Want to help out or contribute?

If you find any typos, errors, or places where the text may be improved, please let us know by providing feedback either in the feedback survey (given during class) or by using GitLab.

On GitLab open an issue or submit a merge request by clicking the "Edit this page " link at the side of this page.

10  Continued learning

10.1 Free online books:

• R for Data Science: Excellent open and online resource for using R for data analysis and data science.
• Fundamentals of Data Visualization: Excellent online resource for using ggplot2 and R graphics. The book mostly focuses on concepts and theory of how to visualize, rather than the practicalities (i.e. no coding involved).
• ModernDive: Statistical Inference via Data Science: Great book on using statistics and data science methods in R.
• Happy Git and GitHub for the useR (highly recommended): Specifically useful is the chapter on Daily Workflows using Git.
• Data Visualization: A practical introduction: A book that goes into practical as well as conceptual detail on how and why to make certain graphs, given your data.
• Online book for R Markdown: The go-to reference for learning and using R Markdown.
• Course material for a statistics class: Excellent course material for teaching statistics and R.

10.2 Quick references:

• RStudio cheatsheets: Multiple, high-quality cheatsheets you can print off to use as a handy reference.
• Tidyverse style guide: To learn about how to write well-styled code in R.

10.3 Articles:

• Good enough practicies in scientific computing: An article listing and describing some practices to use when writing code.
• Best practices in scientific computing.
• Case study of reproducible methodds in Bioinformatics: [@Kim2018a].

10.4 General sites:

• Organizing R Source Code.
• Hands-on tutorial for learning Git, in a web-based terminal.
• Simpler, first-steps guide to using Git.
• RStudio tutorial on using R Markdown.
• Markdown syntax guide.
• Pandoc Markdown Manual (R Markdown uses [pandoc]).
• Adding citations in R Markdown.

10.5 Interactive sites or resources for hands-on learning:

• Interactive tutorials for using R, within R.
• RStudio’s Learning Primers.

10.6 Videos:

• Video on using Git in RStudio.

10.7 Getting help:

• StackOverflow for tidyr.
• StackOverflow for dplyr.
• StackOverflow for ggplot2.
• Tip: Combine auto-completion with :: to find new functions and documentation on the functions (e.g. try typing base:: and then hitting Tab to show a list of all functions found in base R).