HW3

library(tidyverse)

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library(readxl)

## Warning: package 'readxl' was built under R version 4.6.1

district<-read_excel("district.xls")

set1<-district %>% select(DISTNAME,DPETSPEP,DPFPASPEP)

##This was making a data frame

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.     NAs 
##   0.000   5.800   8.900   9.711  12.500  49.000       5

Did it this way before watching you do it with the data frame

summary(set1)

##       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

4) DPFPASPEP has missing values

set2<-set1 %>% na.omit()

summary(set2)

##       DISTNAME       DPETSPEP      DPFPASPEP     
##  Length   :1202   Min.   : 0.0   Min.   : 0.000  
##  N.unique :1191   1st Qu.: 9.9   1st Qu.: 5.800  
##  N.blank  :   0   Median :12.2   Median : 8.900  
##  Min.nchar:   7   Mean   :12.3   Mean   : 9.711  
##  Max.nchar:  50   3rd Qu.:14.2   3rd Qu.:12.500  
##                   Max.   :51.7   Max.   :49.000

There were 1207 observations and now there is a remaining 1202

cor(set2$DPFPASPEP,set2$DPETSPEP)

## [1] 0.3700234

##Weakly correlated so there is another factor that is contributing likely.