Homework 3

library(tidyverse)

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## ✖ dplyr::filter() masks stats::filter()
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library(readxl)
library(dplyr)
district <- read_excel("C:/Users/carmo/Downloads/district.xls")

filtered_district <- subset(district, select = c(DISTNAME, DPETSPEP, DPFPASPEP))

summary(filtered_district)

##    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

#After performing the summary function on the filtered data set, we can see that DPFPASPEP, or the money spent on special education, has missing values.

#In order to remove the missing values, I will use “drop_na”.

clean_filtered <- drop_na(filtered_district)

#After removing the N/A’s from the dataset, there are 1205 observations.

library(ggplot2)

ggplot(clean_filtered, aes(x = DPETSPEP, y = DPFPASPEP)) + geom_point(color = "blue", size = 1)

cor(clean_filtered$DPETSPEP, clean_filtered$DPFPASPEP, method = "pearson")

## [1] 0.3700234

cor(clean_filtered$DPETSPEP, clean_filtered$DPFPASPEP, method = "spearman")

## [1] 0.346629

cor(clean_filtered$DPETSPEP, clean_filtered$DPFPASPEP, method = "kendall")

## [1] 0.2392367

#After running correlation using the three different methods and observing the point graph, it seems as though there is a minor positive correlation between the two variables.