fasteR VS r4ds

The good/bad news is that R has become so popular that there is a overabundance of resources you can use to learn it. Here are a few that helped me get started (though they may be dated at this point, ymmv): [1] [2] [3] [4] [5].

Start with fasteR, then move to Hands on Programming with R and R for Data Science. There is considerable overlap in the early chapters so don’t be afraid to skip parts. If you want to know more about the nuts and bolts of R then try Advanced R.

This tutorial is very good for starters. As is this book.

If you’re sure you need to be focussed on tidyverse then the seminal text would be R for Data Science. If you want even more basics than that then I would start with this link, and a book like Hands on Programming with R.

I’m taking a special topic ECS course and we use R here the profs quick start course https://github.com/matloff/fasteR

Recently I’ve favoured recommending this as one of the best ways to get up to speed with the main basics as quickly as possible. But be prepared that this really is just the start, and you will need to follow the recommendations here or elsewhere for further learning.

Check out his free course on GitHub where you'll see he walks you through getting right into learning R, keeping things simple by using the R Gui command line: https://github.com/matloff/fasteR

For R - the free book "R for Data Science", https://github.com/matloff/fasteR , and the data.table vignettes (I personally prefer using data.table than tidyverse, although there are some useful functions in tidyverse)

https://r4ds.hadley.nz is free and very good

R for Data Science teaches you the tidyverse packages, which makes data wrangling so much easier!

One of the best free resources is the R4DS book by Hadley Wickham. You should make sure you start with the in progress second edition. https://r4ds.hadley.nz/

The first edition of R4DS is quite old now. Check out the soon to be released second edition: https://r4ds.hadley.nz/

R for Data Science (2nd Ed), the updated guide from Hadley Wickham

The big skills gap you have is in practical data exploration and transformation, which will be a large part of any data-centric role. As much as people may have distaste for it, there is no avoiding data manipulation as critical foundational enabler of all inferential and predictive modeling work. SQL is the lingua franca here and well worth picking up the basics (joins, window functions, handling dates and times, etc.), plus learning how to implement similar transformations in R and Python. With appropriately transformed data, you then need to be able to visualize it effectively using tools like Tableau or ggplot2 in R. I would not necessarily seek courses or certificates in it but expect to be evaluated on them in technical interview screenings, so self-study accordingly. R for Data Science by Hadley Wickham is a great free resource for these topics for R.

I just looked and there is now a second edition! https://r4ds.hadley.nz/