The R Consortium supports the R Community through investments in sustainable infrastructure, community programs and collaborative projects. Through the The Funded Project Program, now in it’s fourth year, the R Consortium has invested more that $650,000 USD in over 30 projects that impact the over 2 million R users worldwide.
We are pleased to announce the Spring 2018 grant recipients. We will provide updates on these projects throughout the year. Congratulations to all grant recipients, and look forward to our session at useR!2018 this July where many of our funded projects will showcase their work and tips for leveraging the grant program for driving open collaboration.
Maintaining DBI
Grantee: Kirill Müller
DBI, R’s database interface, is a set of methods declared in the DBI R package. Communication with the database is implemented by DBI backends, packages that import DBI and implement its methods. A common interface is helpful for both users and backend implementers.
The Maintaining DBI Project which follows up on two previous projects supported by the R Consortium will provide ongoing maintenance and support for DBI, the DBItest test suite, and the three backends to open-source databases (RSQLite, RMariaDB and RPostgres).
Ongoing infrastructural development for R on Windows and MacOS
Grantee: Jeroen Ooms
The majority of R users rely on precompiled installers and binary packages for Windows and MacOS that are made available through CRAN. This project seeks to improve and maintain tools for providing such binaries. On Windows we will upgrade the Rtools compiler toolchain, and provide up-to-date Windows builds for the many external C/C++ libraries used by CRAN packages. For MacOS we will expand the R-Hub homebrew-cran with formulas that are needed by CRAN packages but not available from upstream homebrew-core. Eventually, we want to lay the foundation for a reproducible build system that is low maintenance, automated as much as possible, and which could be used by CRAN and other R package repositories.
Developing Tools and Templates for Teaching Materials
Grantee: François Michonneau
The first-class implementation of literate programming in R is one of the reasons for its success. While the seamless integration of code and text made possible by Sweave , knitr, and R Markdown was designed for writing reproducible reports and documentation, it has also enabled the creation of teaching materials that combine text, code examples, exercises and solutions. However, while people creating lessons in R Markdown are familiar with R, they often do not have a background in education or UX design. Therefore, they must not only assemble curriculum, but also find a way to present the content effectively and accessibly to both learners and instructors. As the model of open source development is being adapted to the creation of open educational resources, the difficulty to share materials due to a lack of consistency in their construction hinders the collaborative development of these resources.
This project will develop an R package that will facilitate the development of consistent teaching resources. It will encourage the use of tools and lesson structure that support and improve learning. By providing the technical framework for developing quality teaching materials, we seek to encourage collaborative lesson development by letting authors focus on the content rather than the formatting, while providing a more consistent experience for the learners.
PSI application for collaboration to create online R package validation repository
Grantee: Lyn Taylor (on behalf of PSI AIMS SIG)
The documentation available for R packages currently widely varies. The Statisticians in the Pharmaceutical Industry (PSI) Application and Implementation of Methodologies in Statistics (AIMS) Special Interest Group (SIG) will collaborate with the R-Consortium and representatives from pharmaceutical companies on the setting up of an online repository /web portal, where validation which is of regulatory standard for R packages can be submitted and stored for free use. Companies (or individual R users) would still be liable to make their own assessment on whether the validation is suitable for their own use, however the online repository would serve as a portal for sharing existing regulatory standard validation documentation.
A unified platform for missing values methods and workflows
Grantees: Julie Josse and Nicholas Tierney
The objective is to create a reference platform on the theme of missing data management and to federate contributors. This platform will be the occasion to list the existing packages, the available literature as well as the tutorials that allow to analyze data with missing data. New work on the subject can be easily integrated and we will create examples of analysis workflows with missing data. Anyone who would like to contribute to this exciting project can contact us.
histoRicalg — Preserving and Transfering Algorithmic Knowledge
Grantee: John C Nash
Many of the algorithms making up the numerical building-blocks of R were developed several decades ago, particularly in Fortran. Some were translated into C for use by R. Only a modest proportion of R users today are fluent in these languages, and many original authors are no longer active. Yet some of these codes may have bugs or need adjustment for new system capabilities. The histoRicalg project aims to document and test such codes that are still part of R, possibly creating all-R reference codes, hopefully by teaming older and younger workers so knowledge can be shared for the future. Our initial task is to establish a Working Group on Algorithms Used in R and add material to a website/wiki.
Interested workers are invited to contact John Nash.
Proposal to Create an R Consortium Working Group Focused on US Census Data
Grantee: Ari Lamstein
The Proposal to Create an R Consortium Working Group Focused on US Census Data aims to make life easier for R programmers who work with data from the US Census Bureau. It will create a working group where R users working with census data can cooperate under the guidance of the Census Bureau. Additionally, it will publish a guide to working with Census data in R that aims to help R programmers a) select packages that meet their needs and b) navigate the various data sets that the Census Bureau publishes.