Best R books & Best R courses in 2024

R programming is an open source platform which is developed by Ross Ihaka and Robert Gentleman from University of Auckland during the year 1991.

Best R Courses 2022

 

Best R Books 2022

 

Best R tutorials 2022

R Programming A-Z™: R For Data Science With Real Exercises!

Learn R programming by doing! There are a lot of R courses learn but not easy to understand. R has a very steep learning curve and students are often overwhelmed. This introduction to R course is really step by step. In each new tutorial, we build on what has already been learned and take one more step forward. After each video, you learn a valuable new concept that you can apply immediately. And the best part is you learning R through R examples. You will learn:

Learn to program in R at a good level
Learn how to use R Studio
Learn the basics of R programming
Learn how to create vectors in R
Learn how to create R variables
Learn about integer, doubles, logical, character, and more types in R
Learn how to create a while () loop and a for () loop in R
Learn how to create and use matrices in R
Learn the matrix () function, learn rbind () and cbind ()
Learn how to install packages in R
Learn how to customize R Studio to suit your preferences
Practice working with statistical data in R
Practice working with financial data in R
Practice working with sports data in R

This is the best R course in 2022.

Data Science and Machine Learning Bootcamp with R

Topics include:

Programming with R
Advanced R features
Using R data frames to solve complex tasks
Use R to manage Excel files
Web scraping with R
Connect R to SQL
Use ggplot2 for data visualizations
Use plot for interactive visualizations
Machine learning with R, including:
Linear regression
K Nearest neighbors
K means grouping
Decision trees
Random forests
Twitter data mining
Neural networks and deep learning
Support Vectore machines

This is the best R tutorialin 2022.

R Programming: Advanced Analytics In R For Data Science

Ready to take your best R programming skills to the next level? Do you really want to become proficient in data science and analytics with R? This course is for you! You will learn:

Perform data preparation in R
Identify missing records in dataframes
Locate missing data in your dataframes
Apply the median imputation method to replace missing records
Apply the factual analysis method to replace missing records
Understand how to use the which () function
Know how to reset the dataframe index
Working with the gsub () and sub () functions to replace strings
Explain why NA is a third type of logical constant
Process date-times in R
Convert date-times to POSIXct time format
Create, use, add, modify, rename, access and subsets of lists in R
Understand when to use [] and when to use [[]] or the $ sign when working with lists
Create a time series chart in R
Understand how the Apply family of functions works
Recreate an apply statement with a for () loop
Use apply () when working with matrices
Use lapply () and sapply () when working with lists and vectors
Add your own functions in the apply statements
Nest functions apply (), lapply () and sapply () one inside the other
Use the which.max () and which.min () functions

R Programming for Statistics and Data Science 2022

You will learn:

Learn the basics of R programming
Working with conditional statements, functions, and loops in R
Create your own functions in R
Get your data in and out of R
Discover the main tools of data science with R
Manipulate data with the Tidyverse package ecosystem
Systematically explore data in R
Graphics grammar and the ggplot2 package
Visualize data: plot different types of data and pull insights
Transforming data: best practices for knowing when and how
Index, slice, and subset data
Learn the basics of statistics and apply them in practice
Hypothesis test in R
Understand and perform regression analysis in R
Working with dummy variables
Learn how to make data-driven decisions!
Have fun taking apart Star Wars and Pokemon data, plus more serious data sets

R Programming for Simulation and Monte Carlo Methods

You will learn

Use R software to program probabilistic simulations, often called Monte Carlo simulations.
Use R software to program math simulations and create new math simulation functions.
Use existing R functions and understand how to write their own R functions to perform simulated inference estimates, including probabilities and confidence intervals, and to model other stochastic simulation cases.
To be able to generate different families (and moments) of discrete and continuous random variables.
To be able to simulate parameter estimation, Monte-Carlo integration of continuous and discrete functions and variance reduction techniques

Best R books 2022

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

R for Data Science Import, Tidy, Transform, Visualize, and Model Data by Hadley Wickham and Garrett Grolemund will gain a clear understanding of R to discover the natural laws in the structure of data. Along the way, you will learn to use the versatile R programming language for data analysis from this r programming books. Every time you measure the same thing twice, you get two results, as long as you measure accurately enough. This phenomenon generates uncertainty and opportunities. Author Garrett Grolemund, RStudio Master Instructor, shows you how data science can help you deal with uncertainty and seize opportunities. You will discover:

Data negotiation: how to manipulate data sets to reveal new information
Data Visualization: How to Create Charts and Other Visualizations
Exploratory Data Analysis: Finding Evidence of Relationships in Your Measures
Modeling: How to Get Insights and Predictions from Your Data
Inference: how to avoid being misled by data analytics that cannot provide foolproof results
Throughout the book, he will also learn about the statistical worldview, a way of looking at the world that allows for understanding versus uncertainty and simplicity versus complexity.

This is the best R book in 2022.

R in Action, Third Edition: Data analysis and graphics with R and Tidyverse

by Robert I. Kabacoff makes learning R simple and quick That’s why thousands of data scientists have turned to this book for aid in mastering the language. Rather than being a dry academic tome, every example in this book is applicable to scientific and commercial professionals and aids in the resolution of frequent data problems. From coping with messy and missing data to creating striking visualizations, R specialist Rob Kabacoff takes you on a crash course in statistics. The new tidyverse approach to data analysis and R’s state-of-the-art graphing capabilities with the ggplot2 package are covered in detail in this revised and expanded third edition. You will learn:

Install and setup R and RStudio
R allows you to clean, manage, and analyze data.
For graphs and visualizations, use the ggplot2 library.
R functions can be used to solve data management issues.
Model fitting and interpretation
Test hypotheses and calculate confidence intervals
Principal components and exploratory factor analysis can help to simplify complex multivariate data.
Use time series forecasting to make predictions.
Create amazing infographics and interactive reports.
Techniques for debugging and packaging programs

R for Everyone: Advanced Analytics and Graphics

R for Everyone Advanced Analytics and Graphics 2nd Edition by Jared Lander. Drawing on his unrivaled experience in teaching new users, professional data scientist Jared P Lander has written the perfect R book for anyone new to statistical modeling and programming. Organized for easy and intuitive learning, this guide focuses on the 20% of the R functions you will need to accomplish 80% of modern data tasks. Lander’s standalone chapters start with the absolute basics, offering in-depth practice and R code examples. He will download and install R; navigate and use the R environment; master basic program control, data import and manipulation; and go through several essential R tests. The book includes:

Exploring the R, RStudio, and R packages
Using R for math: types of variables, vectors, call functions, etc.
Take advantage of R data structures, including data.frames, arrays, and lists
Create attractive and intuitive statistical charts
Write user-defined functions
Monitoring program flow with yes, yes, and complex checks
Improve program efficiency with group manipulations
Combine and reshape multiple data sets
String manipulation using R functions and regular expressions
Creating Normal, Binomial, and Poisson Probability Distributions
Basic statistics programming: mean, standard deviation and t-tests
Creation of linear, generalized linear and nonlinear models
Evaluation of the quality of the model and selection of variables
Avoid overfitting, using the Elastic Net and Bayesian methods
Univariate and multivariate time series data analysis
Grouping data via K-means and hierarchical grouping
Prepare reports, slideshows and web pages with Knitr
Create reusable R packages with devtools and Rcpp
Get involved in the global community R

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

The Art of R Programming A Tour of Statistical Software Design by Norman Matloff takes you on a tour of software development with R, from basic data types and structures to advanced topics like closures, recursion, and anonymous functions. No statistical knowledge is required and your programming skills can range from hobbyist to professional to start this book. Along the way, he will learn about functional and object-oriented programming, running mathematical simulations, and reorganizing complex data into simpler and more useful formats. You will also learn how to: Create nifty graphics to visualize complex data sets and functions Write more efficient code using Parallel R and the vectorization R interface with C / C ++ and Python for speed or functionality Find new packages for text analysis, manipulation images, and thousands of other annoying errors with advanced debugging techniques Whether you’re designing airplanes, forecasting the weather, or just need to control your data, The Art of R Programming is your guide to harnessing the power of statistical computing.

Practical Data Science with R 2nd Edition

Practical Data Science with R Second Edition by Nina Zumel and John Mount is an invaluable addition to any data scientist’s library that shows you how to apply the R programming language and useful statistical techniques to everyday business situations, as well as how to effectively present the results to audiences of all levels. To meet the ever-increasing demand for analytics and machine learning, this new edition offers additional R tools, modeling techniques, and more. The book takes a practice-oriented approach to explaining basic principles in the ever-expanding field of data science. You’ll jump right into real-world use cases by applying the R programming language and statistical analysis techniques to carefully explained examples based on marketing, business intelligence, and decision support.

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

An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani provides accessible information in the field of statistical learning, a set of essential tools for making sense of the large and complex data sets that have emerged in fields ranging from biology and finance to marketing and research, astrophysics during the last twenty years. This book presents some of the most important modeling and prediction techniques, as well as relevant applications. Topics include linear regression, classification, resampling methods, reduction approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real life examples are used to illustrate the methods presented. Since the purpose of this manual is to facilitate the use of these statistical learning techniques by professionals in science, industry, and other fields, each chapter contains a tutorial on implementing the analysis and methods presented in R, a software platform. extremely popular open source statistician.

Two of the authors were co-authors of The Elements of Statistical Learning (Hastie, Tibshirani, and Friedman, 2nd ed. 2009), a popular reference work for researchers in statistics and machine learning. An introduction to statistical learning covers many of the same topics, but at a level accessible to a much wider audience. This book is intended for both statisticians and non-statisticians who want to use state-of-the-art statistical learning techniques to analyze their data. The text assumes only a preliminary course in linear regression and no knowledge of matrix algebra.

Advanced R

by Hadley Wickham helps you understand how R works at a fundamental level. It is designed for R programmers who want to deepen their understanding of the language and experienced programmers in other languages ​​who want to understand what makes R different and special. This book will teach you the basics of R; three fundamental programming paradigms (functional, object-oriented and metaprogramming); and powerful debugging and optimization techniques your code. By reading this book, you will learn:

The difference between an object and its name, and why the distinction is important
Important vector data structures, how they fit together, and how you can separate them using subsets
The small details of functions and environments
The condition system, which feeds messages, warnings, and errors.
The powerful functional programming paradigm, which can replace many for loops
The three most important OO systems: S3, S4 and R6
The tidy eval toolkit for metaprogramming, which allows you to manipulate code and control evaluation
Efficient debugging techniques that you can implement regardless of how your code runs
Finding and Eliminating Performance Bottlenecks

R Cookbook

by JD Long and Paul Teetor contains over 275 convenient 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 allow you to be immediately productive with R. Solutions range from basic tasks to inputs and outputs, general statistics, graphs, and linear regression.

Each R recipe addresses a specific problem and includes a discussion that explains the solution and provides an overview of how it works. If you are a beginner, R Cookbook will help you get started. If you are a intermediate user, this book will refresh your memory and broaden your horizons. You’ll get the job done faster and learn more about R in the process.

Create vectors, manage variables, and perform basic functions
Simplify data input and output
Address data structures such as arrays, lists, factors, and data blocks
Work with probabilities, 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

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

by Tilman M. Davies is a comprehensive, beginner-friendly guide to R, the world’s most popular programming language for statistical analysis. Even if you have no programming experience and little more than a grounding in the basics of mathematics, you’ll find everything you need to begin using R effectively for statistical analysis.

You’ll start with the basics, like how to handle data and write simple programs, before moving on to more advanced topics, like producing statistical summaries of your data and performing statistical tests and modeling. You’ll even learn how to create impressive data visualizations with R’s basic graphics tools and contributed packages, like ggplot2 and ggvis, as well as interactive 3D visualizations using the rgl package. Dozens of hands-on exercises (with downloadable solutions) take you from theory to practice, as you learn:

The fundamentals of programming in R, including how to write data frames, create functions, and use variables, statements, and loops
Statistical concepts like exploratory data analysis, probabilities, hypothesis tests, and regression modeling, and how to execute them in R
How to access R’s thousands of functions, libraries, and data sets
How to draw valid and useful conclusions from your data
How to create publication-quality graphics of your results
Combining detailed explanations with real-world examples and exercises, this book will provide you with a solid understanding of both statistics and the depth of R’s functionality. Make The Book of R your doorway into the growing world of data analysis.

R For Dummies

by Andrie de Vries and Joris Meys. Picking up R can be tough, even for seasoned statisticians and data analysts. R For Dummies, 2nd Edition provides a quick and painless way to master all the R you’ll ever need. Requiring no prior programming experience and 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’ll learn how to reshape and manipulate data, merge data sets, split and combine data, perform calculations on vectors and arrays, and so much more including:

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
R For Dummies, 2nd Edition is an ideal introduction to R for complete beginners, as well as an excellent technical reference for experienced R programmers.

Discovering Statistics Using R

by Andy Field, Jeremy Miles, Zoe Field. The R version of Andy Field′s hugely popular Discovering Statistics Using SPSS takes students on a journey of statistical discovery using the freeware R. Like its sister textbook, Discovering Statistics Using R is written in an irreverent style and follows the same ground breaking structure and pedagogical approach. The core material is enhanced by a cast of characters to help the reader on their way, hundreds of examples, self assessment tests to consolidate knowledge, and additional website material for those wanting to learn more.

R Graphics Cookbook: Practical Recipes for Visualizing Data

by Winston Chang provides more than 150 recipes to help scientists, engineers, programmers, and data analysts generate high-quality graphs quickly—without having to comb through all the details of R’s graphing systems. Each recipe tackles a specific problem with a solution you can apply to your own project and includes a discussion of how and why the recipe works.

Most of the recipes in this second edition use the updated version of the ggplot2 package, a powerful and flexible way to make graphs in R. You’ll also find expanded content about the visual design of graphics. If you have at least a basic understanding of the R language, you’re ready to get started with this easy-to-use reference.

Use R’s default graphics for quick exploration of data
Create a variety of bar graphs, line graphs, and scatter plots
Summarize data distributions with histograms, density curves, box plots, and more
Provide annotations to help viewers interpret data
Control the overall appearance of graphics
Explore options for using colors in plots
Create network graphs, heat maps, and 3D scatter plots
Get your data into shape using packages from the tidyverse

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