R vs Stata Are the closest competitors of each other. There are lots of similarities between R and Stata. Both of them are widely used to perform statistics operations. Apart from that, both of these programming languages are close competitors to each other in data science. Let’s find out which programming language is better for data science and why. We are going to start with an overview of both of these programming languages.
Introduction to R
R is one of the best programming languages for statistics. It is the predecessor of the S programming language. It was created for the statisticians. But nowadays, it is widely used for statistical computing and graphics. You can also integrate other programming languages with R. Apart from that, and you can also debug other programming codes in R programming. The initial development of R programming was completed in 1985, and after that development, r programming is widely used by almost every statistician community around the world. But in 1995, R was officially released for public use. R programming’s name is derived from the initials of the name of Ross Ihala and Robert Gentleman. Both are the lead developers of R programming. They developed R programming at the University of Auckland. With the use of R, the statisticians can perform the complex statistical analysis easily.
Stata is one of the best and most powerful statistical software. It is widely used to analyze and manage graphical data visualization. Thus it is used to analyze different data patterns. Therefore, the top-class researchers used it from various fields, i.e., economics, biomedical, and political science. Likewise, Matlab offers both the command line and a graphical user interface that makes it one of the best and most powerful software for programmers. Stata was made available for public use in 1985 by StataCorp. It is one of the best and easiest to use statistical software available in more than 180+ languages worldwide. That is the reason it is widely used by professionals and researchers in various countries around the world.
R vs Stata
Ease of Learning
Learning programming, like R, is never easy for students. Because R is not a programming language, it is a combination of programming and scripting language. If you don’t have a programming background, it will take too much time to learn R programming and command this programming language. But once you master R programming basics, you can do a lot from this programming language. You can learn R from various sources for free. There are a large number of YouTube channels offering free R programming tutorials to the students. Apart from that, R is having one of the largest communities in the world. Here you can contact other programmers to help you with your R programming code. You can also help the other programmer and show your expertise.
On the other hand, Stata is quite easy to learn programming language as compared with R programming. All you need to have the proper training to learn Stata. It also offers the best learning environment to its users in the form of an online community. The Stata community is full of experts who can also help you to get good command over this software. Apart from the community, you can also get the best learning support from Stata in blogs, tutorials, webinars, journals, and much more.
R is not a paid programming language. That is why you don’t get online support in the form of customer support. But R programming is offering the best community support to users. Here you can share your challenges with R programming languages. Apart from that, R official website offers the best documentation, community support, manuals, journals, and many more.
On the other hand, Stata is the paid software; that is why it has one of the best customer support users. You, as a Stata user, will get the after-sales support from their experts. Apart from that, you will get the best support from Stata in the form of FAQs, documentation, video tutorials, news, webinars, and many more if you are stuck while using Stata. Then you will get various kinds of online support over the internet.
R is an open-source programming language. That is the reason you can use R programming for free without paying a single penny to anyone. All you need to pay for your data charges while downloading it from the internet.
On the other hand, Stata is a paid software. It costs you around $180 per user per month. Stata offers a different version for the students, businesses, and government. It also provides the new purchase, upgradation as well as the renewed facility. Stata also offers the three major packages, i.e., the single-user, multi-user, and the on-site license.
R is the best statistics language; it is used for almost all statistics functions i.e.measurement of variability, skewness, and central tendency. It is the best language for data analysis. You can use this package for web application development. R is also used to build predictive models using machine learning algorithms.
On the other hand, Stata is a statistical package. It is quite easy to use; anyone can use it without any programming knowledge. It also offers the command line as well as a graphical user interface. It makes it best for the programmers as well as for the user with no technical background. The programmer can easily utilize the stata using the command line interface. In other words, you can use Stata to its full potential using the command line interface. SPSS has a massive number of advanced components. You can interact with live data and perform almost every operation using the data editor in Stata.
Data management is not so good in r programming, but you can achieve better data preprocessing with tidyverse packages. R also offers one of the best packages i.e. Eshiny.
It is also one of the best data management software in the world. You can have full control over your data using Stata. It is easy to link the data from various sources and then reshape the data easily. You can also manage, edit, and define the data variables in Stata.
R is also offering the best support for data visualization. You can also analyze the discrete and continuous probability in R programming. Apart from that, it has the best validation for statistical models using hypothesis testing.
It is quite easy to create graphs in stata. You can create graphs in Stata either by drag and drop or create the command line. There are many graphs in Stata, and you can utilize them by writing the script in Stata. You can also export the graphs for printing or publication in multiples formats i.e., EPS, TIF, PNG, and SVG. There is also an integrated graph editor in stata to modify the graphs.
Conclusion (R vs Stata)
In the end, I would like to say that R is still the best programming language for data science. It offers various packages and modules that are quite useful in data science operations—all you need to have some patience and basic programming skills to get started with R programming. You can also do a lot more with R programming as compared with Stata.
On the other hand, if you don’t have enough patience and skills to learn a programming language for data science. Then it would help if you went with Stata. It is not that powerful like R programming. But yes, you can perform most of the data science operations with Stata.
If you ask me to choose one of them, then I would go with R. There are a few reasons for it. Likewise, R is an open-source programming language. It offers all the major libraries and modules that will help you get started with data science. Apart from that, you can also extend it using the packages. You will never go out of resources when you try to perform data science with R.