This week's data is from the ACS Survey. The article and underlying data can be found at the Census Website. The PDF report is also available for download if you'd like to try reading in some of the embedded tables.
Please note that the raw excel files are uploaded (6 total), along with the cleaned/tidy data (commute.csv). There is also a cleaned up version of Table 3 from the article, which incorporates summary data around age, gender, race, children, income, and education for modes of travel (bike, walk, other). If you work with the ACS table 3 I'd suggest dplyr::slice() to grab the specific sub-tables from within it!
commute_mode <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-11-05/commute.csv")
| variable | class | description |
|---|---|---|
| city | character | City |
| state | character | State |
| city_size | character | City Size * Small = 20K to 99,999 * Medium = 100K to 199,999 * Large = >= 200K |
| mode | character | Mode of transport, either walk or bike |
| n | double | N of individuals |
| percent | double | Percent of total individuals |
| moe | double | Margin of Error (percent) |
| state_abb | character | Abbreviated state name |
| state_region | character | ACS State region |
# Load Packages -----------------------------------------------------------
library(tidyverse)
library(readxl)
library(here)
library(glue)
library(janitor)
# Read in Data ------------------------------------------------------------
table_num <- 1:6
# Generic read function for this dataset
supp_read <- function(number, ...){
read_excel(here("2019", "2019-11-05", glue::glue("supplemental-table{number}.xlsx")), ...)
}
# 3 datasets for bikes, each of which has a corresponding City Size
small_bike <- supp_read(1, skip = 5) %>%
clean_names() %>%
mutate(city_size = "Small",
percentage_of_workers = as.numeric(percentage_of_workers),
margin_of_error_2 = as.numeric(margin_of_error_2))
medium_bike <- supp_read(2, skip = 5) %>%
clean_names() %>%
mutate(city_size = "Medium")
large_bike <- supp_read(3, skip = 5) %>%
clean_names() %>%
mutate(city_size = "Large")
# Combine datasets
full_bike <- bind_rows(small_bike, medium_bike, large_bike) %>%
set_names(nm = c("city", "n", "percent", "moe", "city_size")) %>%
mutate(mode = "Bike")
# 3 datasets for walking, each of which has a corresponding City Size
small_walk <- supp_read(4, skip = 5) %>%
clean_names() %>%
mutate(city_size = "Small")
medium_walk <- supp_read(5, skip = 5) %>%
clean_names() %>%
mutate(city_size = "Medium")
large_walk <- supp_read(6, skip = 5) %>%
clean_names() %>%
mutate(city_size = "Large")
# Combine datasets
full_walk <- bind_rows(small_walk, medium_walk, large_walk) %>%
set_names(nm = c("city", "n", "percent", "moe", "city_size")) %>%
mutate(mode = "Walk")
# Built in state-level datasets
state_df <- tibble(
state = state.name,
state_abb = state.abb,
state_region = as.character(state.region)
)
# Combine bike and walk data in tidy setup
full_commute <-
bind_rows(full_bike, full_walk) %>%
filter(!is.na(n),
# There are some government-related areas that don't align with cities
!str_detect(tolower(city), "government|goverment")) %>%
separate(city, into = c("city", "state"), sep = ", ") %>%
select(city, state, city_size, mode, everything()) %>%
left_join(state_df, by = c("state"))
full_commute %>%
write_csv(here("2019", "2019-11-05", "commute.csv"))
# ACS Data ----------------------------------------------------------------
acs_data <- read_csv(here("2019", "2019-11-05", "table_3.csv"))
age_data <- acs_data %>%
slice(1:6)
gender_data <- acs_data %>%
slice(9:10) %>%
rename("gender" = age)
race_data <- acs_data %>%
slice(13:18) %>%
rename("race" = age)
children_data <- acs_data %>%
slice(21:24) %>%
rename("children" = age)
income_data <- acs_data %>%
slice(27:36) %>%
rename("income" = age)
education_data <- acs_data %>%
slice(39:43) %>%
rename("education" = age)