library(readxl)
district<-read_excel("district.xls")

New data frame…

sally=district[,c("DISTNAME","DPETSPEP","DPFPASPEP")]

Summary statistics…

summary(district$DPETSPEP)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    0.00    9.90   12.10   12.27   14.20   51.70
summary(district$DPFPASPEP)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   5.800   8.900   9.711  12.500  49.000       5

the minimum variable has missing values

sally_clean<-na.omit(sally)
summary(sally_clean$DPETSPEP)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     0.0     9.9    12.2    12.3    14.2    51.7
summary(sally_clean$DPFPASPEP)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.000   5.800   8.900   9.711  12.500  49.000

1202 observations are left overall

Point graph…

installed.packages("ggplot2")
##      Package LibPath Version Priority Depends Imports LinkingTo Suggests
##      Enhances License License_is_FOSS License_restricts_use OS_type Archs
##      MD5sum NeedsCompilation Built
library(ggplot2)
ggplot(sally_clean, aes(x=DPFPASPEP, y=DPETSPEP))+geom_point()

Weak correlation

Mathematical correlation…

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

I interpreted these results as having a strong correlation the lower the two were but as either one increased the correlation weakened.