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.
What is R Programming
R is one of the most popular statistical programming languages. For statisticians it was created and it is the predecessor of the S language. However, it is now commonly used in statistical computing and graphics. R can also be used with other programming languages. Also, R programming allows you to debug other programming codes. In 1995, R officially released for public use. But firstly, it was released in 1985. And R programming takes its name from Ross Ihala and Robert Gentleman’s initials who are the creators or developers of the R programming language. R programming was created at the University of Auckland. Using R, statisticians may readily do complex statistical analysis.
What is Stata?
One of the best and most important and powerful statistical software packages is the Stata. It’s popular for analysing and managing graphical data visualisation. As a result, to examine various data patterns it is used.
And it is used by the world-class academics or researchers in a variety of subjects, including economics, biological science, and political science. Like, Matlab offers both the GUI and command line. And both of them make it the powerful and the best programmers software.
In more than 180+ languages around the world the Stata is user-friendly statistical software. And in 1985 it was made by StataCorp. It is user-friendly, so it is used by researchers and professionals in many countries.
R vs Stata
Ease of Learning
For statistics students, learning R is difficult. The reason behind this is R is a scripting and programming language. But they can learn the R language. Anyone without programming experience will find it difficult to learn a new programming language.
By using the free resources provided by the R, you can learn R programming. It is an open- source programming language. And for developers, it has a community where anyone may show their skills.
Apart from that, if somebody has an issue with R code, they can help each other. And in R vs Stata, the Stata is easier to learn than R. It is because learning software is easy as compared to learning a programming language.
For users, Stata also offers community. And in the Stata community, you can learn Stata with the help of some experts. And also you can find other users who can assist you with the Stata problems. You will also get the support for your learning from Stata in the form of webinars, blogs, training, tutorials, etc.
As we all know that R is an open-source programming language, which means for everybody it is free to use. And due to this for R programming language, there may be no formal support. However, if you want then by using the community support, documentation, journals, manuals, etc, you can get help with R programming language.
On the other side, Stata is software which is the paid one. And mostly every purchased application is known for its support like the online support or after-sales support. To the users, Stata provides many support services which includes online help, webinars, web resources, documentation, Stata news, and video tutorials. And one thing also, when you use the Stata software, you will never run out of resources.
Everyone can use R for free because it is free to use. You only have to do one thing, that from the internet you only need to download it. After downloading you’ll be able to run it to use without spending any money.
But on the other side, if we talk about the Stata, then you have to pay for it, because it is not free. And the Stata price starts at $180 per user per year. For students, educators, government, and businesses, Stata is available in a variety of versions. It also offers the ability to purchase, upgrade, and renew packages. Single-user, multi-user, and site licences are the three types of licences available.
On the official website of R you may find the most recent version of R, as it releases the updates at regular intervals. Also R provides updates to its packages, allowing you to keep up to date with the data science ecosystem.
And on the other side, also on a one-year interval, the Stata gets the latest update. With the licenced version of Stata, you can download the most recent update.
Applications of R and Stata
Applications of R
- Commonly R is used in descriptive statistics. To represent the data’s important points it is used. For a number of other things R is also used, such as measuring variability, skewness, and central tendency.
- For exploratory data analysis, R is one of the best tools. It has ggplot2, which is one of the greatest data visualisation libraries.
- For analysing both discrete and continuous probability distributions, R provides the most efficient method.
- It also enables hypothesis testing, which can be used to verify statistical models.
- The tidyverse package in R which makes it simple to organise data and perform data preprocessing.
- Eshiny is the most interactive R web application package. This package can be used to create interactive web applications that can be easily integrated on web sites.
- You may also use R to create predictive models that incorporate algorithms of machine learning to assist you to find the future events occurrence.
- Stata has a user-friendly GUI. It is simple to use because it employs a point-and-click interface. The best feature of its user interface is that it can adapt to different types of users, such as new one’s and experts. When using Stata, no one will ever have an issue.
- The menus and dialogue boxes are offered in the Stata graphical user interface. Users can access a variety of important features, such as data analysis, data management, and statistical analysis, by using these dialogue boxes. The data, visuals, and statistical menus are all easily accessible.
- Stata is programmer and developer friendly. And this is because of the feature which it offers and that is the command line feature. It has a number of features in its command line which allows the programmers to type the commands and have them executed. Before executing, the programmers can type the syntax and the command scripts. The command’s solutions will respond and in the result window it displays the results.
- Stata also has a collection of components that are the advanced one’s and that help you to work faster. And to see the live data while performing operations and using the functions, you can use a data editor for this.
- It also has data management features that provide you complete control over your data sets. With the help of Stata, you can link the data sets together and restructure them. Aside from that, Stata allows you to define, edit, and manage variables.
- You can also make more effective graphs with stata. And in two ways Stata allows you to make graphs: the first is by simply pointing and clicking, and using the command line is the second way to make graphs. You must write a script in the command line that generates a huge number of graphs on a regular basis. These graphs are printable, publishable, and exportable. It accepts a variety of file types, including EPS, TIF, PNG, and SVG. You may also use Stata’s integrated graph editor to make changes to the graph.
Conclusion (R vs Stata)
The above information defines R vs Stata effectively. Also, it is beneficial for the students or the learners to understand the major differences between the terms R vs Stata. And we hope that now you should know all about R vs Stata. And you can decide which one is better for data science.
From our point of view, If you have some coding knowledge then you should choose R over the Stata. But, if you don’t have coding knowledge, then you should choose Stata rather than R.
But if in any case, you want our R programming help. Then, feel free to contact us. We are 24*7 available to help you.
R is an open-source programming language. R has a more difficult learning curve, but it is far more powerful and adaptable. R has a lot of features that Stata doesn’t have, basically, there is nothing in stata that you can’t do in R. R is also a good starting point for other programming languages like Python.
In R or Stata, the Stata is easier to learn than R. It is because learning software is easy as compared to learning a programming language. So we can say that Stata is easier than R.