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).