  CONTEXT The R programming language has a huge and active community,
especially in the area of statistical computing. Its interpreted nature allows
for several interesting constructs, like the manipulation of functions at
run-time, that hinder the static analysis of R programs. At the same time,
there is a lack of existing research regarding how these features, or even the
R language as a whole are used in practice. OBJECTIVE In this paper, we conduct
a large-scale, static analysis of more than 50 million lines of real-world R
programs and packages to identify their characteristics and the features that
are actually used. Moreover, we compare the similarities and differences
between the scripts of R users and the implementations of package authors. We
provide insights for static analysis tools like the lintr package as well as
potential interpreter optimizations and uncover areas for future research.
METHOD We analyze 4230 R scripts submitted alongside publications and the
sources of 19450 CRAN packages for over 350000 R files, collecting and
summarizing quantitative information for features of interest. RESULTS We find
a high frequency of name-based indexing operations, assignments, and loops, but
a low frequency for most of R's reflective functions. Furthermore, we find
neither testing functions nor many calls to R's foreign function interface
(FFI) in the publication submissions. CONCLUSION R scripts and package sources
differ, for example, in their size, the way they include other packages, and
their usage of R's reflective capabilities. We provide features that are used
frequently and should be prioritized by static analysis tools, like operator
assignments, function calls, and certain reflective functions like load.
