## Best R Courses 2020

## Best R Books 2020

## Best R tutorials 2020

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

Learn R programming by doing! There are a lot of R courses and conferences out there. However, R has a very steep learning curve and students are often overwhelmed. This course is different! This 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 learn through live examples.

This training is full of real-life analytical challenges that you will learn to solve. We will solve some of these problems together, others will make them homework exercises. In summary, this course has been designed for all skill levels and even if you have no programming or statistics experience, you will be successful in this course!

You will learn:

Learn to program in R at a good level

Learn how to use R Studio

Learn the basics of programming

Learn how to create vectors in R

Learn how to create 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

Understand the law of large numbers

Understanding the normal distribution

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 2020**.

### Data Science and Machine Learning Bootcamp with R

Data Scientist has been ranked number one on Glassdoor and the average salary for a data scientist exceeds $ 120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world’s most interesting problems! This course is designed both for complete beginners with no programming experience or for seasoned developers who want to jump into data science!

This comprehensive course is comparable to other Data Science bootcamps which typically cost thousands of dollars, but now you can learn all of this information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for each lecture, this is one of the most comprehensive data science and machine learning courses on Udemy!

We’ll teach you how to program with R, create amazing data visualizations, and use machine learning with R! Here are some of the topics we’ll learn:

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

### 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!

Professional R Video training, unique datasets designed with years of industry experience in mind, engaging exercises that are both fun and also give you a taste for REAL WORLD analysis .

In this course, you will learn:

How to prepare data for analysis in R

How to run the median imputation method in R

How to work with date-times in R

What are lists and how to use them

What is the Apply family of functions

How to use apply (), lapply () and sapply () instead of loops

How to nest your own functions in functions of type Apply

How to nest the apply (), lapply () and sapply () functions inside each other

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 2020

R programming is a skill you need if you want to work as a data analyst or data scientist in the industry of your choice. And why not you? The data scientist is the highest ranked profession in the United States.

But to do this, you need the tools and skills to manage data. R is one of the best languages to get you where you want to be. Combine that with some statistical know-how, and you’ll be on your way to the title of your dreams. This course brings all of this and more into one easy-to-use package, and it’s the perfect start to your journey.

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.

This course wastes no time and goes straight to hands-on coding in R. But don’t worry if you’ve never coded before, we’re starting with the light and teaching you all the basics as you go! We wanted this to be an equally satisfying experience for newbies and those of you who just want a refresher on R.

What makes this course different from other courses?

Well-paced learning.

Receive top-notch training with content we’ve designed – and rigorously edited – to deliver powerful and effective results.

Even though preferred learning paces differ from student to student, we believe that being challenged just the right amount underpins the learning that remains.

Introductory Guide to Statistics.

We will introduce you to descriptive statistics and the fundamentals of inferential statistics.

We will do it step by step, gradually developing your theoretical knowledge and practical skills.

You will be proficient in confidence intervals and hypothesis testing, as well as regression and cluster analysis.

The essentials of programming – based on R.

Put yourself in the shoes of a programmer, exceed the data scientist average and increase the productivity of your operations.

Data manipulation and analysis techniques in detail.

Learn how to work with vectors, matrices, data blocks, and lists.

Become a fan of the “ Tidyverse package ” – R’s most comprehensive collection of data manipulation tools – allowing you to index and subsets of data, as well as spread (), gather (), order (), subset (), filter (), arrange () and mute ().

Create meaningful visualizations and data graphics.

It is practice makes perfect.

Reinforce your learning through many practical exercises, carried out with love, for you, by us.

What about homework, projects and exercises?

There is a ton of homework that will challenge you in all kinds of ways. You will have the option to tackle projects on your own or access a video tutorial if you get stuck.

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 2020

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

- O Reilly Media
- Wickham, Hadley (Author)
- English (Publication Language)
- 520 Pages - 01/10/2017 (Publication Date) - O'Reilly Media (Publisher)

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

- Kabacoff, Dr. Rob (Author)
- English (Publication Language)
- 608 Pages - 06/06/2015 (Publication Date) - Manning Publications (Publisher)

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

- O'Reilly Media
- Teetor, Paul (Author)
- English (Publication Language)
- 438 Pages - 03/29/2011 (Publication Date) - O'Reilly Media (Publisher)

### Advanced R

- CRC Press
- Wickham, Hadley (Author)
- English (Publication Language)
- 478 Pages - 10/28/2014 (Publication Date) - Routledge (Publisher)