Some folks at work expressed an interest in how to get started learning R. There are lots of resources out there, but I thought I'd share with you what I shared with them as a pathway that I followed that's working out well for me.
You don’t need all these steps but I suggested starting from 1 and working your way through them that way.
- Read R for Data Science (https://r4ds.had.co.nz/). I only made it to Chapter 16 and could already do a lot of the stuff I wanted to do. This book takes a really nice approach of FIRST getting you some results and then working through some programming-specific stuff, so you won’t just be working through a lot of programming concepts without knowing what you are working towards.
- There are exercises (which I largely skipped), but if you do them and want to check your answers, you can read the companion book here: https://jrnold.github.io/r4ds-exercise-solutions/
- If you get stuck, there is a super helpful Slack channel where you can post questions according to the chapter you are on and people will help you. Once you get the hang of manipulating data, I encourage you to practice with some datasets from the Tidy Tuesday challenge. This is a weekly challenge where people create visualisations of the same dataset. It is GREAT for learning how others do things because they often share their code and you can learn a lot. You don’t have to wait for a new dataset, go ahead and look for old datasets that interest you!
- You can view past TidyTuesday submissions and their code here: https://nsgrantham.shinyapps.io/tidytuesdayrocks/ 1.If you want to participate in posting your own visualisations, you’ll need to join Twitter. Here’s a free guide on how to get started on Twitter for R programmers: https://www.t4rstats.com/
- If you like podcasts, there is also a TidyTuesday podcast with short episodes that cover the previous week’s submissions and helpful tips.
I hope that helps you on your journey. Exciting times ahead :).
Image credit: Photo by oxana v on Unsplash
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