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
The missing values are in DFPASPEP
data2<-data1 %>% na.omit()
There are 1,202 observations remaining in data2
cor(data2$DPFPASPEP, data2$DPETSPEP)
## [1] 0.3700234
In our data set we observe a week correlation between the spending on special education and the percent of students in special education.