R and Python: The Rise of Two Popular Programming Languages

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How Python and R computer languages can be used to handle key tasks in business analytics.

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R and Python: The Rise of Two Popular Programming Languages

According to MicroStrategy’s 2020 Global State of Enterprise Analytics Report, 94% of enterprises say data and analytics are important to their business growth and digital transformation. Enterprises investing in analytics are realizing many benefits, such as improved efficiency and productivity, more effective decision-making, and stronger financial performance. For companies to see continued growth from their data analytics efforts, they need to hire talented professionals with extensive analytical skills.

To learn more, check out the infographic below created by the Villanova University’s Online Master of Science in Analytics program.

R and Python: Professionals’ Programming Preferences

The creators of Python define it as “an interpreted, object-oriented, high-level programming language with dynamic semantics.” R, on the other hand, is a language and environment created for graphics and statistical computing. Each one is preferred for different purposes.

Programming Preferences

A 2019 KDnuggets poll found 65.8% of industry professionals liked using Python and 46.6% liked using R. A 2019 survey by Burtch Works found that Python and R were the preferred language for college students and those with 1 to 10 years in the industry. The Burtch Works survey also determined that Python and R were the preferred languages in the tech/telecom, consulting, and retail/CPG industries. Additionally, it was determined that Python and R are equally appreciated in the healthcare/pharmaceutical industry as SAS programming language.

R and Python Benefits & Drawbacks of Both

Each programming language has its own set of pros and cons. The differences between R and Python are quite evident.

Python

Some of the benefits to using Python is its simplicity, as it’s easier to write than C++ or Java, easier to maintain, simplifies preprocessing, and is productive for writing code. Its versatility and flexibility make it suitable for “internet of things” applications, and it also supports object-oriented, procedural and functional programming styles. Additionally, it’s popular in academia, which could lead to the development of a large talent pool for employers to consider.

Conversely, there are some drawbacks to Python. These include ambiguity regarding which version of the software is better, errors showing up only during runtime, and its speed compared to compiled languages.

R

R allows data to be preprocessed with any language or assembly code. It’s also great for statistical applications and data visualizations. While it uses command line, it also has additional features. Additionally, it possesses an extensive functionality and adaptability for developers to build their own tools and methods, which can make it good for fast prototyping and individual projects. R can be used to validate research and check for accuracy, and R commands can be used to create graphs and finalize report. Finally, R contains a vast package ecosystem.

There are two main drawbacks to R. The first is that it may be slower than its competitors. The second is that R’s updates haven’t been as revolutionary and beneficial as the updates to Python.

Choosing the Right Language for the Job

Oftentimes multiple languages are used during one project. However, business analytics professionals should know which programming language is best suited for each task.

Choosing Python

The chief uses for Python include rapid application development and scripting, or glue, language. A large standard library is included with Python, as is GUI programming support, compatibility with Windows, Linux, Unix, Macintosh and other platforms. Python also features extensibility and integration with other languages. Jobs that typically use Python include analytics developer, data analyst, and quality assurance specialist.

Choosing R

R’s numerous uses include linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and graphical display. The program possesses an ability to store and handle data and an ability to create graphs for on-screen and printed use. It also features operators to make calculations on arrays, an integrated collection of intermediate tools for data analysis, conditionals loops, input and output facilities, and user-defined recursive functions. Jobs that use R include market research analyst, analytics consultant, and management analyst.

Conclusion

While Python is growing in popularity among younger business analytics professionals, businesses must recognize the benefits of both Python and R and incorporate each programming language into tasks and projects when appropriate. Python is a more mature programming language that offers more stability for enterprise-scale applications, while r has a wider variety of statistical applications and allows for fast prototype development and one-off analysis.