What to consider before learning a new language for data analysis
Personally going to make a strong argument in favor of Python here. There are a large number of reasons for this, but I'm going to build on some of the points that other people have mentioned here:


Picking a single language: It's definitely possible to mix and match languages, picking d3 for your visualization needs, FORTRAN for your fast matrix multiplies, and python for all of your networking and scripting. You can do this down the line, but keeping your stack as simple as possible is a good move, especially early on.
Picking something bigger than you: You never want to be pushing up against the barriers of the language you want to use. This is a huge issue when it comes to languages like Julia and FORTRAN, which simply don't offer the full functionality of languages like python or R.
Pick Community: The one most difficult thing to find in any language is community. Python is the clear winner here. If you get stuck, you ask something on SO, and someone will answer in a matter of minutes, which is simply not the case for most other languages. If you're learning something in a vacuum you will simply learn much slower.


In terms of the minus points, I might actually push back on them.

Deepening your knowledge of one language is a decent idea, but knowing only one language, without having practice generalizing that knowledge to other languages is a good way to shoot yourself in the foot. I have changed my entire favored development stack three time over as many years, moving from MATLAB to Java to haskell to python. Learning to transfer your knowledge to another language is far more valuable than just knowing one.

As far as feasibility, this is something you're going to see again and again in any programming career. Turing completeness means you could technically do everything with HTML4 and CSS3, but you want to pick the right tool for the job. If you see the ideal tool and decide to leave it by the roadside you're going to find yourself slowed down wishing you had some of the tools you left behind.

A great example of that last point is trying to deploy R code. 'R''s networking capabilities are hugely lacking compared to python, and if you want to deploy a service, or use slightly off-the-beaten path packages, the fact that pip has an order of magnitude more packages than CRAN is a huge help.