First, we introduce the R statistical computing language (R Core Team, 2021), which is the software language we will use to import and clean data as well as create and analyze PLS path models. Software packages are a series of software functions and features with a similar purpose bundled into a single set. This chapter introduces the two software packages that will be used throughout this textbook. Integrated development environment (IDE).We do, however, provide useful open-source resources for learning R, which can be accessed from the R console RStudio environment. This chapter is not intended as a tutorial on the writing of code in the R programming language. Finally, we address creating and managing R projects and scripts, gaining access to documentation and help via various sources.
Then, we discuss the software layout and demonstrate how to interact with the software. We walk the reader through downloading both the R language and the RStudio integrated development environment (IDE). In this chapter, we introduce the R programming language as well as its popular development environment in the form of RStudio.
Today, it has become the language of choice for empirical researchers. The R programming language, in particular, has benefitted from this development alongside of traditional graphical user interface (GUI) software. Computational statistics is now an increasingly popular method of analysis for researchers that combines a vast array of algorithms, statistical methods, and the power of functional coding.
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