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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:
10.5 Interactive sites or resources for hands-on learning:
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 typingbase::
and then hitting Tab to show a list of all functions found in base R).