I've used both extensively (though not for statistics). R's syntax is a little more C-like and consistent than MATLAB's - however the biggest difference is documentation.
R's, like most open source documentation, is rather terse and often very unsatisfactory. This gets especially apparent once you get into 3rd party libraries and use things like Bioconductor. You’ll have no idea how things are designed to be used, and without a guru at hand to walk you through you’ll be in a world of pain. Googling for solutions is also very difficult (even using something like RSeek). There are some archaic boards that sometimes have what you need, but often you'll get stuck and not know what to do. What’s nice about MATLAB is that all the libraries are made by a competent team of engineers and they put in the money/resources to have good documentation. Even the more abstract rarely used libraries have decent documentation. In R, if you try using non standard libraries, you’re gunna get screwed.
The IDEs for R are also worse. RStudio is quite nice, but it's really bare-bones compared to MATLAB's IDE. The one really neat thing about it is that you can host it on a server and then remotely work on your work by just going to a URL.
Also I think there are legacy issues in R (though MATLAB has those too). So there are for instance matrices, dataframes and lists (which are list vectors, but not at all). Why there are these three formats that fundamentally do the same thing is beyond comprehension. (Maybe someone can give some insight) Functions will randomly return one type or another. I always find myself fighting to keep the types consistent and R keeps trying to mess with me. In MATLAB everything is a matrix, so that makes things a lot easier
Fundamentally the issue is that MATLAB has a much larger user-base than R, so you'll just have a much easier working with it.
If R's documentation and community was on the same level as MATLAB's then I would maybe consider recommending it. If you work in genetics and you need to use something like Bioconductor, then R is a must I guess. Most other libraries are Fundamentally it's just some syntax differences.
The expression "You get what you pay for" is really pertinent here.
Note: I personally still use R for plotting, because I’m personally more familiar with it. Otherwise I try not to touch it. Code organization for me always gets messy, but I guess that’s cus I’m used to writing in OO languages.