This [COVID-19 Tracker Shiny app]() allows easy comparison of cumulative outcomes and growth rates of the COVID-19 coronavius between countries using the data provided by the Johns Hopkins University Coronavirus Resource Center.
Users can select any of the country/region units available in the entire JHU GSSE dataset, standardize them on the x-axis using “Days since N”, and automatically generate clean level- and log- plots with dynamically rendered titles and axis labels.
Currently, a maximum of six countries can be compared at a time to allow for better readability of the resulting plots. Users can select between total confirmed cases, deaths, and total recovered as the different y-axis outcome variables.
The data in the app is time-stamped and updated automatically along with the JSU CSSE repo, and there are download buttons for the plots and filtered long-format data tables use for those plots in PNG and CSV formats, respectively.
A separate tab in the app allows for the generation of bar charts of daily case and death totals for the same selection of countries, including a 5-day moving average trend line.
UPDATE (5 Oct. 2021): I created this app so that I could quickly visualize and compare data for the countries that I was personally most interested in during the early days of the pandemic, before the subsequent explosion of dashboards and apps appeared online relating to all things COVID-19. I haven’t had time to work on this in over a year, and others have created far better versions, so I’ve decided to archive the project. For reference, the code for this app remains available on my Github.
Stata to R aims to summarize commonly used Stata commands used in econometric analysis along with their equivalent expressions in R. This cheat sheet is aimed at early Economics students with some basic familiarity of the R software environment.
Feedback or comments are most welcome! Feel free to send me an email or leave your comments directly on the Stata to R post.