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https://github.com/corymccartan/easycensus
Quickly Extract and Marginalize U.S. Census Tables
https://github.com/corymccartan/easycensus
acs-data census census-api r
Last synced: 2 months ago
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Quickly Extract and Marginalize U.S. Census Tables
- Host: GitHub
- URL: https://github.com/corymccartan/easycensus
- Owner: CoryMcCartan
- License: other
- Created: 2022-01-20T20:06:47.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-03-20T20:44:44.000Z (almost 2 years ago)
- Last Synced: 2024-10-12T12:15:40.346Z (3 months ago)
- Topics: acs-data, census, census-api, r
- Language: R
- Homepage: http://corymccartan.com/easycensus/
- Size: 5.91 MB
- Stars: 15
- Watchers: 4
- Forks: 1
- Open Issues: 3
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
Awesome Lists containing this project
README
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
set.seed(5118)
```
# **easycensus** 
[](https://CRAN.R-project.org/package=easycensus)
[](https://opensource.org/license/mit/)
[](https://github.com/CoryMcCartan/easycensus/actions/workflows/R-CMD-check.yaml)
[](https://lifecycle.r-lib.org/articles/stages.html#stable)
Extracting desired data using the proper Census variable names can be time-consuming.
This package takes the pain out of that process.
The use case is best illustrated by an example.
Suppose you want age-by-race information at the tract level.
Unfortunately, the Census Bureau doesn't publish a specific age-by-race table.
You could build one yourself from public-use microdata, but that lacks tract-level geographic information, for privacy reasons.
So you are left trying to find an existing Census product that you can extract age-by-race information from.
Unless you're a Census pro, you won't know what exactly what is off the top of your head.
But suppose you know you'd like to get the data from the decennial census, since it covers the whole nation and asks about age and race.
`easycensus` provides the `cens_find_dec()` function to help you locate exactly which decennial census table to use to get the data you want.
```{r}
library(easycensus)
cens_find_dec(age, race)
```
We can see right away that our best bet is either table `P12` or table `PCT12`, depending on whether we want age in 5-year groups or down to individual years.
Let's say you're OK with the five-year bins.
Then all you need to do to get your data is to call `cens_get_dec()`.
```{r cache=TRUE}
d_cens = cens_get_dec("P12", "tract", state="AK", county="Nome")
print(d_cens)
```
Once you've gotten your labeled data, it's easy to marginalize out the unneeded `sex` variable.
You can either use `group_by()` and `summarize()` as usual, or you can use the `cens_margin_to()` function in `easycensus`.
This has the added advantage of automatically handling margins of error for ACS data.
```{r message=F}
library(dplyr)
d_cens = d_cens %>%
# Drop table margins. Can also use `drop_total=TRUE` in `get_dec_table()`
filter(age != "total", race_ethnicity != "total") %>%
cens_margin_to(age, race=race_ethnicity)
print(d_cens)
```
Finally, you might want to simplify the age and race labels, since they are kind of verbose.
`easycensus` provides a set of `tidy_*()` functions to assist with this.
```{r}
d_cens %>%
mutate(race = tidy_race(race),
tidy_age_bins(age))
```
Dive into the [reference](https://corymccartan.com/easycensus/reference/) to learn more!
## Installation
You can install the released version of easycensus from [CRAN](https://CRAN.R-project.org) with:
``` r
install.packages("easycensus")
```
Or install the development version from [GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("CoryMcCartan/easycensus")
```