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.
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So now, that we have understood what is R Programming Language, let’s have a quick glance at the topics which will be covered in this tutorial:
Features of R
Some important features of R are as follows:
- It is a free and open-source programming language issued under GNU (General Public License).
- It has cross-platform interoperability which means that it has distributions running on Windows, Linux, and Mac. R code can easily be ported from one platform to another.
- It uses an interpreter instead of a compiler, which makes the development of code easier.
- It effectively associates different databases, and it does well in bringing in information from Microsoft Excel, as well as, Microsoft Access, MySQL, SQLite, Oracle, etc.
- It is a flexible language that bridges the gap between Software Development and Data Analysis.
- It provides a wide variety of packages with a diversity of codes, functions, and features tailored for data analysis, statistical modeling, visualization, Machine Learning, and importing and manipulating data.
- It integrates various powerful tools to communicate reports in different forms like CSV, XML, HTML, and pdf, and also through interactive websites, with the help of R packages.
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.
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- 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.
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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:
- 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.
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R Programming Download
To download R, just follow the steps below:
- 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
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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.
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- 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.