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 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 really used. Moreover, we compare the similarities and differences between the scripts of R users and the implementations of package authors and provide insights for static analysis tools. METHOD We analyze 4 230 R scripts submitted alongside publications and the sources of 19 450 CRAN packages. For each of the over 350 000 files, we retrieve the abstract syntax tree (AST), extract basic dataflow information of the program, and collect quantitative information for features of interest that are then summarized. RESULTS We find a high frequency of named-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 in the publication submissions. CONCLUSION R scripts and package sources differ, for example, in their size, the way they include other packages and the usage of R’s reflective capabilities. Moreover, even though R offers a lot of powerful reflective capabilities, most are used seldom or not at all. Still, we find a set of reflective features, like calls to eval and load, that are used frequently and should be supported by static analysis tools.