What are the options when statisticians who only know R need more computing speed Speed isn't a concern for many statistics projects but sometimes it is, for example MCMC. Assume that hardware improvement is not an option (looking for relatively free solutions that produce orders of magnitude speed improvements over using an interpreted language). This post is inspired by the Highest voted question on Cross Validated that discusses using Python as statistics workbench. 
In this answer, csgillespie points out that one reason to use python is the ability to interface with C without having to write cumbersome C code when you need speed (for example when doing MCMC). However, packages do exist that allow you to interface with C and Fortran directly from R. 
Sometimes I need more speed. I do not know either C or Python or Fortran, so as a person who would like to learn a a tool that improves speed when I need it what are the pros and cons of using Python vs. calling C directly in R? Or pros and cons of other options. Preferably the answer should address the learning curve of learning the new tool in addition to its eventual usefulness after mastery. 

The reason for this question: Currently Cross Validated maintains a list of resources for statistical computing. However, That list contains absolutely nothing about how to interface from R or python with compiled languages that can improve speed. Note I feel this question is most appropriate for crossvalidated and not stackoverflow because of the audience (the answer is intended to be for statisticians). However, if you do think this question is off topic please comment so that it can either be edditted for improvement or moved to an appropriate location. 
 A: The answer you point out is an extremely specialized problem: doing MCMC wherein each iteration needs to solve 5 ODEs. For more mainstream MCMC problems, you have access to JAGS (rjags) and Stan (rstan). For extremely specialized problems, R has the Rcpp and inline packages that you might find useful.
I'm not sure how freely Stan lets you do arbitrary C++ code, but that may also be a way to accomplish something similar to what the referenced answer does: use R where it's strong, call Stan where you need MCMC speed, and within Stan do customized evaluation in C++.
I also think that smarter algorithms can make a huge difference in some cases. For example, LaplacesDemon has some cutting-edge samplers that allow it to often be competitive with compiled packages like JAGS or Stan, even though it's entirely written in R. That wouldn't help as much in the referenced answer because so much of the time was spent solving ODE's, not doing MCMC, but even then an algorithm that converges 10x faster needs 1/10 the evaluations.
Are you inquiring about a general need for speed, or do you have some specific examples? It seems a bit harsh to learn Python in order to learn and use C.
A: Are you planning on writing the C or Python that you need? Or are you having an expert in the respective language do it?
If the former, choose Python as it will take you one tenth the time and effort to get functional with as C. Well-written, C is the faster language as it is is only one step removed from the processor instruction set. 
