The R programming language is widely used in a variety of domains. It
was designed to favor an interactive style of programming with minimal
syntactic and conceptual overhead. This design is well suited to data
analysis, but a bad fit for tools such as compilers or program
analyzers. In particular, R has no type annotations, and all
operations are dynamically checked at run-time. The starting point for
our work are the two questions: \emph{what expressive power is needed
to accurately type R code?} and \emph{which type system is the R
community willing to adopt?} Both questions are difficult to answer
without actually experimenting with a type system. The goal of this
paper is to provide data that can feed into that design process. To
this end, we perform a large corpus analysis to gain insights in the
degree of polymorphism exhibited by idiomatic R code and explore
potential benefits that the R community could accrue from a simple
type system. As a starting point, we infer type signatures for
25,215 functions from 412 packages among the most
widely used open source R libraries. We then conduct an evaluation on
8,694 clients of these packages, as well as on end-user code
from the Kaggle data science competition website.