Table of Contents
Special Acknowledgements
Often, it’s said that everything’s bigger in Texas. In difficult times like this, one of the more inspiring ways we live up to this mantra is through an incredible sense of community displayed among the many state agencies, nonprofits, businesses, institutions, and organizations who interface within the Lone Star State. As such, Texas 2036 would be remiss if we didn’t acknowledge the outstanding help and generosity of those who have extended their time, consultation, expertise, and data to inform the resources organized here. That said, we would like to say a huge thank you to the following key folks:
The University of Texas at Austin - Dell Medical School
Throughout the development process, Dell Medical School has been a key provider of feedback in our effort to provide the most relevant and meaningful metrics organized here. As much of the research and what we know about COVID‐19 unfolds weekly (sometimes daily), Dell Medical School has been a trusted thought‐collaborator. As we continue to refine and update this product, it goes without saying that we could not have done this without their help.
The Texas Department of State Health Services (DSHS)
One of the most incredible resources we have encountered while developing this project are the countless professionals at the Texas Department of State Health Services. They have not only made data related to COVID-19 readily available, but have also provided much clarification regarding a number of metrics and methods used in widely reported figures that informed the development of our own data dashboard.
Homebase
Homebase is a free scheduling and time tracking tool used by 100,000+ local businesses and their hourly employees. Homebase’s customers in the US primarily consist of restaurant, food & beverage, retail and services and are largely individual owned/operator managed businesses. Their COVID-19 dataset is based on Homebase data covering 60,000 businesses across the US (over 5,300 in Texas) and 1 million hourly employees active in US metropolitan areas in January 2020.
Convex Design
Convex Design is a small data-driven digital studio with expertise on everything from bar charts to neural nets. Their founders have worked with some of the most cutting-edge organizations out there. This includes helping scientists at NASA/JPL, neuroscientists at HHMI Janelia, journalists at CNN, FiveThirtyEight, and the New Yorker. As well as enterprise teams at Fortune 500 companies like AIG.
Open Source Credits
Data
The COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University
This is the data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). Also, Supported by ESRI Living Atlas Team and the Johns Hopkins University Applied Physics Lab (JHU APL).
Google COVID-19 Community Mobility Reports by Google LLC.
Google’s COVID-19 Community Mobility Reports aim to provide insights into what has changed in response to policies aimed at combating COVID-19. The reports chart movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.
About the basline value of 0: These datasets show how visits and length of stay at different places change compared to a baseline. We calculate these changes using the same kind of aggregated and anonymized data used to show popular times for places in Google Maps.
Changes for each day are compared to a baseline value for that day of the week:
- The baseline is the median value, for the corresponding day of the week, during the 5-week period Jan 3–Feb 6, 2020.
- The datasets show trends over several months with the most recent data representing approximately 2-3 days ago—this is how long it takes to produce the datasets.
The COVID Tracking Project from the Atlantic
The COVID Tracking Project is a volunteer organization launched from The Atlantic and dedicated to collecting and publishing the data required to understand the COVID-19 outbreak in the United States. Since early March, 2020, we have grown from a tiny team with a spreadsheet to a project with hundreds of volunteer data-gatherers, developers, scientists, reporters, designers, editors, and other dedicated contributors.
The New York Times
The New York Times has released a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.
Software
distill by RStudio
distill for R Markdown is based on the Distill web framework, which was originally created for use in the Distill Machine Learning Journal1. Distill for R Markdown combines the technical authoring features of Distill with R Markdown, enabling a fully reproducible workflow based on literate programming 2.
fredr by Sam Boysel
fredr provides a complete set of R bindings to the Federal Reserve Economic Data (FRED) RESTful API, provided by the Federal Reserve Bank of St. Louis. The functions allow the user to search for and fetch time series observations as well as associated metadata within the FRED database.
gt by Richard Iannone, Joe Cheng, and Barret Schloerke
gt provides tools to easily generate information-rich, publication-quality tables from R
highcharter by Joshua Kunst
highcharter is a R wrapper for Highcharts javascript library and its modules. Highcharts is very mature and flexible javascript charting library and it has a great and powerful API. See here for examples.. Texas 2036 holds a Not-for-Profit license of Highcharts and has permission to utilize the following product(s) through that license: Highcharts, Highcharts Maps, and Highcharts Gantt.
idyll by Matt Conlen and Jeffrey Heer
Idyll is a markup language and toolkit for writing interactive articles. Idyll’s reactive document model and standard component library decrease the amount of code needed to create high quality multimedia narratives. Idyll uses web standards to produce output that will load quickly in any web browser and is fully extensible.
janitor by Sam Firke
janitor has simple functions for examining and cleaning dirty data. It was built with beginning and intermediate R users in mind and is optimized for user-friendliness. Advanced R users can already do everything covered here, but with janitor they can do it faster and save their thinking for the fun stuff.
leaflet by RStudio & leaflet.extras by Bhaskar V. Karambelkar
leaflet is an R package that makes it easy to integrate and control Leaflet maps in R. Leaflet is one of the most popular open-source JavaScript libraries for interactive maps. It’s used by websites ranging from The New York Times and The Washington Post to GitHub and Flickr, as well as GIS specialists like OpenStreetMap, Mapbox, and CartoDB. The goal of leaflet.extras package is to provide extra functionality to the leaflet R package using various leaflet plugins.
RStudio by RStudio
RStudio is an integrated development environment (IDE) for R. It includes a console, syntax-highlighting editor that supports direct code execution, as well as tools for plotting, history, debugging and workspace management.
sf by Edzer Pebesma
The sf package is a comprehensive resource for analyzing, manipulating, and visualizing spatial data files within R. It works seamlessly within the tidyverse syntax and, thus, is compatible with a number of different tidyverse-friendly tools.
shiny & shinydashboard by RStudio
shiny is an R package that makes it easy to build interactive web apps straight from R. You can host standalone apps on a webpage or embed them in R Markdown documents or build dashboards. You can also extend your Shiny apps with CSS themes, htmlwidgets, and JavaScript actions. shinydashboard makes it easy to use Shiny to create dashboards.
shinyLP by Jasmine Dumas
The shinyLP package provides functions that wrap HTML Bootstrap code to enable the design and layout of informative landing home pages for Shiny applications.
tidycensus & tigris by Kyle Walker
tidycensus is an R package that allows users to interface with the US Census Bureau’s decennial Census and five-year American Community APIs and return tidyverse-ready data frames, optionally with simple feature geometry included. tigris is an R package that allows users to directly download and use TIGER/Line shapefiles from the US Census Bureau.
tidyverse by Hadley Wickham
The tidyverse is a set of packages that work in harmony because they share common data representations and API design. The tidyverse package is designed to make it easy to install and load core packages from the tidyverse in a single command. If you’d like to learn how to use the tidyverse effectively, the best place to start is R for data science.
waiter & sever by John Coene
waiter is an R package that allows users to programmatically show and hide partial or full page loading screens with spinners or loading bars to keep your users patiently waiting as you load or compute fancy things. sever is an R package that allows users to customise Shiny disconnected screens and error messages.
zoo by Achim Zeileis, Gabor Grothendieck, Jeffrey A. Ryan, Joshua M. Ulrich, and Felix Andrews
zoo is an S3 class with methods for totally ordered indexed observations. It is particularly aimed at irregular time series of numeric vectors/matrices and factors. zoo’s key design goals are independence of a particular index/date/time class and consistency with ts and base R by providing methods to extend standard generics.
Production Credits
This project was produced, designed, and programmed by Matt Worthington. Matt Conlen and Jonathan Dinu designed and programmed the analysis based on Federal Gating Criteria. Substantial feedback and suggestions were provided by Tom Luce, Margaret Spellings, and John Hryhorchuk. Additional contributions were made by Arielle Dortch.
Project coordination led by John Hryhorchuk.
Research by Matt Worthington and John Hryhorchuk. Special thanks to Lisa Kirsch, who provided a number of incredible research connections to individuals around the state of Texas who contributed meaningful insights and expertise to various aspects of this project.
Throughout this process, a number of people submitted voluntary, yet meaningful suggestions related to various components and elements seen in this product. We thank them extensively for their willingness to share and contribute.
Maps and interactive graphics were designed and programmed by Matt Worthington.
Logos for The University of Texas at Austin - Dell Medical School, Homebase, and Convex Design are properties of each entity, and used solely by Texas 2036 to show appreciation for their contributions.
Terms of Use
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Distill — Latest articles about machine learning. (n.d.). Retrieved May 4, 2020, from http://distill.pub/↩
Knuth, D. E. (1984). Literate Programming. The Computer Journal, 27(2), 97–111.↩