In a recent post, Claus shared the link to a GitHub repository where he hosts some of the R programming code with which Claus made the graphics for his dataviz book. The repository is named practical ggplot2, after the R package Clause used to make many of his visuals.
Check it out, the page contains some pearls and the code behind them, which will help you learn to create fabulous visualizations yourself. Some examples:
Here’s the original tweet in case you want to see the responses.
Norm Matloff is a professor of Computer Science at University College Davis. He recently updated his viewpoint on whether R or Python is the best language for Data Science. While I normally hate those opinionated comparisons, Norm’s outline of the two languages’ (dis)advantages is actually quite balanced and well-versed.
I can mostly agree with Norm, although the blog reads as if he has a (slight) bias in favor of R. In his original blog, Norm discusses many different programming topics and provides detailed information on why he considers certain topics big wins, slight edges, or ties between the two programming languages.
In the table below, I’ve tried to summarize Norm’s opinions by converting his words to 0-100 scores per topic for a quicker overview. I’ve converted Norm’s words to scores: his huge win became 100-0, a big win 80-20, a win 70-30, an edge 60-40, and a tie 50-50.
Data Science libraries
Object orientation, metaprogramming
Linked data structures
I personally started my career with R, so that’s definitely my favorite programming language. However, I think that Python is more convenient and faster on certain topics, and closer to more mainstream programming languages, which I why I’m currently learning it next to using R.