Books To learn about R Programming
R is an open-source, free programming language and software environment for computational statistics, graphics, and data analysis. In the mid-1990s, Ross Ihaka and Robert Gentleman, professors at the University of Auckland, New Zealand, developed R. It is freely available to anyone and distributed under the GNU General Public License. Statisticians, data analysts, and data scientists widely use R for performing tasks such as data cleaning, data exploration, data visualization, statistical modeling, and machine learning, as it provides a wide range of graphical and statistical techniques, including linear and non-linear modeling, time-series analysis, clustering, and classification.
Due to its flexibility, scalability, and robust data analysis capabilities, R has become a popular choice for both academic and industry professionals. Organizations like Google, Facebook, and Microsoft also use R for data analysis. Its popularity is due to its scalability, flexibility, powerful data analysis capabilities, and ability to integrate with other programming languages and tools.
Below are some highly recommended R Programming Books that will be helpful in your journey to be a pro at R. Whether you’re a student, researcher, or data enthusiast, these books will help you start with R and build a solid foundation for future learning.
10 Different R Programming Books
| Sr.no | Books | Author | Published | Rating |
| 1. | The Book of R: A First Course in Programming and Statistics | Tilman M. Davies | 2016 | Amazon: 4.5
Goodreads: 4.1 |
| 2. | Discovering Statistics Using R | Andy Field, Jeremy Miles, Zoë Field | 2012 | Amazon:4.5
Goodreads:4.2 |
| 3. | The Art of R Programming | Norman Matloff | 2011 | Amazon: 4.5
Goodreads: 4.0 |
| 4. | Hands-On Programming with R: Write Your Functions and Simulations
|
Garett groelemund | 2014 | Amazon:4.5
Goodreads: 4.3 |
| 5. | R Cookbook 2e: Proven Recipes for Data Analysis, Statistics, and Graphics | Jd Long, Paul Teetor | 2019 | Amazon: 4.6
Goodreads: 4.0 |
| 6. | R in Action | Robert L. Kabacoff |
2015 |
Amazon 4.5
Goodreads- 4.2 |
| 7. | R Packages | Hadley Wickham | 2015 | Amazon 4.7 Goodreads -4.4 |
| 8. | Practical Data Science with R | Nina Zumel, John Mount | 2014 | Amazon 4.3 Goodreads- 4.1
|
| 9. | R for Everyone: Advanced Analytics and Graphics | Jared P. Lander | 2017 | Amazon:4.4
Goodreads: 4.5 |
| 10. | Text Mining with R: A Tidy Approach | Julia Silge, David Robinson | 2017 | Amazon: 4.5
Goodreads: 4.3
|
Let us look at the R Programming books and see which one best suits your needs:-
1. The Book of R: A First Course in Programming and Statistics
Author: Tilman M. Davies
Get this Book here
Book Review
Much ahead of its simple title, the Book takes us on a full-fledged journey of learning R with tools such as ggplot2 and ggvis and visualizations using the rgl package. The Book is meant equally well for data science and statistics lovers alike.
Key Takeaways from that Book
- The Book takes you on a baby step level to reach excellence in programming in R.
- It contains topics such as the fundamentals of R, creating functions, and how to be clean and precise while combining results from data.
- Preferred for people having no prior background in R
2. Discovering Statistics Using R
Author: Andy Field, Jeremy Miles, Zoe Field
Get this Book here
Book Review
This a great book to learn a lot of helpful information regarding R and statistics in a way that leaves you amazed and enthralled for this statistical journey. It incorporates learning fundamentals of statistics with a complete set of illustrations and the addition of fun and creative characters through which the Book is woven and told.
Key Takeaways from that Book
- Great emphasis is done on intermediate-level analysis such as ANOVA and MANOVA.
- Lays a strong foundation in topics such as intelligent Alex’s tools, fitting statistical models, randomization, analyzing data, etc., in a fun and unique approach.
3. The Art of R Programming: A Tour of Statistical Software Design
Author: Norman Matloff
Get this Book here
Book Review
It is recommended to anyone setting their feet in the world of R programming. Despite being old in the market, it’s still much preferred by readers, both professionals and beginners alike.
Key Takeaways from that Book
- Get to create artful graphs, learn how to do text analysis and image manipulation through various r packages, and practice code debugging.
- The highlights of this Book include math and simulations in R, performance tradeoffs, drawing a parallel with some other language to R, etc.
4. Hands-On Programming with R: Write Your Functions And Simulations
Author: Garett Groelemund
Get this Book here
Book Review
This a must-have book for r programming if you are keen to focus more on the programming part instead of built-in functions in R. It takes us through a problem-solving approach to tackling real-world programming problems. Helpful in writing programs on vectorized R code and r’s package systems.
Key Takeaways from that Book
- The exercises are well equipped with many practice questions, and proper implementation of code blocks with the help of illustrations is also present here.
- Gives an extra edge with a hands-on learning scheme about modifying values in logical subsetting, slot machines, S3 systems, etc.
5. R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics
Author: Paul Teetor
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Book Review
Taking on many concepts of r and compiling it exceptionally makes the learnings last longer with the help of crystal problem solving and engages the reader in the Book with its easy flow. A comprehensive recipe collection of several statistical procedures such as null hypotheses and user-oriented tasks to work with.
Key Takeaways from that Book
- Jog your brain through the pathways of R in an accessible and straightforward manner.
- Lists out creating vectors, data frames working with probability, linear regressions, etc.
6. R in Action
Author: Robert L. Kabacoff
Get this Book here
Book Review
The first-of-its-kind amalgamation of the R systems and statistical graphics brings the reader elegant ways to comprehensive incoherent data. It provides a step-by-step guide to understanding modern-day R with suitable examples and illustrations.
Key Takeaways from that Book
- Lays generalized linear models, factor analysis, and resampling statistics crisply and concisely.
- Highlights the elements of ANOVA, general linear models, use practical test cases, and statistical modeling with a fun, engaging voice along with a dash of humor.
- The Book oscillates between being the perfect fit for beginners and a handy tool for professional data scientists.
7. R Packages: Organize, Test, Document, and Share Your Code
Author: Hadley Wickham
Get this Book here
Book Review
Writing r packages was never as easy as it is now after the arrival of this Book. Deals with package developments, CRAN submission process, and reusable R functions in a practical approach that demystifies the concepts of R packages.
Key Takeaways from that Book
- The reader will be able to know more about vignettes, organizing functions into files, and package metadata.
- Learn maintenance and distribution of CRAN, dependencies in practice, and advanced testing techniques with an immersive experience.
- The Book is highly suitable for people who want to indulge in the in and out of dev tools for streamlining.
8. Practical Data Science with R
Authors: Nina Zumel, John Mount
Get this Book here
Book Review
Peppered down with a foray into model preparation, quick Ml references through R, and collecting and analyzing imperative data science’s brand-new tools, this book will not be on your shelf for a long time. Presents the reader with a general overview of data science through meaningful code blocks that can be practiced simultaneously in Rstudio without going into too much technical jargon.
Key Takeaways from that Book
- Merges the business and technical aspects with chapters on project lifestyle, practical data analysis, and doing operations on data.
- They are mainstreamed for technical people eager to widen their horizons in high-level data science.
9. R for Everyone: Advanced Analytics and Graphics
Author: Jared P. Lander
Get this Book here
Book Review
A comprehensive take on the beginner instructions of wanting to program in R. It broadly speculates the worries of a budding learner and dishes out techniques for the explanations to be fresh in one’s mind.
Key Takeaways from that Book
- Defining controlled program flows and non-linear models, efficiently creating probability distributions
- Setting elastic Net and Bayesian methods, helping the code become reproducible with LaTex, and RMarkdown are topics in this edition.
10. Text Mining with R: A Tidy Approach
Author: Julia Silge, David Robinson
Get this Book here
Book Review
Super handy, interesting to read, and filled with tasks in text mining with R to build the reader’s comprehension in packages such as dpyr, broom,ggplot2, etc., the correct read for strengthening your knowledge in sentiment analysis, summarizing, and modeling in natural language processing despite containing some dated versions.
Key Takeaways from that Book
- Examine the tidy text format, n-grams, correlations, and topic modeling in a precise and in-depth way.
- Explore case studies of Usenet text and NASA metadata in a start-to-finish exercise guide.
- Accessible online documentation is available for this Book.
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