This page was built using Wowchemy/Hugo. I created a Git repository and started with the Hugo ‘Academic Resume’ template. The template is very dynamic and a bit overwhelming. So I simplified its structure and changed the layout (colors, fonts, text size etc.). All the content is now written in simple Markdown files that I edit in Atom. Feel free to fork my GitHub repository if you want to build a similar and simple page.
My CV is written in RMardown too. I started with Steve Miller’s Academic CV template. It already comes with a Latex preamble that I adapted. The Big advantage: it directly imports edits from this homepage and vice versa.
I am a huge fan of R, RStudio, Markdown, Atom and other Open Source Software. There are now several excellent open online textbooks on R Programming, (R)Markdown and, Econometrics/Statistics in R etc. Below is a list of my favorites, and some resources others have compiled or created. I use many of them in my research, for teaching and beyond.
R for Data Science, the best introduction!
R Programming, the advanced version by Hadley Wickham
The Awesome Markdown collection of Markdown resources
Basic Syntax and Most Common Errors + Best Practices
R Bookdown by Yihui Xie (the author of the corresponding package), also providing a very nice Hugo template Hugo-XMin
Data Visualization
Data Visualization - A practical introduction, by Kieran Healy
Fundamentals of Data Visualization, by Claus O. Wilke
Glamour of Graphics and the corresponding Grammer of Graphics
A Visual Guide to Ggplot2 is great for learning ggplot2
Colors in R: The Ultimative Color Palettes Collection
Spatial Data in R
The one and only SF - Simple Features Package and the related textbook Geocomputation with R
Access US Census Bureau data via the tidycensus package, by Kyle Walker, or directly load data as SF object using tigris
Econometrics in R
Econometrics with R, by Christoph Hanck, Martin Arnold, Alexander Gerber, and Martin Schmelzer, a great introduction!
Applied Causal Analysis, by Paul C. Bauer
Hands-On Machine Learning with R, by Bradley Boehmke and Brandon Greenwell
On Methods more generally
Causal Inference - The Mixtape has some R resources too, and an additional Mixtape Substack. Other similar and great textbooks are the Textbook on Causal Inference by Martin Huber The Effect
Keeping track of the ‘Diff-in-Diff’ literature: Asjad Naqvi has an amazing compilation of resources for you!