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https://github.com/elipousson/getacs

A R package to work with ACS data from {tidycensus}
https://github.com/elipousson/getacs

american-community-survey r-package tidycensus

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A R package to work with ACS data from {tidycensus}

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README 

        
---

output: github_document

---



```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

```



# getACS



[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

[![Project Status: WIP -- Initial development is in progress, but there has not yet been a stable, usable release suitable for the public.](https://www.repostatus.org/badges/latest/wip.svg)](https://www.repostatus.org/#wip)



The goal of getACS is to make it easier to work with [American Community Survey data](https://www.census.gov/programs-surveys/acs) from the [tidycensus package](https://walker-data.com/tidycensus/) 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](https://gt.rstudio.com/)



As of April 2024, this package uses a development version of `{tigris}`, available at .



## Installation



You can install the development version of getACS from [GitHub](https://github.com/) with:



``` r

# install.packages("pak")

pak::pkg_install("elipousson/getACS")

```



## Usage



```{r setup}

library(getACS)

library(gt)

library(ggplot2)

```



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



```{r}

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:



```{r}

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:



```{r}

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:



```{r}

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()`:



```{r}

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*](https://walker-data.com/census-r) (February 2023).



## Related projects



### Related R packages and analysis projects



-   [{easycensus}](https://github.com/CoryMcCartan/easycensus): Quickly Extract and Marginalize U.S. Census Tables

-   [{cwi}](https://ct-data-haven.github.io/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}](https://camille-s.github.io/camiller/): A set of convenience functions, functions for working with ACS data via `{tidycensus}`

-   [{psrccensus}](https://psrc.github.io/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}](https://github.com/Westat-Transportation/CTPPr): A R package for loading and working with the US Census CTPP survey data.

-   [{lehdr}](https://github.com/jamgreen/lehdr): a package to grab LEHD data in support of city and regional planning economic and transportation analysis

-   [{mapreliability}](https://github.com/austensen/mapreliability): A R package for map classification reliability calculator

-   [Studying Neighborhoods With Uncertain Census Data](https://github.com/geoss/acs_demographic_clusters): Code to create and visualize demographic clusters for the US with data from the American Community Survey



### Related Python libraries



- [census-data-aggregator](https://github.com/datadesk/census-data-aggregator): A Python library from the L.A. Times data desk to help "combine U.S. census data responsibly"

- [census-table-metadata](https://github.com/censusreporter/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()`)