Conference Agenda

R packages are typically tested using expected input/output pairs that are manually coded by package developers. These manually written tests are validated under various CRAN checks that perform both static and dynamic analysis. Manually written tests could still allow subtle bugs, if they do not anticipate all possible inputs, and miss important code paths. In contrast to manually written tests, fuzzers are programs that pass random, unexpected, potentially invalid inputs to a function, expecting it to break or identify subtle bugs.

Regulated industries are built on long histories of software validation to bring confidence to the results that they produce. Historically, this validation has come through licensed software — at a time, such software tools were the preferred tool of practitioners. As free and open source tools rise in popularity, the spectrum of preferred tools grows ever wider. Today, R is the preferred language for many graduating statisticians and biologists. Furthermore, the open nature of these tools brings more ready access to bleeding edge methods expected by regulators.

While the R world brings some amazing pieces of infrastructure for software quality, there is an inevitable gap in translating open source practices to a validation world that is unfamiliar with open software development. With {riskmetric}, we have built a platform for implementing such quality assessments. From code quality to community engagement, we hope to characterize R packages to an extent that industry players can be confident that they can stand behind the tools they use when presenting analysis and software to regulators.

Here we present the {riskmetric} package and surrounding tools to help support R package validation, show how we’ve built a foundation for pulling metrics from a diverse set of information sources and provide next steps in the R Validation Hub’s roadmap to help bridge the gap toward open source tools in regulated industries.

{poorman} is a package that unapologetically attempts to recreate the {dplyr} API in a dependency free way using only {base} R. {poorman} is still under development and doesn’t have all of {dplyr}’s functionality but what is considered the "core" functionality is included and the package is available on CRAN at: https://cran.r-project.org/web/packages/poorman/. The idea behind {poorman} is that a user should be able to take a {dplyr} based script and run it using {poorman} without any hiccups.

{poorman} provides a consistent set of verbs that help you solve the most common data manipulation challenges:

* select() picks variables based on their names.

* mutate() adds new variables that are functions of existing variables.

* filter() picks cases based on their values.

* summarise() reduces multiple values down to a single summary.

* arrange() changes the ordering of the rows.