This book provides a well-written approach to beginning to intermediate-level statistical principles using the R statistical language. It provides some mathematical formulas to help students understand the underlying principles of statistics. It has many excellent social science examples. It provides the statistical understanding with a practical approach to using the most valuable statistical tool—R. Please consider it. I have been looking for a good social science textbook using R—this may be the best so far.
This text successfully presents an introduction to data analysis using R in a highly approachable manner. The use of easy-to-follow examples and conceptual linkage across chapters makes this an outstanding option for undergraduate and graduate stats courses in the social sciences.
A great text with in-depth coverage of statistics concepts with helpful R code segments. Great installation directions and rationale for use of R programming versus others.
This text takes students on a journey through introductory and intermediate statistical methods along with R programming to accomplish the descriptive and inferential statistics. Images of RStudio and samples of R code are woven throughout the text to help students follow along.
An accessible book for any student to learn data analysis, even without a strong math background. It is a student-friendly book that is easy to read, with knowledge checks as the student reads along, and there are great code examples and visualizations that will greatly engage the student.
Very good book for explain R for students!
The book is an essential reading recommendation for the module as it details the application of R programming language in data visualisation and provides the fundamental statistical knowledge needed for a new student to the world of data analytics. Using this book in the module will help students develop the necessary data analytics knowledge and skills in a friendly and incremental manner.