Homework 3

1. create an Rmarkdown document with “district” data (like this one)

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   3.5.2     ✔ tibble    3.3.0
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.1.0     
## ── 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

library(dplyr)


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

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

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

summary(new_data_frame)

##    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

4. Which variable has missing values?

summary(new_data_frame$DPFPASPEP)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   5.800   8.900   9.711  12.500  49.000       5

5. remove the missing observations. How many are left overall?

clean_data<-new_data_frame |> drop_na()

summary(clean_data$DPFPASPEP)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.000   5.800   8.900   9.711  12.500  49.000

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

ggplot(clean_data,aes(x=DPFPASPEP,y=DPETSPEP)) + geom_point() 

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

cor(clean_data$DPFPASPEP,clean_data$DPETSPEP)

## [1] 0.3700234

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

• I THINK this means that there is a positive relationship, but correlation isn’t strong because it should be plus/minus 1?