In Data Science from Scratch, a book introducing data science using Python, Joel Grus said the following about R (pg. 302):
Although you can totally get away with not learning R, a lot of data scientists and data science projects use it, so it’s worth getting familiar with it.
In part, this is so that you can understand people’s R-based blog posts and examples and code; in part, this is to help you better appreciate the (comparatively) clean elegance of Python; and in part, this is to help you be a more informed participant in the never-ending “R versus Python” flamewars.
At the University of Utah, I teach the R lab that accompanies MATH 3070, “Applied Statistics I.”” None of my students are presumed to have any programming experience, and they never hesitate to remind me of that fact, especially when they are starting out. When I create assignments and pick problems, I often can write a one- or three-line solution in thirty seconds that students will sometimes spend four hours trying to solve. They then see my solution and slap their foreheads at its simplicity. I can be tricky with my solutions. For example, suppose you wish to find the sample proportion for a certain property. A common approach (or at least the one used in the textbook our course uses, Using R for Introductory Statistics by John Verzani) looks like this:
When everyone is an activist, everyone can be better off. All too often we resign our responsibility to our community and let others dream. This often is to our detriment; the modernist dream held by city planners turned cities from communal places to places dominated by cars from suburbs. Everyone needs to look at their city and think about how they could improve it and make it enjoyable for themselves and those around them. A city planner in an office building cannot be expected to always make changes that make life for the pedestrian better, and it is the pedestrian’s responsibility to make their desires known. To repeat myself, everyone needs to be an activist.