library(tidyverse)
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library(readxl)

district_data<-read_excel("district.xls")
new_data<-district_data[, c("DISTNAME","DPETSPEP","DPFPASPEP")]
summary(new_data$DPETSPEP)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    0.00    9.90   12.10   12.27   14.20   51.70
summary(new_data$DPFPASPEP)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   5.800   8.900   9.711  12.500  49.000       5
sum(is.na(new_data$DPETSPEP))
## [1] 0
sum(is.na(new_data$DPFPASPEP))
## [1] 5

The variable DPFPASPEP has 5 missing values

clean_data<-na.omit(new_data)
nrow(clean_data)
## [1] 1202

There are 1202 observations left

library(ggplot2)
ggplot(new_data, aes(x=DPFPASPEP, y=DPETSPEP)) +geom_point()
## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).

The graph would suggest there is a correlation between x and y

cor(new_data$DPFPASPEP, new_data$DPETSPEP, use="complete.obs")
## [1] 0.3700234

According to the mathematical check, there is a moderate positive correlation between the two

I interpret the results as spending increases, the percentage of special education students tends to increase. However, the relationship is not very strong