Abstract
Conducting meaningful research is essential in both academics and the industry. A business research report can help managers make informed decisions. Good research adds to the extant literature in academia. Therefore, understanding research methods, especially quantitative data analysis, may help researchers draw valuable insights from available data. This textbook introduces readers to data analysis and inferential statistics with R. The application of R brings various freely available CRAN libraries made for R to the users, which offer cutting-edge and robust data transformation and analysis tools for researchers with virtually limitless possibilities. The book is crisply written and provides examples and a step-by-step guide.
The book is divided into four chapters:
Chapter 1 discusses the basics of R, from installation to basic commands. This chapter introduces the readers to data, variables and operators in R. There are discussions on vectors, lists, matrices, arrays, etc., and their treatment in R. The chapter introduces the readers to built-in R functions and discusses creating user-defined logical functions (if/else) and loops (for/while).
Chapter 2 discusses data frames, dictionaries and data exploration through descriptive statistics. This chapter also discusses importing data from spreadsheets into R. The chapter introduces the reader to markdown scripts and notebooks.
Chapter 3 explores base graphics in R and its applications in data exploration. The chapter also delves into date formats in R and factor data types.
Chapter 4 introduces inferential statistics with R. This chapter introduces Student t-test, Chi-square test, ANOVA, correlation and regression (two-variable, multiple and logistic). The chapter also looks at using step-wise regression for selecting the best-fit model.
This book serves as a good resource for beginner researchers in quantitative research. The solved examples and illustrations are helpful for readers. Future editions of the book may include more advanced R functions and libraries such as Tidy, Dplyr, Caret, GGplot, GGplot2, etc.