In the data science realm, Python is the go-to programming language. R also has a strong following. Both open-source programming languages have advantages and disadvantages. Also, new tools and libaries are frequently added to both for statistical analysis and data science.
In the data science realm, Python is the go-to programming language. R also has a strong following. Both open-source programming languages have advantages and disadvantages. Also, new tools and libaries are frequently added to both for statistical analysis and data science.
While learning both programming languages is the ideal solution, not everyone has the time required. Python is an all-purpose language with straightforward syntax. Statisticians made R, however, and it embraces their specific language.
R
Over about 20 years, academics and statisticians have developed R, which now forms one of the most extensive libraries for performing data analysis. About 12000 packages are currently available in CRAN (open-source repository).
So, an R library is available for virtually any kind of analysis. The wide variety of its library makes R often the first choice for specialized statistical analysis.
Python
Python can perform virtually all of the same tasks as R including specialized engineering tasks and more conventional tasks such as data wrangling and feature selection. Python can also perform web scrapping and can be used in apps.
Moreover, programmers can use Python to deploy and implement machine learning at a large-scale, and Python code is said to be easier to maintain and more robust than R.
Python is catching up to R in terms of its statistical analysis capabilities, and it offers state-of-the-art APIs for machine learning and artificial intelligence.
The majority of data science jobs can be accomplished with just five Python libraries, Pandas, Numpy, Seaborn, Scipy, and Scikit-learn.
Because of this, Python makes replicability and accessibility significantly easier than R. In fact, if you have to use the results of your analysis in an application or website, Python is probably the best choice.
Key Differences
The output is one of the primary differences between R and the other statistical products. R features excellent tools to communicate results of analysis. Rstudio comes with the library knitr, which Xie Yihui created to make reporting elegant, and simple. Therefore, according to R users, communicating the findings from R in a presentation or a document is easy.
Popularity
The IEEE Spectrum ranking is a metric that quantifies a programming language’s popularity. In 2017, Python reached first place compared to a third-place the previous year. R was in 6th place.
As of 2018, Python was still number one, and R was down to number seven.
R or Python Usage
Python was conceived by Guido Van Rossum in the late 1980s and was first released 1991, according to Wikipedia. The language emphasizes code readability, and the code allows both functional and object-oriented programming approaches with understandable, logical code.
History of R
On the other hand, R was developed by academics and scientists to answer statistical problems, machine learning and data analysis.
For Beginners Python is Best
Advantages of Python
Python is straightforward, and its simple syntax makes it among the easiest programming languages to learn. It can be used for most any type of project in data science.
Unlike R, which is exclusively for desktop applications, Python can be used for web applications and can be scaled up. Python is also significantly faster.
R has numerous statistical modeling tools and allows a wide range of statistical distributions. This broad range of R libaries may make it somewhat less reproducible compared to Python, which often only uses five or six libaries to perform most tasks.
Choosing Python or R, or Both
Overall, the choice between R or Python depends on whether your objectives, how much time you have for a project, and which tools your company uses most.
One possibility that is rarely discussed but quite possible is employing a combination of Python and R, according to Parul Pandey writing for towardsdatascience.com.
If you do run into a statistical problem that can only be accomplished in R, Python can import R objects and functions. Mathew Russell explained in a blog post that you can load a standard R function such as ts() through the robjects.r() function, and assign it to a Python variable. Similarly, he said you can use importr to load an R library into a namespace. I cited his blogpost in the bibliography for more about using both.
As a beginner, I have not encountered this issue, but it is good to know that Python can let you use both languages for the same project.
References
M. Russel Interfacing R and Python. Retrieved from http://blog.yhat.com/tutorials/rpy2-combing-the-power-of-r-and-python.html.
Pandey, P. (2019, March 7), R Vrs. Python to R and Python. TowardsDataScience. https://towardsdatascience.com/from-r-vs-python-to-r-and-python-aa25db33ce17
Python (programming language). In Wikipedia. Retrieved on October 25, 2019 from https://en.wikipedia.org/wiki/Python_(programming_language).
R vs Python. In Guru 99. Retrieved October 25, 2019 from https://www.guru99.com/r-vs-python.html.