This repository contains the code used by the UCLA Law COVID-19 Behind Bars Team to create data visualization for social media and other ad-hoc requests.
Note: Only code should be stored here – not the image files themselves.
Our latest data on COVID-19 in carceral facilities is maintained in our data repository, along with a detailed data dictionary.
We updated our scraper ETL pipelines in November 2020. As a result, all data post-November is readily available – but data pre-November is only available for certain states at this time.
We recommend accessing our post-November time series data through an R package that we are developing called behindbarstools:
devtools::install_github("uclalawcovid19behindbars/behindbarstools")
data <- behindbarstools::read_scrape_data(all_dates = TRUE, coalesce = TRUE)
To access out post-November time series data in Python:
import pandas as pd
data = pd.read_csv("http://104.131.72.50:3838/scraper_data/summary_data/scraped_time_series.csv")
Our historical data (pre-November 2020) is available for several states in our historical-data repository. We are in the process of cleaning this data and will be adding additional states as this data becomes available.
Our R package behindbarstools makes it easy to adhere to our visualization style guide through our custom ggplot theme called theme_behindbars and custom color scales (scale_color_bbdiscrete, scale_fill_bbdiscrete, scale_color_bbcontinous, and scale_fill_bbcontinous). These can be added to an existing ggplot object:
datasets::iris %>%
ggplot(aes(x = Petal.Width, y = Petal.Length, color = Species)) +
geom_point() +
theme_behindbars() +
scale_color_bbdiscrete()
Our documentation in behindbarstools provides additional visualization examples.