How can you learn R?
It can be challenging to learn a new programming language, especially when you are starting from scratch with no prior experience. The intent of this article is to provide a brief overview of resources that you can use to learn R. I feel it’s helpful to have with diverse streams of information when learning something new. I mention general categories and then specific examples that have worked for me.
The categories listed may apply to learn a new programming language, in general, to get to an intermediate level. One more thing, here’s a Youtube video to watch at 1.5x, if you would like to watch or listen instead (still getting the hang of making those things). So, without further ado, let’s begin.
1. Enroll in massive open online course(s):
- Coursera (https://www.coursera.org/) — I’ve enjoyed taking many great courses on Coursera and I believe it’s the best platform for MOOCs.
2. Join an interactive online learning platform:
- Datacamp (https://www.datacamp.com/) — probably the most popular interactive learning platform for data science across many languages. Very good for people with limited time availability.
- Dataquest (https://www.dataquest.io/) — the lesser known alternative and my preferred choice for data scientists wanting to quickly fill in skill gaps. Data quest has a focus on interactive lessons with customized learning paths (data analyst, data scientist, and data engineer), guided projects, and insightful career guides (via their blog or newsletter). They take a more personal approach with emails sometimes even coming directly from their founder (Vik Paruchuri). I have nothing but good things to say about them and their platform.
3. Read good programming books:
- R for data science (https://r4ds.had.co.nz/) — probably the best programming book to start from scratch and become familiar with tidyverse at a basic to intermediate level.
- Advanced R (http://adv-r.had.co.nz/) — good follow-up to get a stronger understanding of R.
- Text mining with R (https://www.tidytextmining.com/) — the best book to start text mining in R. Short and straightforward with good examples.
4. Follow popular R blogs or a blog aggregator:
- R views (https://rviews.rstudio.com/) — this blog is directly from Rstudio and has great R tutorials and other informational blog posts.
- R weekly (https://rweekly.org/) — an awesome R blog aggregator. I love the website design.
5. Join a learning community or forum:
- Meetup (https://www.meetup.com/) — Great way to join R or data science learning groups. That’s what I did and I’ve learned a lot and built new connections that way.
- Stack overflow (https://stackoverflow.com/) — number 1 choice for plenty of programmers to quickly debug and troubleshoot problems.
- Github learning communities (https://github.com/) — find and join a learning community on Github ( here’s an example https://github.com/rfordatascience).
6. Read source code on Github:
- Follow your favorite R programmer on Github — make a github account and follow your favorite R programmer. Read their code and learn more or see if you can resolve bugs people are encountering. Put your own code on your Github and share with friends.
7. Participate in coding challenges and competitions:
- TidyTuesday (https://github.com/rfordatascience/ti...) — great opportunity to practice your skills on a dataset and compare your analysis with a large community of R users. You’re encouraged to share your process and results via Twitter and Github.
- Local hackathon or data challenge with a group of friends — Think of it as a skill-building bootcamp in R and quickly develop a Shiny application for competition. That’s what I did when I first started learning R and it helped me out tremendously.
8. Use publicly available datasets:
- Kaggle (https://www.kaggle.com/) — find a dataset on any topic you’re interested in and apply your analysis skills on it. Great way to also put together a project and put it on your Github.
9. Watch coding streams (live or recorded):
- David Robinson’s Youtube Channel (https://www.youtube.com/user/safe4dem...) — David Robinson has been doing awesome TidyTuesday live streams since late 2018. Awesome to see a seasoned R programmer analyze a never seen before dataset from start to finish. He has a long history in the R community (he even co-authored the text mining book I mentioned) and was the Chief Data Scientist at DataCamp.
- Check out other video lessons and tutorials on Youtube (they’re free and plenty are high quality).
