Class Size Regression Analysis

Load Packages

# note I have already installed all of these here!

library(tidyverse) # always! 

── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.3.6      ✔ purrr   0.3.4 
✔ tibble  3.1.8      ✔ dplyr   1.0.10
✔ tidyr   1.2.1      ✔ stringr 1.4.1 
✔ readr   2.1.2      ✔ forcats 0.5.2 
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

library(haven) # for importing .dta files
library(broom) # for tidy regression analysis

Import the Data

# need haven loaded (done in first chunk)
ca_school <- read_dta("https://metricsF22.classes.ryansafner.com/files/data/CASchool.dta")

Explore the Data

ca_school

ca_school %>%
  glimpse()

Rows: 420
Columns: 21
$ observat <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18…
$ dist_cod <dbl> 75119, 61499, 61549, 61457, 61523, 62042, 68536, 63834, 62331…
$ county   <chr> "Alameda", "Butte", "Butte", "Butte", "Butte", "Fresno", "San…
$ district <chr> "Sunol Glen Unified", "Manzanita Elementary", "Thermalito Uni…
$ gr_span  <chr> "KK-08", "KK-08", "KK-08", "KK-08", "KK-08", "KK-08", "KK-08"…
$ enrl_tot <dbl> 195, 240, 1550, 243, 1335, 137, 195, 888, 379, 2247, 446, 987…
$ teachers <dbl> 10.90, 11.15, 82.90, 14.00, 71.50, 6.40, 10.00, 42.50, 19.00,…
$ calw_pct <dbl> 0.5102, 15.4167, 55.0323, 36.4754, 33.1086, 12.3188, 12.9032,…
$ meal_pct <dbl> 2.0408, 47.9167, 76.3226, 77.0492, 78.4270, 86.9565, 94.6237,…
$ computer <dbl> 67, 101, 169, 85, 171, 25, 28, 66, 35, 0, 86, 56, 25, 0, 31, …
$ testscr  <dbl> 690.80, 661.20, 643.60, 647.70, 640.85, 605.55, 606.75, 609.0…
$ comp_stu <dbl> 0.34358975, 0.42083332, 0.10903226, 0.34979424, 0.12808989, 0…
$ expn_stu <dbl> 6384.911, 5099.381, 5501.955, 7101.831, 5235.988, 5580.147, 5…
$ str      <dbl> 17.88991, 21.52466, 18.69723, 17.35714, 18.67133, 21.40625, 1…
$ avginc   <dbl> 22.690001, 9.824000, 8.978000, 8.978000, 9.080333, 10.415000,…
$ el_pct   <dbl> 0.000000, 4.583333, 30.000002, 0.000000, 13.857677, 12.408759…
$ read_scr <dbl> 691.6, 660.5, 636.3, 651.9, 641.8, 605.7, 604.5, 605.5, 608.9…
$ math_scr <dbl> 690.0, 661.9, 650.9, 643.5, 639.9, 605.4, 609.0, 612.5, 616.1…
$ aowijef  <dbl> 35.77982, 43.04933, 37.39445, 34.71429, 37.34266, 42.81250, 3…
$ es_pct   <dbl> 1.000000, 3.583333, 29.000002, 1.000000, 12.857677, 11.408759…
$ es_frac  <dbl> 0.01000000, 0.03583334, 0.29000002, 0.01000000, 0.12857677, 0…

# scatterplot
scatter <- ggplot(data = ca_school)+
  aes(x = str,
      y = testscr)+
  geom_point(color = "blue")+
  labs(x = "Student to Teacher Ratio",
       y = "Test Score")+
  theme_bw(base_family = "Fira Sans Condensed",
           base_size = 20)

scatter