R vs Stata: Which One is Best For Data Science?


Are you wondering about R vs Stata? Do you know what STATA vs R is? If you are a programming or computer science student, then you definitely heard the name of these two programming languages R and STATA. But if you are a beginner or newbie to programming, you may be confused between these two programming languages. 

However, if you are looking for the difference between STATA and R and want to choose the best one, you are in the right place. Keep scrolling! In this blog, we will learn STATA vs R and along with their features so that you can pick a perfect fit.

Before we get into a depth comparison, we should have a look at the overview of both of these languages.

What is STATA?

Stata is one of the most popular and widely used statistical software in the world.

It is used to analyze, manage, and produce a graphical visualization of data.

The primary use of Stata is to analyze the data patterns.

Researchers are using Stata in the field of economics, biomedicine, and political science.

Like only a few software, it offers you the command line as well as the graphical user interface that makes it more powerful.

It was created in the year 1985 by StataCorp.

Stata is the most compelling statistics software; that’s why it is used in more than 180 countries around the world.

And thousands of professionals and researchers trust this software. 

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What is R?

R programming is one of the most influential and most reliable statistics languages in the world.

It is used for statistical computation and graphics.

It offers high-level graphics, interfaces to other languages, and debugging facilities.

R is known as the successor of the S language.

It was designed in the 1980s and has been used by the majority of statistical communities worldwide.

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But the official release of R was in the year 1995.

The primary motive behind the development of R was to allow academic statisticians to perform complex data statistical analyses.

R is derived from the initials of two developers’ names, i.e., Ross Ihala and Robert Gentleman. Both of them were associated with the University of Auckland when they developed R.

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STATA vs R: Popularity

The following graph shows the interest in STATA and R among people.

The above graph shows the interest of people in STATA and R in the last 5 years. As you can clearly notice in the graph, people have shown no interest in STATA in the last 5 years. On the contrary, R is still popular in comparison with STATA.

R Vs STATA: Strengths and Weaknesses


1. Large range of Functions (Over 2,000 packages).
2. Safe and sustainable because of the very large and active developer community.
3. New statistical methods are quickly implemented.
4. All common platforms are supported, like macOS, Linux, Windows, etc.
5. Powerful and flexible scripting languages because it has the support of object-oriented programming.
6. Easy to automate and integrate with Git, LaTeX, ODBC, Apache Hadoop, etc.
7. Available free extensive help resources 
1. A wide range of functions is available.
2. Almost every statistics method can be found in STATA.
3. Easily accessible through the GUI.
4. Compatible with other versions.
5. Can be automated.
6. Available for Windows, Unix, macOS.
7. Investment security guaranteed by a three-year release cycle.
8. Excellent support from the STATA community and extensive literature.


Powerful hardware is needed when working with large data sets.Familiarity with R syntax presents a barrier to entry.Integration with other software is burdensome.A bit sluggish concerning incorporating new methods (version updates)

Let’s learn the core difference between STATA and R.

R vs Stata

1. Ease of Learning

It is quite complicated for statistics students to learn R from scratch.

It is pretty hard for anyone to learn a new programming language without having a programming background.

But, you can learn R with the help of some free sources provided by R.

R is an open-source programming language and has a community for developers where anyone can showcase their expertise.

Apart from that, they can also help each other if anyone faces a problem with the R code. 

On the other hand, learning Stata is quite easy as compared with R.

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Because learning software is always easier than learning a programming language from scratch.

Like R programming, Stata also offers community support to the users.

In their community support, you can find other users who can help you while facing Stata problems.

Apart from that, some experts in their community can help you to learn Stata.

Stata also offers extensive learning support to users in the form of blogs, tutorials, webinars, training, journals, etc.

2. Online Support

As we have already discussed, R is an open-source programming language, which means that it is free to use for anyone.

Therefore you may not find any official support for the R programming language.

But you can find help with R using its documentation, community support, manuals, journals, etc.

On the other hand, Stata is a paid software, and every paid software is known for its online support or after-sales support.

Stata offers extensive support to its users, from online support to FAQs, documentation, video tutorials, web resources, Stata news, and webinars.

You will never find yourself out of resources while using the Stata software. 

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3. Cost 

R is free to use for everyone. You need to install it from the internet, and you can run it without paying a single penny to anyone. 

On the other hand, Stata’s price starts at $179.00 per year per user.

Stata offers different versions for students, education, government, and business.

It also provides the new purchase, upgrade, and renewal facility of the packages.

The license is also divided into two categories, i.e., single-user, multi-user, and site license.

4. Updates

R offers a variety of updates at regular intervals, and you can get the latest update of R on its official site.

Apart from that, R also provides updates on its packages that allow you to stay updated with the data science environment. 

On the other hand, Stata also gets the latest update on a one-year interval.

You can get the latest update with the licensed version of Stata.

STATA VS R: Applications

Applications Of R

  • The primary use of R is in descriptive statistics. It is used to summarize the main features of the data. Apart from that, R is also used for various other purposes like measurement of variability, skewness, and central tendency. 
  • R is also one of the most popular tools for exploratory data analysis. It has one of the best data visualization library that is known as ggplot2
  • R is offering the best way to analyze both discrete and continuous probability distribution.
  • It also allows you to do hypothesis testing that can be used to validate statistical models.
  • It is quite easy to organize the data and data preprocessing in R with the help of its tidyverse package. 
  • Eshiny is the most interactive web application package in R. You can use this package to develop interactive web applications that can easily be embedded on web pages. 
  • You can also develop predictive models in R that work with the integration of machine learning algorithms that help you find future events.
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Applications Of STATA

  • Stata offers an easy-to-use graphical user interface. It is quite simple to use because it uses the point and click GUI. 
  • Stata’s GUI offers menus and dialog boxes. With the help of these dialog boxes, users can access plenty of useful features, i.e., data management, data analysis, and statistical analysis. 
  • Stata is developer and programmer-friendly because it offers the command line feature. Its command line has a set of features that allows programmers to type the command and run them. The solutions of the command will respond and show the results in the result windows. 
  • Stata also offers a set of advanced components that allows you to work more efficiently. You can use a data editor to help you see that live data while using the functions and performing operations. 
  • It also offers data management capabilities that allow you to have full control over data sets. You can link the data set together and reshape them quickly with the help of Stata. 
  • You can also create graphs in stat more effectively. Stata allows you to create graphs in both ways; the first one is merely pointing and clicking, and the second one is with the command line’s help. In the command line, you need to write a script that continuously comes up with a large number of graphs. You can use these graphs in printing, publications, and export. It supports multiple file formats such as EPS, TIF, PNG, and SVG. You can also edit the graph in Stata using the integrated graph editor in Stata. 

Which companies use STATA and R?

Following are companies that are using STATA and R;

Urban Institute, Brookings Institution, George Mason University, The American Red Cross, Chamber of Commerce Of The United States Of AmericaFacebook, Google, Twitter, Microsoft, Uber, Airbnb

R vs Stata: Jobs and Salary


  • Economic Consultant- $73,985
  • Data Analysts- $63,607
  • Research Analysts- $56,071
  • Economist- $97,190
  • Assistant Professor of Economics- $84,500


  • Data Scientist- $89,060
  • Statistician- $71,967
  • Biostatistician- $72,247
  • Data Analyst- $61,271


Now we have seen the in-depth comparison between R and Stata.

R is a programming language that allows you to do more than you can do with the Stata.

I would like you to recommend R for data science if you have a basic knowledge of coding or are familiar with the coding environment.

On the other hand, if you have some coding knowledge or no coding knowledge, you should choose Stata over R.

Because it is quite easy to use and anyone can use it effectively.

The beginners need only prior training to use it like a pro.

But if budget is a big issue for you, then you should choose R.

You can get excellent command over R with the help of a few months’ training.

The training will cost you some money, but it will help you to use the free language for a lifetime.

Now it’s over to you which one you prefer between R vs Stata. 

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Q1. Is STATA faster than R?

STATA took 67.25 seconds to write a file of 458 MB of raw text, while R needed 72.93 seconds to do the same. However, STATA exports 8% faster than R.

Q2. What does STATA cost?

STATA pricing starts at $48.00 per year. STATA does not have the free version, but it provides you with the free trials.