Social Vulnerability Index

Library

# Load packages
library(here)        # relative filepaths for reproducibility
library(tidyverse)   # data wrangling
library(stringi)     # string wrangling
library(kableExtra)  # table formatting
library(tidycensus)  # census data

API Key and Variable Import

# Load API key, assign to TidyCensus Package
source(here::here("analysis/password.R"))
census_api_key(.census_api_key)

import::here( "author",
              "census_division",
              "fips_region_assignment",
              "rank_variables",
              "svi_theme_variables",
              "svi_theme_flags",
             # 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_Jazzy.R"),
             .character_only = TRUE)

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"))

svi_2010 %>% colnames()

##  [1] "GEOID_2010_trt"         "E_TOTPOP_10"            "E_HU_10"               
##  [4] "E_HH_10"                "E_POV150_10"            "ET_POVSTATUS_10"       
##  [7] "EP_POV150_10"           "EPL_POV150_10"          "E_UNEMP_10"            
## [10] "ET_EMPSTATUS_10"        "EP_UNEMP_10"            "EPL_UNEMP_10"          
## [13] "E_HBURD_OWN_10"         "ET_HOUSINGCOST_OWN_10"  "EP_HBURD_OWN_10"       
## [16] "EPL_HBURD_OWN_10"       "E_HBURD_RENT_10"        "ET_HOUSINGCOST_RENT_10"
## [19] "EP_HBURD_RENT_10"       "EPL_HBURD_RENT_10"      "E_HBURD_10"            
## [22] "ET_HOUSINGCOST_10"      "EP_HBURD_10"            "EPL_HBURD_10"          
## [25] "E_NOHSDP_10"            "ET_EDSTATUS_10"         "EP_NOHSDP_10"          
## [28] "EPL_NOHSDP_10"          "E_UNINSUR_12"           "ET_INSURSTATUS_12"     
## [31] "EP_UNINSUR_12"          "EPL_UNINSUR_12"         "E_AGE65_10"            
## [34] "EP_AGE65_10"            "EPL_AGE65_10"           "E_AGE17_10"            
## [37] "EP_AGE17_10"            "EPL_AGE17_10"           "E_DISABL_12"           
## [40] "ET_DISABLSTATUS_12"     "EP_DISABL_12"           "EPL_DISABL_12"         
## [43] "E_SNGPNT_10"            "ET_FAMILIES_10"         "EP_SNGPNT_10"          
## [46] "EPL_SNGPNT_10"          "E_LIMENG_10"            "ET_POPAGE5UP_10"       
## [49] "EP_LIMENG_10"           "EPL_LIMENG_10"          "E_MINRTY_10"           
## [52] "ET_POPETHRACE_10"       "EP_MINRTY_10"           "EPL_MINRTY_10"         
## [55] "E_STRHU_10"             "E_MUNIT_10"             "EP_MUNIT_10"           
## [58] "EPL_MUNIT_10"           "E_MOBILE_10"            "EP_MOBILE_10"          
## [61] "EPL_MOBILE_10"          "E_CROWD_10"             "ET_OCCUPANTS_10"       
## [64] "EP_CROWD_10"            "EPL_CROWD_10"           "E_NOVEH_10"            
## [67] "ET_KNOWNVEH_10"         "EP_NOVEH_10"            "EPL_NOVEH_10"          
## [70] "E_GROUPQ_10"            "ET_HHTYPE_10"           "EP_GROUPQ_10"          
## [73] "EPL_GROUPQ_10"

svi_2010 %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	E_TOTPOP_10	E_HU_10	E_HH_10	E_POV150_10	ET_POVSTATUS_10	EP_POV150_10	EPL_POV150_10	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	E_GROUPQ_10	ET_HHTYPE_10	EP_GROUPQ_10	EPL_GROUPQ_10
01001020100	1809	771	696	297	1809	16.41791	NA	36	889	4.049494	NA	127	598	21.23746	NA	47	98	47.95918	NA	174	696	25.00000	NA	196	1242	15.780998	NA	186	1759	10.574190	NA	222	12.271973	NA	445	24.59923	NA	298	1335	22.32210	NA	27	545	4.954128	NA	36	1705	2.1114370	NA	385	1809	21.282477	NA	771	0	0.0000000	NA	92	11.9325551	NA	0	696	0.0000000	NA	50	696	7.183908	NA	0	1809	0	NA
01001020200	2020	816	730	495	1992	24.84940	NA	68	834	8.153477	NA	49	439	11.16173	NA	105	291	36.08247	NA	154	730	21.09589	NA	339	1265	26.798419	NA	313	2012	15.556660	NA	204	10.099010	NA	597	29.55446	NA	359	1515	23.69637	NA	132	456	28.947368	NA	15	1890	0.7936508	NA	1243	2020	61.534653	NA	816	0	0.0000000	NA	34	4.1666667	NA	13	730	1.7808219	NA	115	730	15.753425	NA	0	2020	0	NA
01001020300	3543	1403	1287	656	3533	18.56779	NA	93	1552	5.992268	NA	273	957	28.52665	NA	178	330	53.93939	NA	451	1287	35.04274	NA	346	2260	15.309734	NA	252	3102	8.123791	NA	487	13.745413	NA	998	28.16822	NA	371	2224	16.68165	NA	126	913	13.800657	NA	0	3365	0.0000000	NA	637	3543	17.979114	NA	1403	10	0.7127584	NA	2	0.1425517	NA	0	1287	0.0000000	NA	101	1287	7.847708	NA	0	3543	0	NA
01001020400	4840	1957	1839	501	4840	10.35124	NA	101	2129	4.744011	NA	310	1549	20.01291	NA	89	290	30.68966	NA	399	1839	21.69657	NA	274	3280	8.353658	NA	399	4293	9.294200	NA	955	19.731405	NA	1195	24.69008	NA	625	3328	18.78005	NA	152	1374	11.062591	NA	10	4537	0.2204100	NA	297	4840	6.136364	NA	1957	33	1.6862545	NA	25	1.2774655	NA	14	1839	0.7612833	NA	19	1839	1.033170	NA	0	4840	0	NA
01001020500	9938	3969	3741	1096	9938	11.02838	NA	188	4937	3.807981	NA	426	2406	17.70574	NA	528	1335	39.55056	NA	954	3741	25.50120	NA	293	5983	4.897209	NA	740	10110	7.319486	NA	837	8.422218	NA	3012	30.30791	NA	759	7155	10.60797	NA	476	2529	18.821669	NA	78	9297	0.8389803	NA	1970	9938	19.822902	NA	3969	306	7.7097506	NA	0	0.0000000	NA	7	3741	0.1871157	NA	223	3741	5.960973	NA	0	9938	0	NA
01001020600	3402	1456	1308	735	3402	21.60494	NA	134	1720	7.790698	NA	242	1032	23.44961	NA	62	276	22.46377	NA	304	1308	23.24159	NA	301	2151	13.993491	NA	355	3445	10.304790	NA	386	11.346267	NA	931	27.36626	NA	440	2439	18.04018	NA	143	924	15.476190	NA	4	3254	0.1229256	NA	723	3402	21.252205	NA	1456	18	1.2362637	NA	433	29.7390110	NA	16	1308	1.2232416	NA	28	1308	2.140673	NA	0	3402	0	NA

svi_2010 %>% nrow()

## [1] 73057

svi_2020 %>% colnames()

##  [1] "GEOID_2010_trt"         "E_TOTPOP_20"            "E_HU_20"               
##  [4] "E_HH_20"                "E_POV150_20"            "ET_POVSTATUS_20"       
##  [7] "EP_POV150_20"           "EPL_POV150_20"          "E_UNEMP_20"            
## [10] "ET_EMPSTATUS_20"        "EP_UNEMP_20"            "EPL_UNEMP_20"          
## [13] "E_HBURD_OWN_20"         "ET_HOUSINGCOST_OWN_20"  "EP_HBURD_OWN_20"       
## [16] "EPL_HBURD_OWN_20"       "E_HBURD_RENT_20"        "ET_HOUSINGCOST_RENT_20"
## [19] "EP_HBURD_RENT_20"       "EPL_HBURD_RENT_20"      "E_HBURD_20"            
## [22] "ET_HOUSINGCOST_20"      "EP_HBURD_20"            "EPL_HBURD_20"          
## [25] "E_NOHSDP_20"            "ET_EDSTATUS_20"         "EP_NOHSDP_20"          
## [28] "EPL_NOHSDP_20"          "E_UNINSUR_20"           "ET_INSURSTATUS_20"     
## [31] "EP_UNINSUR_20"          "EPL_UNINSUR_20"         "E_AGE65_20"            
## [34] "EP_AGE65_20"            "EPL_AGE65_20"           "E_AGE17_20"            
## [37] "EP_AGE17_20"            "EPL_AGE17_20"           "E_DISABL_20"           
## [40] "ET_DISABLSTATUS_20"     "EP_DISABL_20"           "EPL_DISABL_20"         
## [43] "E_SNGPNT_20"            "ET_FAMILIES_20"         "EP_SNGPNT_20"          
## [46] "EPL_SNGPNT_20"          "E_LIMENG_20"            "ET_POPAGE5UP_20"       
## [49] "EP_LIMENG_20"           "EPL_LIMENG_20"          "E_MINRTY_20"           
## [52] "ET_POPETHRACE_20"       "EP_MINRTY_20"           "EPL_MINRTY_20"         
## [55] "E_STRHU_20"             "E_MUNIT_20"             "EP_MUNIT_20"           
## [58] "EPL_MUNIT_20"           "E_MOBILE_20"            "EP_MOBILE_20"          
## [61] "EPL_MOBILE_20"          "E_CROWD_20"             "ET_OCCUPANTS_20"       
## [64] "EP_CROWD_20"            "EPL_CROWD_20"           "E_NOVEH_20"            
## [67] "ET_KNOWNVEH_20"         "EP_NOVEH_20"            "EPL_NOVEH_20"          
## [70] "E_GROUPQ_20"            "ET_HHTYPE_20"           "EP_GROUPQ_20"          
## [73] "EPL_GROUPQ_20"

svi_2020 %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20
01001020100	1941	710	693	352	1941	18.13498	NA	18	852	2.112676	NA	81	507	15.976331	NA	63	186	33.87097	NA	144	693	20.77922	NA	187	1309	14.285714	NA	187	1941	9.634209	NA	295	15.19835	NA	415	21.38073	NA	391	1526	25.62254	NA	58	555	10.45045	NA	0	1843	0.0000000	NA	437	1941	22.51417	NA	710	0	0.0000000	NA	88	12.3943662	NA	0	693	0.0000000	NA	10	693	1.443001	NA	0	1941	0.000000	NA
01001020200	1757	720	573	384	1511	25.41363	NA	29	717	4.044630	NA	33	392	8.418367	NA	116	181	64.08840	NA	149	573	26.00349	NA	139	1313	10.586443	NA	91	1533	5.936073	NA	284	16.16392	NA	325	18.49744	NA	164	1208	13.57616	NA	42	359	11.69916	NA	0	1651	0.0000000	NA	1116	1757	63.51736	NA	720	3	0.4166667	NA	5	0.6944444	NA	9	573	1.5706806	NA	57	573	9.947644	NA	212	1757	12.066022	NA
01001020300	3694	1464	1351	842	3694	22.79372	NA	53	1994	2.657974	NA	117	967	12.099276	NA	147	384	38.28125	NA	264	1351	19.54108	NA	317	2477	12.797739	NA	127	3673	3.457664	NA	464	12.56091	NA	929	25.14889	NA	473	2744	17.23761	NA	263	975	26.97436	NA	128	3586	3.5694367	NA	1331	3694	36.03140	NA	1464	26	1.7759563	NA	14	0.9562842	NA	35	1351	2.5906736	NA	42	1351	3.108808	NA	0	3694	0.000000	NA
01001020400	3539	1741	1636	503	3539	14.21305	NA	39	1658	2.352232	NA	219	1290	16.976744	NA	74	346	21.38728	NA	293	1636	17.90954	NA	173	2775	6.234234	NA	169	3529	4.788892	NA	969	27.38062	NA	510	14.41085	NA	670	3019	22.19278	NA	148	1137	13.01671	NA	89	3409	2.6107363	NA	454	3539	12.82848	NA	1741	143	8.2136703	NA	0	0.0000000	NA	10	1636	0.6112469	NA	72	1636	4.400978	NA	0	3539	0.000000	NA
01001020500	10674	4504	4424	1626	10509	15.47245	NA	81	5048	1.604596	NA	321	2299	13.962592	NA	711	2125	33.45882	NA	1032	4424	23.32731	NA	531	6816	7.790493	NA	301	10046	2.996217	NA	1613	15.11149	NA	2765	25.90407	NA	1124	7281	15.43744	NA	342	2912	11.74451	NA	52	9920	0.5241935	NA	2603	10674	24.38636	NA	4504	703	15.6083481	NA	29	0.6438721	NA	37	4424	0.8363472	NA	207	4424	4.679023	NA	176	10674	1.648866	NA
01001020600	3536	1464	1330	1279	3523	36.30429	NA	34	1223	2.780049	NA	321	1111	28.892889	NA	67	219	30.59361	NA	388	1330	29.17293	NA	306	2380	12.857143	NA	415	3496	11.870709	NA	547	15.46946	NA	982	27.77149	NA	729	2514	28.99761	NA	95	880	10.79545	NA	0	3394	0.0000000	NA	985	3536	27.85633	NA	1464	0	0.0000000	NA	364	24.8633880	NA	0	1330	0.0000000	NA	17	1330	1.278196	NA	0	3536	0.000000	NA

svi_2020 %>% nrow()

## [1] 73057

Functions Wrangling

svi_2010 <- fips_region_assignment(svi_2010)

svi_2010 %>% 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	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	E_GROUPQ_10	ET_HHTYPE_10	EP_GROUPQ_10	EPL_GROUPQ_10
01001020100	01	001	020100	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	1809	771	696	297	1809	16.41791	NA	36	889	4.049494	NA	127	598	21.23746	NA	47	98	47.95918	NA	174	696	25.00000	NA	196	1242	15.780998	NA	186	1759	10.574190	NA	222	12.271973	NA	445	24.59923	NA	298	1335	22.32210	NA	27	545	4.954128	NA	36	1705	2.1114370	NA	385	1809	21.282477	NA	771	0	0.0000000	NA	92	11.9325551	NA	0	696	0.0000000	NA	50	696	7.183908	NA	0	1809	0	NA
01001020200	01	001	020200	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	2020	816	730	495	1992	24.84940	NA	68	834	8.153477	NA	49	439	11.16173	NA	105	291	36.08247	NA	154	730	21.09589	NA	339	1265	26.798419	NA	313	2012	15.556660	NA	204	10.099010	NA	597	29.55446	NA	359	1515	23.69637	NA	132	456	28.947368	NA	15	1890	0.7936508	NA	1243	2020	61.534653	NA	816	0	0.0000000	NA	34	4.1666667	NA	13	730	1.7808219	NA	115	730	15.753425	NA	0	2020	0	NA
01001020300	01	001	020300	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	3543	1403	1287	656	3533	18.56779	NA	93	1552	5.992268	NA	273	957	28.52665	NA	178	330	53.93939	NA	451	1287	35.04274	NA	346	2260	15.309734	NA	252	3102	8.123791	NA	487	13.745413	NA	998	28.16822	NA	371	2224	16.68165	NA	126	913	13.800657	NA	0	3365	0.0000000	NA	637	3543	17.979114	NA	1403	10	0.7127584	NA	2	0.1425517	NA	0	1287	0.0000000	NA	101	1287	7.847708	NA	0	3543	0	NA
01001020400	01	001	020400	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	4840	1957	1839	501	4840	10.35124	NA	101	2129	4.744011	NA	310	1549	20.01291	NA	89	290	30.68966	NA	399	1839	21.69657	NA	274	3280	8.353658	NA	399	4293	9.294200	NA	955	19.731405	NA	1195	24.69008	NA	625	3328	18.78005	NA	152	1374	11.062591	NA	10	4537	0.2204100	NA	297	4840	6.136364	NA	1957	33	1.6862545	NA	25	1.2774655	NA	14	1839	0.7612833	NA	19	1839	1.033170	NA	0	4840	0	NA
01001020500	01	001	020500	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	9938	3969	3741	1096	9938	11.02838	NA	188	4937	3.807981	NA	426	2406	17.70574	NA	528	1335	39.55056	NA	954	3741	25.50120	NA	293	5983	4.897209	NA	740	10110	7.319486	NA	837	8.422218	NA	3012	30.30791	NA	759	7155	10.60797	NA	476	2529	18.821669	NA	78	9297	0.8389803	NA	1970	9938	19.822902	NA	3969	306	7.7097506	NA	0	0.0000000	NA	7	3741	0.1871157	NA	223	3741	5.960973	NA	0	9938	0	NA
01001020600	01	001	020600	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	3402	1456	1308	735	3402	21.60494	NA	134	1720	7.790698	NA	242	1032	23.44961	NA	62	276	22.46377	NA	304	1308	23.24159	NA	301	2151	13.993491	NA	355	3445	10.304790	NA	386	11.346267	NA	931	27.36626	NA	440	2439	18.04018	NA	143	924	15.476190	NA	4	3254	0.1229256	NA	723	3402	21.252205	NA	1456	18	1.2362637	NA	433	29.7390110	NA	16	1308	1.2232416	NA	28	1308	2.140673	NA	0	3402	0	NA

svi_2020 <- fips_region_assignment(svi_2020)

svi_2020 %>% 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	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20
01001020100	01	001	020100	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	1941	710	693	352	1941	18.13498	NA	18	852	2.112676	NA	81	507	15.976331	NA	63	186	33.87097	NA	144	693	20.77922	NA	187	1309	14.285714	NA	187	1941	9.634209	NA	295	15.19835	NA	415	21.38073	NA	391	1526	25.62254	NA	58	555	10.45045	NA	0	1843	0.0000000	NA	437	1941	22.51417	NA	710	0	0.0000000	NA	88	12.3943662	NA	0	693	0.0000000	NA	10	693	1.443001	NA	0	1941	0.000000	NA
01001020200	01	001	020200	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	1757	720	573	384	1511	25.41363	NA	29	717	4.044630	NA	33	392	8.418367	NA	116	181	64.08840	NA	149	573	26.00349	NA	139	1313	10.586443	NA	91	1533	5.936073	NA	284	16.16392	NA	325	18.49744	NA	164	1208	13.57616	NA	42	359	11.69916	NA	0	1651	0.0000000	NA	1116	1757	63.51736	NA	720	3	0.4166667	NA	5	0.6944444	NA	9	573	1.5706806	NA	57	573	9.947644	NA	212	1757	12.066022	NA
01001020300	01	001	020300	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	3694	1464	1351	842	3694	22.79372	NA	53	1994	2.657974	NA	117	967	12.099276	NA	147	384	38.28125	NA	264	1351	19.54108	NA	317	2477	12.797739	NA	127	3673	3.457664	NA	464	12.56091	NA	929	25.14889	NA	473	2744	17.23761	NA	263	975	26.97436	NA	128	3586	3.5694367	NA	1331	3694	36.03140	NA	1464	26	1.7759563	NA	14	0.9562842	NA	35	1351	2.5906736	NA	42	1351	3.108808	NA	0	3694	0.000000	NA
01001020400	01	001	020400	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	3539	1741	1636	503	3539	14.21305	NA	39	1658	2.352232	NA	219	1290	16.976744	NA	74	346	21.38728	NA	293	1636	17.90954	NA	173	2775	6.234234	NA	169	3529	4.788892	NA	969	27.38062	NA	510	14.41085	NA	670	3019	22.19278	NA	148	1137	13.01671	NA	89	3409	2.6107363	NA	454	3539	12.82848	NA	1741	143	8.2136703	NA	0	0.0000000	NA	10	1636	0.6112469	NA	72	1636	4.400978	NA	0	3539	0.000000	NA
01001020500	01	001	020500	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	10674	4504	4424	1626	10509	15.47245	NA	81	5048	1.604596	NA	321	2299	13.962592	NA	711	2125	33.45882	NA	1032	4424	23.32731	NA	531	6816	7.790493	NA	301	10046	2.996217	NA	1613	15.11149	NA	2765	25.90407	NA	1124	7281	15.43744	NA	342	2912	11.74451	NA	52	9920	0.5241935	NA	2603	10674	24.38636	NA	4504	703	15.6083481	NA	29	0.6438721	NA	37	4424	0.8363472	NA	207	4424	4.679023	NA	176	10674	1.648866	NA
01001020600	01	001	020600	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	3536	1464	1330	1279	3523	36.30429	NA	34	1223	2.780049	NA	321	1111	28.892889	NA	67	219	30.59361	NA	388	1330	29.17293	NA	306	2380	12.857143	NA	415	3496	11.870709	NA	547	15.46946	NA	982	27.77149	NA	729	2514	28.99761	NA	95	880	10.79545	NA	0	3394	0.0000000	NA	985	3536	27.85633	NA	1464	0	0.0000000	NA	364	24.8633880	NA	0	1330	0.0000000	NA	17	1330	1.278196	NA	0	3536	0.000000	NA

# National
svi_2010_national <- rank_variables(svi_2010)
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	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	E_GROUPQ_10	ET_HHTYPE_10	EP_GROUPQ_10	EPL_GROUPQ_10
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	36	889	4.049494	0.1790	127	598	21.23746	0.20770	47	98	47.95918	0.5767	174	696	25.00000	0.18790	196	1242	15.780998	0.6093	186	1759	10.574190	0.3790	222	12.271973	0.4876	445	24.59923	0.5473	298	1335	22.32210	0.8454	27	545	4.954128	0.09275	36	1705	2.1114370	0.59040	385	1809	21.282477	0.4524	771	0	0.0000000	0.1224	92	11.9325551	0.8005	0	696	0.0000000	0.1238	50	696	7.183908	0.6134	0	1809	0	0.364
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	68	834	8.153477	0.5754	49	439	11.16173	0.02067	105	291	36.08247	0.3019	154	730	21.09589	0.09312	339	1265	26.798419	0.8392	313	2012	15.556660	0.6000	204	10.099010	0.3419	597	29.55446	0.8192	359	1515	23.69637	0.8791	132	456	28.947368	0.83510	15	1890	0.7936508	0.40130	1243	2020	61.534653	0.7781	816	0	0.0000000	0.1224	34	4.1666667	0.6664	13	730	1.7808219	0.5406	115	730	15.753425	0.8382	0	2020	0	0.364
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	93	1552	5.992268	0.3724	273	957	28.52665	0.45780	178	330	53.93939	0.7152	451	1287	35.04274	0.49930	346	2260	15.309734	0.5950	252	3102	8.123791	0.2596	487	13.745413	0.5868	998	28.16822	0.7606	371	2224	16.68165	0.6266	126	913	13.800657	0.46350	0	3365	0.0000000	0.09298	637	3543	17.979114	0.4049	1403	10	0.7127584	0.3015	2	0.1425517	0.4407	0	1287	0.0000000	0.1238	101	1287	7.847708	0.6443	0	3543	0	0.364
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	101	2129	4.744011	0.2447	310	1549	20.01291	0.17080	89	290	30.68966	0.2044	399	1839	21.69657	0.10540	274	3280	8.353658	0.3205	399	4293	9.294200	0.3171	955	19.731405	0.8643	1195	24.69008	0.5530	625	3328	18.78005	0.7233	152	1374	11.062591	0.34710	10	4537	0.2204100	0.22560	297	4840	6.136364	0.1647	1957	33	1.6862545	0.3843	25	1.2774655	0.5516	14	1839	0.7612833	0.3564	19	1839	1.033170	0.1127	0	4840	0	0.364
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	188	4937	3.807981	0.1577	426	2406	17.70574	0.11050	528	1335	39.55056	0.3753	954	3741	25.50120	0.20140	293	5983	4.897209	0.1655	740	10110	7.319486	0.2211	837	8.422218	0.2408	3012	30.30791	0.8455	759	7155	10.60797	0.2668	476	2529	18.821669	0.63540	78	9297	0.8389803	0.41110	1970	9938	19.822902	0.4330	3969	306	7.7097506	0.6153	0	0.0000000	0.2198	7	3741	0.1871157	0.2535	223	3741	5.960973	0.5483	0	9938	0	0.364
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	134	1720	7.790698	0.5436	242	1032	23.44961	0.28010	62	276	22.46377	0.1035	304	1308	23.24159	0.14070	301	2151	13.993491	0.5510	355	3445	10.304790	0.3656	386	11.346267	0.4232	931	27.36626	0.7200	440	2439	18.04018	0.6912	143	924	15.476190	0.52900	4	3254	0.1229256	0.19840	723	3402	21.252205	0.4519	1456	18	1.2362637	0.3507	433	29.7390110	0.9468	16	1308	1.2232416	0.4493	28	1308	2.140673	0.2298	0	3402	0	0.364

# National
svi_2020_national <- rank_variables(svi_2020)
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	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20
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	18	852	2.112676	0.15070	81	507	15.976331	0.26320	63	186	33.87097	0.2913	144	693	20.77922	0.2230	187	1309	14.285714	0.6928	187	1941	9.634209	0.6617	295	15.19835	0.4601	415	21.38073	0.4681	391	1526	25.62254	0.9011	58	555	10.45045	0.3451	0	1843	0.0000000	0.09479	437	1941	22.51417	0.3902	710	0	0.0000000	0.1079	88	12.3943662	0.8263	0	693	0.0000000	0.09796	10	693	1.443001	0.1643	0	1941	0.000000	0.1831
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	29	717	4.044630	0.41320	33	392	8.418367	0.03542	116	181	64.08840	0.9086	149	573	26.00349	0.4041	139	1313	10.586443	0.5601	91	1533	5.936073	0.4343	284	16.16392	0.5169	325	18.49744	0.2851	164	1208	13.57616	0.4127	42	359	11.69916	0.3998	0	1651	0.0000000	0.09479	1116	1757	63.51736	0.7591	720	3	0.4166667	0.2470	5	0.6944444	0.5106	9	573	1.5706806	0.46880	57	573	9.947644	0.7317	212	1757	12.066022	0.9549
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	53	1994	2.657974	0.22050	117	967	12.099276	0.11370	147	384	38.28125	0.3856	264	1351	19.54108	0.1827	317	2477	12.797739	0.6460	127	3673	3.457664	0.2308	464	12.56091	0.3088	929	25.14889	0.7080	473	2744	17.23761	0.6211	263	975	26.97436	0.8234	128	3586	3.5694367	0.70770	1331	3694	36.03140	0.5515	1464	26	1.7759563	0.3675	14	0.9562842	0.5389	35	1351	2.5906736	0.60550	42	1351	3.108808	0.3415	0	3694	0.000000	0.1831
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	39	1658	2.352232	0.17990	219	1290	16.976744	0.30880	74	346	21.38728	0.1037	293	1636	17.90954	0.1333	173	2775	6.234234	0.3351	169	3529	4.788892	0.3448	969	27.38062	0.9225	510	14.41085	0.1208	670	3019	22.19278	0.8194	148	1137	13.01671	0.4541	89	3409	2.6107363	0.64690	454	3539	12.82848	0.2364	1741	143	8.2136703	0.6028	0	0.0000000	0.2186	10	1636	0.6112469	0.28340	72	1636	4.400978	0.4538	0	3539	0.000000	0.1831
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	81	5048	1.604596	0.09431	321	2299	13.962592	0.17970	711	2125	33.45882	0.2836	1032	4424	23.32731	0.3109	531	6816	7.790493	0.4251	301	10046	2.996217	0.1894	1613	15.11149	0.4553	2765	25.90407	0.7494	1124	7281	15.43744	0.5253	342	2912	11.74451	0.4019	52	9920	0.5241935	0.35230	2603	10674	24.38636	0.4160	4504	703	15.6083481	0.7378	29	0.6438721	0.5037	37	4424	0.8363472	0.33420	207	4424	4.679023	0.4754	176	10674	1.648866	0.7598
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	34	1223	2.780049	0.23780	321	1111	28.892889	0.75870	67	219	30.59361	0.2305	388	1330	29.17293	0.5075	306	2380	12.857143	0.6480	415	3496	11.870709	0.7535	547	15.46946	0.4760	982	27.77149	0.8327	729	2514	28.99761	0.9488	95	880	10.79545	0.3601	0	3394	0.0000000	0.09479	985	3536	27.85633	0.4608	1464	0	0.0000000	0.1079	364	24.8633880	0.9300	0	1330	0.0000000	0.09796	17	1330	1.278196	0.1463	0	3536	0.000000	0.1831

# Regional
svi_2010_regional <- rank_variables(svi_2010, rank_by = "regional", location = "West Region")
svi_2010_regional %>% 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	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	E_GROUPQ_10	ET_HHTYPE_10	EP_GROUPQ_10	EPL_GROUPQ_10
02013000100	02	013	000100	AK	Alaska	Aleutians East Borough	4	West Region	9	Pacific Division	3703	474	267	1212	3695	32.801082	0.75680	111	3163	3.509327	0.11740	25	158	15.82278	0.03240	17	109	15.59633	0.04224	42	267	15.73034	0.01531	1082	3017	35.863441	0.8798	2060	3112	66.19537	0.9997	127	3.429652	0.05340	315	8.506616	0.04000	182	2849	6.388206	0.08106	50	165	30.303030	0.88690	1070	3617	29.5825270	0.9536	3492	3703	94.30192	0.9353	474	8	1.687764	0.3384	42	8.8607595	0.7721	7	267	2.621723	0.4631	77	267	28.8389513	0.97440	2969	3703	80.17823	0.9943
02016000100	02	016	000100	AK	Alaska	Aleutians West Census Area	4	West Region	9	Pacific Division	1774	1056	166	328	1231	26.645004	0.65280	15	1370	1.094890	0.01825	25	95	26.31579	0.18340	16	71	22.53521	0.07759	41	166	24.69880	0.07625	207	1330	15.563910	0.6204	484	973	49.74306	0.9952	53	2.987599	0.04081	182	10.259301	0.05208	147	747	19.678715	0.84870	19	96	19.791667	0.66880	79	1718	4.5983702	0.5482	1154	1774	65.05073	0.7186	1056	22	2.083333	0.3616	0	0.0000000	0.2283	10	166	6.024096	0.6826	84	166	50.6024096	0.99490	1324	1774	74.63360	0.9936
02016000200	02	016	000200	AK	Alaska	Aleutians West Census Area	4	West Region	9	Pacific Division	4485	507	355	1315	4469	29.424927	0.70470	87	3859	2.254470	0.04855	20	94	21.27660	0.08625	35	261	13.40996	0.03444	55	355	15.49296	0.01475	1292	3728	34.656652	0.8710	981	4256	23.04981	0.7611	186	4.147157	0.08056	384	8.561873	0.04031	235	3656	6.427790	0.08275	38	204	18.627451	0.63220	1458	4397	33.1589720	0.9696	3616	4485	80.62430	0.8295	507	85	16.765286	0.7273	32	6.3116371	0.7311	41	355	11.549296	0.8277	30	355	8.4507042	0.72540	3507	4485	78.19398	0.9941
02020000101	02	020	000101	AK	Alaska	Anchorage Municipality	4	West Region	9	Pacific Division	5475	1957	1796	296	5463	5.418268	0.08155	229	2885	7.937608	0.53240	364	1522	23.91590	0.13070	39	274	14.23358	0.03708	403	1796	22.43875	0.05297	121	3476	3.481013	0.1379	863	5853	14.74458	0.4763	236	4.310502	0.08795	1614	29.479452	0.73720	524	4028	13.008937	0.52080	65	1496	4.344920	0.07846	13	5202	0.2499039	0.1070	759	5475	13.86301	0.1579	1957	0	0.000000	0.1131	167	8.5334696	0.7672	134	1796	7.461024	0.7327	11	1796	0.6124722	0.09953	0	5475	0.00000	0.3812
02020000102	02	020	000102	AK	Alaska	Anchorage Municipality	4	West Region	9	Pacific Division	4240	1923	1654	286	4240	6.745283	0.11910	198	2385	8.301887	0.56480	275	1235	22.26721	0.10140	248	419	59.18854	0.76560	523	1654	31.62031	0.20010	242	2799	8.645945	0.3896	838	4982	16.82055	0.5611	259	6.108491	0.18900	1038	24.481132	0.48710	809	3707	21.823577	0.90420	97	1071	9.056956	0.25170	0	4007	0.0000000	0.0421	955	4240	22.52358	0.2947	1923	169	8.788351	0.5803	147	7.6443058	0.7537	33	1654	1.995163	0.3949	103	1654	6.2273277	0.62090	0	4240	0.00000	0.3812
02020000201	02	020	000201	AK	Alaska	Anchorage Municipality	4	West Region	9	Pacific Division	4085	1543	1532	296	4085	7.246022	0.13510	46	2089	2.202011	0.04648	302	969	31.16615	0.30850	187	563	33.21492	0.18520	489	1532	31.91906	0.20740	164	2395	6.847599	0.3060	811	3466	23.39873	0.7701	133	3.255814	0.04808	1199	29.351285	0.73180	320	2468	12.965964	0.51760	171	1083	15.789474	0.53180	7	3810	0.1837270	0.0954	743	4085	18.18849	0.2314	1543	54	3.499676	0.4249	7	0.4536617	0.4768	32	1532	2.088773	0.4053	49	1532	3.1984334	0.39740	0	4085	0.00000	0.3812

# Regional
svi_2020_regional <- rank_variables(svi_2020, rank_by = "regional", location = "West Region")
svi_2020_regional %>% 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	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20
02013000100	02	013	000100	AK	Alaska	Aleutians East Borough	4	West Region	9	Pacific Division	3389	1199	988	698	3379	20.656999	0.5776	86	2414	3.562552	0.3024	67	607	11.037891	0.03573	74	381	19.42257	0.06036	141	988	14.27126	0.02101	354	2646	13.378685	0.64780	1345	3384	39.745863	0.9988	381	11.242254	0.31650	443	13.07170	0.1024	339	2941.000	11.526692	0.3561	135	593.000	22.765599	0.7825	334	3276	10.1953602	0.78580	2939	3389.000	86.72175	0.8534	1199	38	3.169308	0.3895	69	5.7547957	0.7406	30	988	3.0364372	0.4421	220	988.000	22.267207	0.95820	1035	3389	30.5399823	0.9850
02016000100	02	016	000100	AK	Alaska	Aleutians West Census Area	4	West Region	9	Pacific Division	950	694	199	218	719	30.319889	0.7730	15	560	2.678571	0.1843	11	117	9.401709	0.02190	14	82	17.07317	0.04729	25	199	12.56281	0.01342	48	681	7.048458	0.40760	238	721	33.009709	0.9962	116	12.210526	0.37460	195	20.52632	0.4079	113	526.000	21.482890	0.8686	31	98.000	31.632653	0.9233	17	900	1.8888889	0.38640	713	950.000	75.05263	0.7532	694	17	2.449568	0.3573	0	0.0000000	0.2256	7	199	3.5175879	0.4846	68	199.000	34.170854	0.98650	274	950	28.8421053	0.9838
02016000200	02	016	000200	AK	Alaska	Aleutians West Census Area	4	West Region	9	Pacific Division	4758	1319	1107	398	4700	8.468085	0.1779	144	3404	4.230317	0.3891	111	245	45.306122	0.94670	93	862	10.78886	0.02255	204	1107	18.42818	0.06373	297	3527	8.420754	0.47660	699	4724	14.796782	0.8800	314	6.599412	0.08116	822	17.27617	0.2339	292	3902.000	7.483342	0.1022	99	662.000	14.954683	0.5536	433	4586	9.4417793	0.76800	3672	4758.000	77.17528	0.7705	1319	392	29.719485	0.8478	23	1.7437453	0.6232	146	1107	13.1887986	0.8497	96	1107.000	8.672087	0.75590	950	4758	19.9663724	0.9772
02020000101	02	020	000101	AK	Alaska	Anchorage Municipality	4	West Region	9	Pacific Division	5772	2127	1917	416	5772	7.207207	0.1332	223	2691	8.286882	0.7934	296	1679	17.629541	0.16820	30	238	12.60504	0.02848	326	1917	17.00574	0.04385	74	4011	1.844926	0.08361	546	5733	9.523810	0.6946	692	11.988912	0.36100	1481	25.65835	0.6992	771	4252.330	18.131237	0.7612	94	1608.796	5.842877	0.1542	4	5425	0.0737327	0.08891	989	5772.331	17.13346	0.1416	2127	28	1.316408	0.2983	5	0.2350729	0.4599	9	1917	0.4694836	0.1302	24	1916.584	1.252228	0.17850	114	5772	1.9750520	0.8279
02020000102	02	020	000102	AK	Alaska	Anchorage Municipality	4	West Region	9	Pacific Division	4743	1975	1681	633	4738	13.360067	0.3558	75	2465	3.042596	0.2298	383	1350	28.370370	0.59250	122	331	36.85801	0.25890	505	1681	30.04164	0.35500	205	3383	6.059710	0.35300	330	4638	7.115136	0.5427	653	13.767658	0.46540	1186	25.00527	0.6666	472	3447.726	13.690182	0.5134	182	1469.325	12.386640	0.4535	0	4485	0.0000000	0.04046	756	4743.330	15.93817	0.1243	1975	153	7.746835	0.5320	156	7.8987342	0.7792	17	1681	1.0113028	0.2071	0	1681.103	0.000000	0.02755	15	4743	0.3162555	0.5093
02020000201	02	020	000201	AK	Alaska	Anchorage Municipality	4	West Region	9	Pacific Division	4707	1946	1835	706	4707	14.998938	0.4109	88	2269	3.878360	0.3427	219	793	27.616646	0.56120	527	1042	50.57582	0.58770	746	1835	40.65395	0.69090	194	2805	6.916221	0.39940	464	4274	10.856341	0.7531	257	5.459953	0.04952	1279	27.17230	0.7729	390	2999.274	13.003148	0.4650	72	1222.675	5.888728	0.1562	26	4201	0.6189003	0.20280	1282	4706.670	27.23794	0.2849	1946	76	3.905447	0.4167	2	0.1027749	0.4547	96	1835	5.2316076	0.6097	39	1834.897	2.125459	0.28940	0	4707	0.0000000	0.1626

# Divisional
svi_2010_divisional <- rank_variables(svi_2010, rank_by = "divisional", location = "Pacific Division")
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	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	E_GROUPQ_10	ET_HHTYPE_10	EP_GROUPQ_10	EPL_GROUPQ_10
02013000100	02	013	000100	AK	Alaska	Aleutians East Borough	4	West Region	9	Pacific Division	3703	474	267	1212	3695	32.801082	0.75700	111	3163	3.509327	0.08691	25	158	15.82278	0.01337	17	109	15.59633	0.02605	42	267	15.73034	0.004754	1082	3017	35.863441	0.8542	2060	3112	66.19537	0.9999	127	3.429652	0.04240	315	8.506616	0.03961	182	2849	6.388206	0.07775	50	165	30.303030	0.88350	1070	3617	29.5825270	0.93700	3492	3703	94.30192	0.9141	474	8	1.687764	0.29250	42	8.8607595	0.8128	7	267	2.621723	0.4003	77	267	28.8389513	0.96850	2969	3703	80.17823	0.9940
02016000100	02	016	000100	AK	Alaska	Aleutians West Census Area	4	West Region	9	Pacific Division	1774	1056	166	328	1231	26.645004	0.65530	15	1370	1.094890	0.01369	25	95	26.31579	0.09653	16	71	22.53521	0.05099	41	166	24.69880	0.029080	207	1330	15.563910	0.5839	484	973	49.74306	0.9952	53	2.987599	0.03180	182	10.259301	0.05188	147	747	19.678715	0.86420	19	96	19.791667	0.66060	79	1718	4.5983702	0.46890	1154	1774	65.05073	0.6522	1056	22	2.083333	0.31610	0	0.0000000	0.2497	10	166	6.024096	0.6154	84	166	50.6024096	0.99320	1324	1774	74.63360	0.9935
02016000200	02	016	000200	AK	Alaska	Aleutians West Census Area	4	West Region	9	Pacific Division	4485	507	355	1315	4469	29.424927	0.70570	87	3859	2.254470	0.03381	20	94	21.27660	0.03822	35	261	13.40996	0.02120	55	355	15.49296	0.004567	1292	3728	34.656652	0.8445	981	4256	23.04981	0.7621	186	4.147157	0.06676	384	8.561873	0.03989	235	3656	6.427790	0.07933	38	204	18.627451	0.62370	1458	4397	33.1589720	0.95840	3616	4485	80.62430	0.7822	507	85	16.765286	0.69850	32	6.3116371	0.7700	41	355	11.549296	0.7760	30	355	8.4507042	0.69980	3507	4485	78.19398	0.9938
02020000101	02	020	000101	AK	Alaska	Anchorage Municipality	4	West Region	9	Pacific Division	5475	1957	1796	296	5463	5.418268	0.08172	229	2885	7.937608	0.48540	364	1522	23.91590	0.06363	39	274	14.23358	0.02232	403	1796	22.43875	0.018830	121	3476	3.481013	0.1279	863	5853	14.74458	0.4836	236	4.310502	0.07411	1614	29.479452	0.75790	524	4028	13.008937	0.54240	65	1496	4.344920	0.06955	13	5202	0.2499039	0.07262	759	5475	13.86301	0.1072	1957	0	0.000000	0.09395	167	8.5334696	0.8076	134	1796	7.461024	0.6695	11	1796	0.6124722	0.08007	0	5475	0.00000	0.3743
02020000102	02	020	000102	AK	Alaska	Anchorage Municipality	4	West Region	9	Pacific Division	4240	1923	1654	286	4240	6.745283	0.12010	198	2385	8.301887	0.51840	275	1235	22.26721	0.04612	248	419	59.18854	0.73210	523	1654	31.62031	0.108600	242	2799	8.645945	0.3645	838	4982	16.82055	0.5669	259	6.108491	0.17290	1038	24.481132	0.50340	809	3707	21.823577	0.91630	97	1071	9.056956	0.24130	0	4007	0.0000000	0.02799	955	4240	22.52358	0.2295	1923	169	8.788351	0.54130	147	7.6443058	0.7936	33	1654	1.995163	0.3402	103	1654	6.2273277	0.58930	0	4240	0.00000	0.3743
02020000201	02	020	000201	AK	Alaska	Anchorage Municipality	4	West Region	9	Pacific Division	4085	1543	1532	296	4085	7.246022	0.13670	46	2089	2.202011	0.03307	302	969	31.16615	0.19620	187	563	33.21492	0.13830	489	1532	31.91906	0.113500	164	2395	6.847599	0.2875	811	3466	23.39873	0.7707	133	3.255814	0.03812	1199	29.351285	0.75230	320	2468	12.965964	0.53850	171	1083	15.789474	0.52510	7	3810	0.1837270	0.06378	743	4085	18.18849	0.1723	1543	54	3.499676	0.37950	7	0.4536617	0.5183	32	1532	2.088773	0.3498	49	1532	3.1984334	0.36020	0	4085	0.00000	0.3743

# Divisional
svi_2020_divisional <- rank_variables(svi_2020, rank_by = "divisional", location = "Pacific Division")
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	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20
02013000100	02	013	000100	AK	Alaska	Aleutians East Borough	4	West Region	9	Pacific Division	3389	1199	988	698	3379	20.656999	0.5925	86	2414	3.562552	0.2665	67	607	11.037891	0.01803	74	381	19.42257	0.04067	141	988	14.27126	0.006988	354	2646	13.378685	0.61070	1345	3384	39.745863	0.9997	381	11.242254	0.31390	443	13.07170	0.0988	339	2941.000	11.526692	0.3860	135	593.000	22.765599	0.7920	334	3276	10.1953602	0.72620	2939	3389.000	86.72175	0.81100	1199	38	3.169308	0.3474	69	5.7547957	0.7806	30	988	3.0364372	0.36010	220	988.000	22.267207	0.95270	1035	3389	30.5399823	0.9843
02016000100	02	016	000100	AK	Alaska	Aleutians West Census Area	4	West Region	9	Pacific Division	950	694	199	218	719	30.319889	0.7848	15	560	2.678571	0.1560	11	117	9.401709	0.01305	14	82	17.07317	0.03088	25	199	12.56281	0.003541	48	681	7.048458	0.37250	238	721	33.009709	0.9989	116	12.210526	0.37310	195	20.52632	0.4153	113	526.000	21.482890	0.8931	31	98.000	31.632653	0.9318	17	900	1.8888889	0.29830	713	950.000	75.05263	0.69000	694	17	2.449568	0.3163	0	0.0000000	0.2466	7	199	3.5175879	0.39980	68	199.000	34.170854	0.98260	274	950	28.8421053	0.9832
02016000200	02	016	000200	AK	Alaska	Aleutians West Census Area	4	West Region	9	Pacific Division	4758	1319	1107	398	4700	8.468085	0.1862	144	3404	4.230317	0.3539	111	245	45.306122	0.92700	93	862	10.78886	0.01250	204	1107	18.42818	0.024790	297	3527	8.420754	0.43840	699	4724	14.796782	0.9132	314	6.599412	0.07503	822	17.27617	0.2319	292	3902.000	7.483342	0.1088	99	662.000	14.954683	0.5563	433	4586	9.4417793	0.70570	3672	4758.000	77.17528	0.71070	1319	392	29.719485	0.8272	23	1.7437453	0.6618	146	1107	13.1887986	0.80180	96	1107.000	8.672087	0.73650	950	4758	19.9663724	0.9765
02020000101	02	020	000101	AK	Alaska	Anchorage Municipality	4	West Region	9	Pacific Division	5772	2127	1917	416	5772	7.207207	0.1396	223	2691	8.286882	0.7821	296	1679	17.629541	0.08891	30	238	12.60504	0.01632	326	1917	17.00574	0.015840	74	4011	1.844926	0.07404	546	5733	9.523810	0.7456	692	11.988912	0.36010	1481	25.65835	0.7225	771	4252.330	18.131237	0.7949	94	1608.796	5.842877	0.1503	4	5425	0.0737327	0.05497	989	5772.331	17.13346	0.08922	2127	28	1.316408	0.2585	5	0.2350729	0.5017	9	1917	0.4694836	0.09085	24	1916.584	1.252228	0.15320	114	5772	1.9750520	0.8251
02020000102	02	020	000102	AK	Alaska	Anchorage Municipality	4	West Region	9	Pacific Division	4743	1975	1681	633	4738	13.360067	0.3701	75	2465	3.042596	0.1971	383	1350	28.370370	0.47830	122	331	36.85801	0.20770	505	1681	30.04164	0.245400	205	3383	6.059710	0.32020	330	4638	7.115136	0.6017	653	13.767658	0.46830	1186	25.00527	0.6899	472	3447.726	13.690182	0.5484	182	1469.325	12.386640	0.4525	0	4485	0.0000000	0.02391	756	4743.330	15.93817	0.07455	1975	153	7.746835	0.4947	156	7.8987342	0.8173	17	1681	1.0113028	0.15640	0	1681.103	0.000000	0.02249	15	4743	0.3162555	0.4726
02020000201	02	020	000201	AK	Alaska	Anchorage Municipality	4	West Region	9	Pacific Division	4707	1946	1835	706	4707	14.998938	0.4258	88	2269	3.878360	0.3073	219	793	27.616646	0.44320	527	1042	50.57582	0.53120	746	1835	40.65395	0.607400	194	2805	6.916221	0.36480	464	4274	10.856341	0.8018	257	5.459953	0.04652	1279	27.17230	0.7970	390	2999.274	13.003148	0.4994	72	1222.675	5.888728	0.1525	26	4201	0.6189003	0.13850	1282	4706.670	27.23794	0.21200	1946	76	3.905447	0.3758	2	0.1027749	0.4966	96	1835	5.2316076	0.52940	39	1834.897	2.125459	0.25860	0	4707	0.0000000	0.1370

# State
svi_2010_state <- rank_variables(svi_2010, rank_by = "state", location = "WA")
svi_2010_state %>% 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	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	E_GROUPQ_10	ET_HHTYPE_10	EP_GROUPQ_10	EPL_GROUPQ_10
53001950100	53	001	950100	WA	Washington	Adams County	4	West Region	9	Pacific Division	2625	1099	942	701	2408	29.11130	0.7827	89	1103	8.068903	0.611100	177	676	26.18343	0.21350	92	266	34.58647	0.23490	269	942	28.55626	0.1550	224	1900	11.789474	0.6671	406	2613	15.537696	0.6614	583	22.209524	0.9405	566	21.56190	0.3543	353	1933	18.26177	0.7271	96	673	14.26449	0.5239	11	2419	0.4547334	0.2111	228	2625	8.685714	0.12660	1099	67	6.096451	0.5107	97	8.826206	0.6803	22	942	2.3354565	0.6394	40	942	4.246284	0.5398	248	2625	9.447619	0.9384
53001950200	53	001	950200	WA	Washington	Adams County	4	West Region	9	Pacific Division	1785	812	680	574	1762	32.57662	0.8360	0	756	0.000000	0.003114	99	478	20.71130	0.07441	31	202	15.34653	0.03534	130	680	19.11765	0.0173	85	1114	7.630162	0.4318	139	1604	8.665835	0.2576	237	13.277311	0.6284	564	31.59664	0.9128	273	1145	23.84279	0.9093	74	483	15.32091	0.5787	23	1700	1.3529412	0.4173	226	1785	12.661064	0.25810	812	16	1.970443	0.3574	136	16.748769	0.8318	28	680	4.1176471	0.8090	7	680	1.029412	0.1626	0	1785	0.000000	0.3820
53001950300	53	001	950300	WA	Washington	Adams County	4	West Region	9	Pacific Division	6148	1779	1635	2676	6048	44.24603	0.9419	325	2508	12.958533	0.889300	297	1075	27.62791	0.26080	164	560	29.28571	0.14280	461	1635	28.19572	0.1446	1254	3209	39.077594	0.9779	2195	6832	32.128220	0.9827	484	7.872479	0.2394	2287	37.19909	0.9799	670	4392	15.25501	0.5540	161	1482	10.86370	0.3708	1271	5293	24.0128472	0.9882	4365	6148	70.998699	0.96820	1779	53	2.979202	0.4055	962	54.075323	0.9972	243	1635	14.8623853	0.9882	30	1635	1.834862	0.2782	0	6148	0.000000	0.3820
53001950400	53	001	950400	WA	Washington	Adams County	4	West Region	9	Pacific Division	2793	974	946	1155	2793	41.35338	0.9204	187	1379	13.560551	0.905900	104	430	24.18605	0.15160	227	516	43.99225	0.49000	331	946	34.98943	0.3882	584	1491	39.168343	0.9792	601	2616	22.974006	0.9017	287	10.275689	0.4235	901	32.25922	0.9287	274	1666	16.44658	0.6343	204	722	28.25485	0.8863	659	2548	25.8634223	0.9910	2038	2793	72.968135	0.97160	974	105	10.780288	0.6097	52	5.338809	0.5820	175	946	18.4989429	0.9958	39	946	4.122622	0.5308	0	2793	0.000000	0.3820
53001950500	53	001	950500	WA	Washington	Adams County	4	West Region	9	Pacific Division	4533	1496	1396	1857	4526	41.02961	0.9197	259	1954	13.254862	0.899000	385	921	41.80239	0.82820	246	475	51.78947	0.70060	631	1396	45.20057	0.8118	1099	2289	48.012232	0.9889	1006	4708	21.367884	0.8760	286	6.309287	0.1308	1783	39.33377	0.9889	374	2803	13.34285	0.4211	217	1238	17.52827	0.6535	761	3992	19.0631263	0.9702	3611	4533	79.660269	0.98820	1496	128	8.556150	0.5619	50	3.342246	0.5149	167	1396	11.9627507	0.9792	60	1396	4.297994	0.5446	0	4533	0.000000	0.3820
53003960100	53	003	960100	WA	Washington	Asotin County	4	West Region	9	Pacific Division	3933	1810	1554	525	3907	13.43742	0.3640	38	1705	2.228739	0.035290	369	1383	26.68113	0.22980	53	171	30.99415	0.17330	422	1554	27.15573	0.1142	171	2782	6.146657	0.3356	356	4044	8.803165	0.2701	690	17.543860	0.8415	993	25.24790	0.6069	393	3040	12.92763	0.3975	127	1190	10.67227	0.3597	17	3747	0.4536963	0.2104	123	3933	3.127384	0.01107	1810	0	0.000000	0.1097	170	9.392265	0.6920	12	1554	0.7722008	0.3017	0	1554	0.000000	0.0346	0	3933	0.000000	0.3820

# State
svi_2020_state <- rank_variables(svi_2020, rank_by = "state", location = "WA")
svi_2020_state %>% 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	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20
53001950100	53	001	950100	WA	Washington	Adams County	4	West Region	9	Pacific Division	2606	1240	1099	605	2509	24.11319	0.7661	33	1101	2.997275	0.2692	190	762	24.93438	0.58340	110	337	32.640950	0.237700	300	1099	27.297543	0.37790	165	1789	9.22303	0.6694	146	2514	5.807478	0.5516	505	19.378358	0.73310	603	23.13891	0.6072	298	1911.019	15.59377	0.5038	91	620.0101	14.677180	0.6118	27	2495	1.082164	0.4053	463	2606.020	17.766554	0.26760	1240	91	7.3387097	0.5176	151	12.177419	0.8007	12	1099	1.091902	0.2699	101	1099.0137	9.1900586	0.7792	94	2606	3.6070606	0.9122
53001950200	53	001	950200	WA	Washington	Adams County	4	West Region	9	Pacific Division	1762	854	611	428	1762	24.29058	0.7737	31	802	3.865337	0.4042	41	347	11.81556	0.04172	10	263	3.802281	0.006237	51	610	8.360656	0.00346	189	1112	16.99640	0.9018	220	1763	12.478729	0.9190	228	12.939841	0.35820	454	25.76617	0.7732	247	1308.939	18.87025	0.7066	50	426.0483	11.735759	0.4830	19	1655	1.148036	0.4198	570	1761.795	32.353364	0.61130	854	0	0.0000000	0.1000	160	18.735363	0.8920	63	611	10.310966	0.9426	5	610.7908	0.8186109	0.1253	0	1762	0.0000000	0.1082
53001950300	53	001	950300	WA	Washington	Adams County	4	West Region	9	Pacific Division	6527	1853	1753	2094	6479	32.31980	0.8976	170	2669	6.369427	0.7391	314	1265	24.82213	0.57510	70	489	14.314928	0.039500	384	1754	21.892816	0.16120	1452	3577	40.59268	0.9903	1152	6480	17.777778	0.9716	788	12.072928	0.30360	2381	36.47924	0.9848	620	4098.527	15.12739	0.4803	366	1499.5286	24.407670	0.8561	1210	5966	20.281596	0.9882	5219	6527.382	79.955491	0.98340	1853	10	0.5396654	0.2429	1169	63.086886	0.9993	248	1753	14.147176	0.9779	58	1753.4163	3.3078282	0.4478	57	6527	0.8732955	0.6805
53001950400	53	001	950400	WA	Washington	Adams County	4	West Region	9	Pacific Division	3144	949	876	1319	3073	42.92223	0.9723	59	1228	4.804560	0.5412	99	405	24.44444	0.54940	171	471	36.305732	0.315300	270	876	30.821918	0.53150	498	1645	30.27356	0.9710	580	3073	18.874064	0.9813	308	9.796438	0.17430	1263	40.17176	0.9952	317	1810.000	17.51381	0.6346	232	690.0000	33.623188	0.9647	563	2809	20.042720	0.9876	2513	3144.000	79.930025	0.98270	949	111	11.6965227	0.6194	0	0.000000	0.1422	73	876	8.333333	0.9121	137	876.0000	15.6392694	0.9253	93	3144	2.9580153	0.8921
53001950500	53	001	950500	WA	Washington	Adams County	4	West Region	9	Pacific Division	5660	1786	1694	2276	5641	40.34746	0.9619	229	2502	9.152678	0.9038	169	899	18.79867	0.23090	247	795	31.069182	0.212100	416	1694	24.557261	0.25670	1047	2654	39.44989	0.9869	1007	5660	17.791519	0.9723	365	6.448763	0.04288	2241	39.59364	0.9938	611	3419.000	17.87072	0.6609	367	1364.0000	26.906158	0.8934	940	4913	19.132913	0.9834	4356	5660.000	76.961131	0.97720	1786	145	8.1187010	0.5370	85	4.759239	0.5972	223	1694	13.164109	0.9730	42	1694.0000	2.4793388	0.3509	14	5660	0.2473498	0.3928
53003960100	53	003	960100	WA	Washington	Asotin County	4	West Region	9	Pacific Division	4320	2079	1782	605	4320	14.00463	0.4567	109	1727	6.311523	0.7315	240	1645	14.58967	0.08693	53	137	38.686131	0.379100	293	1782	16.442200	0.03668	241	3157	7.63383	0.5733	244	4320	5.648148	0.5329	1337	30.949074	0.96130	968	22.40741	0.5422	662	3351.998	19.74941	0.7488	125	1283.9997	9.735205	0.3779	0	4156	0.000000	0.0491	346	4319.997	8.009265	0.03596	2079	11	0.5291005	0.2401	180	8.658009	0.7301	11	1782	0.617284	0.1761	43	1781.9995	2.4130197	0.3426	0	4320	0.0000000	0.1082

# County
svi_2010_county <- rank_variables(svi_2010, rank_by = "county", location = "Spokane County", state_abbr = "WA")
svi_2010_county %>% 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	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	E_GROUPQ_10	ET_HHTYPE_10	EP_GROUPQ_10	EPL_GROUPQ_10
53063000200	53	063	000200	WA	Washington	Spokane County	4	West Region	9	Pacific Division	5053	2023	1993	2321	5038	46.06987	0.8857	300	2314	12.964564	0.8286	367	1034	35.49323	0.8381	590	959	61.52242	0.9333	957	1993	48.01806	0.9048	507	3123	16.234390	0.9333	1218	4979	24.46274	0.9619	474	9.380566	0.2000	1203	23.80764	0.5048	955	3753	25.44631	0.9143	268	1223	21.91333	0.7524	156	4755	3.2807571	0.88570	1488	5053	29.447853	0.9810	2023	92	4.5477014	0.45710	10	0.4943154	0.3238	52	1993	2.6091320	0.8190	244	1993	12.242850	0.8476	76	5053	1.504057	0.7238
53063000300	53	063	000300	WA	Washington	Spokane County	4	West Region	9	Pacific Division	5028	2164	1961	1318	5012	26.29689	0.6381	148	2378	6.223717	0.4000	526	1338	39.31241	0.9048	338	623	54.25361	0.7143	864	1961	44.05915	0.7905	409	3298	12.401455	0.8190	1063	5065	20.98717	0.8952	567	11.276850	0.3714	1372	27.28719	0.8381	745	3747	19.88257	0.7238	396	1408	28.12500	0.8286	220	4624	4.7577855	0.94290	894	5028	17.780430	0.7905	2164	16	0.7393715	0.23810	0	0.0000000	0.1571	61	1961	3.1106578	0.8762	52	1961	2.651708	0.2762	0	5028	0.000000	0.3238
53063000400	53	063	000400	WA	Washington	Spokane County	4	West Region	9	Pacific Division	4055	1825	1738	1458	3917	37.22236	0.8000	260	1855	14.016172	0.8762	290	802	36.15960	0.8571	561	936	59.93590	0.8952	851	1738	48.96433	0.9333	379	2649	14.307286	0.8857	584	4217	13.84871	0.6190	603	14.870530	0.6667	1035	25.52404	0.6762	661	3045	21.70772	0.8095	317	981	32.31397	0.9143	21	3752	0.5597015	0.40950	754	4055	18.594328	0.8190	1825	236	12.9315068	0.64760	0	0.0000000	0.1571	0	1738	0.0000000	0.1095	198	1738	11.392405	0.8095	99	4055	2.441430	0.7810
53063000500	53	063	000500	WA	Washington	Spokane County	4	West Region	9	Pacific Division	3171	1554	1496	687	3160	21.74051	0.5238	137	1772	7.731377	0.5714	408	1104	36.95652	0.8762	147	392	37.50000	0.2762	555	1496	37.09893	0.6190	169	2248	7.517794	0.5048	390	3033	12.85856	0.5429	455	14.348786	0.6381	684	21.57048	0.3048	481	2431	19.78610	0.6952	240	831	28.88087	0.8571	0	2985	0.0000000	0.09524	422	3171	13.308105	0.6190	1554	12	0.7722008	0.24760	0	0.0000000	0.1571	26	1496	1.7379679	0.7143	117	1496	7.820856	0.6857	0	3171	0.000000	0.3238
53063000600	53	063	000600	WA	Washington	Spokane County	4	West Region	9	Pacific Division	2708	1243	1147	689	2708	25.44313	0.5905	36	1447	2.487906	0.0381	225	841	26.75386	0.5143	187	306	61.11111	0.9143	412	1147	35.91979	0.5810	142	1765	8.045326	0.5714	389	3095	12.56866	0.5238	326	12.038405	0.4476	597	22.04579	0.3429	449	2236	20.08050	0.7429	106	639	16.58842	0.5429	0	2545	0.0000000	0.09524	267	2708	9.859675	0.4000	1243	53	4.2638777	0.42860	0	0.0000000	0.1571	5	1147	0.4359198	0.2476	40	1147	3.487358	0.3810	0	2708	0.000000	0.3238
53063000700	53	063	000700	WA	Washington	Spokane County	4	West Region	9	Pacific Division	4877	2235	2046	683	4877	14.00451	0.3048	134	2553	5.248727	0.2381	346	1701	20.34098	0.1238	143	345	41.44928	0.4000	489	2046	23.90029	0.1048	231	3156	7.319392	0.4762	596	4901	12.16078	0.4381	584	11.974574	0.4190	1372	28.13205	0.8952	440	3592	12.24944	0.2095	287	1339	21.43391	0.7333	29	4463	0.6497871	0.45710	532	4877	10.908345	0.4952	2235	0	0.0000000	0.09524	0	0.0000000	0.1571	15	2046	0.7331378	0.4095	141	2046	6.891496	0.6095	0	4877	0.000000	0.3238

# County
svi_2020_county <- rank_variables(svi_2020, rank_by = "county", location = "Spokane County", state = "WA")
svi_2020_county %>% 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	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20
53063000200	53	063	000200	WA	Washington	Spokane County	4	West Region	9	Pacific Division	4157	2068	1911	1779	4150	42.86747	0.9333	202	2200	9.181818	0.8190	308	1035	29.75845	0.8667	411	876	46.91781	0.5333	719	1911	37.62428	0.7143	279	2887	9.664011	0.8381	372	4157	8.948761	0.9048	431	10.36805	0.09524	815	19.60548	0.2952	751	3342	22.47157	0.8095	287	894	32.10291	0.9048	44	3853	1.1419673	0.6857	708	4157	17.03151	0.6000	2068	201	9.7195358	0.55240	7	0.3384913	0.3810	26	1911	1.3605442	0.4762	215	1911	11.250654	0.7810	6	4157	0.1443349	0.2381
53063000300	53	063	000300	WA	Washington	Spokane County	4	West Region	9	Pacific Division	5324	2229	2130	2010	5302	37.91022	0.9048	115	2646	4.346183	0.4476	562	1390	40.43165	0.9810	267	740	36.08108	0.2667	829	2130	38.92019	0.7714	479	3519	13.611821	0.9619	143	5324	2.685950	0.2000	615	11.55147	0.20000	1416	26.59654	0.8667	821	3908	21.00819	0.7619	473	1315	35.96958	0.9429	175	5068	3.4530387	0.9429	1192	5324	22.38918	0.7905	2229	69	3.0955585	0.35240	29	1.3010319	0.5905	89	2130	4.1784038	0.9143	119	2130	5.586855	0.5905	12	5324	0.2253944	0.3143
53063000400	53	063	000400	WA	Washington	Spokane County	4	West Region	9	Pacific Division	3853	1803	1638	1298	3691	35.16662	0.8381	249	1971	12.633181	0.9524	207	638	32.44514	0.8952	344	1000	34.40000	0.2095	551	1638	33.63858	0.6286	239	2629	9.090909	0.8286	453	3691	12.273097	0.9905	507	13.15858	0.30480	866	22.47599	0.5429	572	2825	20.24779	0.6857	329	807	40.76828	0.9810	121	3600	3.3611111	0.9333	942	3853	24.44848	0.8286	1803	347	19.2457016	0.73330	10	0.5546312	0.4381	41	1638	2.5030525	0.7429	159	1638	9.706960	0.7048	162	3853	4.2045160	0.8667
53063000500	53	063	000500	WA	Washington	Spokane County	4	West Region	9	Pacific Division	3522	1462	1447	867	3515	24.66572	0.6476	104	1848	5.627706	0.6381	235	1019	23.06183	0.5714	152	428	35.51402	0.2476	387	1447	26.74499	0.4000	190	2347	8.095441	0.7238	255	3510	7.264957	0.7714	471	13.37308	0.33330	938	26.63260	0.8762	355	2572	13.80249	0.2381	109	909	11.99120	0.3714	23	3225	0.7131783	0.5810	940	3522	26.68938	0.8952	1462	13	0.8891929	0.28570	0	0.0000000	0.1810	11	1447	0.7601935	0.3810	66	1447	4.561161	0.4857	7	3522	0.1987507	0.2952
53063000600	53	063	000600	WA	Washington	Spokane County	4	West Region	9	Pacific Division	3246	1322	1286	554	3180	17.42138	0.3905	103	1861	5.534659	0.6095	97	906	10.70640	0.0381	215	380	56.57895	0.8286	312	1286	24.26128	0.2667	128	2278	5.618964	0.5143	232	3226	7.191568	0.7524	339	10.44362	0.11430	723	22.27357	0.5238	449	2503	17.93847	0.5714	185	790	23.41772	0.7333	9	3023	0.2977175	0.3714	466	3246	14.35613	0.4667	1322	3	0.2269289	0.20000	8	0.6051437	0.4571	27	1286	2.0995334	0.6571	37	1286	2.877138	0.3524	5	3246	0.1540357	0.2571
53063000700	53	063	000700	WA	Washington	Spokane County	4	West Region	9	Pacific Division	5244	2285	2231	979	5244	18.66896	0.4381	315	3110	10.128617	0.8667	363	1581	22.96015	0.5619	255	650	39.23077	0.3524	618	2231	27.70058	0.4286	205	3615	5.670816	0.5238	448	5244	8.543097	0.8667	649	12.37605	0.25710	1034	19.71777	0.3143	573	4210	13.61045	0.2286	398	1419	28.04792	0.8286	1	4987	0.0200521	0.2000	674	5244	12.85278	0.3714	2285	0	0.0000000	0.09524	0	0.0000000	0.1810	7	2231	0.3137606	0.2286	107	2231	4.796056	0.5048	10	5244	0.1906941	0.2857

svi_2020_national <- svi_theme_variables(svi_2020_national)
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	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20	SPL_THEME1	RPL_THEME1	SPL_THEME2	RPL_THEME2	SPL_THEME3	RPL_THEME3	SPL_THEME4	RPL_THEME4	SPL_THEMES	RPL_THEMES
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	18	852	2.112676	0.15070	81	507	15.976331	0.26320	63	186	33.87097	0.2913	144	693	20.77922	0.2230	187	1309	14.285714	0.6928	187	1941	9.634209	0.6617	295	15.19835	0.4601	415	21.38073	0.4681	391	1526	25.62254	0.9011	58	555	10.45045	0.3451	0	1843	0.0000000	0.09479	437	1941	22.51417	0.3902	710	0	0.0000000	0.1079	88	12.3943662	0.8263	0	693	0.0000000	0.09796	10	693	1.443001	0.1643	0	1941	0.000000	0.1831	2.19120	0.4084	2.26919	0.3503	0.3902	0.3869	1.37956	0.07216	6.23015	0.2314
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	29	717	4.044630	0.41320	33	392	8.418367	0.03542	116	181	64.08840	0.9086	149	573	26.00349	0.4041	139	1313	10.586443	0.5601	91	1533	5.936073	0.4343	284	16.16392	0.5169	325	18.49744	0.2851	164	1208	13.57616	0.4127	42	359	11.69916	0.3998	0	1651	0.0000000	0.09479	1116	1757	63.51736	0.7591	720	3	0.4166667	0.2470	5	0.6944444	0.5106	9	573	1.5706806	0.46880	57	573	9.947644	0.7317	212	1757	12.066022	0.9549	2.45440	0.4888	1.70929	0.1025	0.7591	0.7527	2.91300	0.68620	7.83579	0.4802
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	53	1994	2.657974	0.22050	117	967	12.099276	0.11370	147	384	38.28125	0.3856	264	1351	19.54108	0.1827	317	2477	12.797739	0.6460	127	3673	3.457664	0.2308	464	12.56091	0.3088	929	25.14889	0.7080	473	2744	17.23761	0.6211	263	975	26.97436	0.8234	128	3586	3.5694367	0.70770	1331	3694	36.03140	0.5515	1464	26	1.7759563	0.3675	14	0.9562842	0.5389	35	1351	2.5906736	0.60550	42	1351	3.108808	0.3415	0	3694	0.000000	0.1831	1.86330	0.3063	3.16900	0.8380	0.5515	0.5468	2.03650	0.26830	7.62030	0.4460
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	39	1658	2.352232	0.17990	219	1290	16.976744	0.30880	74	346	21.38728	0.1037	293	1636	17.90954	0.1333	173	2775	6.234234	0.3351	169	3529	4.788892	0.3448	969	27.38062	0.9225	510	14.41085	0.1208	670	3019	22.19278	0.8194	148	1137	13.01671	0.4541	89	3409	2.6107363	0.64690	454	3539	12.82848	0.2364	1741	143	8.2136703	0.6028	0	0.0000000	0.2186	10	1636	0.6112469	0.28340	72	1636	4.400978	0.4538	0	3539	0.000000	0.1831	1.34030	0.1575	2.96370	0.7496	0.2364	0.2344	1.74170	0.16270	6.28210	0.2389
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	81	5048	1.604596	0.09431	321	2299	13.962592	0.17970	711	2125	33.45882	0.2836	1032	4424	23.32731	0.3109	531	6816	7.790493	0.4251	301	10046	2.996217	0.1894	1613	15.11149	0.4553	2765	25.90407	0.7494	1124	7281	15.43744	0.5253	342	2912	11.74451	0.4019	52	9920	0.5241935	0.35230	2603	10674	24.38636	0.4160	4504	703	15.6083481	0.7378	29	0.6438721	0.5037	37	4424	0.8363472	0.33420	207	4424	4.679023	0.4754	176	10674	1.648866	0.7598	1.40481	0.1743	2.48420	0.4802	0.4160	0.4125	2.81090	0.63730	7.11591	0.3654
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	34	1223	2.780049	0.23780	321	1111	28.892889	0.75870	67	219	30.59361	0.2305	388	1330	29.17293	0.5075	306	2380	12.857143	0.6480	415	3496	11.870709	0.7535	547	15.46946	0.4760	982	27.77149	0.8327	729	2514	28.99761	0.9488	95	880	10.79545	0.3601	0	3394	0.0000000	0.09479	985	3536	27.85633	0.4608	1464	0	0.0000000	0.1079	364	24.8633880	0.9300	0	1330	0.0000000	0.09796	17	1330	1.278196	0.1463	0	3536	0.000000	0.1831	2.96830	0.6434	2.71239	0.6156	0.4608	0.4569	1.46526	0.08976	7.60675	0.4440

svi_2020_national <- svi_theme_flags(svi_2020_national, .90)
svi_2020_national %>% arrange(desc(F_TOTAL)) %>% 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
48141000404	48	141	000404	TX	Texas	El Paso County	3	South Region	7	West South Central Division	3380	1648	1221	2390	3380	70.71006	0.9928	1	210	1268	16.56151	0.9697	1	12	154	7.792208	0.02796	0	705	1067	66.07310	0.9281	1	717	1221	58.72236	0.9772	1	745	1955	38.10742	0.9703	1	1234	3370	36.61721	0.9940	1	369	10.917160	0.22390	0	1070	31.65680	0.9356	1	664	2300	28.86957	0.9477	1	365	761	47.96321	0.9741	1	649	3025	21.45455	0.9659	1	3222	3380	95.32544	0.9478	1	1648	701	42.53641	0.9214	1	0	0	0.2186	0	130	1221	10.647011	0.9224	1	344	1221	28.17363	0.9344	1	0	3380	0.0000000	0.1831	0	4.9040	0.9886	5	4.04720	0.9848	4	0.9478	0.9398	1	3.1799	0.8000	3	13.07890	0.9859	13
06037224420	06	037	224420	CA	California	Los Angeles County	4	West Region	9	Pacific Division	2651	943	883	1562	2389	65.38301	0.9868	1	232	1430	16.22378	0.9672	1	13	57	22.807018	0.56500	0	584	826	70.70218	0.9614	1	597	883	67.61042	0.9949	1	894	1588	56.29723	0.9975	1	605	2651	22.82158	0.9514	1	243	9.166352	0.14720	0	543	20.48284	0.4073	0	211	1889	11.16993	0.2666	0	207	571	36.25219	0.9217	1	870	2539	34.26546	0.9937	1	2523	2651	95.17163	0.9465	1	943	540	57.26405	0.9549	1	0	0	0.2186	0	344	883	38.958097	0.9984	1	302	883	34.20159	0.9522	1	253	2651	9.5435685	0.9459	1	4.8978	0.9886	5	2.73650	0.6297	2	0.9465	0.9385	1	4.0700	0.9818	4	12.65080	0.9801	12
12086001401	12	086	001401	FL	Florida	Miami-Dade County	3	South Region	5	South Atlantic Division	6606	2427	2203	4080	6606	61.76203	0.9802	1	314	2673	11.74710	0.9138	1	134	300	44.666667	0.95870	1	1111	1903	58.38150	0.8314	0	1245	2203	56.51384	0.9682	1	1363	4012	33.97308	0.9551	1	1313	6606	19.87587	0.9252	1	732	11.080836	0.23160	0	1985	30.04844	0.9023	1	670	4621	14.49903	0.4697	0	606	1267	47.82952	0.9736	1	854	5756	14.83669	0.9273	1	6592	6606	99.78807	0.9954	1	2427	965	39.76102	0.9125	1	0	0	0.2186	0	378	2203	17.158420	0.9692	1	777	2203	35.27009	0.9546	1	40	6606	0.6055101	0.6241	0	4.7425	0.9867	5	3.50450	0.9317	3	0.9954	0.9870	1	3.6790	0.9403	3	12.92140	0.9845	12
27053005902	27	053	005902	MN	Minnesota	Hennepin County	2	Midwest Region	4	West North Central Division	3896	1173	1087	2343	3739	62.66381	0.9824	1	226	1842	12.26927	0.9232	1	21	71	29.577465	0.77560	0	521	1016	51.27953	0.6904	0	542	1087	49.86201	0.9223	1	719	1932	37.21532	0.9675	1	608	3896	15.60575	0.8581	0	87	2.233059	0.01142	0	1442	37.01232	0.9852	1	640	2440	26.22951	0.9114	1	274	583	46.99828	0.9714	1	444	3462	12.82496	0.9096	1	2911	3896	74.71766	0.8205	0	1173	721	61.46633	0.9616	1	0	0	0.2186	0	226	1087	20.791168	0.9809	1	323	1087	29.71481	0.9398	1	579	3896	14.8613963	0.9619	1	4.6535	0.9834	4	3.78902	0.9713	4	0.8205	0.8136	0	4.0628	0.9815	4	13.32582	0.9873	12
34001002400	34	001	002400	NJ	New Jersey	Atlantic County	1	Northeast Region	2	Middle Atlantic Division	2614	1726	1217	1579	2612	60.45176	0.9773	1	290	1171	24.76516	0.9931	1	69	127	54.330709	0.98500	1	538	1090	49.35780	0.6469	0	607	1217	49.87675	0.9224	1	697	1998	34.88488	0.9590	1	551	2614	21.07881	0.9374	1	516	19.739862	0.70560	0	503	19.24254	0.3280	0	576	2111	27.28565	0.9280	1	257	567	45.32628	0.9667	1	556	2368	23.47973	0.9732	1	2029	2614	77.62050	0.8359	0	1726	1166	67.55504	0.9693	1	0	0	0.2186	0	115	1217	9.449466	0.9062	1	673	1217	55.29992	0.9828	1	223	2614	8.5309870	0.9407	1	4.7892	0.9877	5	3.90150	0.9792	3	0.8359	0.8288	0	4.0176	0.9793	4	13.54420	0.9879	12
34039039300	34	039	039300	NJ	New Jersey	Union County	1	Northeast Region	2	Middle Atlantic Division	6145	1835	1727	3492	6096	57.28346	0.9684	1	350	3006	11.64338	0.9114	1	58	140	41.428571	0.94020	1	829	1587	52.23692	0.7127	0	887	1727	51.36074	0.9352	1	1367	3639	37.56527	0.9686	1	2771	6145	45.09357	0.9987	1	400	6.509357	0.06257	0	2006	32.64443	0.9503	1	443	4139	10.70307	0.2400	0	476	1178	40.40747	0.9470	1	1979	5714	34.63423	0.9941	1	5955	6145	96.90806	0.9624	1	1835	815	44.41417	0.9271	1	0	0	0.2186	0	391	1727	22.640417	0.9849	1	748	1727	43.31210	0.9692	1	114	6145	1.8551668	0.7766	0	4.7823	0.9877	5	3.19397	0.8468	3	0.9624	0.9543	1	3.8764	0.9680	3	12.81507	0.9830	12

Data Analysis Questions

From a data science project management standpoint, how do you believe that functions are most useful? Can you think of scenarios in your current work or previous data projects where it would have been helpful to implement functions? Can you think of scenarios where functions are not the most useful strategy for processing data?

I think R functions are most helpful by introducing iteration/repetition and reducing the amount of code needed in a project. I work as a research scientist in public health, and I can absolutely think of real projects, both past and present, where using functions would be helpful. Before I started my position as a research scientist, I worked in a different department as a lead for our Public Health Emergency Preparedness & Response and Regional Emergency & Disaster Healthcare Coalition programs. As part of our preparedness/planning efforts, I created SVI profiles for each county to understand how social vulnerability characteristics influenced vulnerability to specific hazards. I manipulated the data in Excel, but it would have been a million times better to combine functions and R’s reporting capabilities.

I think writing functions is more resource intensive than writing regular code, so I see it as an investment of time with an expectation of return. If it’s a task that needs to be done once and won’t be repeated, it undermines the purpose of a function. Similarly, writing a function for a small dataset won’t return the invested time.

Describe the CDC SVI index. What is it, and how do we intend to use it for our project?

The CDC’s Social Vulnerability Index calculates scores and rankings to identify areas more vulnerable to emergencies and disasters. It looks at four dimensions, or “themes”, including socioeconomic status, household characteristics, racial & ethnic minority status, and housing type & transportation. For the purpose of this course, we’ll use the SVI as the dependent variable to understand whether tax credit programs (as a policy/intervention) successfully improved community outcomes for vulnerable populations.

Nationally, what are the most & least vulnerable tracts by THEME1, THEME2, THEME3, THEME4, OVERALL for 2020 and 2010.

The tables with the most and least vulnerable tracts are listed below.

# Finish preparing the 2010 data using the steps above
svi_2010_national <- svi_theme_variables(svi_2010_national)
svi_2010_national <- svi_theme_flags(svi_2010_national, .90)

# Find the extremes for 2010
extremes_2010_nat <- svi_2010_national %>% 
  filter(!is.na(F_THEME1) & E_TOTPOP_10 > 100) %>% 
  mutate(min1 = GEOID_2010_trt[which.min(RPL_THEME1)],
         min2 = GEOID_2010_trt[which.min(RPL_THEME2)],
         min3 = GEOID_2010_trt[which.min(RPL_THEME3)],
         min4 = GEOID_2010_trt[which.min(RPL_THEME4)],
         minall = GEOID_2010_trt[which.min(RPL_THEMES)],
         max1 = GEOID_2010_trt[which.max(RPL_THEME1)],
         max2 = GEOID_2010_trt[which.max(RPL_THEME2)],
         max3 = GEOID_2010_trt[which.max(RPL_THEME3)],
         max4 = GEOID_2010_trt[which.max(RPL_THEME4)],
         maxall = GEOID_2010_trt[which.max(RPL_THEMES)]) %>% 
  select(min1:maxall) %>% 
  unique()

extremes_2010_nat %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

min1	min2	min3	min4	minall	max1	max2	max3	max4	maxall
06073011300	06073009901	01003011408	01073005909	19169001200	06065042505	55025001102	01073000700	04013093104	55025001102

# Find the extremes for 2020
extremes_2020_nat <- svi_2020_national %>% 
  filter(!is.na(F_THEME1) & E_TOTPOP_20 > 100) %>% 
  mutate(min1 = GEOID_2010_trt[which.min(RPL_THEME1)],
         min2 = GEOID_2010_trt[which.min(RPL_THEME2)],
         min3 = GEOID_2010_trt[which.min(RPL_THEME3)],
         min4 = GEOID_2010_trt[which.min(RPL_THEME4)],
         minall = GEOID_2010_trt[which.min(RPL_THEMES)],
         max1 = GEOID_2010_trt[which.max(RPL_THEME1)],
         max2 = GEOID_2010_trt[which.max(RPL_THEME2)],
         max3 = GEOID_2010_trt[which.max(RPL_THEME3)],
         max4 = GEOID_2010_trt[which.max(RPL_THEME4)],
         maxall = GEOID_2010_trt[which.max(RPL_THEMES)]) %>% 
  select(min1:maxall) %>% 
  unique()

extremes_2020_nat %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

min1	min2	min3	min4	minall	max1	max2	max3	max4	maxall
06073009901	20091980002	01077011602	01101005608	45019004613	12099008001	48303000605	04001942600	04013093104	48303000605

For your division, what are the most & least vulnerable tracts by THEME1, THEME2, THEME3, THEME4, OVERALL. Do they belong to certain divisions/states?

In 2010, tracts in three California counties had the highest SVI including one in Los Angeles (RPL_THEME1), Fresno (RPL_THEME3), and Contra Costa (RPL_THEME4). The same tract in Clackamas County, Oregon had the highest SVI score for RPL_THEME2 and the overall SVI. Two tracts in San Diego, California scored lowest for RPL_THEME1 and RPL_THEME2. Tracts in two Alaska areas had the lowest SVI including one in the Prince of Wales-Hyder Census Area (RPL_THEME3) and Anchorage Municipality (RPL_THEME4). A tract in Honolulu County, Hawaii scored the lowest overall SVI.

In 2020, California took over all five scores as the highest score. Tracts that ranked highest were located in:

1. Los Angeles County (RPL_THEME1)
2. Imperial County (RPL_THEME2)
3. Los Angeles County (RPL_THEME3) - different tract than RPL_THEME1
4. Fresno County (RPL_THEME4)
5. Imperial County (RPL_THEMES) - same tract as RPL_THEME2

A tract in Pierce County, Washington tied with a tract from San Diego County, California for the lowest SVI score for RPL_THEME1. The same tract in Pierce County scored lowest for RPL_THEME2 too. Tracts in Sacramento County, Alameda County, and San Diego County, California had the lowest SVIs for RPL_THEME3, RPL_THEME4, and RPL_THEMES respectively.

# Prepare the data
svi_2010_divisional <- svi_theme_variables(svi_2010_divisional)
svi_2010_divisional <- svi_theme_flags(svi_2010_divisional, .90)

svi_2020_divisional <- svi_theme_variables(svi_2020_divisional)
svi_2020_divisional <- svi_theme_flags(svi_2020_divisional, .90)

# Find the extremes
extremes_2010_div <- svi_2010_divisional %>% 
  filter(!is.na(F_THEME1) & E_TOTPOP_10 > 100) %>% 
  mutate(min1 = GEOID_2010_trt[which.min(RPL_THEME1)],
         min2 = GEOID_2010_trt[which.min(RPL_THEME2)],
         min3 = GEOID_2010_trt[which.min(RPL_THEME3)],
         min4 = GEOID_2010_trt[which.min(RPL_THEME4)],
         minall = GEOID_2010_trt[which.min(RPL_THEMES)],
         max1 = GEOID_2010_trt[which.max(RPL_THEME1)],
         max2 = GEOID_2010_trt[which.max(RPL_THEME2)],
         max3 = GEOID_2010_trt[which.max(RPL_THEME3)],
         max4 = GEOID_2010_trt[which.max(RPL_THEME4)],
         maxall = GEOID_2010_trt[which.max(RPL_THEMES)]) %>% 
  select(min1:maxall) %>% 
  unique()
print(extremes_2010_div)

## # A tibble: 1 × 10
##   min1        min2        min3       min4  minall max1  max2  max3  max4  maxall
##   <chr>       <chr>       <chr>      <chr> <chr>  <chr> <chr> <chr> <chr> <chr> 
## 1 06073011300 06073009901 021980003… 0202… 15003… 0603… 4100… 0601… 0601… 41005…

extremes_2020_div <- svi_2020_divisional %>% 
  filter(!is.na(F_THEME1) & E_TOTPOP_20 > 100) %>% 
  mutate(min1 = GEOID_2010_trt[which.min(RPL_THEME1)],
         min2 = GEOID_2010_trt[which.min(RPL_THEME2)],
         min3 = GEOID_2010_trt[which.min(RPL_THEME3)],
         min4 = GEOID_2010_trt[which.min(RPL_THEME4)],
         minall = GEOID_2010_trt[which.min(RPL_THEMES)],
         max1 = GEOID_2010_trt[which.max(RPL_THEME1)],
         max2 = GEOID_2010_trt[which.max(RPL_THEME2)],
         max3 = GEOID_2010_trt[which.max(RPL_THEME3)],
         max4 = GEOID_2010_trt[which.max(RPL_THEME4)],
         maxall = GEOID_2010_trt[which.max(RPL_THEMES)]) %>% 
  select(min1:maxall) %>% 
  unique()

print(extremes_2020_div)

## # A tibble: 1 × 10
##   min1        min2        min3       min4  minall max1  max2  max3  max4  maxall
##   <chr>       <chr>       <chr>      <chr> <chr>  <chr> <chr> <chr> <chr> <chr> 
## 1 06073009901 53053072906 060670071… 0600… 06073… 0603… 0602… 0603… 0601… 06025…

# If we want to go back and find these tracts in their entirety:
extremes_table_2010_div <- svi_2010_divisional %>% 
  filter(GEOID_2010_trt %in% as.character(extremes_2010_div)) %>% 
  select(GEOID_2010_trt, state_name, county, matches("^RPL_"))

extremes_table_2010_div %>% arrange(state_name, county) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	state_name	county	RPL_THEME1	RPL_THEME2	RPL_THEME3	RPL_THEME4	RPL_THEMES
02020002813	Alaska	Anchorage Municipality	0.008282	0.0216300	0.0984600	0.002807	0.0005521
02198000300	Alaska	Prince of Wales-Hyder Census Area	0.057700	0.1648000	0.0005981	0.085760	0.0348800
06013369001	California	Contra Costa County	0.925500	0.9830000	0.7821000	0.987200	0.9859000
06019007801	California	Fresno County	0.924200	0.7206000	0.9858000	0.932100	0.9344000
06037224310	California	Los Angeles County	0.986700	0.6316000	0.9038000	0.979500	0.9748000
06073009901	California	San Diego County	0.013710	0.0001840	0.4239000	0.082820	0.0004601
06073011300	California	San Diego County	0.000092	0.0003681	0.4937000	0.083460	0.0002761
15003008611	Hawaii	Honolulu County	0.000184	0.0016560	0.5820000	0.002807	0.0000920
41005980000	Oregon	Clackamas County	0.634000	0.9978000	0.4090000	0.839200	0.9978000

extremes_table_2020_div <- svi_2020_divisional %>% 
  filter(GEOID_2010_trt %in% as.character(extremes_2020_div)) %>% 
  select(GEOID_2010_trt, state_name, county, matches("^RPL_"))

extremes_table_2020_div %>% arrange(state_name, county) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	state_name	county	RPL_THEME1	RPL_THEME2	RPL_THEME3	RPL_THEME4	RPL_THEMES
06001451505	California	Alameda County	0.0251200	0.0811600	0.2719000	0.0009202	0.001288
06019000800	California	Fresno County	0.9309000	0.9873000	0.6902000	0.9876000	0.986900
06025010101	California	Imperial County	0.8727000	0.9880000	0.8769000	0.7500000	0.987800
06037224420	California	Los Angeles County	0.9875000	0.7086000	0.9155000	0.9802000	0.982000
06037540800	California	Los Angeles County	0.6542000	0.6373000	0.9912000	0.3516000	0.639200
06067007101	California	Sacramento County	0.0167500	0.0588900	0.0004141	0.1799000	0.016470
06073009901	California	San Diego County	0.0002761	0.0003681	0.3860000	0.0831000	0.000092
53053072906	Washington	Pierce County	0.0002761	0.0001840	0.5246000	0.8216000	0.020800

For the most vulnerable state in your division, what are the most & least vulnerable tracts by THEME1, THEME2, THEME3, THEME4, OVERALL? Do they belong to certain counties?

The most vulnerable state in the Pacific Division is California. In 2010, two tracts in San Diego had the lowest SVI score in RPL_THEME1, RPL_THEME2, and RPL_THEMES. In 2020, one tract dropped off, but the remaining tract maintained the lowest SVI in the same three areas. Los Angeles, Alameda, Fresno, and San Diego counties appeared in the results for both 2010 and 2020.

# Find which states have highest overall SVI
aggregate(svi_2010_divisional$RPL_THEMES, by=list(svi_2010_divisional$state_name), FUN=mean)

##      Group.1         x
## 1     Alaska 0.4298082
## 2 California 0.5326944
## 3     Hawaii 0.4561219
## 4     Oregon 0.4301651
## 5 Washington 0.3782204

aggregate(svi_2020_divisional$RPL_THEMES, by=list(svi_2020_divisional$state_name), FUN=mean)

##      Group.1         x
## 1     Alaska 0.4920315
## 2 California 0.5287623
## 3     Hawaii 0.4893055
## 4     Oregon 0.4305941
## 5 Washington 0.3845878

# Isolate California
svi_2010_CA <- svi_2010_divisional %>% 
  filter(state == "CA")

svi_2020_CA <- svi_2020_divisional %>% 
  filter(state == "CA")

# Find the extremes

extremes_2010_CA <- svi_2010_CA %>% 
  filter(!is.na(F_THEME1) & E_TOTPOP_10 > 100) %>% 
  mutate(min1 = GEOID_2010_trt[which.min(RPL_THEME1)],
         min2 = GEOID_2010_trt[which.min(RPL_THEME2)],
         min3 = GEOID_2010_trt[which.min(RPL_THEME3)],
         min4 = GEOID_2010_trt[which.min(RPL_THEME4)],
         minall = GEOID_2010_trt[which.min(RPL_THEMES)],
         max1 = GEOID_2010_trt[which.max(RPL_THEME1)],
         max2 = GEOID_2010_trt[which.max(RPL_THEME2)],
         max3 = GEOID_2010_trt[which.max(RPL_THEME3)],
         max4 = GEOID_2010_trt[which.max(RPL_THEME4)],
         maxall = GEOID_2010_trt[which.max(RPL_THEMES)]) %>% 
  select(min1:maxall) %>% 
  unique()
print(extremes_2010_CA)

## # A tibble: 1 × 10
##   min1        min2        min3       min4  minall max1  max2  max3  max4  maxall
##   <chr>       <chr>       <chr>      <chr> <chr>  <chr> <chr> <chr> <chr> <chr> 
## 1 06073011300 06073009901 060379200… 0600… 06073… 0603… 0605… 0601… 0601… 06055…

extremes_2020_CA <- svi_2020_CA %>% 
  filter(!is.na(F_THEME1) & E_TOTPOP_20 > 100) %>% 
  mutate(min1 = GEOID_2010_trt[which.min(RPL_THEME1)],
         min2 = GEOID_2010_trt[which.min(RPL_THEME2)],
         min3 = GEOID_2010_trt[which.min(RPL_THEME3)],
         min4 = GEOID_2010_trt[which.min(RPL_THEME4)],
         minall = GEOID_2010_trt[which.min(RPL_THEMES)],
         max1 = GEOID_2010_trt[which.max(RPL_THEME1)],
         max2 = GEOID_2010_trt[which.max(RPL_THEME2)],
         max3 = GEOID_2010_trt[which.max(RPL_THEME3)],
         max4 = GEOID_2010_trt[which.max(RPL_THEME4)],
         maxall = GEOID_2010_trt[which.max(RPL_THEMES)]) %>% 
  select(min1:maxall) %>% 
  unique()
print(extremes_2020_CA)

## # A tibble: 1 × 10
##   min1        min2        min3       min4  minall max1  max2  max3  max4  maxall
##   <chr>       <chr>       <chr>      <chr> <chr>  <chr> <chr> <chr> <chr> <chr> 
## 1 06073009901 06073009901 060670071… 0600… 06073… 0603… 0602… 0603… 0601… 06025…

# If we want to go back and find these tracts in their entirety:
extremes_table_2010_CA <- svi_2010_divisional %>% 
  filter(GEOID_2010_trt %in% as.character(extremes_2010_CA)) %>% 
  select(GEOID_2010_trt, county, matches("^RPL_"))

extremes_table_2010_CA %>% arrange(county) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	county	RPL_THEME1	RPL_THEME2	RPL_THEME3	RPL_THEME4	RPL_THEMES
06001443200	Alameda County	0.126600	0.0877900	0.730400	0.002807	0.0461900
06013369001	Contra Costa County	0.925500	0.9830000	0.782100	0.987200	0.9859000
06019007801	Fresno County	0.924200	0.7206000	0.985800	0.932100	0.9344000
06037224310	Los Angeles County	0.986700	0.6316000	0.903800	0.979500	0.9748000
06037920026	Los Angeles County	0.320400	0.0808000	0.001288	0.082540	0.0965300
06055200900	Napa County	0.829300	0.9913000	0.460000	0.083740	0.9913000
06073009901	San Diego County	0.013710	0.0001840	0.423900	0.082820	0.0004601
06073011300	San Diego County	0.000092	0.0003681	0.493700	0.083460	0.0002761

extremes_table_2020_CA <- svi_2020_divisional %>% 
  filter(GEOID_2010_trt %in% as.character(extremes_2020_CA)) %>% 
  select(GEOID_2010_trt, county, matches("^RPL_"))

extremes_table_2020_CA %>% arrange(county) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	county	RPL_THEME1	RPL_THEME2	RPL_THEME3	RPL_THEME4	RPL_THEMES
06001451505	Alameda County	0.0251200	0.0811600	0.2719000	0.0009202	0.001288
06019000800	Fresno County	0.9309000	0.9873000	0.6902000	0.9876000	0.986900
06025010101	Imperial County	0.8727000	0.9880000	0.8769000	0.7500000	0.987800
06037224420	Los Angeles County	0.9875000	0.7086000	0.9155000	0.9802000	0.982000
06037540800	Los Angeles County	0.6542000	0.6373000	0.9912000	0.3516000	0.639200
06067007101	Sacramento County	0.0167500	0.0588900	0.0004141	0.1799000	0.016470
06073009901	San Diego County	0.0002761	0.0003681	0.3860000	0.0831000	0.000092