library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.2
## ✔ 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(readxl)
district<-read_excel("district.xls")
CleanDistrict<-district%>%select( "DISTNAME", "DPETSPEP","DPFPASPEP")
glimpse(CleanDistrict)
## Rows: 1,207
## Columns: 3
## $ DISTNAME <chr> "CAYUGA ISD", "ELKHART ISD", "FRANKSTON ISD", "NECHES ISD", …
## $ DPETSPEP <dbl> 14.6, 12.1, 13.1, 10.5, 13.5, 14.5, 14.7, 10.4, 11.6, 11.9, …
## $ DPFPASPEP <dbl> 28.9, 8.8, 8.4, 10.1, 6.1, 9.4, 9.9, 10.9, 9.2, 10.3, 10.7, …
summary(CleanDistrict)
## 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
CleanDistrict%>%rename(DistrictName=DISTNAME, DistrictPer=DPETSPEP, DistrictSpend=DPFPASPEP)
## # A tibble: 1,207 × 3
## DistrictName DistrictPer DistrictSpend
## <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
## 7 SLOCUM ISD 14.7 9.9
## 8 ANDREWS ISD 10.4 10.9
## 9 PINEYWOODS COMMUNITY ACADEMY 11.6 9.2
## 10 HUDSON ISD 11.9 10.3
## # ℹ 1,197 more rows
CleanDistrict%>%drop_na(DPFPASPEP)
## # A tibble: 1,202 × 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
## 7 SLOCUM ISD 14.7 9.9
## 8 ANDREWS ISD 10.4 10.9
## 9 PINEYWOODS COMMUNITY ACADEMY 11.6 9.2
## 10 HUDSON ISD 11.9 10.3
## # ℹ 1,192 more rows
ggplot(CleanDistrict, aes(x = DPETSPEP, y = DPFPASPEP)) + geom_point() + labs(title = "Special Education Spending vs Percent",
x = "Percent Special Ed", y = "Spending Special Ed")
## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
CleanDistrict %>% select(DPETSPEP, DPFPASPEP) %>% cor(use = "complete.obs")
## DPETSPEP DPFPASPEP
## DPETSPEP 1.0000000 0.3700234
## DPFPASPEP 0.3700234 1.0000000
- Which variable has missing values? District Spending
- remove the missing observations. How many are left overall? 4
- Create a point graph (hint: ggplot + geom_point()) to compare
DPFPASPEP and DPETSPEP. Are they correlated? Somewhat
- Do a mathematical check (cor()) of DPFPASPEP and DPETSPEP. What is
the result? .37
- How would you interpret these results? (No real right or wrong
answer – just tell me what you see). There’s a moderate positive
relationship between the two. The percentage of special education
students goes up, spending usually goes up too, but it’s not a perfect
match. The correlation of about 0.37 tells me there’s a connection, but
it’s not super strong, which makes sense because spending is probably
influenced by other factors too. relationship.