R and Python are great programming languages in their own right. With each having its set of benefits and specialties, the outcome of R vs Python in various conditions may differ as both of them have specific advantages they contribute to certain processes and prove to be more effective than the other in various situations.
However, skilled programmers can use both to their advantage and work their way around them. As languages, both of them can be used for multiple applications in data science and computing. That being said, Python is used for specialised data science fields, IT operations and deployment much more than R.
And, R is built by statisticians for statistical purposes. Mainly in the fields of analytics and statistical analysis, Python is also more universal in general, offering a readable syntax while R sticks to its language boundaries.
This article will cover R and Python both in detail, focusing on how R performs against Python or fundamentally, the topic of R vs Python with special importance on their differences.
R and Python are open-source languages and have huge developer forums and communities behind them which further enhance their viability. The huge involvement in these communities and the extensive use of these programming languages also assure that new tools and updated libraries keep getting added with all the platforms or IDEs supporting these languages becoming more effective as well.
What is R?
R is a programming language based on the S-PLUS programming language powered additionally by lexical scoping semantics. Most of the codes in S-PLUS are not altered and can be still seen being executed in R. Further, R provides an open-source software environment highly useful for statistical and graphical purposes. It is observed to be used mostly by statisticians and data miners. Notably, R is maintained by the R Foundation for Statistical Computing.
R saw a lot of development since the time of S-PLUS, the commercial version of S. R’s predecessor S was founded in 1976 by John Chambers while he was working at Bell Labs. R was officially announced in 1995 with CRAN or the Comprehensive R Archive Network following soon by 1997. The official beta version which was declared to be stable was first released by 2000. As of 2021, R is one of the most popular programming languages globally.
R’s official software environments are GNU packages that are primarily written in R, Fortran, and C. Being written in R itself, it is a bit self-hosting in nature. R promotes object-oriented programming which has strong statistical computing standards and also supports the usage of Python, C++ or Fortran codes for manipulation and during computationally intensive tasks.
R offers state-of-the-art static graphics which produces high-quality graphs which are capable of even including mathematical symbols. R has support for interactive or dynamic graphics as well.
What is Python?
Python is a high-level programming language that is interpreted in nature which fundamentally means that it can be run without being compiled into machine language. Python is very versatile and adaptive and can be used for large-scale projects as well as smaller processes.
It follows an object-oriented methodology alongside a functional and structural approach that promotes logical, clear and readable codes. It is backed by an expansive standard library which furthers the viability of Python.
Python is a successor to the ABC programming language and was initially being developed during the late 1980s, finally being released in 1991 as Python 0.9.0. A newer and upgraded Python 2.0 saw its release in 2000 with Python 3.0, the best version to date, following in 2008.
Python 3 saw a lot of fame and increasing popularity alongside its release. It quickly grew to become one of the most popular programming languages in the world. According to Stack Overflow’s highly acclaimed Developer Survey, during 2020, Python was rated as the second most popular language globally.
Python allows developers the freedom to choose specific approaches or mix various programming paradigms, thus serving as a multi-paradigm language. It heavily promotes object-oriented and structured programming while supporting functional and aspect-oriented programming.
With the help of various extensions and plugins, Python can also support logic programming. Python is modular and extensible in nature and designed to be able to be manipulated and upgraded with ease. Hence, Python does not have all of its functionalities built into its core, thus making it easier to build a programmed interface into an existing application.
Python is known for being simple, clear and logical. It is also known for providing developers with the option of choosing their coding methods.
R vs Python
R vs Python is a pretty debatable topic as both R and Python hold their ground in their specific fields. Both the languages have highly valuable benefits. And, they offer enormous utility in various projects. Here is how R performs against Python:
- R Vs Python Uses: R is more focused on statistics and data analysis while Python is geared towards production, operations and deployment.
- R Vs Python Users: R is used by scholars and researchers while Python is used extensively by programmers and developers.
- R Vs Python Adaptability and Versatility: Python is more flexible and versatile than R with features such as matrix computation and optimisation while R boasts of an easily available library.
- R Vs Python Difficulty and Ease of Use: R is comparably a bit difficult to learn and meant for statisticians and heavy statistical use while Python is easy to learn with linear and increasing difficulty with time which developers can adjust to.
- R Vs Python Popularity: R was the popular choice before 2015. However, Python grew in popularity immensely since then, approximately being around five times more used than R by 2018. Python has taken over as the preferred option of many data scientists in recent years and continues to grow in popularity.
- R Vs Python Integrations: R is stable and works best when run locally while Python is comfortable with app integrations and working in unofficial software environments and platforms.
- R Vs Python Objectives and Tasks: R’s primary objective is to easily acquire targeted or primary results. Python on the other hand is built to deploy algorithms and various programming paradigms.
- R Vs Python Database Support: Both R and Python are capable of handling huge databases.
- IDE and Software Environments: Rstudio can be used for R while Python has various options such as Spyder and Ipython Notebook.
- Libraries and Packages: Zoo, Ggplot2, Tidyverse and Caret are meant for R while TensorFlow, Scikit-learn and Pandas are popular choices for Python.
- Disadvantages of Python: It has a lesser amount of libraries as compared to R. It is more geared towards the deployment of algorithms and not specialised for graphical and statistical workflows.
- Disadvantages of R: It is harder to learn and there is inter-dependency between libraries. Also, it is less versatile than Python and limited.
Advantages of Python
- Incredible with deployments and production
- Best-in-class mathematical computations
- Provides code readability
- Helps in developing predictive models and extremely advanced computational models
- Allows the implementation of machine learning algorithms and promotes artificial intelligence
- Helps share data through notebooks
- Very fast and allows rapid execution
- Availability of “Functions”
Advantages of R
- Creation of incredible graphs and interactive graphical representations
- Massive catalogue for data analysis
- Boasts of Shiny which helps make incredible web applications directly from R
- R offers a Github interface
- R supports RMarkdown which allows many dynamic and static output formats such as HTML, MS Word and PDF
- R can be used for descriptive statistics and summary statistics such as central tendency, finding kurtosis, measurement of variability and skewness
- R can be used for both discrete and continuous probability distributions. For instance, one can use the ppois() function to allow Poisson distribution while using the dbinom() function, binomial distribution can be plotted
Applications of R
Even though R is limited to mainly being used extensively for statistical purposes, it has major contributions in many fields. Here are some applications of R:
- Finance: R offers a wide range of statistical tools that can accomplish various financial functions such as risk measurement, credit-risk modelling and market analysis. R also helps in producing interactive visualisations and graphs for financial reports. It can be used alongside Hadoop to promise customer analysis and segmentation as well.
- Medicine and Healthcare: R facilitates many functions in the fields of medical research, genetics, epidemiology, bioinformatics and medicine. From empowering patient and disease analysis to chemical discoveries, R is extensively used to perform exploratory analysis during pre-clinical trials and for effectively working with drug-safety data. It can also help in analysing genomic data or for modelling epidemiological requirements.
- Social Media: From its uses in social media data mining to customer behaviour analysis, R has a wide range of uses in social media as well. Most of the data in social media are unstructured, hence R is heavily used to target, extract and analyse this data to promote social media analytics for various functions like customer segmentation, audience targeting and building relational graphs. R helps forecast sales and suggest and recommend products to customers.
Applications of Python
Here are some applications of Python
- Web Development: Python has various frameworks which make it easy for web applications to be built at a rapid pace. The frameworks allow developers to not start again from the scratch and provide integrations and a secure base through fundamental backend logic. Python allows the integration of FTP, HTTPS and SSL and lets formats such as JSON, e-Mail and XML be processed.
- Artificial Intelligence and Machine Learning: Python allows computers to be trained with historical data and training data sets. Even better, Python promotes self-learning through unsupervised and supervised learning where the computer is able to learn by itself with the help of algorithms. NumPy and Scikit-Learn are great examples of libraries that support AI and machine learning.
- Desktop Applications and Business Applications: Python allows heavy desktop applications and light applications to be built. Python helps in creating interactive user interfaces and other available toolkits such as PYQT and wxWidgets. It helps create various applications across multiple platforms and operating systems.
It also helps in the development of ERP or business software as well. ERP stands for enterprise resource planning and these applications are great for business management and financial administration. Python offers features such as being scalable, readable and expandable which are all bonuses for ERP and other business applications.
- Web Extraction and Computer-aided Applications: CAD is the short form for computer-aided designing which is quite complex for other toolkits to handle unless Python gets involved, making it simple and usable. Python allows objects and their functions to be used effectively during building CAD applications. Python also allows users to extract massive amounts of data from web pages or websites which prove helpful when lists or collections of information need to be extracted for research and development.
- Game Development: Python can be extensively used for game development as there are various libraries such as PySoy, a 3D game engine that supports Python 3. PySoy provides a great environment for game development.
Frequently Asked Questions
R is much more preferred for data analysis as it is ideal for statistical analysis while Python provides a more general approach to data analysis. Python is ideal for data science and its components, but it is not as specialised as R for statistically inclined methodologies.
Python and R can both be declared as the two best choices for data analysis, even though R is better at statistical analysis. Python can also hold its own against R in data analysis by offering more versatility and computational functions.
Python is the ideal choice for machine learning (ML) and undoubtedly one of the best options to assist machines in learning from data sets or for training AI. Even R has immense support for machine learning but Python is growing to be the leading choice for AI and ML.
No, R is harder to learn than Python. The latter is a much easier programming language to pick up in comparison to the statistically inspired R.
One can learn R or Python based on the individual requirement and fields that he/she belongs from. Generally, it is more ideal to learn Python first. However, for statisticians and researchers, R is recommended.
Yes, Python can be used in R by just declaring the variables and indicating the chunk to run on Python instead of R.
Yes, Python is a general-purpose language empowered with versatility. Python armed with the right packages can achieve all that needs to be done through R. So yes, Python can definitely replace R.
R is the best tool available for data science due to its immense collection of libraries and packages which allow data mining, time series analysis and other analytical operations.
R is designed to be good at business analytics with its foundational core being focused on statistical analysis and statistical problems. Python is great for integration and manipulation of analytical deployments but it is not the best tool for business analytics as R can prove to be much more useful in this field with better econometrics and communicational abilities.
Python has all the abilities to take on R and replace it in data science. However, for statistical purposes and statisticians, R is always more effective and user-friendly. Hence, it is highly dependent on the objectives or tasks at hand when wondering whether to use R or Python.
The amount of time at hand or that one can use for the project and the tool allotted to the project by the company or demanded to be used by it make all the difference. Python is much faster and allows rapid deployment unlike R. So, time and speed form a huge factor when deciding upon R vs Python. Both are great tools but each has its fundamental core uses that either call out to developers or statisticians.