library(readxl)
district <- read_excel("district.xls")
library(tidyverse)
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head(district)
## # A tibble: 6 × 137
##   DISTNAME DISTRICT DZCNTYNM REGION DZRATING DZCAMPUS DPETALLC DPETBLAP DPETHISP
##   <chr>    <chr>    <chr>    <chr>  <chr>       <dbl>    <dbl>    <dbl>    <dbl>
## 1 CAYUGA … 001902   001 AND… 07     A               3      574      4.4     11.5
## 2 ELKHART… 001903   001 AND… 07     A               4     1150      4       11.8
## 3 FRANKST… 001904   001 AND… 07     A               3      808      8.5     11.3
## 4 NECHES … 001906   001 AND… 07     A               2      342      8.2     13.5
## 5 PALESTI… 001907   001 AND… 07     B               6     3360     25.1     42.9
## 6 WESTWOO… 001908   001 AND… 07     B               4     1332     19.7     26.2
## # ℹ 128 more variables: DPETWHIP <dbl>, DPETINDP <dbl>, DPETASIP <dbl>,
## #   DPETPCIP <dbl>, DPETTWOP <dbl>, DPETECOP <dbl>, DPETLEPP <dbl>,
## #   DPETSPEP <dbl>, DPETBILP <dbl>, DPETVOCP <dbl>, DPETGIFP <dbl>,
## #   DA0AT21R <dbl>, DA0912DR21R <dbl>, DAGC4X21R <dbl>, DAGC5X20R <dbl>,
## #   DAGC6X19R <dbl>, DA0GR21N <dbl>, DA0GS21N <dbl>, DDA00A001S22R <dbl>,
## #   DDA00A001222R <dbl>, DDA00A001322R <dbl>, DDA00AR01S22R <dbl>,
## #   DDA00AR01222R <dbl>, DDA00AR01322R <dbl>, DDA00AM01S22R <dbl>, …
summary(district$DISTNAME)
##    Length     Class      Mode 
##      1207 character character
summary(district$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
district_data <- district %>% select(DISTNAME,DPETSPEP,DPFPASPEP)
district_clean<-district_data %>% filter(!is.na(DPFPASPEP))

#The value that has missing values is :DPFPASPEP”. There are 5 n/a’s.

ggplot(district_clean,aes(DPETSPEP,DPFPASPEP)) + geom_point()

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