A Comprehensive Guide to R Programming for Data Analytics provides a comprehensive presentation of univariate and multivariate statistical models within the general linear model and generalized linear model framework to analyze simple and complex data using R software. This book presents popular R packages that are used in data mining (e.g., caret-classification and regression, lubridate-dates and times, string-R for string data) and visualization (e.g., ggplot, ggthemes, ggtext). The R packages used to analyze data using a particular statistical model are explained through real-world and publicly available datasets. R codes are presented in a manner that helps readers understand the program code syntax.

Examples of real-world data sets from a variety of academic disciplines are provided so that a wide audience can learn R programming to analyze data in their research. The book provides tips, recommendations, and strategies to troubleshoot common issues in R syntax, as well as definitions of key terms. Checkpoints are included to recap the concepts learned in each chapter. The book helps readers enhance their conceptual understanding and practical application of statistical models to real-world datasets, and enables readers to gain competency in R programming, which is an important skill in today’s data-driven market.

• Presents a wide array of statistical models to accommodate data analytics for various data types, including cross-sectional, clustered, longitudinal, time-series, non-parametric, and big data
• Illustrates the identification and explanation of common syntax errors in R and how to resolve them in each chapter, including explanations on how to adjust the R codes based on variable names, data analysis, and output options within a particular statistical model
• Presents categorical data analysis measures, including statistics such as chi-square, Mann-Whitney, Kruskal-Wallis, Wilcoxon signed rank and rank sum tests, as well as Fisher’s exact test, conditional and marginal odds ratio, relative risk, and risk ratio using the Cochran-Mantel-Haenszel statistic and Hosmer-Lemeshow chi-square test