library(tidyverse)
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library(readxl)
district<-read_excel("district.xls")
districtnew <-district |> select(DISTNAME, DPETSPEP, DPFPASPEP)
summary (districtnew)
##    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

DPFPASPEP is the variable with 5 NA observations

districtclean <- districtnew |> drop_na()

1202 observations are left

plot(districtclean$DPETSPEP,districtclean$DPFPASPEP) 

cor(districtclean$DPETSPEP,districtclean$DPFPASPEP)
## [1] 0.3700234

there is a slight correlation between the observations. Because the value is not significanly close to 1, the correlation is just over .37.