Thus, researchers who learn and adopt computational methods will have the advantage of being able to apply and adapt the latest techniques to their research, while also being competent and conversant with industry trends. Similarly, academic research is rapidly adopting computational methods, involving the implementation of analytic techniques for inferential analysis and machine learning into computer programs (Hair & Sarstedt, 2021). Additionally, we will provide examples of R code throughout this textbook, so we start by looking at the syntax and formatting that we will use to distinguish code from regular text.ĭata analytics using computationally intensive methods is becoming an increasingly important, strategic capability for companies to transform the data collected during business activities into information that can assist effective decision-making and policy creation. This textbook will not serve as a comprehensive resource for learning R, so we will share further resources for learning this programming language and helpful documentation on the Internet. We then offer a basic introduction to writing analytic scripts in R. We will explain how to download and install the software required, how to interact with the software, and how to store your data and code. We will then introduce the RStudio (RStudio Team, 2021) application, which is an integrated development environment that enables you to easily and productively conduct computational analyses using the R language. 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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