Unless you're doing in-depth statistical analysis, there's not really any reason to know anything about R. It isn't a useful general purpose language.
That is to say, while your suggestion is certainly valid and appreciable, the reason why there are rarely primers for content about R (specifically) is that the intended audience is primarily academic, where the audience is already familiar with the technology. if you take an applied statistics class, you'll learn how to use R there.
If you're not already in the club, there isn't much reason for you to join it unless you're leaving industry to do scientific-level statistical analysis.
This isn't me saying "Mind your own business!". I'm just giving the perspective of the writers for most R content.
But couldn't R have applications in business (industry)?
A businessperson once asked me about R after he read a non-technical article about it in a major newspaper. This person has never been in academia. Why was he curious? What could he possibly see in R?
I see R as a "disruptor" to the market that SPSS has had under lock and key for as long as I can remember. Perhaps statistics has utility outside of academics? Maybe R can give anyone the power of something like SPSS, for free.
Now what I want to know is how to convert "screenr" into a more open format. Can you convert to video? R is not dependent on a GUI. Maybe you could convert to screen(1) format, along with an mp3.
Certainly it does. Perhaps I shouldn't have emphasized academics so much.
Anyone who does in-depth statistical analysis can use R. It is equivalent to SPSS, SAS, and matlab in its applicability, plus its free.
People involved in 'big data' and serious analytics in business are already using R a lot of the time. The other group of people in business who use the closed source applications above, however, aren't as technical, and have a pretty big roadblock ahead of them.
The issue is just a question about ease of use. There are companies out there who offer products that compare with the above (http://www.revolutionanalytics.com/), but they still cost money. The 'problem' of R in business is that there aren't any good GUI's for it that allow you to just go menu>ANOVA>3 way factorial>[settings dialog]>Calculate and then get there.
Of course there are a few other reasons why those other applications exist and thrive in spite of R, but I think the limited use cases and lack of a comprehensive GUI are its biggest roadblocks.
Regarding R for BigData, do you think R is going to continue to be a stable of the analytical toolset as data sizes continues to grow?
I use R to prototype machine learning techniques on a small set of data, or visualize some summary statistics. But if I want to run K-Means Clustering or Support Vector Machine algorithms on 1,000,000,000 rows of data, I've found that running R on Hadoop is tricky. There are some libraries out there ( for example, RHadoop https://github.com/RevolutionAnalytics/RHadoop/wiki/rmr ) but they require writing your algorithm in such a manner that algorithms must be adapted to run within map() and reduce() functions. My understanding is that the built-in functions that make R so useful will often not adapt well to a mapreduce algorithm.
From what I've seen, once an algorithm is prototyped in something like R/Matlab, if the data size warrants it, it's best to re-write the algorithm in Java MapReduce or use Apache Mahout.
There is a difference between an optional GUI and a mandatory one. I don't see why an optional GUI would hurt R, but I have almost no experience in this area. Would you mind explaining?
It is not a great general purpose language. I use it to create automated reports and analyses. It's got every statistical test under the sun in the libraries, and the graphs are very flexible, provided you are willing to tweak a lot.
Well between SAS and SPSS they have had the market under lock and key. Yes R is widely used in industry these days. Yes, unless you've got serious statistical work to do it is a fairly horrible language to work with.
That is to say, while your suggestion is certainly valid and appreciable, the reason why there are rarely primers for content about R (specifically) is that the intended audience is primarily academic, where the audience is already familiar with the technology. if you take an applied statistics class, you'll learn how to use R there.
If you're not already in the club, there isn't much reason for you to join it unless you're leaving industry to do scientific-level statistical analysis.
This isn't me saying "Mind your own business!". I'm just giving the perspective of the writers for most R content.