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library(readxl)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
district<-read_excel("district.xls")
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>, …
your_variable_here<-district %>% select(DISTNAME,DPETSPEP,DPFPASPEP)

head(your_variable_here)
## # A tibble: 6 × 3
##   DISTNAME      DPETSPEP DPFPASPEP
##   <chr>            <dbl>     <dbl>
## 1 CAYUGA ISD        14.6      28.9
## 2 ELKHART ISD       12.1       8.8
## 3 FRANKSTON ISD     13.1       8.4
## 4 NECHES ISD        10.5      10.1
## 5 PALESTINE ISD     13.5       6.1
## 6 WESTWOOD ISD      14.5       9.4
summary(your_variable_here)
##    DISTNAME            DPETSPEP       DPFPASPEP     
##  Length:1207        Min.   : 0.00   Min.   : 0.000  
##  Class :character   1st Qu.: 9.90   1st Qu.: 5.800  
##  Mode  :character   Median :12.10   Median : 8.900  
##                     Mean   :12.27   Mean   : 9.711  
##                     3rd Qu.:14.20   3rd Qu.:12.500  
##                     Max.   :51.70   Max.   :49.000  
##                                     NA's   :5

Which variable has missing values? BOTH Mins on DPETSPEP, DPFPASPEP. Everything else has a value over 0.

I cleaned both variables.

your_variable_here_cleaned<-your_variable_here %>% filter(DPETSPEP>0)

mean(your_variable_here_cleaned$DPETSPEP)
## [1] 12.29867
your_variable_here_cleaned<-your_variable_here %>% filter(DPFPASPEP>0)

mean(your_variable_here_cleaned$DPFPASPEP)
## [1] 9.718651
compare_two<-district %>% select(DISTNAME,DPETSPEP,DPFPASPEP)

compare_two<-compare_two %>% filter(DPETSPEP>0)

ggplot(compare_two,aes(DPETSPEP,DPFPASPEP)) + geom_point()
## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).

cor(your_variable_here_cleaned$DPFPASPEP, your_variable_here_cleaned$DPETSPEP)
## [1] 0.371033