library(tidyverse)
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## ✖ dplyr::filter() masks stats::filter()
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(readxl)


district<-read_excel("district.xls")
new_df<-district%>%select(DISTNAME,DPETSPEP,DPFPASPEP)
summary(new_df)
##    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 has 5 NA’s/ Missing variables.

new_df2<-new_df%>%filter(DPFPASPEP>0)

The observations changed from 1207 to 1201.

ggplot(new_df2,aes(x=DPFPASPEP,y=DPETSPEP))+geom_point()

cor(new_df2$DPETSPEP,new_df2$DPFPASPEP)
## [1] 0.371033

The correlation is minimal; only 1/3 %.