## code to prepare `DATASET` dataset goes here
library(tidyverse)
tempfile_path <- tempfile()
download.file("http://davidcard.berkeley.edu/data_sets/njmin.zip", destfile = tempfile_path)
tempdir_path <- tempdir()
unzip(tempfile_path, exdir = tempdir_path)
codebook <- read_lines(file = paste0(tempdir_path, "/codebook"))
variable_names <- codebook %>%
`[`(8:59) %>%
`[`(-c(5, 6, 13, 14, 32, 33)) %>%
str_sub(1, 13) %>%
str_squish() %>%
str_to_lower()
dataset <- read_table2(paste0(tempdir_path, "/public.dat"),
col_names = FALSE)
dataset <- dataset %>%
select(-X47) %>%
`colnames<-`(., variable_names) %>%
mutate_all(as.numeric) %>%
mutate(sheet = as.character(sheet))
dataset <- dataset %>%
mutate(chain = case_when(chain == 1 ~ "bk",
chain == 2 ~ "kfc",
chain == 3 ~ "roys",
chain == 4 ~ "wendys")) %>%
mutate(state = case_when(state == 1 ~ "New Jersey",
state == 0 ~ "Pennsylvania")) %>%
mutate(region = case_when(southj == 1 ~ "southj",
centralj == 1 ~ "centralj",
northj == 1 ~ "northj",
shore == 1 ~ "shorej",
pa1 == 1 ~ "pa1",
pa2 == 1 ~ "pa2")) %>%
mutate(meals = case_when(meals == 0 ~ "None",
meals == 1 ~ "Free meals",
meals == 2 ~ "Reduced price meals",
meals == 3 ~ "Both free and reduced price meals")) %>%
mutate(meals2 = case_when(meals2 == 0 ~ "None",
meals2 == 1 ~ "Free meals",
meals2 == 2 ~ "Reduced price meals",
meals2 == 3 ~ "Both free and reduced price meals")) %>%
mutate(status2 = case_when(status2 == 0 ~ "Refused 2nd interview",
status2 == 1 ~ "Answered 2nd interview",
status2 == 2 ~ "Closed for renovations",
status2 == 3 ~ "Closed permanently",
status2 == 4 ~ "Closed for highway construction",
status2 == 5 ~ "Closed due to Mall fire")) %>%
mutate(co_owned = if_else(co_owned == 1, "Yes", "No")) %>%
mutate(bonus = if_else(bonus == 1, "Yes", "No")) %>%
mutate(special2 = if_else(special2 == 1, "Yes", "No")) %>%
mutate(type2 = if_else(type2 == 1, "Phone", "Personal")) %>%
select(sheet, chain, co_owned, state, region, everything()) %>%
select(-southj, -centralj, -northj, -shore, -pa1, -pa2) %>%
mutate(date2 = lubridate::mdy(date2)) %>%
rename(open2 = open2r) %>%
rename(firstinc2 = firstin2)
dataset1 <- dataset %>%
select(-ends_with("2"), -sheet, -chain, -co_owned, -state, -region, -bonus) %>%
mutate(type = NA_character_,
status = NA_character_,
date = NA)
dataset2 <- dataset %>%
select(ends_with("2")) %>%
#mutate(bonus = NA_character_) %>%
rename_all(~str_remove(., "2"))
other_cols <- dataset %>%
select(sheet, chain, co_owned, state, region, bonus)
other_cols_1 <- other_cols %>%
mutate(observation = "February 1992")
other_cols_2 <- other_cols %>%
mutate(observation = "November 1992")
dataset1 <- bind_cols(other_cols_1, dataset1)
dataset2 <- bind_cols(other_cols_2, dataset2)
njmin <- bind_rows(dataset1, dataset2) %>%
select(sheet, chain, state, region, observation, everything())
#
# ggplot(dataset, aes(wage_st)) + geom_density(aes(fill = state), alpha = 0.3) +
# facet_wrap(vars(observation)) + theme_blog() +
# theme(legend.title = element_blank(), plot.caption = element_text(colour = "white")) +
# labs(title = "Distribution of starting wage rates in fast food restaurants",
# caption = "On April 1st, 1992, New Jersey's minimum wage rose from $4.25 to $5.05. Source: Card and Krueger (1994)")
usethis::use_data(njmin, overwrite = TRUE)
eitc <- read_table2("http://www.montana.edu/cstoddard/562/oldkeys/eitc.out")
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