behindbarstools

Overview

behindbarstools is an R package with the set of data tools used by the UCLA Law COVID-19 Behind Bars Project – a data project that collects and reports facility-level data on COVID-19 in jails, prisons, and other carceral facilities. behindbarstools includes a variety of functions to help pull, clean, wrangle, and visualize our data.

Warning: This package is actively under development.

Installation

# Install directly from GitHub 
devtools::install_github("uclalawcovid19behindbars/behindbarstools")

Usage Examples

Reading Data

The read_scrape_data() function can be used to load our data.

behindbarstools also includes functions to more easily load related data from other organizations including the Vera Institute’s Jail Population Data through read_vera_pop() and the Department of Homeland Security’s Homeland Infrastructure Foundation-Level Data through read_hifld_data().

library(behindbarstools)

# Pull latest data
latest_scraped <- read_scrape_data()

# Pull historical scraped data for California 
scraped_CA <- read_scrape_data(all_dates = TRUE, 
                               state = "California")

Processing Data

The majority of the functions in behindbarstools help standardize our ETL and data cleaning process. This includes functions to help with the following:

• Cleaning facility names, e.g.clean_fac_col_txt(), clean_facility_name()
• Coalescing data from various sources, e.g. coalesce_with_warnings(), group_by_coalesce()
• Enforcing data validation, e.g. is_valid_state(), is_federal()
• Standardizing our data scraping infrastructure, e.g. ExtractTable(), get_src_by_attr()

See our package documentation for more information and examples for each function.

Visualizing Data

behindbarstools also includes functions to create data visualizations. This includes a custom ggplot2 theme called theme_behindbars() that incorporates our team’s style guide. All plotting functions return ggplot objects, making it easy to customize and add additional layers.

# Plot cumulative COVID-19 cases in the Los Angeles Jails over the past 30 days  
plot_fac_trend(fac_name = "Los Angeles Jails", 
               state = "California", 
               metric = "Residents.Confirmed", 
               plot_days = 30, 
               auto_label = TRUE) + 
    theme_behindbars(base_size = 14) + 
    ggplot2::ylim(3500, 4000) + 
    ggplot2::theme(legend.position = "none")

# Plot the 3 facilities with the largest recent spikes in active COVID-19 cases  
plot_recent_fac_increases(metric = "Residents.Active",
                          plot_days = 60, 
                          num_fac = 3, 
                          auto_label = TRUE) + 
    theme_behindbars(base_size = 14) + 
    ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, hjust = 1), 
                   plot.tag.position = c(0.80, 0.05))