R is a programming language and software environment for statistical computing and graphics. It was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently developed by the R Development Core Team. The latest version of R is 4.1.2.

R has a strong community of developers and users around the world, making it one of the most popular open source platforms for statistical computing, predictive analytics and machine learning.

The platform is used in various industries including pharmaceuticals, healthcare, finance and social sciences among others.

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The average salary for an R Programmer in the United States is $80,886.

The salary range for an R Programmer typically falls between $71,353 and $91,230.

*Source salary.com

Best R Programming Books

What are the best books on R programming?

For anyone interested in learning about or advancing their knowledge of this powerful statistical programming language, it’s important to choose the right texts.

Let’s take a look at some of the best R programming books, so you can get started on this challenging but rewarding endeavor!

Read: A Learner’s Guide to the Best Visual Basic Books


  • These books cover the basics of R programming
  • Teaches different steps involved in R coding
  • How to use the R Studio?
  • Installing packages and libraries
  • Examples of using R in Business
  • Data types in R
  • Basic Operations in R
  • More advanced operations in R (Vectors, Matrices, Lists)
  • Input and Output in R (Console and Files)
  • Subsetting data in R (Subset(), subsetting data frames, subsetting matrices)
  • Conditionals, loops, and functions in R
  • Graphics in R using ggplot2
  • Advanced Functions
  • Common pitfalls and FAQs

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List of the Best R Programming Books

1. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data

by Hadley Wickham, Garrett Grolemund

Best Suited For : Absolute Beginners

R for Data Science: Import, Tidy, Transform, Visualize, and Model Data by Hadley Wickham, Garrett Grolemund

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R for Data Science: Import, Tidy, Transform, Visualize, and Model Data by Hadley Wickham, Garrett Grolemund — Hadley Wickham is an Assistant Professor and the Dobelman FamilyJunior Chair in Statistics at Rice University. His research focuses on how to make data analysis better, faster and easier, with a particular emphasis on the use of visualization to better understand data and models.

Garrett Grolemund is a statistician, teacher and R developer who currently works for RStudio. Garrett received his Ph.D at Rice University.

In R for Data Science, you’ll learn how to wrangle data into shape and make it talk using one of today’s most popular programming languages.

In this book, you’ll learn how to:

Wrangle—transform your datasets into a form that is convenient for analysis
Program—learn powerful R tools for solving data problems with greater clarity and ease
Explore—examine your data, generate hypotheses, and perform a quick test
Model—provide a low-dimensional summary that captures true “signals” in your dataset
Communicate—learn R Markdown for integrating prose, code, and results.


This is one of the best books I have come across while I was preparing for R Programming course. The chapters are good in detail and covers all the topics necessary to start learning the language.

Part I Explore

Chapter 1 is on Data Visualization with ggplot2
Chapter 2 is on Workflow: Basics
Chapter 3 is on Data Transformation with dplyr
Chapter 4 is on Workflow: Scripts
Chapter 5 is on Exploratory Data Analysis
Chapter 6 is on Workflow: Projects

Part II Wrangle

Chapter 7: Tibbles with Tibble
Chapter 8 is on Data Import with Readr
Chapter 9 is on Tidy Data with Tidyr
Chapter 10 is on Relational Data with Dplyr
Chapter 11 is on Strings with Stringr
Chapter 12 is on Factors with Forcats
Chapter 13 is on Dates and Times with Lubridate

Part III Program

Chapter 14 is on Pipes with Migrittr
Chapter 15 is on Functions
Chapter 16 is on Vectors
Chapter 17 is on Iteration with purrr

Part IV Model

Chapter 18 is on Model Basics with Modelr
Chapter 19 is on Model Building
Chapter 20 is on Many Models with Purrr and Broom

Part V Communicate

Chapter 21 is on R Markdown
Chapter 22 is on Graphics for Communication with ggplot2
Chapter 23 is on Markdown Formats
Chapter 24 is on R Markdown Workflow


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2. The Book of R: A First Course in Programming and Statistics

by Tilman M. Davies

Best Suited For : Beginners

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The Book of R: A First Course in Programming and Statistics by Tilman M. Davies

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The Book of R: A First Course in Programming and Statistics by Tilman M. Davies — Tilman M. Davies is a senior lecturer at the University of Otago in New Zealand. He he teaches statistics and R. He has been programming in R for over 15 years.If you’re interested in data analysis, statistics or computer programming, then The Book of R: A First Course in Programming and Statistics is for you.

This book is for beginners with no prior experience needed. It will guide you through a wide range of topics including statistical tests, hypothesis testing, sampling methods and basic graphics.

Although it was written with R specifically in mind (this might be the main reason why I like it), most of its information can be applied to other tools and languages as well.


I scanned though a numerous book on R programming before I finally had my hands on this book. Well-written and well-explained chapters with examples. Good book!


Chapter 1 is on Getting Started
Chapter 2 is on Numerics, Arithmetic, Assignment, and Vectors
Chapter 3 is on Matrices and Arrays
Chapter 4 is on Non-numeric Values
Chapter 5 is on Lists and Data Frames
Chapter 6 is on Special Values, Classes, and Coercion
Chapter 7 is about Basic Plotting
Chapter 8 is about Reading and Writing Files


Chapter 9 is about Calling Functions
Chapter 10 is about Conditions and Loops
Chapter 11 is on Writing Functions
Chapter 12 is on Exceptions, Timings, and Visibility


Chapter 13 is on Elementary Statistics
Chapter 14 is on Basic Data Visualization
Chapter 15 is on Probability
Chapter 16 is on Common Probability Distributions


Chapter 17 is on Sampling Distributions and Confidence
Chapter 18 is on Hypothesis Testing
Chapter 19 is on Analysis of Variance
Chapter 20 is on Simple Linear Regression
Chapter 21 is on Multiple Linear Regression
Chapter 22 is on Linear Model Selection and Diagnostics


Chapter 23 is on Advanced Plot Customization
Chapter 24 is on Going Further with the Grammar of Graphics
Chapter 25 is on Defining Colors and Plotting in Higher Dimensions
Chapter 26 is onInteractive 3D Plots

Appendix A is about Installing R and Contributed Packages
Appendix B is about Working with RStudio

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3. Hands-On Programming with R: Write Your Own Functions and Simulations

by Garrett Grolemund, Hadley Wickham

For: Intermediate to Advanced Users

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Hands-On Programming with R: Write Your Own Functions and Simulations by Garrett Grolemund, Hadley Wickham

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Hands-On Programming with R: Write Your Own Functions and Simulations by Garrett Grolemund, Hadley Wickham — Garrett Grolemund is a statistician, teacher and R developer. He is a Ph.D. from Rice University. He develops R software, and he has co-authored the lubridate R package.

If you want to be a true data ninja, there’s no better way than getting your hands dirty with code. This book will get you started with R, and teaches everything from programming basics (conditional statements, loops) to visualizing data using R’s graphing functions. With Hands-On Programming with R, you’ll not only learn how to write amazing programs; but you’ll also gain a deeper understanding of statistics and data science by learning how to build your own simulations and apply them in practice.


This book on R programming is full of practice examples, you can easily follow for your projects. I have found this book very useful.

Part I. Project 1: Weighted Dice

Chapter 1 starts with The Very Basics
The R User Interface
Sample with Replacement
Writing Your Own Functions

Chapter 2 is on Packages and Help Pages
Getting Help with Help Pages

Part II. Project 2: Playing Cards

Chapter 3 details R Objects
Atomic Vectors
Data Frames
Loading Data
Saving Data

Chapter 4 is on R Notation
Selecting Values
Deal a Card
Shuffle the Deck
Dollar Signs and Double Brackets

Chapter 5 is about Modifying Values
Changing Values in Place
Logical Subsetting
Missing Information

Chapter 6 is on Environments
Working with Environments
Scoping Rules

Part III. Project 3: Slot Machine

Chapter 7 is about Programs
if Statements
else Statements
Lookup Tables
Code Comments

Chapter 8 is on S3
The S3 System
Generic Functions
S3 and Debugging
S4 and R5

Chapter 9 is about Loops
Expected Values
for Loops
while Loops
repeat Loops

Chapter 10 is on Speed
Vectorized Code
How to Write Vectorized Code
How to Write Fast for Loops in R
Vectorized Code in Practice

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4. Python and R for the Modern Data Scientist: The Best of Both Worlds

by Rick J. Scavetta, Boyan Angelov

Best Suited For : Intermediate to Advanced Users

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Python and R for the Modern Data Scientist: The Best of Both Worlds by Rick J. Scavetta, Boyan Angelov

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Python and R for the Modern Data Scientist: The Best of Both Worlds by Rick J. Scavetta, Boyan Angelov — Rick Scavetta has worked as a data scientist and is a co-founder since 2012. His online courses at DataCamp have been attended by more than 200,000 students since 2016. He has contributed to O’Reilly and Manning’s advanced data science studies.

Boyan Angelov is a Senior Data Scientist, Engineering Manager, and Consultant. He also contributes to the development of science projects in the xAI field.

R is an extremely popular programming language in data science today. It’s one of three fundamental languages that every aspiring data scientist should know, alongside Python and SQL.

If you’re serious about getting into data science, then learning R will make your job easier when it comes to wrangling data sets and performing statistical analysis.

Python on its own is powerful, but adding R capabilities can give you a leg up in terms of time spent and quality of output. Here’s our guide to getting started with learning how to use R for effective data analysis!


  • Build up upon your current language skills to learn Python and R
  • You will gain knowledge of strengths and weaknesses of both the languages
  • Identify how does the modern open source ecosystem is a fit for R and Python.
  • Understand the integration of R and Python in a single workflow
  • Use cases to identify how to use both the languages


It really focuses on the inner workings of the R language in a clear and simple way to follow with many good exercises and examples.

Part I Discovery of a New Language

Chapter 1 is In the Beginning

Part II Bilingualism I: Learning a New Language

Chapter 2 is on R for Pythonistas
Chapter 3 is on Python for UseRs

Part III Bilingualism II: The Modern Context

Chapter 4 is on Data Format Context
Chapter 5 is on Workflow Context

Part IV Bilingualism III: Becoming Synergistic

Chapter 6 is on Using the Two Languages Synergistically
Chapter 7 is titled A Case Study in Bilingual Data Science

A. A Python: R Bilingual Dictionary


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5. R For Dummies

by Andrie de Vries, Joris Meys

Best Suite For : Beginners

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R For Dummies by Andrie de Vries, Joris Meys

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R For Dummies by Andrie de Vries, Joris Meys — Andrie de Vries is a high-performance mathematician, a predictable mathematical model and a large data computer, mainly using R, the language of open source mathematical analysis. He is one of the authors of “R for Dummies”. This guide is a great place to start. It’s aimed at beginners, so you won’t be bogged down by any math theory. Instead, it covers some of the basics and sets you up with a good foundation to begin your journey.

You may have to take a little time working through each concept, but once you get going, it’s pretty easy to pick up on things.

The For Dummies guides are always easy reads—they tend to be colorful and engaging while keeping things basic enough for anyone new to learn from them.

We recommend starting here if you’re completely new to R programming—it will set you up with a solid base of knowledge before moving on to more advanced texts.


This book “R for Dummies”:

  • Includes download, install, and configuration of R
  • Cover details for getting data in and out of R
  • Covers wide array of statistical techniques
  • Contains practical tips on working with graphics
  • Provides advice on different regression models and ANOVA


You must read this book on R if you really want to make good progress in R programming.

Part I: R You Ready?

Chapter 1 is on Introducing R: The Big Picture
Chapter 2 is on Exploring R
Chapter 3 is on The Fundamentals of R

Part II: Getting Down to Work in R

Chapter 4 is on Getting Started with Arithmetic
Chapter 5 is on Getting Started with Reading and Writing
Chapter 6 is on Going on a Date with R
Chapter 7 is on Working in More Dimensions

Part III: Coding in R

Chapter 8 is on Putting the Fun in Functions
Chapter 9 is on Controlling the Logical Flow
Chapter 10 is on Debugging Your Code
Chapter 11 is on Getting Help

Part IV: Making the Data Talk

Chapter 12 is about Getting Data into and our of R
Chapter 13 is about Manipulating and Processing Data
Chapter 14 is on Summarizing Data
Chapter 15 is on Testing Differences and Relations

Part V: Working with Graphics

Chapter 16 is about Using Base Graphics
Chapter 17 is about Creating Faceted Graphics with Lattice
Chapter 18 is about Looking at ggplot2 Graphics

Part VI: The Part of Tens

Chapter 19 is on Ten Things You Can Do in R That You Would’ve Done in MS Excel
Chapter 20 is titled Ten Tips on Working with Packages


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6. Advanced R

by Hadley Wickham

Best Suited For : Advanced Users

Advanced R by Hadley Wickham

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Advanced R by Hadley Wickham — Hadley Alexander Wickham was born on 14 October 1979 in New Zealand. He is a statistician and senior scientist at RStudio Inc. and Professor of Mathematics at the University of Auckland, Stanford University, and Rice University. He is best known for developing open source programming R language for data visualization, including ggplot2, and tidyverse packages, that support a tidy data approach to data science.

This is book is for advanced R users and it doesn’t focus on any programming language except R. The book is written by Hadley Wickham who is a statistician and focuses on his personal experience to write his learning process while developing statistical analysis software.

The book is divided into three parts: Working with Data, Building Models, and Advanced Topics. This book also explains how to create graphics in R such as different plot types and graphs. Besides, you will also find an overview of other methods that are implemented in other statistics software packages such as PROC IML in SAS, SPSS and Stata etc.


  • Detailed coverage of functions and environments
  • Important OO systems: S3, S4 and R6
  • Toolkit for metaprogramming
  • Innovative debugging techniques


I would recommend that you read this book along with developing your own project wherein you can apply the main ideas from the book.

The new book incorporates many new concepts of metaprogramming, which can unleash the power of R which I was unaware of earlier.

Chapter 1 starts with Introduction

Part I Foundations

Chapter 2 Data Structures
Chapter 3 Subsetting
Chapter 4 is on Vocabulary
Chapter 5 is on Style Guide
Chapter 6 is on Functions
Chapter 7 is on OO Field Guide
Chapter 8 is on Environments
Chapter 9 is on Debugging, condition handling, and defensive programming

Part II Functional Programming

Chapter 10 is on Functional Programming
Chapter 11 is on Functionals
Chapter 12 is on Function Operators

Part III Computing on the language

Chapter 13 is on Non-standard evaluation
Chapter 14 is in Expressions
Chapter 15 is on Domain Specific Languages

Part IV Performance

Chapter 16 is on Performance
Chapter 17 is on Optimising Code
Chapter 18 is on Memory
Chapter 19 deals with High Performance Functions with Rcpp
Chapter 20 deals with R’s C interface


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7. The Art of R Programming: A Tour of Statistical Software Design

by Norman Matloff

Best Suited For : Beginner to Advanced

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The Art of R Programming: A Tour of Statistical Software Design by Norman Matloff

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The Art of R Programming: A Tour of Statistical Software Design by Norman Matloff — Norman Matloff has taught computer science and statistics at the University of California, Davis. He has authored articles for the New York Times, Forbes Magazine, and the Washington Post. He is the co-author of The Art of Debugging by No Starch Press.

It’s a deep dive into all things programming related. If you’re new to programming, it will make your head spin. But if you’re an experienced programmer and want to learn more about programming in R, there’s no better book on R in existence.

This is one of those rare books that will teach you how to think like a programmer. You’ll learn how different algorithms are implemented, how they work and how they should be used.


  • Regression Analysis of Exam Grades
  • Predicting Discrete-Valued Time Series
  • Use of rbind() and cbind() Functions and Alternatives
  • How to work with Scalars, Vectors, Arrays, and Matrices
  • Writing to Nonlocals with the Superassignment Operator
  • Overview of String-Manipulation Functions


I must say this book “The Art of R Programming” is pretty good. I am in love with the detailed examples of codes and love the way in which it flows throughout the chapters.

If one really wants to be a skilled R programmer at this time, you cannot find a better guide than The Art of R Programming by Norman Matloff. I strongly recommend this book.

Chapter 1 begins with Getting Started
Chapter 2 is on Vectors
Chapter 3 is on Matrices and Arrays
Chapter 4 is on Lists
Chapter 5 is on Data Frames
Chapter 6 is on Factors and Tables
Chapter 7 is on R Programming Structures
Chapter 8 is on Doing Math and Simulations in R
Chapter 9 is on Object-Oriented Programming
Chapter 10 is on Input/Output
Chapter 11 is on String Manipulation
Chapter 12 is about Graphics
Chapter 13 is about Debugging
Chapter 14 is on Performance Enhancement: Speed and Memory
Chapter 15 is on Interfacing R to Other Languages
Chapter 16 is on Parallel R
Appendix A: Installing R
Appendix B: Installing and Using Packages

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8. R in Action: Data Analysis and Graphics with R

by Dr. Rob Kabacoff

Best Suited For : Advanced Users

R in Action: Data Analysis and Graphics with R by Dr. Rob Kabacoff

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R in Action: Data Analysis and Graphics with R by Dr. Rob Kabacoff — Dr. Rob Kabacoff is a seasoned researcher and teacher who specializes in data analysis. He also maintains the popular Quick-R website at statmethods.net.

This is an excellent book for people who want to get started in R programming. The focus of this book is on how to use R interactively.

This is a comprehensive book on how to use R for data analysis and graphics. Dr. Kabacoff provides several detailed examples of how to perform complex tasks with R and describes a wide range of visualization techniques that you can use with R.

The author uses a variety of datasets in his examples so you can learn how to use data for actual analytical tasks rather than just learning generic programming techniques.

The book starts off with an overview of data analysis using specific tools including Stata, SPSS, SAS, Excel, Python and others. After providing information about all these different tools, Dr. Kabacoff explains what sets apart each tool from another one and why you might want to choose one over another in specific circumstances.


  • 2nd edition has new chapters on data mining, forecasting, and dynamic report writing.
  • Use of R language with examples relevant to technical, scientific, and business developers.
  • Plenty of practical examples showing how to make graphs based off data sets.
  • Develop data analysis and data visualization (graphing) techniques.


The book focuses mainly on graphing, statistics, and plotting. This is the area where R outperforms other programming languages and it is well explained in this book.

I have paid hundreds of dollars for learning R during my college days. I found this book much better than those course books which cost me a mullah! I recommend this book for college goers.


Chapter 1 starts with Introduction to R
Chapter 2 is on Creating a dataset
Chapter 3 is on Getting started with graphs
Chapter 4 is on Basic data management
Chapter 5 is on Advanced data management


Chapter 6 is on Basic graphs
Chapter 7 is on Basic statistics


Chapter 8 is on Regression
Chapter 9 is on Analysis of variance
Chapter 10 is on Power analysis
Chapter 11 is on Intermediate graphs
Chapter 12 is on Resampling statistics and bootstrapping


Chapter 13 is on Generalized linear models
Chapter 14 is on Principal components and factor analysis
Chapter 15 is on Time series
Chapter 16 is on Cluster analysis
Chapter 17 is on Classification
Chapter 18 is on Advanced methods for missing data


Chapter 19 is on Advanced graphics with ggplot2
Chapter 20 is on Advanced programming
Chapter 21 is on Creating a package
Chapter 22 is on Creating dynamic reports
Chapter 23 is on Advanced graphics

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9. Discovering Statistics Using R

by Andy Field, Jeremy Miles, Zoe Field

Best Suited For : Novices

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Discovering Statistics Using R by Andy Field, Jeremy Miles, Zoe Field

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Discovering Statistics Using R by Andy Field, Jeremy Miles, Zoe Field — Andy Field is Professor at the University of Sussex and teaches Quantitative Methods. He has published more than 100 research papers, and over 17 books. His books are on statistics and R.

This book is written for novices and covers data analysis, probability distributions, regression models, smoothing techniques, bootstrapping and Monte Carlo simulations.

Readers are assumed to have no previous experience in statistics or programming. The authors include all of their code from each chapter so readers can run it on their own computers as they read along.

This feature is especially helpful for users new to coding who may be concerned about how to install packages or run code. They also include an appendix full of resources that contains links to websites where they download datasets and software used in their examples such as R Commander (an open source tool) and data sets compiled by StatLib.


  • Covers enough theory to making your understanding conceptually strong.
  • Smooth transition from ANOVA to advanced techniques such as MANOVA and multilevel models.
  • Covers real-world examples and how to apply them.
  • Plenty of examples, self-assessment tests to assess your understanding of R.


This book Discovering Statistics by Andy Field makes teaches R in a fun way.

Discovering Statistics has made my learning of R and statistics easy and fun. I was out of touch from the subject, but this book brought me back to the fold. I have found this book very useful.

Chapter 1 starts with Why is my evil lecturer forcing me to learn statistics
Chapter 2 is named Everything you ever wanted to know about statistics
Chapter 3 is The R environment
Chapter 4 is on Exploring data with graphs
Chapter 5 is on Exploring assumptions
Chapter 6 is on Correlation
Chapter 7 is on Regression
Chapter 8 is on Logic regression
Chapter 9 is on Comparing two means
Chapter 10 is on Comparing several means: ANOVA (GLM 1)
Chapter 11 is on Analysis of covariance, ANCOVA (GLM 2)
Chapter 12 is on Factorial ANOVA (GLM 3)
Chapter 13 is on Repeated-measures designs (GLM 4)
Chapter 14 is on Mixed designs (GLM 5)
Chapter 15 is on Non-parametric tests
Chapter 16 is on Multivariate analysis of variance (MANOVA)
Chapter 17 is on Exploratory factor analysis
Chapter 18 is on Categorical data
Chapter 19 is on Multilevel linear models

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10. R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics

by JD Long, Paul Teetor

Best Suited For : Beginner to Intermediate

51YLVsva7BL. SX379 BO1,204,203,200
R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics by JD Long, Paul Teetor

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R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics by JD Long, Paul Teetor — J.D. Long is an enthusiast of R programming, Python, AWS and colorful metaphors. He is a host at conferences on R. He is working as an agricultural economist in New York City. J.D. Long is a founder of the Chicago R User Group.

Paul Teeter holds Masters degree in statistics and computer science. He specializes in analytics and software engineering with deep interest in quantitative development.

This is an excellent reference on many of R’s features. It’s well organized, thorough, and written in a way that makes it easy to follow. The book includes data visualization tips, along with references to other helpful books and online resources.

Additionally, it has a fair number of tutorials that can help you grasp even complex statistical methods like regression analysis quickly.

This book is great for programmers who need quick solutions and explanations on how to solve problems with their code.

If you find yourself regularly needing help in R—whether it’s just learning more about its functions or figuring out how best to use them in your existing algorithms—you might want to consider buying a copy of The R Cookbook as soon as possible.


  • The book guides you well through  input and output, general statistics, graphics, and linear regression topics.
  • This book has a plenty of how-to recipes, each of which help you solve a specific problem.
  • Thoughtfully written for a beginner, but an intermediate user can broaden his horizon.


The Cookbook series has always been my favorite, especially this book. The chapters are well organized and easy to understand.

This book was my first choice when I started using R. Statistics and sample codes are very well elaborated with tons of useful tips to improve R coding capabilities.

Chapter 1 starts with Getting Started and Getting Help
Chapter 2 is titled Some Basics
Chapter 3 is on Navigating the Software
Chapter 4 is on Input and Output
Chapter 5 is on Data Structures
Chapter 6 is on Data Transformations
Chapter 7 is on Strings and Dates
Chapter 8 is on Probability
Chapter 9 is on General Statistics
Chapter 10 is on Graphics
Chapter 11 is on Linear Regression and ANOVA
Chapter 12 is on Useful Tricks
Chapter 13 is on Beyond Basic Numerics and Statitics
Chapter 14 is on Time Series Analysis

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11. R for Data Analysis in Easy Steps – R Programming Essentials

by Mike McGrath

Best Suited For: Beginners

51tl+JJyOuL. SX366 BO1,204,203,200
R for Data Analysis in easy steps – R Programming Essentials by Mike McGrath

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R for Data Analysis in Easy Steps – R Programming Essentials by Mike McGrath — Mike McGrath has years of experience as a computer programmer. He has authored many books on different programming languages viz.- C++; Javascript, PHP, SQL, Linux, Java, and Visual Basic Express. He has written books on R programming.

This book is for everyone who would like to start learning R programming. You don’t need any programming experience, although having some knowledge of spreadsheets will be useful.

The book covers all aspects of data analysis in R and demonstrates how you can use it to quickly and easily solve your own problems. Inside you will find full coverage of a range of functions including statistics, data management, graphics, time series analysis and more.

This friendly guide has been created with beginners in mind, with plenty of examples and exercises to ensure that you get everything from it that you want out of it.


  • Core programming principles are explained well throughout
  • Learn how to create “matrices”
  • Learn how to create “data frames” from imported data sets
  • Learn to produce advanced visualizations
  • Learn how to create Line graphs, Histograms, Bar charts, Scatter graphs, etc.


R for Data Analysis in Easy Steps has an easy-to-follow style that makes learning easy for anyone wanting to learn graphic visualizations. This book will teach how to gain insights from gathered data.

This book explains core programming principles of R in a clear and concise way, with plenty of examples and tips to follow along as you move on from chapter to chapter. I would recommend this book to anyone starting out in R.

Chapter 1 is on Getting started
Chapter 2 is on Storing values
Chapter 3 is on Performing Operations
Chapter 4 is on Testing Conditions
Chapter 5 is on Employing Functions
Chapter 6 is on Building Matrices
Chapter 7 is on Constructing Data Frames
Chapter 8 is on Producing Quick Plots
Chapter 9 is on Storytelling with Data
Chapter 10 is on Plotting Perfection

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12. Practical Machine Learning in R

by Fred Nwanganga, Mike Chapple

Best Suited For : Beginners to Advanced Users

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Practical Machine Learning in R by Fred Nwanganga, Mike Chapple

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Practical Machine Learning in R by Fred Nwanganga, Mike Chapple — Fred Nwanganga is a Ph.D. from University of Notre Dame. He has done his MBA from Indiana University. His core areas of expertise are Machine Learning, AI, Cloud Computing, Data Science, Data Warehousing and R. He teaches Machine Learning, Unstructured Data Analytics, and Data Management to both graduate and undergraduate students.

Mike Chapple is a professor of IT, Operations, and Analytics at the University of Notre Dame’s where he teaches graduate and undergraduate courses in cybersecurity and business analytics.

He has written 25 books, including Cyberwarfare: Information Operations in a Connected World, and the CISSP Study Guide. Mike has done BS and Ph.D. degrees in computer science & engineering from Notre Dame.

He has done MS from the University of Idaho in computer science and an MBA from Auburn University.

A detailed introduction to machine learning, data mining, and statistical programming.

This text teaches you how to use R to develop production-quality predictive analytics applications for a variety of domains. You’ll start with introductory concepts in statistics and programming before diving into core algorithms like regression analysis and support vector machines.

Several chapters introduce advanced topics such as clustering, unsupervised learning methods, and parallel processing with multicore/GPU computing. Includes exercises in each chapter that allow you to practice what you’ve learned.

A detailed appendix explains how to set up your own local copy of all software used throughout the book so that you can follow along or reference material easily on your own computer at any time later on.


This book will teach you:

  • the concepts of different types of machine learning algorithms
  • to work with large datasets
  • write and execute R scripts with RStudio
  • to use R with Tidyverse
  • how to work with Naïve Bayes and logistic regression
  • to evaluate and improve upon machine learning models


The book is easy to follow with plenty of examples and graphical illustrations. I was able to work on these examples to test some of my ML models.

The book is perfect for everyone whether he is a novice or an experienced. I have gained a lot of understanding from this book. I highly recommend this book to everyone.

Part I Getting Started

Chapter 1 starts with What is Machine Learning
Chapter 2 is on Introduction to R and RStudio
Chapter 3 is on Managing Data

Part II Regression

Chapter 4 covers Linear Regression
Chapter 5 covers Logistic Regression

Part III Classification

Chapter 6 is on k-Nearest Neighbors
Chapter 7 is on Naive Bayes
Chapter 8 covers Decision Trees

Part IV Evaluation and Improving Performance

Chapter 9 is on Evaluating Performance
Chapter 10 is on Improving Performance

Part V Unsupervised Learning

Chapter 11 is on Discovering Patterns with Association Rules
Chapter 12 is on Grouping Data with Clustering

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13. Computer Programming for Beginners: 3 Books in 1: R, Phython and SQL Coding Languages

by Anthony McGuire, Alexander Rigoni

Best Suited For : Beginners

Computer Programming for Beginners: 3 Books in 1: R, Phython and SQL Coding Languages by Anthony McGuire, Alexander Rigoni

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Computer Programming for Beginners: 3 Books in 1: R, Phython and SQL Coding Languages by Anthony McGuire, Alexander Rigoni — If you are searching for some books on computer programming to assist you with beginning your coding career, look no further.

This 3-in-1 book features books on three of today’s most in-demand computer languages: R, Python and SQL.

Each book is written by an expert in its respective language to ensure that you not only learn how to use each language effectively but understand it as well.

Covering basic commands to help you get started writing programs, Computer Programming for Beginners: 3 Books in 1 is sure to be a valuable resource that will serve both novice and veteran coders alike.


  • Includes three books on R, Phython and SQL Coding.
  • Python section is good for newbies in Python.
  • SQL for beginners covers easy and basic steps.
  • Book on R covers statistics and has in-depth coverage of R’s functionality.


I am new to coding. And trust me, this book made me learn a lot even as an absolute beginner. The basic programming concepts of SQL, Python and R are covered very well from very basic. I love this book.

The author covers all the three aspects of data science SQL, Python and R nicely in the book. This book is for absolute beginners who are just starting out.

How R is Used in Data Science?
R Applications
The forecaster’s toolbox.
Forecast errors
Prediction intervals
Time series regression models
Selecting predictors
Adjusted R2
Akaike’s information
Schwarz’s Bayesian information criterion
Forecasting with regression
Forecasting with a nonlinear trend
Correlation, causation and forecasting
STL decomposition
Simple exponential smoothing
Holt-Winters’ seasonal method
Holt-Winters’ damped method

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14. R for Excel Users: An Introduction to R for Excel Analysts

by John L Taveras

Best Suited For : Intermediate Users

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R for Excel Users: An Introduction to R for Excel Analysts by John L Taveras

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R for Excel Users: An Introduction to R for Excel Analysts by John L Taveras — John is a BA in Maths and Economics from Wesleyan University and an MBA from MIT Sloan School of Management. John has more than a decade of experience working on R, SQL and Excel to produce data driven insights.

This book teaches R as a dialect of Excel. It’s helpful if you’re accustomed to using Microsoft Office’s built-in tools, such as VLOOKUP, PivotTables, and Macros.

If you’re an experienced Excel user who wants to transition to a more full-featured tool, then look no further than Taveras’ book.

You don’t need any prior experience with R before reading it—it takes time to master R on its own—but it helps if you have a passing familiarity with statistics and data analytics workflows.


  • Language / lingo used is simple and easy to grasp for beginners.
  • Learn data manipulation.
  • Creating, filtering, modifying, summarizing and reshaping data sets.
  • Deep coverage of topics like vectors and functions.


If you are starting out on your journey to R and you want it to be complemented with Excel, this book could be for you. This book makes use of Excel for R users easy and fun.

Part 1 starts with Introduction & Set Up

Chapter 1 is on Getting Set Up
Chapter 2 is on Programming Basics
Chapter 3 is on Quick Start – Analysis Examples

Part 2 – Building Blocks: Cells and Formulas

Chapter 4 is on Cells are Vectors
Chapter 5 is on Formulas are Functions

Part 3 – Data Frames

Chaper 6 is on Import and Create Data Sets
Chapter 7 is on Inspect Your Data
Chapter 8 is on Working with Columns
Chapter 9 is on Working with Rows
Chapter 10 is on Manipulating Rows and Columns with dplyr

Part 4 – Shape your Dataset

Chapter 11 is on Combine Data Tables
Chapter 12 is on PivotTables – Summarize and Transpose your Data

Part 5 – Advanced Topics

Chapter 13 is on Working with lists
Chapter 14 is on Programming: Loops and Control Flow
Chapter 15 is on Writing your own functions
Chapter 16 is on Apply Family of Functions
Chapter 17 is on Text / String Extraction

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15. Advancing into Analytics: From Excel to Python and R

by George Mount

Best Suited For : Intermediate Users

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Advancing into Analytics: From Excel to Python and R by George Mount

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Advancing into Analytics: From Excel to Python and R by George Mount — George holds a bachelor’s degree in economics from Hillsdale College. He has done master’s in Finance and Information Systems from Case Western Reserve University. George writes on topics related to data education, data analysis, and workforce development.

For companies large and small, data is an important asset that they use to make key decisions. Without properly collecting, analyzing and interpreting data, business leaders are missing a huge opportunity to grow their bottom line.

This practical guide shows you how to advance your skills in analytics by moving from Excel spreadsheets to more advanced techniques such as Python or R.

Inside you’ll learn how these tools can help your organization understand its customers, forecast future trends and plan for growth.

You’ll also discover how easy it is to apply these tools with real-world examples of machine learning in action—and helpful hints on troubleshooting when things go wrong.


  • The book will help you to conduct exploratory data analysis.
  • Hypothesis testing using R programming.
  • Key statistical concepts explained with proper spreadsheets.
  • Guide you to transition your skills from Excel to Python and R.


Mr. Mount covers the topics well. The book has plenty of charts, graphs, and examples with tips and warnings to make topics engaging and interesting. The content of this book is very good and covers all the aspects necessary for a beginner to learn R programming.

I found this book well-written and explanatory on all the topics. I highly recommend this book for R enthusiasts.

Part I Foundations of Analytics in Excel

Chapter 1 is on Foundations of Exploratory Data Analysis
Chapter 2 is on Foundations of Probability
Chapter 3 is on Foundations of Inferential Statistics
Chapter 4 is on Correlation and Regression
Chapter 5 is on The Data Analytics Stack

Part II From Excel to R

Chapter 6 is on First Steps with R for Excel Users
Chapter 7 is on Data Structures in R
Chapter 8 is on Data Manipulation and Visualization in R
Chapter 9 is on Capstone: R for Data Analytics

Part III From Excel to Python

Chapter 10 is on First Steps with Python for Excel Users
Chapter 11 is on Data Structures in Python
Chapter 12 is on Data Manipulation and Visualization in Python
Chapter 13 is on Capstone: Python for Data Analytics
Chapter 14 is on Conclusion and Next Steps

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16. Cleaning Data for Effective Data Science

by David Mertz

Best Suited For : Advanced Users

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Cleaning Data for Effective Data Science: Doing the other 80% of the work with Python, R, and command-line tools by David Mertz

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Cleaning Data for Effective Data Science: Doing the other 80% of the work with Python, R, and command-line tools by David Mertz — David holds a BA degree in Philosophy and Mathematics from the University of Colorado Boulder.

He holds MA and Ph.D. in Philosophy from the University of Massachusetts, Amherst. He has been a member and a keynote speaker at Python Community. Presently, he is a Partner and Senior Trainer at KDM Training. He has authored many books so far.

Unlike other languages in data science, you can’t use Python or R to simply execute a set of commands and then get an answer.

Instead, you often have to clean your data for analysis with many steps including combining datasets, generating features and then putting them into training and testing sets.

This book teaches you how to do all that effectively using command-line tools such as Python’s Pandas library or R’s Dplyr package. You will also learn about other considerations like ensuring privacy for data collected from social media or regular expressions for cleaning messy data files.


This book covers:

  • Nice handy techniques to prepare data for analysis and modeling.
  • Common data formats.
  • Usage of tools like pandas, scikit-learn, Tidyverse, SciPy and Bash made easy.
  • Work on time series data to perform de-trending and interpolation.
  • Tips and tricks to identify and remediate data integrity.


This book is good for data science aspirants as it covers all the stuffs needed for data integrity and maintain data hygiene in an easy to understand and follow language.

If you have some basic understanding of Python and R code you can easily follow the instructions and guides in this book to conveniently import data into any of the applications that you use.

Errors that occur during data processing can be too frustrating. This book covers those errors and describes the methods to clean the data. You learn how to manipulate data.

Part I – Data Ingestion

Chapter 1 starts with Tabular Formats
Chapter 2 is on Hierarchical Formats
Chapter 3 is on Repurposing Data Sources

Part II – The Vicissitudes of Error

Chapter 4 is on Anomaly Detection
Chapter 5 is on Data Quality

Part III – Rectification and Creation

Chapter 6 is on Value Imputation
Chapter 7 is on Feature Engineering

Part IV – Ancillary Matters


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17. An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani

Best Suited For : Beginner to Advanced Users

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An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani

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An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani — Gareth James is a Deputy Dean of the USC Marshall School of Business. He has been a professor of Data Sciences and Operations. He has done his BSc/BCom from University of Auckland, New Zealand. He is also a PhD. in Statistics from Stanford University, California.

His research areas are Functional Data Analysis, Statistical problems in Marketing and High Dimensional Regression. He is an elected fellow of the Institute of Mathematical Statistics.

Daniela Witten is a professor of Statistics and Biostatistics at the University of Washington. She has received many awards. Some of the awards that she has received are – NSF CAREER Award, NIH Director’s Early Independence Award, Simons Investigator Award, and Sloan Research Fellowship.

This book assumes that you know nothing about statistics and is a great place to start if you have no background in data analysis.

By using real-world examples (many of which are taken from biology, ecology, sociology, economics, and medicine), An Introduction to Statistical Learning teaches you all of the concepts without burying you in equations.

The online course supplement is interactive and covers 100+ hours of training videos with real data sets. You don’t even need to know how to code – most students learn everything they need just by watching lectures and doing homework problems. ​ This book is fantastic for both beginners who want to understand what stats is all about and experts who want an introduction to machine learning.


  • The book covers modeling and prediction techniques.
  • Color graphics and real-world examples.
  • Tutorials to help you implement R in read world scenarios.
  • Covers advanced statistical learning techniques.


I was not good at maths, but even then this book didn’t disappoint me. This covers the basic maths and the contents are very easy to follow and consume. I never struggled with this book. A nice read!

This book on statistics and R is easy to follow for college students. If you have some basics of maths and statistics clear, you are going to enjoy this book thoroughly. Though the book ‘The Elements of Statistical Learning’ does the handholding right from the very basic to advanced topics, but I found this book also covering statistics and R in deep.

Chapter 1 begins with Introduction
Chapter 2 is on Statistical Learning
Chapter 3 is on Linear Regression
Chapter 4 talks about Classification
Chapter 5 is on Resampling Methods
Chapter 6 is on Linear Model Selection and Regularization
Chapter 7 is on Moving Beyond Linearity
Chapter 8 is on Tree-Based Methods
Chapter 9 is on Support Vector Machines
Chapter 10 is on Unsupervised Learning

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18. Statistics for Linguists: An Introduction Using R

by Bodo Winter

Best Suited For : Intermediate to Advanced Users

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Statistics for Linguists: An Introduction Using R by Bodo Winter

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Statistics for Linguists: An Introduction Using R by Bodo Winter — Body Winter is a Lecturer in the Department of English Language and Applied Linguistics at the University of Birmingham, UK. He teaches Cognitive Linguistics. He has authored many books, with Statistics for Linguists being his best.

Statistics for Linguists is a gentle introduction to statistics and statistical software that uses real-world examples from linguistics to teach a range of techniques. Data sets and exercises are freely available online.

A separate website provides additional examples, data sets, and other resources for instructors using the book in courses at universities. The first half of Statistics for Linguists introduces R—the open source language used in courses at many universities—and teaches basic statistical procedures.


  • The book covers simple to advanced uses of linear models
  • The book focuses on conceptual issues.
  • Easy to understand and comprehend language.
  • Perfect for students of Linguistics statistics courses.


The contents are good and easy to read and follow. This could be a perfect book for anyone willing to learn more about statistics in Linguistics and Psychology and allied fields.

Chapter 1 begins with Introduction to R
Chapter 2 is on The Tidyverse and Reproducible R Workflows
Chapter 3 is on Descriptive Statistics, Models, and Distributions
Chapter 4 is on Introduction to the Linear Model: Simple Linear Regression
Chapter 5 is on Correlation, Linear, and Nonlinear Transformations
Chapter 6 is on Multiple Regression
Chapter 7 is on Categorical Predictors
Chapter 8 is about Interactions and Nonlinear Effects
Chapter 9 talks about Inferential Statistics 1: Significance Testing
Chapter 10 is on Inferential Statistics 2: Issues in Significance Testing
Chapter 11 is on Inferential Statistics 3: Significance Testing in a Regression Context
Chapter 12 is on Generalized Linear Models 1: Logistic Regression
Chapter 13 details Generalized Linear Models 2: Poisson Regression
Chapter 14 is on Mixed Models 1: Conceptual Introduction
Chapter 15 is on Mixed Models 2: Extended Example, Significance Testing, Convergence Issues
Chapter 16 is about Outlook and Strategies for Model Building
Index of R Functions

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19. R Graphics Cookbook: Practical Recipes for Visualizing Data

by Winston Chang

Best Suited For : Intermediate to Advanced Users

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R Graphics Cookbook: Practical Recipes for Visualizing Data by Winston Chang

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R Graphics Cookbook: Practical Recipes for Visualizing Data by Winston Chang — Winston Chang works on data visualization and software development tools for R. He is a software engineer at RStudio. He holds a Ph.D. in Psychology from Northwestern University. He has created the Cookbook for R website. This website contains recipes for common tasks in R.

Whether you’re a seasoned programmer or just getting started, R Graphics Cookbook provides more than 150 recipes to help you quickly create beautiful graphs and visualizations.

You’ll learn how to produce every kind of chart imaginable, as well as useful visualizations such as scatter plots, tree maps, heat maps, interactive graphs, and more.

Each recipe is demonstrated using real-world datasets and accompanied by easy-to-follow instructions that clarify each step of the process. By working through these examples one by one, you’ll quickly become proficient with each technique in no time at all.


  • This Cookbook has more than 150 recipes for all its users.
  • Specific recipe for specific problems.
  • Usage of updated  version of the ggplot2 package and tidyverse.
  • Control over the graphics.


This is a step-by-step guide to learn R programming. The book contains graphs and plenty of instructions on how to group bars, make proper use of colors, how to adjust bar width and height and how to make a stacked bar graphs. The book has a selection of 3D graphs.

This book has been a savior for me. I have learned how to work on ggplot, how to reorder, and how to make a bar chart. This book is well written with plenty of easy to understand graphics.

The best thing I like about this book is that each recipe in this book lists a problem and a solution. Wonderfully covered!

Chapter 1 starts with R Graphics;
Chapter 2 is on Quickly Exploring Data
Chapter 3 is on Bar Graphs
Chapter 4 is on Line Graphs
Chapter 5 is on Scatter Plots
Chapter 6 is on Summarized Data Distributions
Chapter 7 is on Annotations
Chapter 8 is on Axes
Chapter 9 is on Controlling the Overall Appearance of Graphs
Chapter 10 is on Legends
Chapter 11 is on Facets
Chapter 12 details Using Colors in Plots
Chapter 13 is on Miscellaneous Graphs
Chapter 14 is on Output for Presentation
Chapter 15 is on Getting your Data into Shape

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20. Practical Data Science with R

by Nina Zumel, John Mount

Best Suited For : Intermediate to Advanced Users

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Practical Data Science with R by Nina Zumel, John Mount

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Practical Data Science with R by Nina Zumel, John Mount — Nina Zumel is a Data Scientist. She is also a Principal and Co-Owner of Win-Vector, LLC. Nina specializes in the development of analytical applications including risk modelling, price management, data mining, emergency management and visualization text.

Nina holds a BS in Electrical Engineering and Computer Science from University of California, and a Ph.D. in Robotics from Carnegie Mellon University.

John Mount is a BA in Mathematics from UC Berkley and a Ph.D. in Computer Science from Carnegie Mellon University. He is a Principal at Win-Vector, LLC. John specializes in analysis and design of algorithms. He has worked on different applications on optimization, statistics, and machine learning.

Practical Data Science with R is an introductory text designed to help intermediate R users apply their knowledge in a hands-on way.

This book provides a diverse set of clear examples and its availability online makes it easy to try out examples as you work through each chapter.

The book also comes with an online data playground where you can manipulate sample data sets and explore topics that are discussed in each chapter at your own pace.


  • The 2nd Edition has plenty of additional R tools, modeling techniques.
  • Practice-oriented approach to explain basic principles of R.
  • Advanced data preparation techniques using the vtreat package.
  • Regularization methods.
  • Model explainability.
  • Use case of xgboost / gradient boosting.


A comprehensive coverage of all the important topics in this book. The examples are simple enough to follow and practice. A good book overall!

The authors Nina and John are renowned data scientists. They have made this book to be a good training material for machine learning classes. Nice book!


Chapter 1 starts with The data science process
Chapter 2 is on Loading data into R
Chapter 3 is on Exploring data
Chapter 4 is on Managing data


Chapter 5 is on Choosing and evaluating models
Chapter 6 covers Memorization methods
Chapter 7 is on Linear and logistic regression
Chapter 8 is on Unsupervised methods
Chapter 9 covers Exploring advanced methods


Chapter 10 is on Documentation and deployment
Chapter 11 is on Producing effective presentations

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21. R Programming: A Step-by-Step Guide for Absolute Beginners

by Daniel Bell

Best Suited For : Absolute Beginners

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R Programming: A Step-by-Step Guide for Absolute Beginners by Daniel Bell

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R Programming: A Step-by-Step Guide for Absolute Beginners by Daniel Bell — If you’re completely new to R, then Daniel Bell’s A Step-by-Step Guide for Absolute Beginners is a great place to start.

This book includes everything from installing R on your computer to creating data visualizations. As a bonus, it comes with real code examples and exercises at the end of each chapter.

It also covers topics such as understanding object-oriented programming concepts in R and using RStudio (the most popular IDE used by analysts). This book really is a step-by-step guide that will get you up and running in no time!


This book is for:

  • Anyone new to R Programming.
  • Anyone willing to improve on their R Programming skills.
  • Computer programming professionals.
  • Lecturers, educators, academics, and students willing to focus more on R, Data Analysis, Knowledge Development and Computer Science.


As the name suggests, this book is for absolute beginners in R programming. A good book to start with if you are new to this field. It will give you a good foundation. Go for it!

Chapter 1 starts with R Basics
Chapter 2 is on R Data Types
Chapter 3 is on R Variables and Constants
Chapter 4 is on R Operators
Chapter 5 is about Decision-Making in R
Chapter 6 is on R Loops
Chapter 7 is on R Functions
Chapter 8 is on R Classes and Objects
Chapter 9 is on R for Data Science
Chapter 10 covers R for Machine Learning

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Read: 15 Best Python Programming Books for Beginners and Experienced [Learn Faster]

Free Resources to Learn R Programming

Getting started with R is easy if you have a good foundation. You can find lots of books and tutorials about basic programming, but for R, there are some specific places to look for.

Try going to your favorite search engine and typing in “Learn R Programming”. Doing so will give you access to lots of resources that can help you get up to speed. Once you have something basic under your belt, try a course that walks through algorithms and approaches for data manipulation.

Finally, dive deeper into one specific area of interest by reading any papers or articles on your topic from academic journals. The more material that’s out there on a subject,the easier it is to find good information!

If you’re just getting started, I would recommend DataCamp’s free Intro to R course on Datacamp and then An Introduction to Data Science in R by MIT on edX.

Using code-academy: one good place to start is by signing up with code academy and following their free course on R programming. Using r-bloggers: r-bloggers has lots of useful tutorials on how to program in different languages including R, Python, SAS etc

After that, you’ll probably want to read some books. You can watch some of these free YouTube video tutorials on R Programming.

R Programming Tutorial – Learn the Basics of Statistical Computing


Who are the founders of R programming?

In October 1993, Ross Ihaka and Robert Gentleman of Bell Labs in Murray Hill, New Jersey announced a new project called ‘S’, intended to be an improved version of BASIC. The project was conceived by Ron Ross and named by John Chambers. S language is used for statistical applications. It has been maintained since 2000 by a core team at New Zealand’s University of Auckland led by Robert Gentleman and Ross Ihaka, who continue to maintain and enhance it.

What does R stands for in R programming?

You may be familiar with some of R’s general characteristics, such as its open-source nature and use of statistical packages, but what about its name? The R in R programming stands for resemblance. The creators chose to make a language that closely resembled S, one of their previous successes. Over time, additional features were added to make it easier for non-statisticians to work with. Both languages are now useful in many fields outside of just statistics.

Is it easy to learn R?

Learning a new programming language is daunting—even more so when you’re trying to wrap your head around advanced functions and techniques. Fortunately, R has a massive and active community that provides plenty of resources to help you get started. The documentation for R is top-notch (and better yet, it’s all on-line). There are forums where you can ask questions and participate in discussions with other programmers. And there are thousands of freely available tutorials, videos, and books to help you get started.

How can I learn R programming?

The best way to learn programming is to learn by doing. Though there are plenty of options for self-paced online tutorials and interactive courses that can get you started with coding, it’s hard to beat reading a good book from start to finish as a means of learning programming fundamentals. While more advanced books are written with proficiency in mind, introductory and intermediate level books help lay down some fundamental concepts that you can build upon and use in real life applications.

What can I do with R?

The programming language R has a growing and devoted following among statisticians, data miners, data scientists, and other analysts who work with data. You can do a lot with R: You can crunch numbers. You can produce charts and graphs of all sorts. You can use its well-developed statistical functions to analyze datasets. And you can visualize your results in ways that let you make sense of complex datasets quickly and easily. But to do any of these things, you have to know how to use it first!

How do I start coding in R?

The best way to get started with R is by simply downloading and installing it. To do so, go to CRAN, an open-source repository of software for use in statistical computing, and search for R using your web browser. Download it using your system’s package manager (Ubuntu users should use Synaptic) or just click on one of their installation files. In case you didn’t know, a package manager is a program that can be used to install packages from repositories like CRAN. It’s like an app store for software!

Is R easy or python?

This might sound like a silly question, but it’s an important one to answer before you learn any programming. If you are starting out in software development or data science, chances are that you already have some experience with coding (or at least familiarity with other languages). If so, then you should ask yourself whether or not your preferred language is readily available for use with your preferred statistical framework and environment. There’s little point in diving into both R and Python if they can’t work together seamlessly. Similarly, there’s little point in using two different statistical frameworks if they rely on two different languages. Ultimately, which language is better for beginners will come down to which interface feels more intuitive based on your past experience.

What is R programming used for?

R programming is a key part of data analysis and data science. It’s most widely used for statistical analysis and machine learning, as well as in areas like bioinformatics and other forms of scientific research. R programming allows users to manipulate large amounts of data, while still providing a high level of control over how that data is organized and manipulated. If you want to learn R, there are many good books to help you get started! We’ve put together a list in this article. Enjoy!

What is the difference between SAS and SPSS?

Although both programs work similarly and can produce similar results, SAS and SPSS are very different in terms of approach, user base, scope of application, support and software packages. Because there are many differences between these two widely used statistical packages, many people wonder which one is better for them to use. Each program has advantages that make it a good choice in some situations and a poor choice in others.

Can you use R instead of SPSS?

SPSS has been around since 1968, so it’s no surprise that many students (and faculty) might ask whether they can use R in their classes instead. The short answer: you should absolutely not try to use R in place of SPSS.

Is R Object Oriented?

No, says Hadley Wickham, R is not a pure functional language. Nor is it an object-oriented language. That said, I would argue that to some extent it borrows good ideas from both camps: there are objects in R and many of them carry their own methods; and everything in R is an expression that returns a value.

Is R Worth Learning in 2022?

The short answer is yes. If you’re familiar with other programming languages, you may wonder why you would need to learn something as esoteric as R. If that’s your stance, then it’s unlikely that R will be a good fit for you, and we don’t recommend making it a priority.


If you want to learn more about R programming, there are a lot of books that will help. In our article, we’ve provided a list of the best ones around. We hope these resources have been helpful!

Have you ever read any of these books? What was your experience with them? If there is anything else we can do for you, please let us know by dropping us an email. Happy reading!

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