R Tutorial | Learn R Programming Language

R Programming Tutorial is designed for beginners and experts. This free R Tutorial gives you knowledge basic to advanced of concepts of the R programming language. Here you will get a detailed introduction, features, installation, variables, data types, operators, if statements, vectors, data handling, graphics, and statistical modeling of R programming.

What is R Programming?

R is an interpreted programming language and also a software environment that is widely used for statistical computing and data analysis. R was developed by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand.

R is an open-source programming language and it is available on widely used platforms e.g. Windows, Linux, and Mac. It generally comes with a command-line interface and provides a vast list of packages for performing tasks. R is an interpreted language that supports both procedural programming and object-oriented programming.

Hello World Program in R Language

Here is an example of the first Hello World program in R Programming Language. To print in R language you just need to use a Print function.

Output

Hello World! 

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Prerequisites for R Programming

While there are no strict prerequisites for learning R programming, certain foundational knowledge can significantly ease your learning journey of R programming:

Essential:

• Basic computer literacy
• Logical thinking and problem-solving skills

Helpful (but not mandatory):

• Prior programming experience
• Mathematics and statistics.
• Data analysis concepts

Note: Remember, the most important factor to learn any programming language is constant practice with dedication and a structured approach.

How to Install R

• Go to https://cloud.r-project.org/ and download the latest version of R for Windows, Mac or Linux.
• When you have downloaded and installed R, you can run R on your Command prompt or any IDE.

R Tutorial – Table of Content

Why Use R Programming Language?

R programming language is a best resource for data science, data analysis, data visualization and machine learning. R provides various statistical techniques like statistical tests, clustering and data reduction. Graph making is easy eg. pie chart, histogram, box, plot, etc. R is totally free and open-source Programming language. The community support with the R language is very large and it works on all OS. R programming comes with many packages (libraries of functions) to solve various problems.

Applications of R Programming Language

Some of the important applications of R Programming Language are listed below:

• R is used in wide range of industries for example academics, government, insurance, retail, energy, media, technology, and electronics.
• R helps in importing and cleaning data and data analysis.
• R is used in data science. R language provides us many libraries for data science e.g. Dplyr, Ggplot2, shiny, Lubridate, Knitr, Caret, Janitor.

FAQs on R Tutorial

Q.1 What is Rstudio ?

Answer

:

Rstudio is the IDE for programming in R. It is used to write scripts, access files, and make graphics. It is widely used in data science, machine learning, and research.

Q.2 What are some popular packages in R?

Answer

:

R include ggplot2 the packages for data visualization, dplyr for data manipulation, tplyr for data cleaning.

Q.3 What’s the difference between R and Python?

Answer

:

R programming	Python programming
Data visualization libraries and tools are good in R language.	R has poor data visualization than python.
Production is poor than python.	Production is better than R.
Model Interpretability is good in R programming language.	Model Interpretability is not good in python.
R has relative complex syntax and learning.	Syntax is simple in python.
R is used when the data analysis process requires analysis and processing.	Python is used when the data analysis process require integrated with web applications.
Model creation is similar to Python.	Model creation is similar to R.

Q.4 Which is more demanding language? Python or R?

Answer

:

R has more demand than Python in Data science. Specific skills are needed in compare to Python which is a multi-purpose language.