library(tidyverse)
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library(readxl)

district <- read_excel("district.xls")
data1 <- district %>% select(DISTNAME, DPETSPEP, DPFPASPEP)
summary(data1)
##       DISTNAME       DPETSPEP       DPFPASPEP     
##  Length   :1207   Min.   : 0.00   Min.   : 0.000  
##  N.unique :1196   1st Qu.: 9.90   1st Qu.: 5.800  
##  N.blank  :   0   Median :12.10   Median : 8.900  
##  Min.nchar:   7   Mean   :12.27   Mean   : 9.711  
##  Max.nchar:  50   3rd Qu.:14.20   3rd Qu.:12.500  
##                   Max.   :51.70   Max.   :49.000  
##                                   NAs    :5
# see at the bottom of this chart, it says NA missing is 5

The missing values are in DPFPASPEP

data2 <- data1 %>% na.omit()

There are 1,202 observations remaining in the data

cor(data2$DPFPASPEP, data2$DPETSPEP)
## [1] 0.3700234

Interpretation: Spending on special education does not seem to be very closely related to the amount of students in Special Ed. Weak correlation.