R vs Stata: What Are The Major Differences Between Both?

R vs Stata: What Are The Major Differences Between Both?

Stata considers being well-designed and user-friendly for performing simple analysis. When a user wants to program a task, Stata becomes more challenging. R, on the other hand, needs a variety of abilities in order to execute even the most basic analysis. In this blog, we’ll look at the differences between R vs Stata

When it comes to data science, most students find the distinction between R and Stata to be challenging. Moreover, as statistics students, we must know which language is preferable for data science between R and Stata. Before we go into the comparison, let’s look at the definitions of both programming languages.

What is R programming all about?

R is often regarded as the most powerful and reliable statistical programming language. This language is mostly used for calculations and visuals in mathematics. This language has a number of advantages, including cross-language compatibility, excellent visuals, and debugging tools. Moreover, the language is also referred to as the S language’s ancestor.

This language was created in the 1980s and utilized by the majority of statistical communities throughout the world at the time. R, on the other hand, first released in 1995. The major goal of R’s creation was to allow statisticians to do complex data statistical analysis. Basically, the name of two developers, Ross Ihala and Robert Gentleman, inspired this language.

What is Stata all about?

Stata is often regarded as the most widely used statistics program on the planet. The major goal of this language is to alter, inspect, and visualize data in a graphical format. It’s mostly used to decipher data patterns. Moreover, Stata is used by the majority of academics in the fields of healthcare, business, and civic science.

Stata, like other software, is regarded as the most powerful since it provides a user command line as well as a graphical user interface.

Moreover, Stata is not a free program to install on your computer. It, unlike an open-source application, does not let you write your own functions. Therefore you confine to the functions it currently has. Finally, its capabilities are restricted to numeric or categorical data. And it cannot examine spatial or other forms of data.

List of top 3 applications of R and Stata


  • It mostly uses in detailed statistics. This is preferred by statisticians to examine the data’s primary properties. R may also use for a variety of additional tasks, such as central tendency, analysis of variability, and central tendency analysis.
  • R also regards as one of the most widely used tools for exploratory data analysis. The most useful data visualization package in R is ggplot2, which also know as ggplot2.
  • Shiny is the most interactive web application package in the R language. This package helps the creation of interactive web apps that can easily put on websites.


  • Stata has an easy-to-use graphical user interface. After all, it is user-friendly since it makes use of the point and sticks to the graphical user interface. The most useful feature of Stata’s user interface is its ability to adapt to different sorts of users, such as beginners and experts.
  • Stata also has data management capabilities that allow users to have total control over their data sets. It allows a user to connect data sets together and edit them fast. STATA also assists users in editing and maintaining Stata variables.
  • Moreover, Stata also has the ability to easily produce graphs in two ways: the first is by simply targeting and clicking, and the second is by the command line’s support. You should develop a command-line script that generates various graphs on a regular basis. 

R vs Stata: A comparison on different factors


You didn’t have to pay a little bit for R, and anybody may use it. You must download and install this language from the internet in order to use it.

Stata, on the other hand, costs $179.00 a year for one user. Stata also has a variety of options for educators, learners, administrators, and marketers. It also provides the most updates, up-to-date marketing, and renewal options for the packages.

Learning Ease

When it is about R vs Stata, the R language is difficult for students to grasp. The reason is that it is a programming and scripting language. Learning a new programming language is quite challenging for a newbie. That is why R provides several free resources to help you learn the language. Moreover, R is an open-source programming language that provides a wonderful opportunity for community developers to demonstrate their skills.

Stata, on the other hand, is much easier to learn than R. Learning software is easier for most students than learning a programming language from the start. Stata, like R programming, has a community support system for its users. In a scenario, when you have difficulty with Stata, you’ll be able to discover another individual who can help you.

Online Assistance

As previously said, R is an open-source programming language, implying that it is user-friendly and that anybody may use it. That is why R will not give you any official service. However, you may get assistance by reading its documentation, guidebooks, community guide, and publications, among other things.

In contrast, a user must pay a fee to use a Stata language. Simply said, there is a need to pay for software, and every purchase of software value for its online assistance. Stata also provides excellent assistance to its users.


When we compare R vs Stata, R delivers a variety of updates at regular intervals. And it’s simpler to discover the most recent R update on the official site.

Stata, on the other hand, provides annual upgrades to its most recent edition. They will also offer you the most recent version of Stata, which permit.

Let’s wrap it up!


We’ve put up a detailed comparison of R vs Stata in this blog. In comparison to Stata, R is a programming language that allows users to do a variety of activities. After reading this blog, you will be able to choose which programming language is ideal for you. And you will get a thorough understanding of both languages.

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