The goal of getACS is to make it easier to work with American Community Survey data from the tidycensus package by Kyle Walker and others.

This package includes:

• Functions that extend tidycensus::get_acs() to support multiple tables, geographies, or years
• Functions for creating formatted tables from ACS data using the gt package

As of April 2024, this package uses a development version of {tigris}, available at https://github.com/elipousson/tigris.

You can install the development version of getACS from GitHub with:

# install.packages("pak")
pak::pkg_install("elipousson/getACS")

library(getACS)
library(gt)
library(ggplot2)

The main feature of {getACS} is support for returning multiple tables, geographies, and years.

acs_data <- get_acs_geographies(
  geography = c("county", "state"),
  county = "Baltimore city",
  state = "MD",
  table = "B08134",
  year = 2022,
  quiet = TRUE
)

The package also includes utility functions for filtering data and selecting columns to support the creation of tables using the {gt} package:

tbl_data <- filter_acs(acs_data, indent == 1, line_number <= 10)
tbl_data <- select_acs(tbl_data)

commute_tbl <- gt_acs(
  tbl_data,
  groupname_col = "NAME",
  column_title_label = "Commute time",
  table = "B08134"
)

as_raw_html(commute_tbl)

The gt_acs_compare() function also allows side-by-side comparison of geographies:

commute_tbl_compare <- gt_acs_compare(
  data = tbl_data,
  id_cols = "column_title",
  column_title_label = "Commute time",
  table = "B08134"
)

as_raw_html(commute_tbl_compare)

gt_acs_compare_vars() is a variant on gt_acs_compare() where the default values support comparisons with values in columns and geographical areas in rows:

commute_tbl_compare_vars <- acs_data |>
  filter_acs(indent == 1, line_number > 10) |>
  gt_acs_compare_vars(
    table = acs_data$table_id
  )

as_raw_html(commute_tbl_compare_vars)

The package also includes several functions to support creating plots with the {ggplot2} package including geom_acs_col() and labs_acs_survey():

plot_data <- acs_data |>
  filter_acs(indent == 1, line_number > 10) |>
  select_acs() |>
  fmt_acs_county(state = "Maryland")

plot_data |>
  ggplot() +
  geom_acs_col(
    fill = "NAME",
    position = "dodge",
    color = NA,
    alpha = 0.75,
    perc = TRUE,
    errorbar_params = list(position = "dodge", linewidth = 0.25)
  ) +
  scale_y_discrete("Means of transportation to work") +
  scale_fill_viridis_d("Geography") +
  labs_acs_survey(
    .data = acs_data
  ) +
  theme_minimal()

The geom_acs_col() function calls geom_acs_errorbar() (passing the errorbar_params argument as additional parameters) and scale_x_acs() or scale_y_acs() (depending on whether orientation = "y" or the default value of NA).

For more information on working with Census data in R read the book Analyzing US Census Data: Methods, Maps, and Models in R (February 2023).

• {easycensus}: Quickly Extract and Marginalize U.S. Census Tables
• {cwi}: Functions to speed up and standardize Census ACS data analysis for multiple staff people at DataHaven, preview trends and patterns, and get data in more layperson-friendly
• {camiller}: A set of convenience functions, functions for working with ACS data via {tidycensus}
• {psrccensus}: A set of tools developed for PSRC (Puget Sound Regional Council) staff to pull, process, and visualize Census Data for geographies in the Central Puget Sound Region.
• {CTPPr}: A R package for loading and working with the US Census CTPP survey data.
• {lehdr}: a package to grab LEHD data in support of city and regional planning economic and transportation analysis
• {mapreliability}: A R package for map classification reliability calculator
• Studying Neighborhoods With Uncertain Census Data: Code to create and visualize demographic clusters for the US with data from the American Community Survey

• census-data-aggregator: A Python library from the L.A. Times data desk to help “combine U.S. census data responsibly”
• census-table-metadata: Tools for generating metadata about tables and fields in a Census release based on sequence lookup and table shell files. (Note: the pre-computed data from this repository is used to label ACS data by label_acs_metadata())