Network Analysis for Kyungpook Univ.

rm(list=ls()) #remove all the objects in the work space
setwd("/Users/se776257/OneDrive - University of Central Florida/Desktop/Prof. An/02 Teaching/Guest lecture/2026 Kyungpook/Method workshop/Network/Outcome/") #the folder where you extract data and save files 

You need to download necessary R packages. R is like Roblox; R provides a platform and users create fun games (packages) and share them with other users. Today, the following packages are needed to play the ‘Network Analysis’ game!

library(haven)
library(igraph)

## Warning: package 'igraph' was built under R version 4.3.3

## 
## Attaching package: 'igraph'

## The following objects are masked from 'package:stats':
## 
##     decompose, spectrum

## The following object is masked from 'package:base':
## 
##     union

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:igraph':
## 
##     as_data_frame, groups, union

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

Import raw data file

df <- read.csv(file="network_2025_06_30.csv")

Check the number of individuals in the dataset

length(unique(df$master_PID))

## [1] 15179

Let’s see the frequency by exit type

table(df$dest_unshelter)

## 
##     0     1 
## 28697  2451

table(df$dest_shelter)

## 
##     0     1 
## 29184  1964

table(df$dest_housing)

## 
##     0     1 
## 29893  1255

table(df$dest_no_interview)

## 
##     0     1 
## 28680  2468

table(df$dest_deceased)

## 
##     0     1 
## 31118    30

table(df$dest_dbl_up)

## 
##     0     1 
## 27921  3227

table(df$dest_institution)

## 
##     0     1 
## 30477   671

table(df$dest_temp_housing)

## 
##     0     1 
## 29459  1689

table(df$dest_other)

## 
##     0     1 
## 24557  6591

See the column: projecttype

table(df$projecttype)

## 
##                                                                      
##                                                                  247 
##                                                 1. Emergency Shelter 
##                                                                 5784 
##   10.  PH – Housing with Services (no disability required for entry) 
##                                                                   62 
##                                          12. Homelessness Prevention 
##                                                                 1378 
##                                            13. PH - Rapid Re-Housing 
##                                                                 1478 
##                                                14. Coordinated Entry 
##                                                                 5371 
##                                                                   15 
##                                                                10785 
##                                                    15. Youth shelter 
##                                                                    1 
##                                              2. Transitional Housing 
##                                                                  700 
## 3. PH - Permanent Supportive Housing (disability required for entry) 
##                                                                  165 
##                                                   4. Street Outreach 
##                                                                 1222 
##                                                     6. Services Only 
##                                                                 3437 
##                                                             7. Other 
##                                                                  518

#we only need numbers for network analysis, so let's remove text.
df$projecttype <- gsub("^(\\d+)\\..*", "\\1", df$projecttype)
#See if you correctly cleaned the data
table(df$projecttype)

## 
##           1    10    12    13    14    15     2     3     4     6     7 
##   247  5784    62  1378  1478  5371 10786   700   165  1222  3437   518

Now, only select those who exited unsheltered (dest_unshelter == 1)

db <- df %>%
  filter(dest_unshelter == 1)

Extract two columns of data and create a network data.frame

y <- data.frame(db$projecttype, db$next_entry)
colnames(y) <- c("source", "target")

Delete cases whose the next entry is NA

y <- y[!is.na(y$source), ]
y <- y[!is.na(y$target), ]

We will keep “unsheltered”- the final destination to show in the network

y$target[y$target =="16"] <- "UNSHELTER"
y <- y[!is.na(y$target), ]
y <- y[!is.na(y$source), ]

See how the network data look like

table(y$target)

## 
##         1        10        12        13        14        15         2         3 
##       201         1         7        67       463       395        12         1 
##         4         6         7 UNSHELTER 
##        65        76        20      1143

Save the network data as CSV file

write.csv(y, file = "networkgephi_project_unshelter.csv")

Do the same job for other projecttype: Shelter, housing, no_interview, deceased, dbl_up, institution, temp_housing, and others