Resources for R and Data Science

by Julie Lowndes

September 29, 2016

Conducting an Ocean Health Index assessment requires collaborative coding in R, and we are often asked for the best way to learn R as well as other data science concepts and tools. There are many great resources available but here are some of the free, online resources we have found helpful as we’ve been learning:

• R for data science by Hadley Wickham and Garrett Grolemund (book)
• RStudio’s webinars by RStudio (on-demand videos)
• RStudio’s cheatsheets by RStudio (PDFs)
• R Packages by Hadley Wickham (book)
• Advanced R by Hadley Wickham (book)
• Happy Git With R by Jenny Bryan (short course)
• UBC Stats545: Data wrangling, exploration, and analysis with R by Jenny Bryan (university course)
• Swirl (interactive course taught in R)
• Software Carpentry (workshops, teaching community, self-paced courses)
• Data Carpentry (workshops, teaching community, self-paced courses)
• #rstats on Twitter (online discussion)
• Not so standard deviations by Roger Peng and Hilary Parker (podcast)
• R-Bloggers (blog)

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Our OHI team has also created materials to help teach these concepts.

• Intro to Open Data Science (2-day workshop or self-paced course on the RStudio-GitHub workflow, under active development)
• Software Carpentry workshop at Oxford University (2-day workshop, self-paced course on the RStudio-GitHub workflow)
• Introduction to RStudio Awesomeness (blog)
• Github Quickstart for Scientists (2-hour workshop, self-paced course)
• Spatial analysis in R: Rasters (2-hour workshop, self-paced course)
• Spatial analysis in R: Vectors (2-hour workshop, self-paced course)
• Data visualization using ggplot2 (2-hour workshop, self-paced course)
• Making free websites with RStudio’s R Markdown (2-hour workshop, self-paced course)

Update:

Also check out our publication Our path to better science in less time using open data science tools (Lowndes et al. 2017, Nature Ecology and Evolution). An accompanying website ohi-science.org/betterscienceinlesstime lists media coverage of our publication, including a Q&A interview in Nature.

We also have a list of references in the academic literature and media discussing the importance of open data science tools in science.