# import and clean data from Washington Post database on fatal police shootings
# source: https://github.com/washingtonpost/data-police-shootings
library(tidyverse)
library(tidymodels)
# get police-shootings data
police_shootings_raw <- read_csv(file = "https://raw.githubusercontent.com/washingtonpost/data-police-shootings/master/fatal-police-shootings-data.csv")
# select only relevant columns
police_shootings <- police_shootings_raw %>%
select(manner_of_death, armed, age, gender, race, state,
signs_of_mental_illness, threat_level, flee, body_camera) %>%
# recode variables for easier tidymodeling
mutate(
# convert logical columns to factors
signs_of_mental_illness = factor(signs_of_mental_illness,
labels = c("No", "Yes")),
body_camera = factor(body_camera, labels = c("No", "Yes")),
# fix gender miscoding - typo of D which probably meant F
gender = if_else(gender == "D", "F", gender),
# use full labels for categorical variables
gender = recode(.x = gender,
"M" = "Male",
"F" = "Female"),
race = recode(.x = race,
"A" = "Asian",
"B" = "Black, non-Hispanic",
"H" = "Hispanic",
"N" = "Native American",
"O" = "Other",
"W" = "White, non-Hispanic")
) %>%
# use sentence case for all character columns
mutate(across(.cols = c(where(is.character), -state), .fns = str_to_sentence)) %>%
# keep only complete observations for pedagogical purposes
drop_na()
# store in package
usethis::use_data(police_shootings, overwrite = TRUE)
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