Data Science is an emerging field that allows various industries for manipulating data, extracting information, and generating valuable insights from large amounts of data. For this, data scientists use a number of tools, and software programs. Some of the most popular ones that many data scientists use are Python, Apache Hadoop, R, SPSS, SAS, and Stata. These programming languages enable you to perform complex statistical calculations and turn the result into visualizations.
Python is mostly used to implement Artificial Intelligence(AI) applications and Machine Learning models. R and Scala are mostly dedicated to Data Science applications like data analysis, visualizations, and predictive modeling. However, as a data science enthusiast or professional you should know how to use all these languages and popular libraries associated with Data Science operations. With Data Science Training, you can acquire the essential skills and practice how to apply them on different fronts.
Now, let’s discuss the R and Stata programming languages and the differences between them based on different scenarios.
What is R?
R is a proficient programming language that data scientists use to analyze huge datasets and perform vigorous measurements using statistical models. It allows you to extract new information from raw datasets, establish relationships between two or more distinctive data variables, and find anomalies or redundant values. Check out this video to learn various aspects of R programming designed for aspirants.
R is an open-source language, which means you can extend the environment using different packages and custom libraries. R can be used on a web browser through a client-server architecture or on a local machine with Integrated Development Environment(IDE) like RStudio. Some of the best features of the R language are:
- Open-source and a fully functional language
- Supports both structured and object-oriented programming concepts
- Provides debugging and unit testing functionalities
- Large developers community that allows newbies to learn the language easily
- Enables the developers to produce clean and readable syntax with consistency
- Advanced Machine Learning and Deep Learning models enable you to create insightful visualizations
- Easy to integrate with different frameworks like Hadoop, KNIME, Teradata, Git, and PySpark
What is Stata?
Stata is a commercial and one of the most popular statistical software used by various economists and data scientists for statistical analysis. The software is used by many industries and scientists to manage large datasets, analyze them, and produce graphical representations out of them. Invented in 1985, Stat is mainly used to find data patterns and establish relationships between different data sets and dimensions.
Stata is a powerful tool that you can use through both graphical user interface(GUI) and command-line interface. The software was created by StataCorp in 1985. It is now used in various fields such as biomedicine, IT economics, space, research, and more. Following are some of the best features of Stata software:
- Provides numerous functions and statistical models to perform complex calculations.
- Simple GUI with easily accessible features.
- Developers can automate various tasks and can also work with older versions of different software tools.
- High portability allows the developers and programmers to use Stata on various platforms such as Linux, macOS, Windows, etc.
- Less expensive compared to other statistical software and programming languages.
R vs Stata
Now, let’s understand the difference between the R and Stata based on different features. But before that, if you want to learn more about the R language, then check out this R Tutorial and sharpen your skills in Data Science.
R is used as both scripting and programming language and therefore requires some programming background. If you’re new to programming, the learning curve for R is going to be pretty steep. However, a huge community of developers and online resources will help you learn the language and work on different projects. Stata on the other hand is easier to learn and features extensive online resources like blogs, training courses, tutorials, and more.
As discussed earlier, R is an open-source language and features huge community support. But, you’ll not get official support as you get from commercial software providers. In contrast, Stata is a paid software and provides 24/7 online support to its users.
R is freely available for various platforms and you can download it from the internet. Without paying a cent, you can modify, add more features, and use it for both business and educational purposes.
Stata is a paid software and charges up to US$179.00 per user every year. Based on the preferences, Stata also features licenses for single, multiple, and website users.
R is mainly used for descriptive analysis and pattern recognition. Data Scientists use R to perform probability distributions, hypothesis testing, and data preprocessing. It also acts as an exploratory tool and visualization through different libraries like ggplot2, BioConductor, Shiny, and dplyr.
Stata features a simple GUI which allows the user to produce insightful results without doing much programming. In addition, Stata features attractive visualizations like graphs, scatter plots, charts, and maps. With a graph editor in Stata, you can add different data variables and establish a relationship between them.
Both R and Stata are used by various organizations and data scientists to perform mathematical calculations and process the data in an efficient way. Both of these tools have some benefits and drawbacks in terms of usage, availability, and affordability. Therefore, it depends on the developer which tool or programming language fulfills your requirements.