When I started my data science journey, I came to know about a few tools. Some of the tools are quite easy, and some are a lot more complex. Yup, complex for absolute beginners. But once you get started with these programming languages, then you find them easy for you. But there is always a problem for everyone when they start to learn new technologies.
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.
What is R Programming
R is one of the best programming languages for statistics. It is the predecessor of the S programming language and created for statisticians. But nowadays, it is widely used for statistical computing and graphics. You can also integrate other programming languages with R. Apart from that, 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. The name of R programming is derived from the initials 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, statisticians can perform complex statistical analysis easily.
Features of R Programming
- It is an open Source programming language that allows free commercial use.
- Strong graphic support with a variety of libraries.
- Widest range of packages to perform plenty of functions.
- Specially designed to perform complex statistics calculations
- Cross platform support
- Integration with almost every programming language.
- Compatible with latest technologies
- Large community support with a massive number of experienced programmers to help each other.
What is Stata?
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 of 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. The features of Stata also give you the best answer of why use Stata:-
- Stata can perform almost every single statistics function such as regression, time series, variance, bayesian analysis, ANOVA and many more.
- Fully object-oriented programming language support
- Graphical user interface with great graphical capability
- Integration with other programming languages such as Java, Python and many more.
- Easy to use the new command with scripting language support
- Convert HTML file to word document.
- Easy to import, manipulate and generate networks.
Is R Faster Than Stata
As a whole, I can say that R is faster than Stata. It is because it can process a large number of data sets within a few minutes. But if you have a smaller number of datasets, then Stata is quite faster than R programming. Again, Stata depends on the memory; if enough memory is available, you can process the smaller amount of data within a few minutes.
Is Stata a programming language
No, Stata is not a programming language. However, it is the most powerful statistics software that offers programming features. If you would like to do programming in Stata, then it allows you to code in it. That is why most of the users also consider Stata coding language. However, it is a statistical package that allows you to code. But you can’t code like any other programming or coding language in Stata. In other words, there is a limitation of coding or programming in Stata.
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 the basics of R programming, then 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 has 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 easier to learn than 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 get a good command of this software.
Apart from the community, you can also get the best learning support from Stata in blogs, tutorials, webinars, journals, and much more. Therefore, in this comparison of Stata vs R, we can say that Stata is the clear winner.
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 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. So in this comparison of R versus Stata, Stata is the clear winner if you are an absolute beginner.
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. It offers a different version for the students, businesses, and government. You can also get the new purchase, upgradation as well as the renewed facility. Besides, It offers the three major packages, i.e., the single-user, multi-user, and 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. In addition, 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 the 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 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. In this comparison of Stata r. We can say that there is no clear winner.
R is one of the leading programming languages for data science. It has all the libraries and packages that are quite useful for data science. You can perform almost every single operation of data science using R programming, i.e., data mining, data wrangling, data visualization and so on.
On the other hand, Stata is also a good software for data science. You can perform almost every data science operation in Stata. It offers lots of features such as data wrangling, data visualization, statistics and modeling etc.
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 as R programming. But yes, you can perform most of the data science operations with Stata.
If you ask me to choose one of them, I will 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. Now, and you may be confident enough to select the best between Stata and R.