District Homework

library(tidyverse) 

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library(readxl)
district <- read.csv("district.csv")

newdata <- district |> select(DISTNAME,DPETSPEP,DPFPASPEP)

summary(newdata)

##    DISTNAME            DPETSPEP       DPFPASPEP     
##  Length:1207        Min.   : 0.00   Min.   : 0.000  
##  Class :character   1st Qu.: 9.90   1st Qu.: 5.800  
##  Mode  :character   Median :12.10   Median : 8.900  
##                     Mean   :12.27   Mean   : 9.711  
##                     3rd Qu.:14.20   3rd Qu.:12.500  
##                     Max.   :51.70   Max.   :49.000  
##                                     NA's   :5

the column with the msising varibles is DPFPASPEP and it it has 5 missing varibles.

cleandata <- newdata |> drop_na()

plot(cleandata$DPETSPEP,cleandata$DPFPASPEP)

There is cleandata from where all the districtdata used to be but more clustered all over the area of the left botom area of the graph.

cor(cleandata$DPETSPEP,cleandata$DPFPASPEP)

## [1] 0.3700234