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