The final project will use all of the information you have learned in
this course to create a report on a network. For the final project, you
will use data from the Phoenix Open Data Portal. Specifically, you will
use co-arrest data. These data represent incidents where individuals
were arrested together. For the final project, imagine that you work for
a police department and your supervisor has asked you to create a report
on co-offending networks. Your supervisor has provided you two networks:
the PhxArrestNet2023 network of co-offending for 2023 and
the PhxArrestNet2022 network of co-offending for 2022.
Both networks are stored as .rds files in the data folder on the SNA
Textbook site. As before, use the readRDS() function,
to load the file. Be sure to make sure the sna and
network packages are loaded, using library(),
so that R recognizes the objects as being of class
network.
Use the following code to load the 2023 network:
# set the url
loc <- "https://github.com/jacobtnyoung/sna-textbook/raw/main/data/data-PHX-arrest-2023-net.rds"
# for the 2023
PhxArrestNet2023 <- readRDS( url( loc ) )Use the following code to load the 2022 network:
# set the url
loc <- "https://github.com/jacobtnyoung/sna-textbook/raw/main/data/data-PHX-arrest-2022-net.rds"
# for the 2022
PhxArrestNet2022 <- readRDS( url( loc ) )As with the labs, There is a template for the final project. The
template is set up as a report in that the code chunks include the
option echo = FALSE. What this does is hides the code in
the final report. When you render your report, the code will be hidden
so that it reads cleanly.
IMPORTANT: Do you report entire matrices or objects. These print out very long and are cumbersome to look through.
What can you tell me about this network? To answer your
supervisor’s question, provide an analysis of the
PhxArrestNet2023 network. This analysis should include a
visualization of the network as well as a description of the structural
properties such as the density and the degree distributions. Be sure to
focus on the interpretation of the structural properties (e.g. what does
the average degree centrality score mean?).
Who are the central actors in this network? To answer your
supervisor’s question, provide an analysis of the “person” matrix for
the PhxArrestNet2023 network. Create an object of class
matrix using the as.sociomatrix() function in
the network package. Then, focus on describing the
centrality of actors. You should examine the three types of centrality
we discussed (i.e. degree, closeness, and betweenness). Be sure to
emphasize any differences in the measures in terms of interpreting the
“who is central?” question. Also, be sure to include visualizations that
incorporate each centrality measure.
How has the co-offending network changed from 2022 to 2023?
To answer your supervisor’s question, provide a comparative analysis of
the PhxArrestNet2022 and the PhxArrestNet2023
networks. Think about the ways we discussed for comparing networks this
semester. Your analysis should compare the two-mode networks as well as
the “person” matrices.
Download the template for this final project prior to beginning the final project.
When you have completed your assignment, click the “Knit” button to
render your .RMD file into a .HTML report.
Upload both your .RMD and .HTML files to
the appropriate link for this assignment on the Canvas page for this
course.
Remember to ensure the following before submitting your assignment.
See Google’s R Style Guide for examples of common conventions.
.RMD files are knit into .HTML and other
formats procedural, or line-by-line.
install.packages() or
setwd() are bound to cause errors in knittinglibrary() in a previous chunkIf All Else Fails: If you cannot determine and fix
the errors in a code chunk that’s preventing you from knitting your
document, add eval = FALSE inside the brackets of
{r} at the beginning of a chunk to ensure that R does not
attempt to evaluate it, that is: {r eval = FALSE}. This
will prevent an erroneous chunk of code from halting the knitting
process.