10 Must-Read Books On R Programming In 2024

Here is a list of the top 10 R programming books that will help you learn how to code in 2024.

R is still the most popular programming language for data science and statistical research, even though the fields are always changing. As we move into 2024, there is still a huge need for skilled R coders.

Having the right tools available is important whether you are new to R and want to learn more or you are an experienced R user who wants to improve your skills. Please find below a carefully chosen list of the ten best R programming books that will help you learn and master this powerful language.

“R for Data Science” by Garrett Grolemund and Hadley Wickham:

For anyone interested in R for data science, this book is a must-read. It gives a complete explanation of how to change data, show it visually, and do statistical analysis. Grolemund and Wickham’s book is a must-read for both new and experienced practitioners because it is easy to understand and full of useful information based on real-life cases.

“R Cookbook” by Paul Teetor:

The “R Cookbook” is a must-have for anyone who wants to learn how to solve common problems in R code. Teetor gives you a set of recipes that can help you with a lot of different jobs, from changing data to using advanced visualization tools. There are clear explanations and examples of code for each recipe, which makes it easy for readers to use the answers in their own projects.

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“Advanced R” by Hadley Wickham:

After going over the basics in “R for Data Science,” “Advanced R” goes into more detail about how to program in R. Wickham looks at advanced methods for working with data, showing data, and doing statistical analysis.

This gives us a lot of useful information about how to make code run faster and use R to its fullest potential. If you want to take your R programming skills to the next level, you need to read this book.

“R Graphics Cookbook” by Winston Chang:

For insights and results to be communicated in an interesting way, they need to be visualized well. In “R Graphics Cookbook,” Chang gives a huge collection of ideas for using R to make powerful visualizations.

This book covers a lot of different methods, from simple plots to complex graphics. It is a must-have for anyone who wants to learn how to visualize data in R.

“Data Manipulation with R” by Winston Chang:

Changes to data are an important part of any data analysis job. Chang has written this book to give you a complete guide to cleaning, changing, and summarizing data in R. Readers will learn how to quickly change data to get useful insights and make smart decisions with the help of real-life cases and step-by-step guides.

“R in a Nutshell” by Joseph Adler:

As the title suggests, “R in a Nutshell” is a short but complete guide to programming in R. Adler goes over the basics of the language, including its syntax and popular uses. This makes it a great book for people who are new to R programming and want to get started quickly.

“R for Everyone” by Jared P. Lander:

“R for Everyone” is another great resource for people who are new to R because it gives a friendly introduction to the language and how it can be used.

This book is great for people who have little to no experience with programming because Lander writes in an easy-to-understand way and uses real-life examples to help readers understand difficult ideas.

“R in Action” by Robert I. Kabacoff:

R in Action is a hands-on guide with lots of examples of how to use R to analyze and display data. This book gives useful information on how to deal with data, make descriptive and inferential statistics, make graphs that look good, and build and test predictive models.

“Machine Learning with R” by Brett Lantz:

Machine Learning with R is a useful book for learning about machine learning with R. You will learn about different machine learning methods, such as classification, regression, clustering, and recommendation systems, by using real-world datasets as examples.

There are also ways to evaluate and improve models, use cross-validation and put feature engineering into practice in this book.

“R Programming for Data Science” by Roger D. Peng:

Last on our list is another great book by Roger D. Peng that is all about using R code for data science. This book is helpful for both new and experienced data scientists because it covers a wide range of topics related to data manipulation, data display, and statistical analysis.

In conclusion, data scientists, statisticians, and analysts can find a lot of work in the area of R programming. The ten books listed above will help you learn R programming well in 2024 and beyond, whether you’re just starting out or want to improve your skills.

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