"In this second edition of ISL, we have greatly expanded the set of topics covered. In particular, the second edition includes new chapters on deep learning (Chapter 10), survival analysis (Chapter 11), and multiple testing (Chapter 13). We have also substantially expanded some chapters that were part of the first edition: among other updates, we now include treatments of naive Bayes and generalized linear models in Chapter 4, Bayesian additive regression trees in Chapter 8, and matrix completion in Chapter 12. Furthermore, we have updated the R code throughout the labs to ensure that the results that they produce agree with recent R releases."
This is great. I read the first edition cover to cover years ago as I first got interested in data science. 6 years and 3 data scientist jobs later and I still find myself going back from time to time.
I use Python daily, but I still find the way R / Tidyverse handles df manipulations much more user-friendly and efficient. My understanding is that R has a health ecosystem of packages for dealing with spectrometry data. It is not field so I am not speaking from experience.
I own the first edition. Made it through the entire book during the pandemic with a study partner.
There are other resources:
https://github.com/melling/ISLR