A Visual Introduction to Hierarchical Models, by Michael Freeman

Hierarchical models I have covered before on this blog. These models are super relevant in practice. For instance, in HR, employee data is always nested within teams which are in turn nested within organizational units. Also in my current field of insurances, claims are always nested within policies, which can in turn be nested within…

Online Workshop Tidy Data Science in R, by Jake Thompson

Here’s a website hosting for a five-day hands-on workshop based on the book “R for Data Science”. The workshop was originally offered as part of the Stats Camp: Summer Statistical Institute in Lawrence, KS and hosted by the Center for Research Methods and Data Analysis and the Achievement and Assessment Instituteat the University of Kansas. It is designed for those who…

Comprehensive Introduction to Command Line for R Users

Too little time, too many things of interest. Here’s a resource that’s still on my to-do list: A Comprehensive Introduction to Command Line for R Users by rsquaredacademy.com In this tutorial, you will be introduced to the command line. We have selected a set of commands we think will be useful in general to a…

How Do I…? R Code Snippets by Sharon Machlis

Sharon Machlis is the author of Practical R for Mass Communication and Journalism. In writing this book, she obviously wrote a lot of R code. Now, Sharon has been nice enough to share all 195 tricks and tips she came across during her writing with us, via this handy table. Sharon’s list contains many neat…

The Causal Inference Book: DAGS and more

Harvard (bio)statisticians Miguel Hernan and Jamie Robins just released their new book, online and accessible for free! The Causal Inference book provides a cohesive presentation of causal inference, its concepts and its methods. The book is divided in 3 parts of increasing difficulty: causal inference without models, causal inference with models, and causal inference from…

Getting started with Python in Visual Studio Code

After several years of proscrastinating, the inevitable finally happened: Three months ago, I committed to learning Python! I must say that getting started was not easy. One afternoon three months ago, I sat down, motivated to get started. Obviously, the first step was to download and install Python as well as something to write actual…

Overview of built-in colors in R

Most of my data visualizations I create using R programming — as you might have noticed from the content of my website. Though I am colorblind myself, I love to work with colors and color palettes in my visualizations. And I’ve come across quite some neat tricks in my time. For instance, did you it’s…

Learn Programming Project-Based: Build-Your-Own-X

Last week, this interesting reddit thread was filled with overviews for cool projects that may help you learn a programming language. The top entries are: Build Your Own X, by Dani Stefanovic Project-based Learning, by Tu Tran Projects from Scratch, by Algory L. Project-based Tutorials in C, by Robby Awesome DIY Software, by Cameron Eagans…

Tidy Machine Learning with R’s purrr and tidyr

Jared Wilber posted this great walkthrough where he codes a simple R data pipeline using purrr and tidyr to train a large variety of models and methods on the same base data, all in a non-repetitive, reproducible, clean, and thus tidy fashion. Really impressive workflow!