Reviewer response for version 1
I feel odd reviewing this paper - since I have little technical expertise in assessing human survey-based studies. 
My interest in this topic is as a highly biased object of the study, specifically, as an enthusiastic user of R in the classroom, both for statistics and ecological modeling courses. 
[Is this what the animals I do research on feel like when they read my papers?]
Overall, the information presented is useful. 
The survey appears to have been conducted responsibly, with a reasonably high response rate (though with possible self-selection bias). 
The introduction provides a useful overview of the history and context of R's and the discussion is comprehensive and thoughtful. 
The availability of the survey results is a welcome contribution.
While the main purpose of the article seems to be to argue and advocate for the use of R in classrooms, it is not always clear how the survey results inform that argument. 
On the one hand, the fact that there are courses in 65% of institutions and across fields might encourage other professors to adopt R. On the other hand (given my bias) it is a shocking disservice to students that 35% of institutions use R in ZERO courses. 
There was a bit of a missed opportunity in that the article presents just a snapshot in time. 
It would have been interesting to see how the rate of R use has increased (which would have been possible by asking professors about their use of R 5 or 10 years ago). 
I imagine the rate of increase would have been very nearly explosive.*
* In any case, the claim that "R is not being adopted ... as fast as it perhaps should be" is more of an opinion (even if softly put, and one I completely agree with) than supported by the results. 
There are other slight disconnects between claims in the discussion and the survey, but then for this kind of pseudo-advocating article/essay this is perhaps more acceptable than what I am used to.
* - As a single datapoint: when I first proposed incorporating R into an introductory undergraduate statistics course at the Unversity of Washington in 2012 - not even five years ago - the idea was met with surprise and some scepticism by other instructors that the students could "hack it", 
The experiment ended up being an unequivocal success, with many students claiming that was the most useful portion of the course [certainly compared to looking up t-values in a table at the back of a textbook!] and I believe is now standard in the curriculum.
Results and Figures:
The weakest point in this paper is the ugliness of the figures (which is ironic, considering that one of the main selling points of R is the ability to make beautiful graphics). 
I understand that the results are simple counts, but the presentation could still be improved. 
Figure 1 is completely unnecessary, unless it were cross-tabulated against, e.g., subject of course (an important missing bit of information), for example sorted into statistics/ mathematics/ computer science vs. life/ social sciences.
In almost all of the bar plots, you could use horizontal bars, ordered top to bottom from highest count to lowest count, and go ahead and include all of the "Other" categories [e.g., in figure 2, climatology, population genetics, econoinformatics, and plotting]. 
Those results are interesting, and there's plenty of room if you abandon the fat vertical bars. 
These could also be cross-tabulated and color-stacked against subject, or at least "graduate" / "undergraduate".
Figure 6 (though seasonally appropriate) would be much improved if it were presented as a mosaic plot (i.e.: mosaicplot(table(R.Use, Willingness))), which is much better for comparing the relative shift across categories, while reflecting the sample sizes as well. 
It is, incidentally, interesting that so few people answer "9" compared to "8" (I guess 9/10 of "Very" is a more slippery concept than 4/5 of "Very"!) 
There's a psychological effect here somewhere, but in the meantime you might be better off pooling 1-3, 4-6, 7-9.
I must confess I would have no idea how to answer the question in Figure 10 - there is so much overlap. 
I really don't see how one can separate "Modelling" (and, often, "Statistics") from "Data manipulation" "Simulation", "Data Exploration", "Visualization", etc.
Discussion
Among the tools which facilitate the use of R in the classroom, one of the most important is the use of 'knitr' and 'Rmarkdown' to easily generate documents that combine text, math, code, figures and output. 
This is a very important omission. 
Perhaps the single most practical use of "knitting" documents is for teaching material - including lectures, labs and homework assignments - in particular for learning R. Also, report generation itself is a useful and totally accessible skill to teach, particularly considering the importance of reproducibility of analysis (another important advantage of R over point-and-click tools).
Minor:
Discussion - there's a "does'nt" that should be "doesn't".
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