R Best Practice
1
Introduction
1.1
End-User Computing
1.2
Risk Analysis
1.3
Documentation
1.4
Testing
1.5
Reproducibility
1.6
Change control
1.7
Access control
1.8
Appendix: Solvency II: internal model approval process data review findings
1.8.1
Sub-risk 5: IT environment, technology and tools
2
Software
2.1
Core R Applications
2.2
Supporting Software
2.3
Package Versions
3
Writing R Code
3.1
Writing Style
3.1.1
Tidyverse
3.1.2
Files
3.1.3
Syntax
3.1.4
Functions
3.1.5
Pipes
3.1.6
Documentation
3.2
Structure
3.2.1
Projects
3.2.2
Folders
3.2.3
README
3.2.4
R Packages
3.3
R Markdown
3.3.1
Markdown
3.3.2
R Code Chunks
3.3.3
R Notebooks
3.3.4
Executing chunks
3.3.5
Saving and sharing
4
Recommended Packages
4.1
Data Wrangling
4.1.1
Tidy Data
4.1.2
Importing Data
4.1.3
Transforming Data
4.2
Visualising Data
4.2.1
Grammer of Graphics
4.2.2
Interactive Graphics
5
Development Practices
5.1
Package Development
5.1.1
Documentation
5.1.2
Testing
5.2
Version Control
5.2.1
Git
5.2.2
GitHub Flow
5.2.3
Git Flow
5.2.4
GitKraken
5.3
Managing Package Dependencies
6
Advanced Topics
6.1
Non-Standard Evaluation
6.1.1
Strings and quoting
6.1.2
Base R evaluation
6.1.3
Tidy Evaluation
7
Cheatsheets
References
Published with bookdown
R Best Practice
Chapter 7
Cheatsheets