All About R Programming

Whenever you come across powerful statistics, analytics, and visualizations that are used by business leaders, researchers, and data scientists, chances are R Programming Language is behind them. Don’t know much about R? Well, we got you covered. This blog talks about everything you need to know about R, from what it is and when it was created to whether it’s popular and what companies use R. So if you’re up for a deep dive into R, read in full.

What is R?

R is basically an open-source programming language created by statisticians for statisticians. While other languages are general-purpose, R Programming Language is known specifically to help with statistical computing and data visualization and is used by data analysts, scientists, and data scientists. However, you should know one thing R is not just a programming language. It’s also an environment for the users who wish to perform statistical analysis & modeling but don’t have programming skills.

What Falls Under Basic and Advanced R Programming?

Similar to other programming languages, R is also divided into parts: basic and advanced R. Here’s what falls under these parts:

Basic R

Basic R, as the name tells, includes basic knowledge of R Programming Language. It includes writing R scripts for practical applications, the core of R, and data manipulation. If you’re fluent in Basic R, you can easily:

  • Produce data plots of different types.
  • Perform data manipulation.
  • Create descriptive statistics.
  • Create simple functions.
  • Conduct specification test and multiple linear regression analysis.
  • Work with different data structures and data types such as list, matrix, data frame, etc.

Advanced R

Advanced R Programming Language includes more in-depth concepts. This part of R includes efficient data manipulation, advanced plotting, and writing efficient scripts to be able to solve complex applications. Advanced R also includes the below concepts:

  • Return Values
  • Parameters
  • Debugging
  • Variable Scope
  • Data extraction
  • Advanced R graphics (ggplot2)
  • Advanced-Data Manipulation
  • Several Useful Packages

Mastering Advanced R will allow you to:

  • Write complex R functions.
  • Perform advanced data manipulation like merging or reshaping the data.
  • Use Advanced Plot Package.
  • Perform data analysis on all types of data.

Who uses R?

Finance Companies

The finance industry actively makes use of data science. And guess what the best tool/language for data science is? It’s R-programming language. R offers finance companies a suite of statistical functionalities that they can use to carry out different tasks such as adjusting risk performance and performing visualizations such as:

  • Drawdown Plots
  • Density Plots
  • Candlestick Charts
  • & More

Research Institutes

Several research institutions, such as agricultural universities, and educational institutes teaching political science, are actively shifting from SPSS to R for analyzing data. Even universities such as the University of California and Cornell University teach their students to R for effective data analysis and crafting visualizations.


Banks, similar to financial companies, actively use R for risk analytics such as credit risk modeling. Also, R is used along with Hadoop to analyze customer retention and quality. What banks use R, you may think? Well, Bank of America uses R to analyze financial losses and for financial reporting.

Healthcare Companies

Several companies that operate in the epidemiology, drug discovery, bioinformatics, and genetics domain make use of R. Such companies, with the help of R, crunch tones of data which makes data analysis a piece of cake. Also, R is used by companies who work to develop drugs for analyzing drug safety data and performing pre-clinical trials. What’s more, the Bioconductor Package of R offers numerous functionalities using which scientists can carefully analyze the genomic data. Last but not least, data scientists in the epidemiology domain use R to extensively analyze and predict the spread of diseases.

Social Media Companies

Social media has always been data intensive. Millions, probably billions of users, scroll Facebook, Instagram, and Twitter feeds each day. And based on their data or preferences, they’re shown ads, or their user experience is modified. And all this is made easy with R-programming language.

Using R, several social media companies, including Facebook, analyze user sentiments to enhance the overall UX. You can even find several tutorials for analyzing Facebook comments and other data on the internet. SocialMediaMineR is an R-based tool that consumes one or more URLs and returns the info on the popularity of every URL on the internet.

How Has R Evolved Over the Years?

In the beginning, R was a tough language to master. It was structured differently and was quite confusing to work on. However, keeping these issues in mind, Hadley Wickham (statistician and Chief Scientist at RStudio Inc.) came up with tidyverse — a collection of packages.

And tidyverse changed the game. It made data manipulation pretty intuitive and trivial. Also, plotting a graph became easy. You could just define a vector using some values and plot a simple graph using the plot function. Here’s an example of how easy it is to plot a graph using R:

R Programming Language — Plot Graph

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Running the above code will produce something like this:

R Programming Language — Code

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You could even plot a bar graph just by changing the plot(cars) function to barplot(cars). Easy, right?

R Programming Language — Bar Graph

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Here’s what the output of the above code would look like:

R Programing Language — Output

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Apart from plotting graphs, you can easily implement algorithms for machine learning using R. What’s more, using R, you can connect to other databases such as Hadoop or communicate with other programming languages. For instance, you can call C++, Java, and Python classes in R. Now, this makes the job quite easy.

All in all, over the years, R has evolved to become easy to master and extremely functional programming language for statisticians, data analysts, data scientists, academicians, etc. Now, more than ever, people from all domains are opting for R for statistical computation purposes.

Here’s a brief evolution of R from 1976 to 2019:

R Programming Language — R History and the evolution of R

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When Was R created?

First implemented in the early 1990s, R-programing language was crafted by Robert Gentleman and Ross Ihaka at the University of Auckland, New Zealand. The initial version of R was released in 1995, and the stable beta version came out in Feb 2000. R-programming language was modeled closely to the S programming language for statistical computing. For more details on how R got started and how it’s connected to S, you can check out Ross Ihaka’s brief account.

Is R a Popular Programming Language?

It won’t be wrong to say that R is a popular programming language. However, a better statement would be that R is increasingly becoming popular. Here are The top ten languages in TIOBE’s Programming Community index for August 2020:

This table shows how R has moved upward from position 20 to position 8 in just 1 year. But what’s the reason behind R becoming popular. Here are some:

R Programming Language — Change Log

R-Programming language is Open Source

One of the biggest reasons why R-programming language is becoming popular is that it’s open-source. It’s the reason why people in the academic domain are moving from paid software such as SPSS to R. Also, you can use R on any platform, be it Linux, Windows, or MAC. What’s more, there are several free programming libraries that get the job done. However, you may have to buy commercial libraries, especially if you’re dealing with TBs of data.

R-Programming Language Comes with All the Statistical Tools You Need

R language offers you all the data analytics tools from basic to advanced. These tools can help you access data in different formats and perform different operations such as:

  • Merges
  • Transformations
  • Aggregations

You can also find tools for modern and traditional statistical models such as GLM, Tree models, Regression, and ANOVA. The best thing is all this comes in an object-oriented framework. And this makes it easy to extract the information and perform operations on the same.

R-Programming Language Offers Supreme Level Visualizations

R-programming language offers unmatched charting and graphing capabilities influenced by famous data visualization thought leaders such as Bull Cleveland and Edward Tufte. You can easily visualize data in the form of:

  • Graphs
  • Charts
  • Multi Panel Lattice
  • Scatter Plots

What’s more, if you’re a programmer, you can easily create custom graphics.

R-Programing Language has an Actively Growing Community

Another reason behind R’s growing popularity is the ever-growing vibrant community. R Programming Language has attracted a huge following of statisticians, engineers, and scientists because they find it easy to use and master.

You can easily find like-minded people on stack overflow, Quora, or Reddit who’ll help you with your queries. Here’s a graph that shows how significantly R’s (in blue) traffic on Stack Overflow has grown from 2012 to 2018.:

R Programming Language — Traffic to Programming Language

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Please Note. This graph is not meant to compare R with other languages as other languages aren’t specifically meant for data analysis. It just shows how R’s community is growing.

R Programming Language Offers You a Host of Useful Packages

Want different functionalities for statistical analysis? R got you covered. R programming language has several built-in packages that you can use to perform numerous functionalities.

For instance, data scientists use packages such as “dplyr” and “ggplot2” for data manipulation and plotting. Another is the Cared package, which helps data scientists implement ML via a unified API. Several ML algos have already been implemented using this package.

What Companies use R-Programming Language?


How does Twitter use R Programming Language?

R-programming language is an important part of the Data science toolbox on Twitter. Data Scientists at Twitter use this language for monitoring and this improving user experiences.


How does Facebook use R Programming Language?

Facebook is known to use the R Programming Language for behavior analysis pertaining to profile pictures and status updates.


How does Microsoft use R Programming Language?

Microsoft is a huge organization that uses R to apply machine learning algos on data from Azure, Bing, Office applications, and other departments such as marketing, sales, and finance.

eSmart Systems

How does eSmart Systems use R-programming language?

eSmart Systems, a company in Norway, optimizes the power grid using data from smart meters based on forecasting models crafted using the R programming language.


How does NYT use R-programming language?

The New York Times uses R to improve its traditional way of reporting. It also uses R for interactive data analysis for forecasting elections and finding out a person’s birthplace based on their dialect.

Apart from the above, several other companies, such as Mckinsey, Ford, IBM, Airbnb, HP, ANZ, etc., use R Programming Language for a variety of purposes (all related to statistics and data visualization).


R is both a free programming language and an environment used by individuals from almost every domain for statistical computation, data visualization, and data analysis. Also, R boasts a huge active community that is exponentially expanding each year. Since its launch, the R Programming Language has become better and easier to implement. In addition, new libraries have been added that made handling and visualizing thousands of GBs of data pretty easy.

So, if you’re in the data analytics domain or manage loads of data and want a free yet powerful tool for effective data handling and visualization, R-programming language is what you should go for.

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