Our setup code:

# install.packages('ggpubr','ggforce','ggalt')

library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --
## v ggplot2 3.3.3     v purrr   0.3.4
## v tibble  3.1.0     v dplyr   1.0.5
## v tidyr   1.1.3     v stringr 1.4.0
## v readr   1.4.0     v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(ggpubr)
library(ggforce)
library(ggalt)
## Registered S3 methods overwritten by 'ggalt':
##   method                  from   
##   grid.draw.absoluteGrob  ggplot2
##   grobHeight.absoluteGrob ggplot2
##   grobWidth.absoluteGrob  ggplot2
##   grobX.absoluteGrob      ggplot2
##   grobY.absoluteGrob      ggplot2
google <- read_csv('https://github.com/NickCH-K/causalbook/raw/main/EventStudies/google_stock_data.csv') %>%
  pivot_longer(cols = c('Google_Return','SP500_Return'))
## 
## -- Column specification --------------------------------------------------------
## cols(
##   Date = col_date(format = ""),
##   Google_Return = col_double(),
##   SP500_Return = col_double()
## )
spend <- read_csv('https://vincentarelbundock.github.io/Rdatasets/csv/stevedata/gss_spending.csv')
## Warning: Missing column names filled in: 'X1' [1]
## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_double()
## )
## i Use `spec()` for the full column specifications.
#https://vincentarelbundock.github.io/Rdatasets/doc/stevedata/gss_spending.html

Google’s Alphabet Announcement

On August 10, 2015, Google announced that they were reorganizing the company to fit underneath Alphabet, a new umbrella company. The GOOG stock would now be stock in Alphabet. Did this announcement affect the Google stock price?

Life by Education

## Warning: Missing column names filled in: 'X1' [1]
## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_double()
## )
## i Use `spec()` for the full column specifications.
spend
Name Class Label Values
BA logical Has Bachelor or Grad Degree TRUE FALSE
income_above_median numeric Income above median Num: 0.413 to 0.761
conservative numeric 5-7 on political views scale Num: 0.315 to 0.333
news_reader numeric Reads news at least a few times a week or daily Num: 0.164 to 0.305
full_time numeric Works full-time Num: 0.439 to 0.586
BA income_above_median conservative news_reader full_time
FALSE 0.4126984 0.3333333 0.1644254 0.4388005
TRUE 0.7609148 0.3147353 0.3047753 0.5856742 
spend <- spend %>%
  pivot_longer(cols = c('income_above_median','conservative','news_reader','full_time'))
no_ba <- spend %>% 
  slice(1:4) %>%
  rename(no_ba_value = value)
ba <- spend %>%
  slice(5:8) %>%
  rename(ba_value = value)
df <- no_ba %>% 
  left_join(ba, by = 'name')