As data science and data engineering become the hottest buzzwords, open-source programming languages Python and R are paving the way for data-driven innovation through artificial intelligence and machine learning capabilities.
While both these languages are very similar, each with its own set of strengths and weaknesses, some evident differences set them apart. Read on as we deep dive into the basics of Python and R, so you can make the right choice for building your data science application.
What Python Offers
As a high-level, general-purpose, open-source, object-oriented programming language, Python comes built in with data structures and dynamic typing – making it a popular language for developing data science applications. With easy-to-read, learn and use syntaxes, Python's libraries are free for distribution.
What makes programmers love Python for deploying machine learning at a large scale is its:
- Advanced debugging capabilities and sophisticated functionalities that improve code efficiency
- Clean and robust structure and massive libraries that make exploratory data analysis stress-free
- Embedded codes and vast integration capabilities that enable seamless expansion of data science applications across platforms
- Support for data science-specific tasks such as handling large dimensional arrays, data manipulation and analysis, and building data visualizations
- Suite of advanced deep and machine learning libraries that can be used to develop sophisticated data models and plugged directly into production
What R Offers
An open-source programming language used for statistical analysis, R offers a wide range of statistical techniques such as linear and non-linear modeling, statistical tests, clustering, and more. Offering seamless compatibility for UNIX, Windows, and macOS, R allows developers to leverage user-specific functions and build additional functionality.
What makes R a popular language for data science is its
- Broad variety of tools and libraries for cleansing and prepping data, creating innovative visualizations, and training advanced machine learning algorithms
- Seamless integration with C and C++ codes during run time that allows the language to be extended
- The ability to leverage different packages for the development of machine learning and data analysis projects
- Platform independence that makes it easy for developers to build cutting-edge applications across different operating systems
- Improved data wrangling capacity that allows developers to transform messy codes into structured ones
Comparing the Two
Both Python and R are the preferred languages for data science, but they are used for different purposes:
- While Python takes a general approach to data wrangling, R has its roots in statistical analysis.
- Python is well-suited for deploying machine learning at large scale while R is apt for small scale applications
- Programmers use Python to enable basic data analysis and R for specialized analytics as it leverages advanced statistical models.
- Python supports all types of data formats from CSV to SQL, web, and JSON, while R is primarily used to import data from Excel, CSV, and text files.
- Python can be used to explore mid to large data sets by filtering, sorting, and displaying data in a matter of seconds, while R can be used to carry out statistical analysis of small datasets by applying probability distributions, different statistical tests, and machine learning and data mining techniques.
- While Python offers standard libraries for data modeling and analysis, with R, programmers often have to rely on packages outside R's core functionality to import, manipulate, visualize and report on data.
- Python is ideal for engineering workflows while R is better suited for teams without who just need to carry out statistical analysis
- Like Python, R too has a short learning curve and is ideal for beginners but for developing advanced functionality, R requires teams to develop deep expertise
- Python offers very basic visualization features that can be used to generate simple graphs and charts; R, on the other hand, is built to demonstrate the results of statistical analysis and enables the generation of advanced plots, including complex scatter plots with regression lines.
Making the Right Decision
Choosing the right language depends on many factors: how experienced your developers are with programming languages, what your data science needs are, the degree of statistical analysis you need, and the problems you are trying to solve.
- If you are just beginning on your data science journey, Python's easy-to-read syntax makes the learning curve extremely linear and smooth. If you want to develop progressive data science functionality and have deep programming experience, R's advanced functionality allows for data analysis tasks to be done in minutes.
- If you want to enable simple data analysis and machine learning, Python is a good choice. However, if you want to enable cutting-edge data exploration and experimentation, R's programming capabilities pave the way for unmatched statistical learning.
- If you want to enable large-scale data analysis, Python's suite of specialized deep learning libraries is a perfect choice. If you want to build complex data models, R is ideal for visualizing data in different graphs and formats.
In conclusion, the question isn't about which is better but how to use both languages to meet the needs of specific use cases. Understanding the pros and cons of each language is essential to uncover which language is most suited for your business requirement. In all probability, you might end up using a combination of both Python and R to enable simple, early-stage, and complex late-stage data analysis and exploration.