funesrequiem

I can't recommend the "R for Data Science" (https://r4ds.had.co.nz) book enough, which is written by one of the creators of the tidyverse, Hadley Wickham. This opinion might get challenged here, but if you're going to use R primarily for data science/analysis and not for programming I think it's a better idea to start learning it with the tidyverse than with base R (beyond the basics, of course, which are also covered in the book).

I use R professionally for biostatistics and I can't remember the last time I had to use the base syntax because something couldn't be done with the tidyverse approach.

And what would you say the remaining 20% use? Because I've never seen anyone using R outside of RStudio.

Hi, would it be possible to contact you to ask some career questions related to the pharmaceutical industry and data science? I'm a biostatistician who uses R for everything and lately I've been thinking about doing a career change, but I'm a bit lost with all the available options.

I don't know if you've heard about it, because it is a relatively recent development, but the tidymodels ecosystem of packages (https://www.tidymodels.org) is also breaching the gap from data exploration/visualization to advanced modeling and machine learning in a way that feels really natural if you're used to the tidyverse way of doing things. It's developed by RStudio as the improved version of caret. I've been using it for differential gene expression analysis and it's a game changer in how much time it saves me.