Introduction to R Programming - Applications and Features

What is R Programming Language?

R is a programming language and an analytics tool that was developed in 1993 by Robert Gentleman and Ross Ihaka at the University of Auckland, Auckland, New Zealand. It is extensively used by Software Programmers, Statisticians, Data Scientists, and Data Miners. It is one of the most popular Data analytics tools used in Data Analytics and Business Analytics. It has numerous applications in domains like healthcare, academics, consulting, finance, media, and many more. Its vast applicability in Statistics, Data Visualization, and Machine Learning have given rise to the demand for certified trained professionals in R.

Check this Intellipaat video on R Programming:

Features of R

Some important features of R are as follows:

Feature	Description
Free and Open-Source	R is a free and open-source programming language distributed under the GNU General Public License.
Cross-Platform Interoperability	R has distributions that run on Windows, Linux, and Mac, allowing for easy porting of R code across different platforms.
Interpreter-Based Development	R uses an interpreter, which facilitates the development of code by allowing for immediate execution and testing.
Database Integration	R effectively connects to various databases and can import data from sources like Microsoft Excel, Access, MySQL, SQLite, Oracle, etc.
Bridging Software Development and Data Analysis	R serves as a flexible language that bridges the gap between software development and data analysis tasks.
Rich Package Ecosystem	R provides a vast collection of packages with diverse codes, functions, and features tailored for data analysis and modeling tasks.
Statistical Modeling Capabilities	It offers a range of tools for statistical modeling, allowing for advanced analysis of data and generation of models.
Data Visualization	R is equipped with powerful tools for creating a wide array of visualizations to aid in data exploration and interpretation.
Machine Learning Capabilities	R supports machine learning with various libraries and packages, making it suitable for building and evaluating ML models.
Data Import and Manipulation	R provides robust functionality for importing, cleaning, and manipulating data, making it suitable for data pre-processing tasks.
Report Generation	It integrates tools for generating reports in formats such as CSV, XML, HTML, and PDF, and can also create interactive web-based reports.

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Steps to perform Data Analysis in R

Import: The first step is to import data into the R environment. It means that you take the data stored in files, databases, HTML tables, etc., and load it into an R data frame to perform data analysis on it.
Transform: In this step, first, we make our data tidy by making each column a variable, and each row an observation. Once we have tidy data, we narrow down on it to find observations of our interest, create new variables that are functions of existing variables, and find summary statistics of the observations.

Visualization: It is used to make our data more understandable by representing data in graphical form. Visualization makes it easy to recognize patterns, find trends, and exceptions in our data. It enables us to convey information and results in a quick and visual way.

Model: Models are complementary tools for visualization. These are fundamentally mathematical or computational tools used to answer questions related to our observations.

Communication: In this last step of data analysis, we focus on communicating the results from visualization and modeling with others. R provides the ease to produce well-designed print-quality plots for sharing worldwide.

Why learn R Programming

Here are some advantages of R programming and why learning it might be a good idea for you:

R is a platform-independent programming language. This means that whichever operating system you use, your R program will work just fine.
R is very easy to learn and understand. If you have a good understanding of statistics, R programming will make your tasks easier.
R libraries provide one of the best and most insightful data visualizations.
R programming is one of the most popular programming languages for data science and machine learning.
R can easily be integrated with various other programming languages such as C and C++.
R is a free language; anyone can download and use it without having to purchase a license. It is also open-source.
The demand for R is growing at a very fast rate and it is currently a trend in the industry.
R has a huge community of users and extensive community support to help you with the learning process.

A lot of programmers choose R over Python these days. Here’s why:

Even novices can start doing data analysis quickly on R, which was designed specifically keeping statisticians in mind.
R is better suited, as compared to Python when it comes to statistical learning. R programming has exceptional libraries for exploring and experimenting with data.
With amazing graphics, R is perfect for data visualization.

Now let us learn about some of the applications of R programming.

Applications of R Programming

R is very widely used for data science. In addition to giving us an environment for statistical design, R programming also gives us many libraries for data science. Some of them are:
- Dplyr
- Ggplot2
- Shiny
- Lubridate
- Knitr
- Quanteda.dictionaries
- RCrawler
- Caret
- RMarkdown
- Leaflet
- Janitor
R also helps in importing and cleaning data and quantitative analysis.
R has applications in a wide range of industries such as academics, healthcare, government, insurance, energy, finance, retail, media, manufacturing, technology, and electronics.

Watch this R Programming for Beginners Video Tutorials

R Programming Download

To download R, just follow the steps below:

Go to the link Download R

For Windows, Click on base

Download the latest version of R on your system.

R Programming Installation

To install R on your system, execute the downloaded file
Follow the installation instructions and leave all default settings in the installation options

What is RStudio?

RStudio is a free and open-source IDE (integrated development environment) for programming in R. It makes it easier to write scripts, interact with objects in the R environment, access files, and make graphics more accessible to a casual user. It is available in two versions:

RStudio Desktop edition, where a program runs locally as a regular desktop application.
RStudio Server edition, which allows a user to access RStudio using a web browser while it runs on a remote server.

Prepackaged distributions of RStudio Desktop are available for Windows, macOS, and Linux.

Installing RStudio

Go to the link Download RStudio

Click on Download RStudio

Download and install RStudio Desktop on your system.

This tutorial has covered topics like what is an R programming language, the steps it uses to perform data analysis, and the features it provides. It also has discussed the Installation procedure of both R and RStudio, and why RStudio is preferred as an IDE for R. After installation we are ready to start with the coding part, which will be included in the next tutorial.