# R vs Stata: Which One Is Best Data Science Software?

Do you want to know the difference between R and Stata? Do you know what R vs Stata is? You must have heard of the R and STATA programming languages if you are a programming student. Those who are new to programming and unfamiliar with these two programming languages, they may be confused.

To choose the best STATA or R, you need to know the differences between them. Keep reading! In this blog, we are going to share with you the comparison between R vs Stata. As a student of statistics, you should know about R vs Stata, like which one is better for data science.

Before going deeper, Let’s start with a short introduction to each of these terms.

Overview

## What is R?

The R programming language was created in 1993 by Ross Ihaka and Robert Gentleman. R offers a wide range of statistical and graphical methods. Examples include machine learning algorithms, linear regression, time series, and statistical inference. C, C++, and Fortran should be considered in the case of heavy computational tasks. Most R libraries are written in R, but you can also use libraries written in C, C++, and Fortran.

With R, data analysis occurs in a sequence of steps: programming, transforming, discovering, modeling, and communicating.

• Programming: R is an easy-to-use programming language.
• Transforming: R is a collection of libraries designed specifically for data science.
• Discovering:  Investigate the data, refine your hypothesis and analyze them.
• Modeling: R provides a comprehensive set of features for identifying the right model for your data.
• Communicating: Write reports using R Markdown with R code and graphs or build Shiny apps to share with the world.

## What is Stata?

Stata offers users powerful tools for managing, analyzing, and visualizing data. It is primarily used by economists, biomedical researchers, and political scientists to examine data patterns. This software has a command line and graphical user interface, making it more intuitive to use.

There are many other statistical software programs you may encounter during your career involving data, so you must know them.

Statistics software Stata is available in more than 180 languages worldwide. It was created in 1985 by StataCorp.The official release of R was in 1995. Researchers and professionals in many countries use it because it is user-friendly. Research statisticians developed R to perform complex data analyses.

## R vs Stata

### Online Support

The R programming language is an open-source language, which means that it is available for free to everyone. As a result, there may be no legal support for the R programming language. You can get help with the R programming language by using community support, documentation, journals, manuals, etc.

On the other hand, Stata is a paid software. Almost every purchased application is supported by online support or after-sales support. Stata offers many support services to its users, including online support, webinars, web resources, documentation, Stata news, and video tutorials. One more thing about Stata is that you will never run out of resources.

### Cost

Because R is free, anyone can use it. You have to download it from the Internet. You can use the program immediately after downloading it without spending any money.

On the other hand, Stata is a paid program, so you will have to pay for it. Stata prices start at \$180 per user per year. Stata is available in numerous versions that can be used by students, educators, governments, and businesses. Purchasing, upgrading, and renewing packages are possible. There are three types of licenses: single-user, multi-user, and site licenses.

### Easy to learn

Learning R can be challenging for statistics students. It is because R is a scripting and programming language. However, they can learn R. It will be difficult for anyone with no programming experience to learn a new programming language.

The free R resources will help you learn R programming. The language is open-source. Additionally, it offers a community where developers can display their skills.

Normally, R releases updates to its official website regularly so that you can find the most recent version of R on the website. R also provides updates to its packages, enabling you to keep up with the data science environment.

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## R vs Stata: Applications

### Applications of R

• R is primarily used for detailed statistics. It is used to analyze the main characteristics of data. Additionally, we utilized R for other tasks such as variability analysis and central tendency.
• We can examine both discrete and constant probability distributions with this language.
• Besides performing system testing, R enables users to authenticate statistical models.
• R programmers can easily organize and preprocess data using the tidy verse package of the R language.
•  A web application package called Shiny is available for the R language. It allows users to easily create interactive web applications placed on web pages.
• R also provides a powerful tool for generating imminent models that integrate machine learning algorithms to predict future events.

### Applications of Stata

• Stats provides a simple, intuitive graphical user interface. Ultimately, it is user-friendly because it makes good use of the point and has a good graphical user interface. One of the best aspects of Stata’s user interface is that it can be adjusted to work with newbies and experienced users.
• In Stata, users can manage and interpret mathematics through tables and dialog boxes. The user can easily access the information, graphics, and statistics menu.
• It provides high-level elements that help you run more efficiently. In addition, you can use a data editor tool to perform functions and conduct operations while discovering the live data.
• Using Stata, the user can also gain complete control over multiple data sets through its data management functions. Users can manage data in a unified manner and modify them quickly by using it. STATA also makes it possible to annotate, edit, and maintain Stata variables.
• Stata provides multiple ways to create graphs; the easiest and most intuitive is to point and click, and the second way is to use the command line. The script should be written in the command line to continually generate graphs. This data can then be used for publications, magazines, and exports. It supports various file formats, including PNG, EPS, SVG, and TIF.

## R vs Stata: Strengths and weakness

### R

Strengths

• A wide range of functions is available.
• A new statistical method is implemented quickly.
• Automation and integration are very simple (for example, with Git, LaTeX, ODBC, Oracle R Enterprise, Apache Hadoop, Microstrategy, etc.)
• Community support is very good, as is third-party support available for free.
• A comprehensive help section (manuals, tutorials, etc.) is available for free.
• The scripting language is very powerful and flexible (e.g., it supports object-oriented programming).
• Windows, Linux, macOS, and all other common platforms are supported.
• Developed by a very large, active developer community, making it future-proof
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Weaknesses

• It can be challenging to learn the R syntax
• Usually, the stability and quality of little-used packages aren’t as good as the core distribution
• When working with large data sets, powerful hardware is required

### STATA

Strengths

• STATA provides almost every established statistical method.
• A GUI makes it easy to access
• Easily automated
• Older versions are compatible
• Stable community support, extensive literature available
• Compatible with Windows, macOS, and Unix
• Affordably priced when compared to commercial competition
• Three-year release cycle provides investment security

Weaknesses

• Updates are a little slow
• It isn’t easy to integrate with other software
• Open only one data set at a time

## Conclusion (R Vs Stata)

In the following article, R vs Stata are compared in detail. As compared to Stata, R offers users the flexibility to perform different tasks. The article will help you to get extensive knowledge about these programming languages and you will be able to identify which language is best for you.

Based on our experience, you should choose R over Stata if you have some coding knowledge. If you don’t have any coding knowledge, you should pick Stata over R.