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.
You will:
Acquire and install R and RStudioImport and export data from multiple file formatsAnalyze data and generate graphics (including confidence intervals)Interactively conduct hypothesis testingCode multiple and moderated regression solutions
Who This Book Is For
Programmers and data analysts who are new to R. Some prior experience in programming is recommended.