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The objective of this text is to introduce RStudio to practitioners and students and enable them to use R in their everyday work. It is not a statistical textbook, the purpose is to transmit the joy of analyzing data with RStudio. Practitioners and students learn how RStudio can be installed and used, they learn to import data, write scripts and save working results. Furthermore, they learn to employ descriptive statistics and create graphics with RStudio. Additionally, it is shown how RStudio can be used to test hypotheses, run an analysis of variance and regressions. To deepen the learned content, tasks are included with the solutions provided at the end of the textbook. This textbook has been recommended and developed for university courses in Germany, Austria and Switzerland.
List of contents
Comment.- 1 R and RStudio.- 2 Data analysis basics with RStudio.- 3 Data tourism (simulated).- 4 Describing data with RStudio.- 5 Testing normal distribution with RStudio.- 6 Testing hypotheses with RStudio,- 7 Linear regression with RStudio.- 8 Further reading.- 9 Appendix: 1 Questionnaire, 2 Data "tourism.xlsx" including legend, 3 How to deal with missing data, 4 Solutions for the task.
About the author
Franz Kronthaler is Professor of Statistics and Economics at the University of Applied Sciences of the Grisons in Switzerland. He teaches statistics at the bachelor and master level for more than ten years and authored the German textbook “Statistik angewandt mit Excel – Datenanalyse ist keine Kunst”.
Silke Zöllner is a Research Associate at the Institute of Business and Regional Economics at Lucerne University of Applied Sciences and Arts. She is a doctoral candidate and teaches statistics at the bachelor level.
Summary
The objective of this text is to introduce RStudio to practitioners and students and enable them to use R in their everyday work. It is not a statistical textbook, the purpose is to transmit the joy of analyzing data with RStudio. Practitioners and students learn how RStudio can be installed and used, they learn to import data, write scripts and save working results. Furthermore, they learn to employ descriptive statistics and create graphics with RStudio. Additionally, it is shown how RStudio can be used to test hypotheses, run an analysis of variance and regressions. To deepen the learned content, tasks are included with the solutions provided at the end of the textbook. This textbook has been recommended and developed for university courses in Germany, Austria and Switzerland.
Additional text
“This book can be used for self-study by those in business administration, engineering, and the social, health, or biological science fields, to become competent in statistical programming. … it can be used as supplementary material for statistics-related courses. … Each chapter ends with a summary of R commands and appropriate exercises. Moreover, the appendix presents detailed code solutions to facilitate faster code-learning among readers. … this book can evoke readers’ interests in analyzing data and reduce the learning difficulty.” (Mei-Hsien Lee, Biometrics, Vol. 77 (4), December, 2021)
“I enjoyed reading this book. The authors were good at creating a complete tool for beginners to start along the path of essential statistical analysis in R. I recommend this book, for its content, writing, and organization, to undergraduate or graduate students of disciplines other than statistics but also to professionals (non-statisticians) who would like to acquire or improve their analysis skills and understand the depth of R functionality, especially nowadays that R use is very popular among data analysts.” (Georgios Nikolopoulos, ISCB News, iscb.info, Vol. 72, December, 2021)
Report
"This book can be used for self-study by those in business administration, engineering, and the social, health, or biological science fields, to become competent in statistical programming. ... it can be used as supplementary material for statistics-related courses. ... Each chapter ends with a summary of R commands and appropriate exercises. Moreover, the appendix presents detailed code solutions to facilitate faster code-learning among readers. ... this book can evoke readers' interests in analyzing data and reduce the learning difficulty." (Mei-Hsien Lee, Biometrics, Vol. 77 (4), December, 2021)
"I enjoyed reading this book. The authors were good at creating a complete tool for beginners to start along the path of essential statistical analysis in R. I recommend this book, for its content, writing, and organization, to undergraduate or graduate students of disciplines other than statistics but also to professionals (non-statisticians) who would like to acquire or improve their analysis skills and understand the depth of R functionality, especially nowadays that R use is very popular among data analysts." (Georgios Nikolopoulos, ISCB News, iscb.info, Vol. 72, December, 2021)