Resources

I try to generate my own material 'de novo' to give an alternate perspective to existing resources. This means that other resources are still a rich source of information, and if I say something poorly here you can turn to them.

→ R

R for Data Science is how I learned R. It teaches R through the tidyverse, rather than through 'base R', but I think base R is something you can pick up later.

R: What they forgot contains a lot of 'glue' knowledge about doing stuff on the computer gets left behind. Jenny Bryan does a great job making this knowledge explicit in this book. This one I do strongly recommend perusing at least the chapter names for so you know what this book can help you with when you eventually need it.

Happy Git with R (also by Jenny Bryan) which teaches you the bare basics of what git is, why you should use it, and how to get up and running. The sooner you get started with git the better: it's a safety net, a electronic lab notebook, and a collaboration tool all in one - and it's also an industry standard.

→ Bio-R

SummarizedExperiments form the backbone of many Bioconductor based analyses, so it's good to know what their anatomy is, how to get in and out of them, and what to do with them. The linked vignette does a good job explaining it, but I find the image in the vignette to be the most useful.

DESeq2 is the standard tool for doing differential expression on RNAseq data. The vignette is immense but incredibly thorough - I'd recommend searching through it if you ever have a DESeq2-based question.

→ More advanced R

The ggplot2 book is a great resource for more advanced plotting needs and questions. ggplot2 is well known for being a succinct yet powerful plotting library. R4DS (above) will probably give you all the intro you need, but if you want or need to learn more, the ggplot2 book is great.

R Packages is my recommended resource if you are ever interested in creating your own R package. It is truly a gem and a wonderful reference, takes you through the whole process from creating to publishing.

Advanced R is where you go if you're interested of learning the 'whys' of R, or learning about R's object oriented stuff like S3, S4, and R6. It hasn't been updated in a while, so some parts may be inaccurate now, but it's still an excellent resource.