Finding the best Pokémon type combinations for a maximally effective team.
In this post, I try something new and share an analysis I did without stopping to explain the code along the way (with a few exceptions). I analyze a dataset on Bob Ross paintings from last week’s Tidytuesday, an initiative by the R for Data Science online learning community. Each Monday, a new dataset is posted on GitHub with a short description. You can see some analyses and visualizations people have done by searching for the #tidytuesday hashtag on Twitter.
Have you ever had a “first this then that” question? For example, maybe you’re an e-commerce business and you want all the times people clicked on an item and then added it to their cart within 2 days, or the last page they visited before registering. Or you work with pharmaceutical data and need to know what drugs people took before drug x and which drugs they took afterward and when.
I recently completed Colin Fay’s excellent DataCamp course, Intermediate Functional Programming with purrr (full disclosure: I work at DataCamp, but part of why I joined was that I was a big fan of the short, interactive course format). Although I’ve used the purrr package before, there were a lot of functions in this course that were new to me. I wrote this post to hopefully demystify purrr a bit for those who find it overwhelming and illustrate some of its lesser known functions.
In early 2018, I gave a few conference talks on “The Lesser Known Stars of the Tidyverse.” I focused on some packages and functions that aren’t as well known as the core parts of ggplot2 and dplyr but are very helpful in exploratory analysis. I walked through an example analysis of Kaggle’s 2017 State of Data Science and Machine Learning Survey to show how I would use these functions in an exploratory analysis.