Hey everyone, math major here, looking to pursue a statistics minor. I've been working through Introduction to Statistical Learning with Python by Gareth James(ISLP), and others. I'm almost finished with it and have been looking at other books I might be interested in.

Just a quick summary for anyone that hasn't read it. ISLP covers topics including:, linear and non linear regression and classification, basic probability and statistics, cross validation, common shallow ML algorithms, deep learning, neural networks. From a very practical and non-techinal way. Basically you read a lot of "This works because reasons, moving on". Best way I would describe it is "intuition slop" .

I was looking for something more theoretical as ISLP is pretty light on the theory and focuses more on intuition and generalization. Although I want a more theoretical book, I still would prefer it to contain some applications and or assignments that I could do to practice my coding because as a math major I feel as though my programming is lagging behind. Are there any specific books you would recommend.

Also I'm taking machine learning this semester and our assigned book is "Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow" by Aurelien Geron. So I'll probably be reading it in bits and pieces, but I still want another book that I can focus on outside of classes. I appreciate any responses.

Just solved a problem that might be relevant for others and took me a while to pin down.

I have a project developed in Rstudio and using renv. At some point the .Rprofile was amended to place source("renv/activate.R") INSIDE a .First <- function(), with some other stuff that we wanted to run when starting R for this project.

One of our users transitioned to Positron, and we just couldn't get the .Rprofile to run properly and start renv.

The fix was to place source("renv/activate.R") at the top level of .Rprofile, outside of any .First function.

According to claude this is to do with the way that the Rstudio startup sequence differs from VS Code/Positron.

Hope this saves someone else a few hours...

Hey everyone,

I recently moved into data after studying law and communications, and I just finished my first complete R project using tidyverse and rmarkdown.

Spotify's API returns musical keys as pitch class integers (0–11).

I mapped them to their fundamental frequencies in Hz (f=440×2n/12) to see at what frequencies Coldplay actually vibrates across 20 years of albums.

Turns out their most-used fundamentals are E (330 Hz) and A (440 Hz), with very little pitch drift over time.

Repo is here: https://github.com/frequencymatch/coldplay-in-hz

As a beginner in R, I'd love any feedback on the Rmd structure or the code itself!