval County	459	\$66,878,307
FL	Orange County	717	\$64,320,444
FL	Broward County	993	\$48,975,143
FL	Lee County	361	\$34,965,000
FL	Taylor County	21	\$32,880,000
FL	Leon County	184	\$31,875,000
FL	Polk County	514	\$23,450,000
FL	Pinellas County	381	\$21,235,000

Here we can see the largest amounts of dollars spent in Florida, as well as how they comare to the amount of flags.

LIHTC

svi_divisional_county_lihtc_projects <- svi_divisional_county_lihtc %>% filter(post10_lihtc_project_cnt > 0)

summary(svi_divisional_county_lihtc_projects$flag_count10)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    3.00   16.50   25.00   56.33   62.00  407.00

svi_divisional_county_lihtc_projects <- svi_divisional_county_lihtc_projects %>% filter(post10_lihtc_project_dollars > 0)

summary(svi_divisional_county_lihtc_projects$flag_count10)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    3.00   16.25   27.00   59.10   62.50  407.00

summary(svi_divisional_county_lihtc_projects$post10_lihtc_project_dollars)

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##    63397   612954   942126  2038582  1543086 12039478

Similar to our NMTC group, we can see that our LIHTC group has a large range of both flags and dollars spent. Our minimum is now 3, with our maximum amount of flags at 407. Our minimum money spent is now $63,397, with our maximum being over $12,000,000.

Also similarly to our last group, we can see that our means are much smaller than our maximums, suggesting that large outliers are once again impacting our data.

# Scatterplot
# y is our independent variable (LIHTC Project Dollars),  
# x is our dependent variable (SVI flag count)
ggplot2::ggplot(svi_divisional_county_lihtc_projects,
                aes(x=flag_count10,
                    y=post10_lihtc_project_dollars)) +
        geom_point() +
        geom_smooth(method="lm") +
        scale_y_continuous(labels = scales::dollar_format()) 

# Pearson's r calculation
cor(svi_divisional_county_lihtc_projects$flag_count10, svi_divisional_county_lihtc_projects$post10_lihtc_project_dollars, method = "pearson")

## [1] 0.7792099

boxplot(svi_divisional_county_lihtc_projects$flag_count10)

Once again, our Pearson’s R Calculation suggests a fairly strong correlation. However, a vizual representation of our data does confirm that outliers may be impacting this.

svi_divisional_lihtc_cluster <- svi_divisional_county_lihtc_projects %>% 
                            select(county_name, post10_lihtc_project_dollars, 
                                   flag_count10) %>% 
                            remove_rownames %>% 
                            column_to_rownames(var="county_name")

# Remove nulls, if in dataset
svi_divisional_lihtc_cluster <- na.omit(svi_divisional_lihtc_cluster)


# Scale numeric variables
svi_divisional_lihtc_cluster <- scale(svi_divisional_lihtc_cluster)


svi_divisional_lihtc_cluster %>% head(5)

##                          post10_lihtc_project_dollars flag_count10
## District of Columbia, DC                    3.3855306    4.1691145
## Broward County, FL                          0.1831488    1.2810530
## Duval County, FL                           -0.2637197    0.5380662
## Escambia County, FL                        -0.1853096    0.1306219
## Hillsborough County, FL                     2.8320293    1.0293962

set.seed(123)
k2_lihtc_div <- kmeans(svi_divisional_lihtc_cluster, centers = 2, nstart = 25)
set.seed(123)
k3_lihtc_div <- kmeans(svi_divisional_lihtc_cluster, centers = 3, nstart = 25)
set.seed(123)
k4_lihtc_div <- kmeans(svi_divisional_lihtc_cluster, centers = 4, nstart = 25)
set.seed(123)
k5_lihtc_div <- kmeans(svi_divisional_lihtc_cluster, centers = 5, nstart = 25)

# plots to compare
p_k2_lihtc_div <- factoextra::fviz_cluster(k2_lihtc_div, geom = "point", data = svi_divisional_lihtc_cluster) + ggtitle("k = 2")

p_k3_lihtc_div <- factoextra::fviz_cluster(k3_lihtc_div, geom = "point", data = svi_divisional_lihtc_cluster) + ggtitle("k = 3")

p_k4_lihtc_div <- factoextra::fviz_cluster(k4_lihtc_div, geom = "point",  data = svi_divisional_lihtc_cluster) + ggtitle("k = 4")

p_k5_lihtc_div <- factoextra::fviz_cluster(k5_lihtc_div, geom = "point",  data = svi_divisional_lihtc_cluster) + ggtitle("k = 5")

grid.arrange(p_k2_lihtc_div, p_k3_lihtc_div, p_k4_lihtc_div, p_k5_lihtc_div, nrow = 2)

elbow_plot(svi_divisional_lihtc_cluster)

##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15
## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
## [1] 6
## [1] 7
## [1] 8
## [1] 9
## [1] 10
## [1] 11
## [1] 12
## [1] 13
## [1] 14
## [1] 15

p_k3_lihtc_div <- factoextra::fviz_cluster(k3_lihtc_div, geom = "point", data = svi_divisional_lihtc_cluster) + ggtitle("k = 3")

p_k3_lihtc_div

Once again, it appears that our data would be better represented as three clusters.

svi_divisional_lihtc_cluster_label <- as.data.frame(svi_divisional_lihtc_cluster) %>%
                                  rownames_to_column(var = "county_name") %>%
                                  as_tibble() %>%
                                  mutate(cluster = k3_lihtc_div$cluster) %>%
                                  select(county_name, cluster)

svi_divisional_county_lihtc_projects2 <- left_join(svi_divisional_county_lihtc_projects, svi_divisional_lihtc_cluster_label, join_by(county_name == county_name))

# View county counts in each cluster
table(svi_divisional_county_lihtc_projects2$cluster)

## 
##  1  2  3 
## 41  3  6

# Cluster 1 Scatterplot
# y is our independent variable (LIHTC Project Dollars),  
# x is our dependent variable (SVI flag count)

svi_divisional_county_lihtc_projects2 %>% 
  filter(cluster == 1) %>%
  ggplot2::ggplot(aes(x=flag_count10,
                    y=post10_lihtc_project_dollars)) +
        geom_point() +
        geom_smooth(method="lm") +
        scale_y_continuous(labels = scales::dollar_format()) 

svi_divisional_county_lihtc_projects2 %>% 
  filter(cluster == 1) %>%
  select(flag_count10,post10_lihtc_project_dollars) %>%
  cor(method = "pearson")

##                              flag_count10 post10_lihtc_project_dollars
## flag_count10                    1.0000000                    0.2414151
## post10_lihtc_project_dollars    0.2414151                    1.0000000

Looking at our first and largest cluster, we can see that there is no strong correlation between the amount of flags and the amount of money spent.

# Cluster 2 Scatterplot
# y is our independent variable (LIHTC Project Dollars),  
# x is our dependent variable (SVI flag count)

svi_divisional_county_lihtc_projects2 %>% 
  filter(cluster == 2) %>%
  ggplot2::ggplot(aes(x=flag_count10,
                    y=post10_lihtc_project_dollars)) +
        geom_point() +
        geom_smooth(method="lm") +
        scale_y_continuous(labels = scales::dollar_format()) 

svi_divisional_county_lihtc_projects2 %>% 
  filter(cluster == 2) %>%
  select(flag_count10,post10_lihtc_project_dollars) %>%
  cor(method = "pearson")

##                              flag_count10 post10_lihtc_project_dollars
## flag_count10                    1.0000000                    0.2309379
## post10_lihtc_project_dollars    0.2309379                    1.0000000

Our number 2 cluster, which contains our largest outliers, confims this as well.

# Cluster 3 Scatterplot
# y is our independent variable (LIHTC Project Dollars),  
# x is our dependent variable (SVI flag count)

svi_divisional_county_lihtc_projects2 %>% 
  filter(cluster == 3) %>%
  ggplot2::ggplot(aes(x=flag_count10,
                    y=post10_lihtc_project_dollars)) +
        geom_point() +
        geom_smooth(method="lm") +
        scale_y_continuous(labels = scales::dollar_format()) 

svi_divisional_county_lihtc_projects2 %>% 
  filter(cluster == 3) %>%
  select(flag_count10,post10_lihtc_project_dollars) %>%
  cor(method = "pearson")

##                              flag_count10 post10_lihtc_project_dollars
## flag_count10                    1.0000000                   -0.1082347
## post10_lihtc_project_dollars   -0.1082347                    1.0000000

Finally, our number three cluster shows a slight negative correlation, also confirming that there is not a strong relationship between the amount of money spent and the amount of flags.

# Join our LIHTC projects data with our shapefile geocoordinates
svi_divisional_county_lihtc_sf <- left_join(svi_divisional_county_lihtc_projects, divisional_county_sf, join_by("FIPS_st" == "STATEFP", "FIPS_county" == "COUNTYFP"))

svi_divisional_county_lihtc_sf %>% head(5) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

State	County	Division	post10_lihtc_project_cnt	tract_cnt	post10_lihtc_project_dollars	post10_lihtc_dollars_formatted	fips_county_st	FIPS_st	FIPS_county	state_name	region_number	region	division_number	flag_count10	pop10	flag_by_pop10	flag_count_quantile10	flag_pop_quantile10	flag_count20	pop20	flag_by_pop20	flag_count_quantile20	flag_pop_quantile20	county_name	geometry
DC	District of Columbia	South Atlantic Division	21	53	11695555	\$11,695,555	11001	11	001	District of Columbia	3	South Region	5	407	157923	0.0025772	1	0.6	387	192201	0.0020135	1	0.4	District of Columbia, DC	MULTIPOLYGON (((-76.95772 3…
FL	Broward County	South Atlantic Division	3	17	2561000	\$2,561,000	12011	12	011	Florida	3	South Region	5	166	71933	0.0023077	1	0.6	160	84636	0.0018904	1	0.4	Broward County, FL	MULTIPOLYGON (((-80.08484 2…
FL	Duval County	South Atlantic Division	4	12	1286341	\$1,286,341	12031	12	031	Florida	3	South Region	5	104	40471	0.0025697	1	0.6	90	40771	0.0022075	1	0.6	Duval County, FL	MULTIPOLYGON (((-81.39745 3…
FL	Escambia County	South Atlantic Division	1	9	1510000	\$1,510,000	12033	12	033	Florida	3	South Region	5	70	30376	0.0023045	1	0.6	64	26675	0.0023993	1	0.6	Escambia County, FL	MULTIPOLYGON (((-87.54197 3…
FL	Hillsborough County	South Atlantic Division	7	17	10116734	\$10,116,734	12057	12	057	Florida	3	South Region	5	145	42317	0.0034265	1	0.8	149	50266	0.0029642	1	0.8	Hillsborough County, FL	MULTIPOLYGON (((-82.75593 2…

# Create classes for bivariate mapping 
svi_divisional_county_lihtc_sf <- bi_class(svi_divisional_county_lihtc_sf, x = flag_count10, y = post10_lihtc_project_dollars, style = "quantile", dim = 3)

# View data
svi_divisional_county_lihtc_sf %>% head(5) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

State	County	Division	post10_lihtc_project_cnt	tract_cnt	post10_lihtc_project_dollars	post10_lihtc_dollars_formatted	fips_county_st	FIPS_st	FIPS_county	state_name	region_number	region	division_number	flag_count10	pop10	flag_by_pop10	flag_count_quantile10	flag_pop_quantile10	flag_count20	pop20	flag_by_pop20	flag_count_quantile20	flag_pop_quantile20	county_name	geometry	bi_class
DC	District of Columbia	South Atlantic Division	21	53	11695555	\$11,695,555	11001	11	001	District of Columbia	3	South Region	5	407	157923	0.0025772	1	0.6	387	192201	0.0020135	1	0.4	District of Columbia, DC	MULTIPOLYGON (((-76.95772 3…	3-3
FL	Broward County	South Atlantic Division	3	17	2561000	\$2,561,000	12011	12	011	Florida	3	South Region	5	166	71933	0.0023077	1	0.6	160	84636	0.0018904	1	0.4	Broward County, FL	MULTIPOLYGON (((-80.08484 2…	3-3
FL	Duval County	South Atlantic Division	4	12	1286341	\$1,286,341	12031	12	031	Florida	3	South Region	5	104	40471	0.0025697	1	0.6	90	40771	0.0022075	1	0.6	Duval County, FL	MULTIPOLYGON (((-81.39745 3…	3-2
FL	Escambia County	South Atlantic Division	1	9	1510000	\$1,510,000	12033	12	033	Florida	3	South Region	5	70	30376	0.0023045	1	0.6	64	26675	0.0023993	1	0.6	Escambia County, FL	MULTIPOLYGON (((-87.54197 3…	3-3
FL	Hillsborough County	South Atlantic Division	7	17	10116734	\$10,116,734	12057	12	057	Florida	3	South Region	5	145	42317	0.0034265	1	0.8	149	50266	0.0029642	1	0.8	Hillsborough County, FL	MULTIPOLYGON (((-82.75593 2…	3-3

# Create map with ggplot
svi_divisional_county_lihtc_map <- ggplot() +
  # Map county shapefile, fill with bi_class categories
  geom_sf(data = svi_divisional_county_lihtc_sf, mapping = aes(geometry=geometry, fill = bi_class), color = "white", size = 0.1, show.legend = FALSE) +
  # Set to biscale palette
  bi_scale_fill(pal = "GrPink", dim = 3) +
  # Add state shapefiles for outline
  geom_sf(data=divisional_st_sf, color="black", fill=NA, linewidth=.5, aes(geometry=geometry)) +
  labs(
    title = paste0("Correlation of 2010 ", census_division, " SVI Flag Count \n and 2011 - 2020 LIHTC Tax Dollars")
  ) +
  # Set them to biscale
  bi_theme(base_size = 10)

# Create biscale legend
svi_divisional_county_lihtc_legend <- bi_legend(pal = "GrPink",
                    dim = 3,
                    xlab = "SVI Flag Count",
                    ylab = "LIHTC Dollars",
                    size = 8)

# Combine map with legend using cowplot
svi_divisional_county_lihtc_bivarmap <- ggdraw() +
  draw_plot(svi_divisional_county_lihtc_map) +
  # Set legend location
  draw_plot(svi_divisional_county_lihtc_legend, x= -.02,  y = -.05,
 width=.20)


# View map
svi_divisional_county_lihtc_bivarmap

It appears that this time, the majority of high flags are still in Florida; there is no significant impact on the map in our other states, though there is a slight increase in spending without high flag counts above North Carolina.

svi_divisional_county_lihtc_sf %>% filter(State == "FL") %>% select(State, County, flag_count10, post10_lihtc_dollars_formatted) %>% arrange(desc(flag_count10)) %>% head(6)

##   State              County flag_count10 post10_lihtc_dollars_formatted
## 1    FL   Miami-Dade County          300                    $12,039,478
## 2    FL      Broward County          166                     $2,561,000
## 3    FL Hillsborough County          145                    $10,116,734
## 4    FL   Palm Beach County          132                     $4,702,545
## 5    FL        Duval County          104                     $1,286,341
## 6    FL         Polk County           86                     $1,271,000

saveRDS(svi_divisional_lihtc, file = here::here(paste0("data/wrangling/", str_replace_all(census_division, " ", "_"), "_svi_divisional_lihtc.rds")))

saveRDS(svi_national_lihtc, file = here::here(paste0("data/wrangling/", str_replace_all(census_division, " ", "_"), "_svi_national_lihtc.rds")))

saveRDS(svi_divisional_nmtc, file = here::here(paste0("data/wrangling/", str_replace_all(census_division, " ", "_"), "_svi_divisional_nmtc.rds")))

saveRDS(svi_national_nmtc, file = here::here(paste0("data/wrangling/", str_replace_all(census_division, " ", "_"), "_svi_national_nmtc.rds")))

</div>