| R in Action, 3rd Ed (Manning) |
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Author: Dr. Robert I. Kabacoff With high salaries to be earned as a data scientist, R seems to be a useful language to learn R. Is R in Action the way to do it? This is the third edition of a book that first appeared in 2011. I gave it a rating of 4.5 out of 5 when I reviewed the first edition and upped the rating to 4.6 in my review of the second edition. At the end of that review my conclusion was: If you want to discover how to do statistics using R then this is a really good place to start. Highly recommended if you know stats and not R. Whereas the original edition had four sections - Getting Started, Basic Methods, Intermediate Methods and Advanced Methods, the second edition added Expanding Your Skills and the book grew by almost 200 pages. This revised and expanded third edition has added almost 50 pages and author Robert Kabacoff helpfully provides a list of what's new in this edition. The book has also seen a change to its subtitle which now reads: Data analysis and graphics with R and Tidyverse
According to Wikipedia, The tidyverse is a collection of open source packages for the R programming language that "share an underlying design philosophy, grammar, and data structures" of tidy data. Tidyverse is new since the previous edition of this book and Kabacoff writes: The advantages offered by tidyverse packages are changing the way data scientists write code in R .... In fact, the opportunity to describe how to use these packages for data analysis and visualization was a major motivation for writing a third edition of this book. Installing, loading and learning about packages are one of the topics in Chapter 1 and all the packages used in the book are listed in its Appendix E. ggplot2, one of the tidyverse packages, is used extensively in this edition, bringing a new focus on graphs and visualizations. It first appears in Chapter 4: Getting started with graphs, which is a new chapter in Part 1 of the book. Kabacoff explains that: ggplot2 aims to provide a comprehensive, grammar-based system for generating graphs in a unified and coherent manner, allowing users to create new and innovative data visualizations and the chapter is devoted to creating and elaborating a series of graphs, starting with a simple bivariate scatter plot and getting more sophisticated with trend lines and the use of facets and customization features such as scales, labels and themes. ggplot2 is revisited in other new chapters in each of the other original sections: Part II now starts with Chapter 6: Basic graphs. Here you'll find methods for creating common univariate graphs - bar plots, pie charts, histograms, density plots, box plots, treemaps and dot plots. Chapter 11: Intermediate graphs is in Part III and covers graphical methods for bivariate and multivariate relationships, including scatter plots, bubble plots, line graphs, corrgrams - a relatively recent tool for visualizing data in correlation matrices and mosaic plots. Chapter 19: Advanced graphs in Part IV focuses on customization - creating a graph the precisely meets your needs. It covers adding annotations, combining multiple graphs into a single plot and creating interactive graphs. This edition also adds examples for topics that were added in the second edition, clustering, classification and time series. Kabacoff says that he deliberately chooses small data sets so that the examples are easy to follow. I thoroughly approve of that. Coverage of missing values (In Part IV Chapter 18) has been expanded with new sections on k-nearest neighbor and random forest approaches to imputing missing values. In Part V there are new sections in Chapter 20: Advanced programming on nonstandard evaluation and visual debugging and in Chapter 21: Creating dynamic reports on parameterized reports and common coding errors together with expanded coverage of R Markdown. Finally Chapter 22: Creating a package has been completely rewritten to incorporate the use of new tools for streamlined package creation, together with new sections on how to share and promote packages through CRAN, GitHub and software-generated websites. At the end of this review of the changes to the third edition my conclusion is that this is now an excellent guide to using R for data analysis and graphing/data visualization using ggplot2. R in Action: Data Analysis and Graphics with R (2e) For recommendations of Big Data books see Reading Your Way Into Big Data in our Programmer's Bookshelf section. For more Book Watch just click. Book Watch is I Programmer's listing of new books and is compiled using publishers' publicity material. It is not to be read as a review where we provide an independent assessment. Some, but by no means all, of the books in Book Watch are eventually reviewed. To have new titles included in Book Watch contact BookWatch@i-programmer.info Follow @bookwatchiprog on Twitter or subscribe to I Programmer's Books RSS feed for each day's new addition to Book Watch and for new reviews.
R in Action: Data Analysis and Graphics with R (2e) For recommendations of Big Data books see Reading Your Way Into Big Data in our Programmer's Bookshelf section. For more Book Watch just click. Book Watch is I Programmer's listing of new books and is compiled using publishers' publicity material. It is not to be read as a review where we provide an independent assessment. Some, but by no means all, of the books in Book Watch are eventually reviewed. To have new titles included in Book Watch contact BookWatch@i-programmer.info Follow @bookwatchiprog on Twitter or subscribe to I Programmer's Books RSS feed for each day's new addition to Book Watch and for new reviews.
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| Last Updated ( Wednesday, 12 October 2022 ) |
