r-for-research

R for Research: Introduction to good practices

These materials were developed for an LSHTM workshop introducing good practices using R for research. The workshop includes:

Theme	Content	Key tools
Project set up	Reproducibility: RStudio projects Modularity: separating data, code, and reporting Documentation: READMEs and dependencies	RStudio Projects renv
Writing code	Code style guidelines Writing, testing, and getting help Refactoring: managing existing code	lintr stylr
Communicating & collaborating	Reporting with Rmarkdown Collaboration with Github	Rmarkdown Github

Workshop content draws on:

• A highly-recommended discussion of good enough practices in scientific computing
  • Wilson G, Bryan J, Cranston K, Kitzes J, Nederbragt L, Teal TK (2017) Good enough practices in scientific computing. PLoS Comput Biol 13(6): e1005510. https://doi.org/10.1371/journal.pcbi.1005510
• Several guides and textbooks, linked below

Workshop materials

• View workshop slides
• Go to Github repository
  • Download the tutorials:
    • R Markdown
    • Github
• Feedback welcome! Add your comments or suggestions for improving the workshop by posting in the repository forum.
  • Open an Issue

Learning more

General resources

Most of these resources cover the key topics of the session (workflow, style guidance, R Markdown, and Git). If one doesn’t make sense to you, try another for a different explanation.

• Open Data Science with R: https://carpentries-incubator.github.io/open-science-with-r
• R for Statistical Programming: https://smac-group.github.io/ds/
• Efficient R: https://csgillespie.github.io/efficientR/
• Reproducible Data Science: https://ecorepsci.github.io/reproducible-science/index.html

Setting up RStudio Projects

• Creating efficient workflows: https://csgillespie.github.io/efficientR/workflow.html
• Managing dependencies with renv: https://ecorepsci.github.io/reproducible-science/renv.html

Coding

• Code style guides: https://style.tidyverse.org
• Writing functions: https://r4ds.hadley.nz/functions

Reporting and collaborating

• Rmarkdown:
  • https://r4ds.had.co.nz/r-markdown.html
  • https://rmarkdown.rstudio.com/docs/articles/rmarkdown.html
• Github:
  • https://rfortherestofus.com/2021/02/how-to-use-git-github-with-r
  • https://happygitwithr.com

Questions & resources

• Using R with large or confidential data sets
  • This guide covers many useful practices, including the highly recommended data.table package:
    • https://marketsplash.com/tutorials/r/how-to-handle-large-datasets-in-r
  • Arrow is a great option for storing very large (roughly >8GB) datasets efficiently, and can be read into R (almost) as easily as a .csv file:
    • https://r4ds.hadley.nz/arrow.html
  • When using Github with large or confidential data sets, one strategy might be to use the .gitignore file to prevent Github from tracking or uploading a given data file:
    • https://docs.github.com/en/get-started/getting-started-with-git/ignoring-files
• Writing reports with Rmarkdown
  • An example of a paper using Rmarkdown:
    • https://github.com/epiforecasts/euro-hub-ensemble/tree/main/analysis
  • Using trackdown for tracking changes with google docs:
    • https://cran.r-project.org/web/packages/trackdown/vignettes/trackdown-workflow.html

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