COVID-19 Tracker
COVID-19 Tracker Shiny app for easy country comparisons

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. The data in the app is timestamped 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.

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.

Additional features and edits will be added on an ongoing basis.

Code for the app available on Github.

Feedback, comments and pull requests are welcome. Feel free to send me an email or leave your comments directly on the COVID-19 tracker post.

Stata to R
Stata to R Survival cheat sheet for econometrics in R

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.

Economics and Machine Learning
Economics and Machine Learning Resources for students

This page contains a selection of resources covering the intersection between the fields of Economics and Machine Learning. By no means comprehensive, this collection of readings, videos, online courses, syllabi and miscellaneous links reflect my own interests in the application of machine learning approaches to social problems traditionally of interest to economists, and is aimed at early Economics students interested in learning more about machine learning and how to integrate machine learning approaches to the field of applied economics.