R Tutorial for Beginners

by Randy Moore

Today, we are starting a tour of the R programming language in which we will explore its different and essential concepts. This R DataFlair Tutorial Series is designed to help beginners to get started with R and experienced to brush up their R programming skills and gain perfection in the language. R is one of the most widely used programming languages for statistical modeling. It has become the lingua franca of Data Science. In this article, we will provide you with the introduction to R programming language, its examples and we will also see how R is transforming the Data Science industry. We will also go through the various editors, environments through which you can run the R code. Contents R Tutorial – Be a Data Science rock star with R Chapter 1 Be a Data Science rock star with R . Chapter 2 Be a Data Science Super Hero with R Chapter 3 Taking you closer to your Data Science dream Chapter 4 Features of R Programming . Chapter 5 Pros and Cons of R Programming Language Chapter 6 Why Learn R? .. Chapter 7 Discover various Opportunities and Scope of R Programming! Chapter 8 Why Top Companies are using R Programming Chapter 9 How to Install R, RStudio and R Packages Chapter 10 R and Hadoop Integration Chapter 11 R Data Types Chapter 12 OLS Regression in R Chapter13 RStudio Tutoria Chapter 14 Data Structures in R Chapter 15 - R Vector Operations with Examples Chapter 16 -R List .. Chapter 17 How to Create & Access R Matrix Chapter 18 R Array Function and Create Array in R . Chapter 19 R Data Frame Chapter 20 R Factor . Chapter 21 Control Structures in R Chapter 22 Functions in R Chapter 23 R Vector Functions . Chapter 24 Numeric and Character Functions in R ... Chapter 25 Matrix Function in R .. Chapter 26 R Recursive Function (Recursion) Chapter 27 Arguments in R Programming Language Chapter 28 R Vs Python Chapter 29 R, Python or SAS Chapter 30 Top Data Analytics Tools of 2019 .. Chapter 31 Data Analytics with R, Tableau and Excel . Chapter 32 R Books – Select, Learn & Become a Data Science Expert!

288 pages, Kindle Edition

Published December 24, 2019