R Tutorial | Learn R Programming 2023

R Tutorial | Learn R Programming 2023

6 September 2023 0 By Anshul Pal

R is programming language which provides an environment for statistical computing and graphical presentation to analyze and visualize data. It is similar to S language. R programming language is designed by statisticians Rob Ihaka and Robert Gentlemen. Currently R is develops by the R Development Core Team. The history of R goes back about 20-30 years ago. Currently in April 2023 R ranked as 16th in the TIOBE Index and in it’s peak time it is indexes as 8th in 2020. It allows you to integrate with programming procedures written in C, C++, Python, .Net, etc. R provides also a software which is used to analyze statistical information, graphical representation, reporting, and data modeling. It is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems like Linux, Windows and Mac. 

R programming language is used by :-

  • Bioinformaticians
  • Statisticians
  • Data miners
  • Data analysis
  • Statistical Software

You can Run R programs in two different ways:

    • By using an online environment.
    • Installing R software in your local machine.

Installation of R Software

You can install R in different operating system according to your need. R has binaries for all the major operating system. Such as Windows, MacOS, and Linux.

Here is a article link – How to install R programming language & R Studio in Windows

Syntax of R Programming

R Programming is a language that many people use to work with data. It’s famous for analyzing data. Making code in R is easy. “Hello World!” is a simple program in all languages. Now, let’s learn how R works using the “Hello World” program. We can type code in the command prompt or use a special R script file.

# This is a simple program in R
# Print "Hello, World!" to the console
print("Hello, World!")

To execute this file in Windows and other operating systems, the process will remain the same as mentioned below.

R Programming Syntax

When we press enter it will give us the following output:

R Programming Output

Relation between R and Big Data

R programming language has limitations when it comes to handling big data. So, instead of using R for large-scale production tasks, many businesses use it as a tool for exploring and investigating data. Data scientists use R to perform complex analyses on smaller sets of data. Once they discover important patterns or relationships in the data, they incorporate those findings into their enterprise-level tools.

To make R more suitable for big data tasks, several software companies, such as IBM, Microsoft, Oracle, SAS Institute, TIBCO, and Tableau, have added support for R in their software products. This integration allows R to be used more effectively in the modern world of big data. Additionally, there are R packages available for popular open-source big data platforms like Hadoop and Spark, which further extends R’s capabilities in handling large datasets.

R vs Python Which one is better?

Python and R are two important languages in data science. People use them to find, display, and understand important information in data, which can inform business decisions. These languages have special tools for collecting, exploring, modeling, and showing data, as well as doing statistical analysis. These tools make it easier for data scientists to work with data and draw useful conclusions.

Python has a huge community that makes it versatile in data science. It’s widely used for data analysis thanks to its excellent data-focused packages like Pandas and NumPy, which simplify importing, analyzing, and visualizing data. On the other hand, R Programming has a rich ecosystem for standard machine learning and data mining. It specializes in statistical analysis of large datasets and provides various tools for exploring data, making it simpler to work with probability distributions and perform different statistical tests.

Facts about R Programming Language

  • R is based on the S programming language and incorporates lexical scoping semantics inspired by Scheme. It gets its name from the first two R authors and plays on the name of S.
  • R supports both procedural programming (which involves procedures, records, modules, and procedure calls) and object-oriented programming (which includes classes, objects, and generic functions).
  • R is an interpreted language, meaning it doesn’t require a compiler to turn code into a runnable program. This makes running R scripts faster and more efficient.
  • There are over 100,000 R packages available via CRAN or GitHub, allowing you to perform a wide range of tasks with just one line of code, from regression analysis to Bayesian methods.
  • R is one of the fastest-growing data science languages and is widely used, second only to SQL, with 70% of data miners using it.

With packages like rmarkdown, you can create reproducible Word documents and PowerPoint presentations from your R code easily. Connecting to various databases is simple with the dbplyr package, allowing R users to work independently and retrieve data from common database types. You can also use packages like bigquery for high-performance data stores. In R, you can build and host interactive web apps with just a few lines of code using packages like flexdashboard and rsconnect. You can even turn your web apps into Nintendo Entertainment System (NES)-style games with the nessy package and deploy them like any other Shiny app.

Suggested Reads!