PS4: Exploration of Social Capital Atlas
Chris Barber
2022-09-19
library(tidycensus)
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v dplyr 1.0.7
## v tidyr 1.1.4 v stringr 1.4.0
## v readr 2.1.1 v forcats 0.5.1## Warning: package 'purrr' was built under R version 4.1.3## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(tigris)## To enable
## caching of data, set `options(tigris_use_cache = TRUE)` in your R script or .Rprofile.##
## Attaching package: 'tigris'## The following object is masked from 'package:tidycensus':
##
## fips_codeslibrary(sf)## Linking to GEOS 3.9.1, GDAL 3.2.1, PROJ 7.2.1; sf_use_s2() is TRUElibrary(ggthemes)## Warning: package 'ggthemes' was built under R version 4.1.3library(viridis)## Warning: package 'viridis' was built under R version 4.1.3## Loading required package: viridisLiteThese plots will display some data collected from Social Capital Atlas.
# read data
soccap <- read_csv("https://data.humdata.org/dataset/85ee8e10-0c66-4635-b997-79b6fad44c71/resource/ec896b64-c922-4737-b759-e4bd7f73b8cc/download/social_capital_county.csv")## Rows: 3089 Columns: 26
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): county_name
## dbl (25): county, num_below_p50, pop2018, ec_county, ec_se_county, child_ec_...
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.# make state fips code
soccap <- soccap %>%
mutate(county_fips = as.character(county),
county_fips = str_pad(county_fips, width = 5, side = "left", pad = "0"),
state_fips = str_sub(county_fips, 1,2))
#df & shp for TN
Tennessee <- soccap %>%
filter(state_fips == "47")
Tennessee <- Tennessee %>% mutate(GEOID = county_fips)
TN_counties <- counties(state = "TN", cb = TRUE)##
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|======================================================================| 100%TN_joined <- inner_join(TN_counties, Tennessee, by = "GEOID")We’ll focus on counties in Tennessee. For instance, the plot below demonstrates the Facebook connections between the percent of friends of low-income people that have high-income (on the x axis) and county’s “clustering” score or what percent of an individual’s friend pairs are also friends with each other.
It seems apparant that counties with smaller popoulations have a higher “friend pariing intensity”. I guess it’s true everyone in small towns knows each other, right? Also with the notable exception of Memphis in (big yellow dot in bottom right), it appears that higher populations loosely correlate with friend networks of more economic diversity.
## Warning: Removed 1 rows containing missing values (geom_point).
The next two choropleth maps offer a statewide view of economic connectedness across the whole state. Williamson County is the outlier here. Its county seat is Franklin, a wealthy suburb of Nashville, so it would make sense low-income people there have higher concentrations of wealthy friends.
TN_joined %>%
ggplot(aes(fill = ec_county)) +
geom_sf()+
scale_fill_viridis(option = "plasma")+
theme_void()+
labs(title = "Tennessee Economic Connectedness Score (percent)",
subtitle = "By county",
caption = "Chris Barber; 9/19/2022; Source: Social Capital Atlas",
fill = "")+
theme(legend.position = "bottom") #how can I adjust the title position? 
TN_joined %>%
ggplot(aes(fill = ec_county)) +
scale_fill_binned(limits = c(0, 1.4), breaks = c(0,0.2,0.4,0.6,0.8,1,1.2,1.4))+
geom_sf()+
scale_fill_viridis(option = "inerno")+
theme_void()+
labs(title = "Tennessee Economic Connectedness Score (percent)",
subtitle = "By county",
caption = "Chris Barber; 9/19/2022; Source: Social Capital Atlas",
fill = "")+
theme(legend.position = "bottom") #how can I make the legend wider? ## Warning in viridisLite::viridis(256, alpha, begin, end, direction, option):
## Option 'inerno' does not exist. Defaulting to 'viridis'.## Scale for 'fill' is already present. Adding another scale for 'fill', which
## will replace the existing scale.
#the choropleths mostly demonstrate the same thing, but with more nuance to the latter