## Best R Courses 2021

## Best R Books 2021

## Best R tutorials 2021

### 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 2021**.

### 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

You will learn

Program in R

Use R for data analysis

Create data visualizations

Use R to manage csv, excel, SQL or web scraping files

Use R to easily manipulate data

Use R for machine learning algorithms

Use R for data science

This is the **best R tutorial**in 2021.

### R Programming: Advanced Analytics In R For Data Science

Ready to take your 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 2021

R for Statistics and Data Science is the course that will take you from a complete beginner in programming with R to a professional who can perform data manipulation on demand. It gives you the skillset you need to approach a new data science project with confidence and be able to critically evaluate your work and that of others. 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

R programming for simulation and Monte Carlo methods focuses on using R software to program probabilistic simulations, often referred to as Monte Carlo simulations. Typical simplified “real world” examples include simulating the odds of a baseball player having a twenty-game season “streak” sequentially with “batting” or estimating the probable total number of taxis in a row. strange town when one observes a certain sequence of numbered taxis passing by a particular street corner over a period of 60 minutes. In addition to detailing half a dozen (sometimes fun) “ real world ” extended application examples, the course also explains in detail how to use existing R functions and how to write your own R functions, to perform simulated inference estimates, including probabilities. and confidence intervals, and other cases of stochastic simulation. Techniques for using R to generate different characteristics of various families of random variables are explained in detail. The course teaches the skills necessary to implement various approaches to simulate continuous and discrete random variable probability distribution functions, parameter estimation, Monte-Carlo integration and variance reduction techniques. The course partially uses the spuRs Comprehensive R Archive Network (CRAN) package to demonstrate how to structure and write programs to perform mathematical and probabilistic simulations using R statistical software.

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 2021

### 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.

### 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

### R in Action: Data Analysis and Graphics with R

R in Action Data Analysis and Graphics with R by Dr. Rob Kabacoff presents both the R language and the examples that make it so useful for business developers. Focusing on practical solutions, the book offers a crash course in statistics and covers elegant methods for dealing with messy and incomplete data that are difficult to analyze with traditional methods. You will also master R’s extensive graphical capabilities to explore and present data visually. And this expanded second edition includes new chapters on time series analysis, cluster analysis, and classification methodologies, including decision trees, random forests, and support vector machines. The book contains:

Complete tutorial on the R language

Using R to manage, analyze and visualize data

Techniques for debugging programs and creating packages

OOP to R

More than 160 graphics

### 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

Advanced R Second Edition 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

The second edition is a complete update:

New fundamental chapters: “Names and values”, “Flow of control” and “Conditions”

full coverage of object-oriented programming with chapters on S3, S4, R6 and how to choose between them

Much deeper coverage of metaprogramming, including the new orderly evaluation framework

use of a new package like rlang (http://rlang.r-lib.org), which provides a clean interface for low-level operations, and purr (http://purrr.tidyverse.org/) for functional programming

Using color in code blocks and numbers

### R Cookbook

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

The Book of R: A First Course in Programming and Statistics is a comprehensive and easy-to-use guide to R, the world’s most popular programming language for statistical analysis. Even if you don’t have any programming experience and a little more than the basics of math, you will find everything you need to start using R effectively for statistical analysis.

You’ll start with the basics, such as managing data and writing simple programs, before moving on to more advanced topics, such as producing statistical summaries of your data and conducting statistical modeling and testing. You will even learn how to create impressive data visualizations with basic R graphical tools and contributed packages, such as ggplot2 and ggvis, as well as interactive 3D visualizations using the rgl package.