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Admin application
We would like to get high school students aware of R and interested in R programming. For several years we have declined to participate in GCI, because we felt that the statistical focus of R may be too complex for high school students. However this year we would like to participate by proposing some simple tasks related to fixing issues, docs, failing tests, and open-source best practices (setting up CI, etc).
Our organization has prepared by participating in GSOC for 10+ years. The primary admin (Toby Hocking) plans to recruit mentors from the international R community, and has lots of experience proposing and mentoring coding projects himself.
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How do you plan to deal with any holidays or vacations mentors may have planned during the contest period?
We plan to (1) make sure mentors know that they need to provide email feedback for their tasks in at least 24 hours, and (2) ask each mentor to indicate times when they plan to be away from email, and (3) ask each mentor to indicate a backup mentor to contact when they are away.
Unresponsive mentors have been an issue for us with GSOC as well. For example some mentors in R-GSOC'19 failed to provide student evals. Part of the problem may be that there were multiple mentors per project, so it was not clear who was responsible for the eval. To handle this we plan to (1) communicate responsibilities to mentors clearly, and (2) enforce a policy that non-responsive mentors will not be allowed to participate with R project in for future GSOC/GCI programs.
R Project for Statistical Computing
https://raw.githubusercontent.com/rstats-gsoc/gci2019/master/Rlogo-724x724.png
R is a free software environment for statistical computing and graphics
R includes
- efficient data handling, storage, and linear algebra,
- a large collection of tools for data analysis and machine learning,
- graphical facilities for display either on-screen or on hardcopy, and
- a programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities.
The term “environment” is intended to characterize R as a fully planned and coherent system, rather than an incremental accretion of very specific and inflexible tools, as is frequently the case with other data analysis software.
R is designed around a true programming language, and it allows users to add new functions. Much of the R system is itself written in the R programming language, which makes it easy for users to follow the algorithmic choices made. For computationally-intensive tasks, C, C++ and Fortran code can be linked and called at run time. Advanced users can write C code to manipulate R objects directly.
Many users think of R as a statistics system. We prefer to think of it of an environment within which statistical techniques are implemented. R can be easily extended via packages. There are about eight recommended packages which are supplied with the standard R distribution, and many thousands more are available through the Comprehensive R Archive Network (CRAN).
machine learning, data visualization, statistics, data science
r, C++, c, fortran
GPL-2