Each chapter starts with a list of learning outcomes and concludes with a summary of any R functions introduced in that chapter, along with exercises to test your new knowledge. The text opens with a hands-on installation of R and CRAN packages for both Windows and macOS. The bulk of the book is an introduction to statistical methods (non-proof-based, applied statistics) that relies heavily on R (and R visualizations) to understand, motivate, and conduct statistical tests and modeling.
Beginning R 4 shows the use of R in specific cases such as ANOVA analysis, multiple and moderated regression, data visualization, hypothesis testing, and more. It takes a hands-on, example-based approach incorporating best practices with clear explanations of the statistics being done.
- Acquire and install R and RStudio
- Import and export data from multiple file formats
- Analyze data and generate graphics (including confidence intervals)
- Interactively conduct hypothesis testing
- Code multiple and moderated regression solutions
Programmers and data analysts who are new to R. Some prior experience in programming is recommended.
Chapter 2: Installing Packages and Using Libraries
Chapter 3: Data Input and Output
Chapter 4: Working with Data
Chapter 5: Data and Samples
Chapter 6: Descriptive Statistics
Chapter 7: Understanding Probability and Distributions
Chapter 8: Correlation and Regression
Chapter 9: Confidence Intervals
Chapter 10: Hypothesis Testing
Chapter 11: Multiple Regression
Chapter 12: Moderated Regression
Chapter 13: Analysis of Variance