I'm coursing a research/PhD access master degree and all I can tell you R is one of the most popular tools. If it is used in your university go for it.

It's not just the features of the R programming language. It is also free, open source and can be integrated easily with many external tools. For example you can make a paper using Markdown(RMarkdown) and convert it to ->TEX->PDF. Or you can use Rmarkdown to make a presentation converting it to ->TEX->beamer slides. Or just output a html document/webpage. It is also well integrated with online databases and resources.

While it's true that it can be a bit less intuitive or not as visualization focused as compared to some other tools, it is just as powerful under the hood.

Other open source tools like python are good for programming but when it comes to data analysis they can be a little bit cumbersome. For example, python is quite object oriented and for straightforward purposes with low reusability the amount of code needed can be large.

There are other tools like Matlab, Mathematica, SPSS... Matlab is good at visualization and Mathematica has really nice features aimed to improve understanding. However these are closed source and cost a lot of money to you or the university.

Personally I work in macro and fixed income market analysis (strategist), and I can heartily recommend R as your first language. Indeed, coming from a CS background, I first applied Python to many problems, and resisted R which was not a "grown up" programming language, in my opinion (some would make the same accusation on Python). However I dipped my toe in the water one day because R had a Bloomberg terminal add in and Python did not (at the time), and after about a month of uphill learning curve the eureka moments started materializing thick and fast. I cannot recommend R enough, as a problem exploration language. It just beats Python hands down when it comes to grabbing some (usually dirty) data, mangling it around, cleaning it, and then install.package'ing a bunch of potentially useful libraries which allow you to do everything you could possibly imagine to a small to medium sized data set. And crucially, static graphing. Nothing else comes close for this use case.

Now...caveats. R is not a production programming language. If you find yourself creating something truly useful for many users, that requires robust programming language structures such as threading, proper memory management, server-capability, or indeed, speed, R is going to become frustrating. Yes a whole bunch of people will tell you "it's possible, I do it, etc", but that is not its sweet spot. Also, if your data set is bigger than 2-3 gig or so, you're going to start hitting R's memory management wall. It's slow. You'll then be better off with Python, C, or indeed, Scala, or possibly, Apache Spark. The common thing about these caveats, however, is that they're definitely second order problems, later in your career life cycle, than the excellent mainstream data science tool which is R for people who have outgrown Excel, but are not full fledged computer scientists, and who want to get (lots of) stuff, done.

(by the way, pre-empting comments. Yes Pandas is great, but no it's not quite R).

Thanks for the replies everyone. I'll definitely save this link and pick up the book when it comes out!

If you want to attain a deeper understanding of coding, I suggest you try another language once you get the working basics of R. Maybe some basic MOOC on Python or C. You'll understand concepts of CS that are hard to grasp by learning only R.

It looks like it starts at a low enough level that you should be fine without a programming background.

Though they suggest Garrett's book [1] as a companion to R for Data Science in the Prerequisites section [2].

> And is R the appropriate language to learn?

I'd think so. Possibly either R or Python, or both (if you want to get beyond Excel).

[1] https://www.amazon.com/dp/1449359019 [2] http://r4ds.had.co.nz/intro.html

Yes