N. Krause HW #3

1. create an Rmarkdown document with “district” data

library(tidyverse)

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## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(readxl)

district<-read_excel("district.xls")

2. create a new data frame with “DISTNAME”, “DPETSPEP” (percent special education) and “DPFPASPEP” (money spent on special education). call the dataframe whatever you want

data1<-district %>%select(DISTNAME,DPETSPEP,DPFPASPEP)

3. give me “summary()” statistics for both DPETSPEP and DFPASPEP. You can summarize them separately if you want.

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

4. Which variable has missing values?

The variable with missing values is “DPFPASPEP” which has 5 missing listed as “NAs”

5. remove the missing observations. How many are left overall? (REMOVE WITH drop_na)

data2<-data1 %>% na.omit()

There are 1,202 observations remaining in data2

6. Create a point graph (hint: ggplot + geom_point()) to compare DPFPASPEP and DPETSPEP. Are they correlated?

Question removed

7. Do a mathematical check (cor()) of DPFPASPEP and DPETSPEP. What is the result?

cor(data2$DPFPASPEP,data2$DPETSPEP)

## [1] 0.3700234

8. How would you interpret these results? (No real right or wrong answer – just tell me what you see)

My interpretation of the correlation is that there is somewhat of a correlation between the two variables, however, it is a weak correlation.