https://r4ds.had.co.nz/ you could use this to get started.

Software Carpentry community [1] has a few very good tutorial guides and workshops for those who are new to R. Their main aim is for teaching basic laboratory skills for research computing. Others who are already proficient in R can utilize and adopt these materials for their own training and workshops.

The first guide is for clinical settings, doing data analysis for inflammation in patients who have been given a new treatment for arthritis [2]. Another is a general introduction to R for non-programmers using gapminder data for reproducible scientific analysis [3][4].

[1]https://software-carpentry.org/lessons/index.html

[2]http://swcarpentry.github.io/r-novice-inflammation/

[3]http://swcarpentry.github.io/r-novice-gapminder/

[4]https://www.gapminder.org/data/

Tilman M. Davies’s book, The Book of R: A First Course in Programming and Statistics is a good place to start. See [1].

[1] https://www.amazon.com/Book-First-Course-Programming-Statist...

What do you currently use for data analysis?

I’ve taken courses on statistical computing in R and statistical computing in SAS in my statistics degree. We were always told that SAS is the standard for anything health care, pharmaceutical, or where regulation and publication comes into play.

Anecdotally, my friends who did PhDs in biochem and immunology all used SAS for their data analysis.

Have I been misled or is this up to individual preference?

This seems like a reasonable place to start: https://online-learning.harvard.edu/subject/r