Learn how to use R 4, write and save R scripts, read in data files, use built-in functions, and write custom statistical functions. This in-depth tutorial includes key R 4 features such as a new color palette for charts, an enhanced reference counting system, and normalization of matrix and array types where matrix objects will formally inherit from the array class, eliminating many inconsistencies.  Each chapter starts with a list of learning outcomes and concludes with a summary of any R functions introduced in that chapter, a list of specific cited references for more reading, and some exercises to test your new knowledge. The text opens with a hands-on installation of R (and a limited number of packages) for both Windows and macOS. The bulk of the book is an introduction to statistical methods (a non-proof-based introduction) that relies heavily on R (and R visualizations) to understand, motivate, and perform statistical outcomes.  Beginning R 4 shows the use of R in specific cases such as one-way ANOVA analysis, linear and logistic regression, data visualization, parallel processing, bootstrapping, and more. It takes a hands-on, example-based approach incorporating best practices with clear explanations of the statistics being done.  Additionally, the text comprehensively includes learning outcomes for Texas common course number Math 1342 (elementary statistical methods).  What You Will Learn Acquire and install R Import and export data and scripts Analyze data and generate graphics Program in R to write custom functions Use R for interactive statistical explorations Conduct bootstrapping and other advanced techniques   Who This Book Is For  Programmers and data analysts who are new to R.  Some prior experience in programming is recommended.  weiterlesen