Home Internet County Explorer

P. Mae Cooper
6/8/2020

Home Internet

Home internet access crucial in time of Coronavirus
Not everyone has access
Poverty, age, and race all play roles in access

Why this application

Application lets you play with three variables and their relation to Home Internet Access across counties
Allows user to limit range to eliminate outliers or examine parts of the distribution
Uses American Community Survey data
Clearly shows relationship across multiple variables

Example Plot

library(tidycensus)

vars<- c("B28002_001","B28002_013","S1701_C03_002","DP05_0018","DP05_0077P","DP05_0078P","DP05_0079P","DP05_0080P","DP05_0071P")
nms<- c("TOTPOP","NOINT","POVPCT","MEDAGE","WHT","BLK","IND","ASN","HSP")

county18<-get_acs("county",survey = "acs1", key = "993d02931a941bd1f5af535c637800916aa504b2",variables=vars)

cty18_wide<-data.frame(county18$GEOID[county18$variable==vars[1]],
                       county18$NAME[county18$variable==vars[1]])
    for(i in 1:9){
  cty18_wide[i+2]<-county18$estimate[county18$variable==vars[i]]}


names(cty18_wide)<-c("GEOID","NAME",nms)

cty18_wide$INTPCT<- with(cty18_wide,round((1-(NOINT/TOTPOP))*100,1))

y<-cty18_wide$INTPCT
x<-cty18_wide$POVPCT
fit<- lm(y ~ x)

plot(x=x,y=y,xlab="Percent of children in Poverty",ylab="Percent of Households with Home Internet")
abline(fit)

plot of chunk unnamed-chunk-1

Potential Improvements for Future

Add more variables (e.g. population density)
Add dropdown to limit to particular states
Make the plot prettier