Learn how to – Best Books To Master R Programming in 2023.
For most people, the beginning of a new year is another opportunity to re-calibrate, re-focus, crate new plans, engage in the so called resolutions and much more that the human species are wired to do. 2023 is roving with vigor, with speed, with vitality and with a promise. Many have aspirations, goals and achievements to make a reality and for those who would wish to add a skill into their catalogue, then you’ve dived into the right spot.
In this article, we are going to look at some books you might be interested in if you would wish to study and master R Programming. In case you would like a little bit of introduction about R, then here we go.
R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. Source: R-project
One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control.
If you would wish to Master R, the following books should be close to you during your season of learning:
1. R in Action: Data Analysis and Graphics with R
Dr. Rob Kabacoff, a seasoned researcher and teacher who specializes in data analysis, is Vice President of Research for MRG, a global organizational development firm based in Portland Maine and Dublin Ireland. He has more than 20 years of experience providing research and statistical consultation to government, academia, and private industry. His strong career experience coupled with his leadership and skill makes him the best teacher and mentor.
The experienced author, Dr. Rob, in the book teaches you how to use the R language by presenting examples relevant to scientific, technical, and business developers. Focusing on practical solutions, the book offers a crash course in statistics, including elegant methods for dealing with messy and incomplete data. You will also master R’s extensive graphical capabilities for exploring and presenting data visually.
And this expanded second edition includes new chapters on forecasting, data mining, dynamic report writing, time series analysis, cluster analysis, and classification methodologies, including decision trees, random forests, and support vector machines
What you will find Inside
- Complete R language tutorial
- Using R to manage, analyze, and visualize data
- Techniques for debugging programs and creating packages
- OOP in R
If data analysis is your interest, get the most from a seasoned researcher and teacher who specializes in data analysis to receive your mentorship and solid instruction. Get your copy from Amazon by following the link provided below:
2. R For Dummies
For those who are beginning on any subject of interest, the Dummy series of books provides plain, unsentimental and thorough introduction for them. It has been accredited as the most basic by many readers and we are sure that R For Dummies is no peculiar.
As an ideal introduction to R for complete beginners, as well as an excellent technical reference for experienced R programmers, R For Dummies provides a quick and painless way to master all the R you will ever need. Packed with tons of practical examples, step-by-step exercises, and sample code, this friendly and accessible guide shows you how to know your way around lists, data frames, and other R data structures, while learning to interact with other programs, such as Microsoft Excel. You will learn how to reshape and manipulate data, merge data sets, split and combine data, perform calculations on vectors and arrays, and so much more.
The guide by Andrie de Vries, and Joris Meys takes the intimidation out of working with R and arms you with the knowledge and know-how to master the programming language of choice among statisticians and data analysts worldwide.
Juices you will get inside
- Covers downloading, installing, and configuring R
- Includes tips for getting data in and out of R
- Offers advice on fitting regression models and ANOVA
- Provides helpful hints for working with graphics
If you are a beginner and would like to be assisted from your level of understanding to a point of confidence, R for Dummies is here to get you there in a simple, elegant and clear manner. Check this guide out from Amazon below and get started with R programming the uncomplicated way.
3. R Cookbook
R Cookbook is a product of two minds who will warm your mind with a good grasp of R. In R, it is not obvious how to accomplish many tasks, even simple ones. The simple tasks are easy once you know how, yet figuring out that “how” can be maddening. Meet the authors who are going to make it easier for you.
J.D. Long is a misplaced southern agricultural economist currently working for Renaissance Re in New York City. He is an avid user of Python, R, AWS and colorful metaphors, and is a frequent presenter at R conferences as well as the founder of the Chicago R User Group. His co-author, Paul Teetor is a quantitative developer with Masters degrees in statistics and computer science. He specializes in analytics and software engineering for investment management, securities trading, and risk management.
The authors have crafted this resource such that if you are a beginner, the book will get you started faster. If you are an intermediate user, the book will be useful for expanding your horizons and jogging your memory.
You will get to perform data analysis with R quickly and efficiently with more than 275 practical recipes in this expanded second edition. The R language provides everything you need to do statistical work, but its structure can be difficult to master. These task-oriented recipes make you productive with R immediately. Solutions range from basic tasks to input and output, general statistics, graphics, and linear regression.
Each recipe addresses a specific problem and includes a discussion that explains the solution and provides insight into how it works. If you’re a beginner, R Cookbook will help get you started. If you’re an intermediate user, this book will jog your memory and expand your horizons. You will get the job done faster and learn more about R in the process.
As you proceed, you will:
- Create vectors, handle variables, and perform basic functions
- Simplify data input and output
- Tackle data structures such as matrices, lists, factors, and data frames
- Work with probability, probability distributions, and random variables
- Calculate statistics and confidence intervals and perform statistical tests
- Create a variety of graphic displays
- Build statistical models with linear regressions and analysis of variance (ANOVA)
- Explore advanced statistical techniques, such as finding clusters in your data
Join thousands of other data analysis aspirants in the mastery of R using this cookbook. You can order your copy from Amazon below:
4. Machine Learning with R
If you are a data scientist, student, or any other practitioner who wants a clear, accessible guide to machine learning with R, kindly spend some time to peruse through Brett Lantz’s work. Brett Lantz has spent more than 10 years using innovative data methods to understand human behavior.
The author observes that machine learning, at its core, is concerned with transforming data into actionable knowledge and R offers a powerful set of machine learning methods to quickly and easily gain insight from your data.
This book provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings.
This new 3rd edition updates the classic R data science book to R 3.6 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning.
What you will learn
- Discover the origins of machine learning and how exactly a computer learns by example
- Prepare your data for machine learning work with the R programming language
- Classify important outcomes using nearest neighbour and Bayesian methods
- Predict future events using decision trees, rules, and support vector machines
- Forecast numeric data and estimate financial values using regression methods
- Model complex processes with artificial neural networks ― the basis of deep learning
- Avoid bias in machine learning models
- Evaluate your models and improve their performance
- Connect R to SQL databases and emerging big data technologies such as Spark, H2O, and TensorFlow
Find powerful new insights in your data; discover machine learning with eloquent instruction by Brett Lantz. Click below to get your copy delivered from Amazon:
5. Beyond Spreadsheets with R
Spreadsheets are powerful tools for many tasks, but if you need to interpret, interrogate, and present data, they can feel like the wrong tools for the task. That’s when R programming is the way to go.
The author, Jonathan Carroll, holds a PhD in theoretical astrophysics from the University of Adelaide, Australia, and is currently working as an independent contractor providing R programming services in data science.
With his work you will get to learn how to go from raw data to meaningful insights using R and RStudio. Each carefully crafted chapter covers a unique way to wrangle data, from understanding individual values to interacting with complex collections of data, including data you scrape from the web. You will build on simple programming techniques like loops and conditionals to create your own custom functions. Youwill come away with a toolkit of strategies for analyzing and visualizing data of all sorts.
What you will find inside
- How to start programming with R and RStudio
- Understanding and implementing important R structures and operators
- Installing and working with R packages
- Tidying, refining, and plotting your data
In case you are already comfortable with Excel and would like better tools to make your complex data more presentable without the hassle involved in writing Excel formulaes, pick Beyond Spreadsheets with R and get the magic working.
Click below to take your copy home from Amazon.
6. Learning R: A Step-by-Step Function Guide to Data Analysis
The writer of this book, Richie, is a data scientist himself with a background in chemical health and safety, and has worked extensively on tools to give non-technical users access to statistical models.
In this simple guide, Richie teaches you how to perform data analysis with the R language and software environment, even if you have little or no programming experience. You will study how to go from raw data to meaningful insights using R and RStudioWith the tutorials in this hands-on guide, you will learn how to use the essential R tools you need to know to analyze data, including data types and programming concepts. It is straightforward and succint.
The second half of Learning R shows you real data analysis in action by covering everything from importing data to publishing your results. Each chapter in the book includes a quiz on what you have learned, and concludes with exercises, most of which involve writing R code.
What you will experience:
- Writing a simple R program, and discover what the language can do
- Using data types such as vectors, arrays, lists, data frames, and strings
- Executing code conditionally or repeatedly with branches and loops
- Applying R add-on packages, and package your own work for others
- Learning how to clean data you import from a variety of sources
- Understand data through visualization and summary statistics
- Using statistical models to pass quantitative judgments about data and make predictions
- Learning what to do when things go wrong while writing data analysis code
Get the opportunity of being taken through R without getting bogged down in explanations of statistics and data modeling. You will find that it is well written and simple to follow from the beginning to the end. If you have some prior programming experience, you will find this resource a gift. Click below to order your copy from Amazon today.
7. Hands-On Programming with R
Author Garrett Grolemund, foreword writer Hadley Wickham are on a mission to make you learn how to program by diving into the R language, and then use your newfound skills to solve practical data science problems.
With this book, the author ensures that you learn how to load data, assemble and disassemble data objects, navigate R’s environment system, write your own functions, and use all of R’s programming tools.
RStudio Master Instructor Garrett Grolemund not only teaches you how to program, but also shows you how to get more from R than just visualizing and modeling data. You’ll gain valuable programming skills and support your work as a data scientist at the same time.
You will get to:
- Work hands-on with three practical data analysis projects based on casino games
- Store, retrieve, and change data values in your computer’s memory
- Write programs and simulations that outperform those written by typical R users
- Use R programming tools such as if else statements, for loops, and S3 classes
- Learn how to write lightning-fast vectorized R code
- Take advantage of R’s package system and debugging tools
- Practice and apply R programming concepts as you learn them
Garrett, a statistician, teacher and R developer, is passionate about helping people avoid the frustration and unnecessary learning he went through while mastering data analysis. He shares what he has learnt the hard way using plain language and simple techniques to ensure that you get the most out of reading the book. It is a worthwhile resource. Get yours from Amazon today by clicking on the link below:
8. R for Everyone
R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. Jared P. Lander, the author believes that R for Everyone is the solution. Jared is the owner of Lander Analytics, a statistical consulting firm based in New York City, the organizer of the New York Open Statistical Programming Meetup and an adjunct professor of statistics at Columbia University
Drawing from his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modelling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you will need to accomplish 80 percent of modern data tasks.
Lander’s self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You will download and install R; navigate and use the R environment; master basic program control, data import, and manipulation; and walk through several essential tests. Then, building on this foundation, you will construct several complete models, both linear and nonlinear, and use some data mining techniques.
Some of what is covered includes:
- Exploring R, RStudio, and R packages
- Using R for math: variable types, vectors, calling functions, and more
- Exploiting data structures, including data.frames, matrices, and lists
- Creating attractive, intuitive statistical graphics
- Writing user-defined functions
- Controlling program flow with if, ifelse, and complex checks
- Improving program efficiency with group manipulations
- Combining and reshaping multiple datasets
- Manipulating strings using R’s facilities and regular expressions
- Creating normal, binomial, and Poisson probability distributions
By the time you are done, you will not just know how to write R programs, but will be ready to tackle the statistical problems you care about most. Grab Lander’s broad experience and mentorship today as a beginner and start your data journey the best way. Click below to order yours from Amazon.
9. Practical Data Science with R
Written by two renowned data scientists in industry, Nina Zumel and John Mount, brought their minds together to offer interesting perspectives that cover many aspects of practical data science. The authors shae anecdotes and lessons full of useful shared experience and practical advice.
This invaluable addition to any data scientist’s library shows you how to apply the R programming language and useful statistical techniques to everyday business situations as well as how to effectively present results to audiences of all levels. To answer the ever-increasing demand for machine learning and analysis, this new edition boasts additional R tools, modelling techniques, and more.
Practical Data Science with R, Second Edition takes a practice-oriented approach to explaining basic principles in the ever-expanding field of data science. You’ll jump right to real-world use cases as you apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support.
Purchase this resource to get a good understanding of the basic to the not-so basics of R offered by two amazing authors. Your copy awaits in Amazon. Click below to check it out.
10. R for Data Science
One of the authors of this book, Garrett Grolemund, is a statistician, teacher and R developer who currently works for RStudio. The other mind in the book, Hadley Wickham, is an Assistant Professor and the Dobelman FamilyJunior Chair in Statistics at Rice University. He is an active member of the R community and has written and contributed to over 30 R packages.
In this book, you will learn how to use R to turn raw data into insight, knowledge, and understanding. This resource introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible.
Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modelling your data and communicating the results. You will get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way.
You will learn how to:
- Wrangle: Transform your datasets into a form convenient for analysis
- Program: Learn powerful R tools for solving data problems with greater clarity and ease
- Explore: Examine your data, generate hypotheses, and quickly test them
- Model: Provide a low-dimensional summary that captures true “signals” in your dataset
- Communicate: Learn R Markdown for integrating prose, code, and results.
Reading this book is synonymous to having your food cooked by your best chef at home. One of the authors works for R studio and the other is very experienced in the subject matter. The only work involved is to turn the pages and drink their knowledge and follow their advice. Click below to get started as soon as possible.
Concluding Words
In statistical circles, you will realize that R is prominently hailed as its lingua franca. This prominence is the main reason why R is dominates other programming languages for developing statistical tools. With data science surging in demand and opportunities increasing every day, having sound skills in this area is worth the painful hours you will invest while studying and practicing. Many silver linings are on this Data Science Cloud. Do not miss a chance of having yours.
Other books you might enjoy reading are shared within the articles below:
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