Summer interns
This article is originally published at https://www.rstudio.com/blog/
We are excited to announce the first formal summer internship program at RStudio. The goal of our internship program is to enable RStudio employees to collaborate with current students (broadly construed: if you think of yourself as a student you quality) to create impactful and useful applications that will help both RStudio users and the broader R community, and help ensure that the community of R developers is representative of the community of R users. You will have the opportunity to work with some of the most influential data scientists and R developers and work on widely used R packages.
To be qualified for the internship, you need some existing experience writing code in R and using git + GitHub. To demonstrate these skills, your application needs to include a link to a package, Shiny app, or data analysis repository on GitHub. It’s ok if you create it specially for this application; we just want some evidence that you’re already familiar with the basic mechanics of collaborative development in R.
RStudio is a geographically distributed team which means you can be based anywhere in the USA (next year, we’ll try and support international interns too). That means, unless you are based in Boston or Seattle, you will be working 100% remotely, although we will pay for travel to one face-to-face meeting with your mentor. You will meet with your mentor for at least an hour a week, but otherwise you’ll be working on your own.
Projects
We are recruiting interns for the following five projects:
Bootstrapping methods: Implement 1) classic bootstrap methods (confidence intervals and other methods) to work with rsample, yardstick, and potentially infer as well as 2) modern bootstrap methods for performance estimation (e.g. 632, 632+ estimates) for rsample.
Skills needed: knowledge of bootstrapping methods (e.g. Ch 5 of Davidson and Hinkley) and tidyverse tools and packages. C++ would be advantageous but not required.
Mentor: Max Kuhn
broom: broom provides a bunch of methods to turn models into tidy data frames. It’s widely used but has lacked developer bandwidth to move it forward. Your job will be to resolve as many pull requests and issues as possible, while thinking about how to re-organise broom for long term maintainability.
Skills needed: experience with one or more modelling packages in R; strong communication skills
Mentor: David Robinson.
ggplot2: ggplot2 is one of the biggest and most used packages in the tidyverse. In this internship you will learn enough about the internals that you can start contributing. You will learn the challenges of working with a large existing codebase, in an environment when any API change is likely to affect existing code.
Skills needed: experience creating ggplot2 graphics for data analysis; previous package development experience.
Mentor: Hadley Wickham
Shiny: Shiny lets R programmers quickly create interactive web applications with R. The focus of this internship will be on addressing open issues and working on general user interface improvements. You will learn about how Shiny works, and gain experience working on a project that is at the interface of data analysis and web programming.
Skills needed: experience with JavaScript and CSS; some experience creating your own Shiny apps.
Mentor: Winston Chang
The Tidies of March: Construct ~30 tidyverse data analysis exercises inspired by the Advent of Code. The main goal is to create an Advent of Code type of experience, but where the exercises cultivate and reward mastery of R, written in an idiomatic tidyverse style.
Skills needed: documented experience using the tidyverse to analyze data and an appreciation of coding style/taste. Experience with the R ecosystem for making websites.
Mentor: Jenny Bryan.
Apply now!
The internship pays $USD 6000, lasts 10 weeks, and will start around June 1. Applications close March 12.
We value diverse viewpoints, and we encourage people with diverse backgrounds and experiences to apply.
Thanks for visiting r-craft.org
This article is originally published at https://www.rstudio.com/blog/
Please visit source website for post related comments.
