Not so Easy to Understand
R's learning curve is partially due to its syntax, or the rules of the programming language and how some symbols are defined. R reads less like English, and in general is more difficult for beginners to wrap their heads around.
Excels at its Tasks
Nonetheless, data visualization in particular is known to be easier with R than with Python, and R has many built-in functionality and useful tools that makes doing certain tasks more straightforward. R excels at statistical computing, so in the long run R may turn become easier to use.
R was not designed to be fast. Since R projects are not easy to integrate into web apps, this is not a critical flaw that will affect your customers. However, the slow speed can still be annoying or even problematic when analyzing very large datasets.
R is known to have memory limitations, which becomes a problem when trying to analyze large datasets. R uses a lot of memory, and was designed to take up physical memory, so if your RAM isn't big enough, you need to workaround and use your harddrive instead. This may make computation even slower.
First of all, community size is important, because the larger a programming language community is, the more support you'd be likely to get. As you step into the programming world, you'll soon understand how vital support is, as the developer community is all about giving and receiving help. Moreover, the larger a community, the more people will be building useful tools to make development in that particular language easier. As of now, there are over 600 notable programming languages world-wide.
So, with that context in mind, let's get into the details of the R community size.
11th Most-Followed Programming Language on StackOverflow
StackOverflow is a programming Q&A site you will no doubt become intimate with as a coding beginner.
8th Largest Meetup Community
At meetups, you can generally network and learn from fellow developers. Meetups often offer mentorship to those who want it as well. There are over 270 R groups at Meetup.com, totaling over 130k members worldwide. Thus, in terms of programming languages, R has the 8th largest Meetup community.
R is completely focused on statistical computing, so the community size may not be as large as other general-purpose programming languages, but it is still nonetheless quite large and active especially considering the context. According to the Oracle survey in 2012, R has over 2 million users worldwide. Considering it's 2016 now, one can only expect there are many more R users now.
Over 199k GitHub Projects
While this number is probably not so impressive when compared with Python, it's worth noting that one of R's biggest strength is the CRAN (Comprehensive R Archive Network) repository, which has 7700+ packages specialized for all sorts of data analysis needs. Currently these packages are very mature, so while the library quantity might not be as large as, say, Python's, R wins in the quality of its tools.
Salary information from gooroo.io
As companies gather more and more data, the demand for data scientists are also greatly increasing. Demand for R developers will no doubt be on the rise.
With data analysis becoming more and important in helping business's understand their customers, operational efficiency, and more, R will only become increasingly important.
R is the 18th most popular language according to the TIOBE Index
However, a programming language's ability to stay relevant and survive also depends on whether the language is getting new blood.
Interest in learning R grew by 51.7％ in 2015
According to Google Trends, the number of people who are interested in learning R is growing steadily. Thus, R has a pretty optimistic outlook for the time being