For the past few months, I’ve been getting more and more messages from people asking me a very simple question: ‘what’s your advice for getting started with R?’. I know the answer is not straightforward: what you want to do and what you’ve done before are likely to determine your starting point. In addition, what works for me may not work for others and maybe a book that is like a bible for me will not appeal you at all. However, after some thought and resource-wrangling, I’ve decided to put together in this blogpost a list of resources that I personally find or have found useful for different stages of R-related work. This is basically a ‘tidy version’ of my R bookmark folder and does not aim to be comprehensive – you can also find a lot of other lists online, but hopefully it can be useful for some of you. Let’s get started!
Getting started with R and data analysis 👩🏼💻
This is probably what most people writing to me are interested in – and it’s by large the most complicated to answer. The main reason I find for this is that there are zillion of amazing tutorials online but let’s face it – if you don’t apply what you learn in these tutorials to some real life task, it’s rather unlikely that you’ll retain anything of what you learned while filtering mtcars rows (duh). Having said this, I first learned to code with R using two main resources:
- R for Data Science, by Hadley Wickham and Garrett Grolemund. My now ex-boyfriend gifted this book to me around three years ago, and it completely changed the way I thought about data analysis and coding as this very-complicated-thing-very-smart-people-do. It’s a great book, it goes step by step, it has good, hands-on exercises (you can easily find solutions online) and it will give you a very comprehensive overview on how to do basically whatever with your data. I still use mine a lot (fun fact, whenever I open it, it automatically opens at the page where the gather and spread figures are – long live to those). Also, the online version is free 😊
- Datacamp.com Definitely the site where I have learned more R stuff (according to my dashboard I’ve finished 46 courses there lol). They combine videos and small-task-based exercises, which is a great way to learn if you like instant reward. You’ll find plenty of online courses on basically everything you may want to do in R, but maybe a good starting point is to look at their ‘Data Scientist with R’ As a disclaimer, it’s a paying site, and you may want to google them before to see if you feel comfortable with the issues they have had, but I feel it’d be very dishonest from my side to provide a list of resources for learning R without including the resource that I think is the best one.
This being said, I cannot stress enough how important it is that you take what you have learned and go play with real data. You can use your own, or check the #TidyTuesday project for new datasets every week (also the best community on Twitter).
A couple more resources that I haven’t used myself but think can be useful:
- Tidyverse seminars by Jorge Cimentada (very comprehensive for data analysis, wrangling, visualization)
- Workshop in Exploratory Data Analysis, by Otho Mantegazza
Data visualization with ggplot2 🎨
The second most repeated sets of questions I get, by far, are all on the line of ‘how do you plot this/ can you help me making my plots look prettier/ do you know how this is done in ggplot…?’.
Tabs I always have open while using ggplot
- Modify components of a theme – whatever you want to change to make a plot prettier is probably there, and you want to be familiar with most of the arguments for a quick working flow.
- R Color Reference Sheet
- For a long while, I used to code with the R Graphics cookbook pdf version open. I don’t need it any more, but it’s a great, recipe-based book, has a very clear table of contents and most of what you may want to change when starting with ggplot is probably there.
Useful ggplot resources
- Default aes by geom
- Double plots and two axes in ggplot2
- Reordering and facetting for ggplot2
- Changing Glyph in legend in ggplot2
- Easy Way to Mix Multiple Graphs on The Same Page
- Gradient of n colors ranging from color 1 and color 2
Specific types of plots
Making plots prettier
- Wes Anderson Palettes (my favorite ones by far)
- Game of Thrones inspired palette for R
- Hrbr themes
- The ggplot glow-up – making lovely data visualizations in R
- The ggplot Flipbook
- Take a sad plot and make it better
- Designing ggplots – making clear figures that communicate