R and Python are both open-source programming languages with a large community. They are very popular among data analysts. New libraries or tools are added continuously to their respective catalog. R is mainly used for statistical analysis while Python provides a more general approach to data science.
While Python is often praised for being a general-purpose language with an easy-to-understand syntax, R’s functionality is developed with statisticians in mind, thereby giving it field-specific advantages such as great features for data visualization. Both R and Python are state of the art in terms of programming language oriented towards data science and hence learning both of them is, of course, the ideal solution. But R and Python require a time-investment, and such luxury is not available for everyone.
Let us see how these two programming languages relate to each other, by exploring the strengths of R over Python and vice versa and indulging in basic comparison between these two.
Python can do almost all the tasks that R can, like data wrangling, engineering, feature selection, web scraping and so on. But Python is known as a tool to deploy and implement machine learning at a large-scale, as Python codes are easier to maintain and remains more robust than R. The programming language is up to date with many data learning and machine learning libraries. It provides APIs for machine learning or AI. Python is also usually the first choice when there is a need to use the results of any analysis in an application or a website.
R has been developed by academicians and statisticians in over 2 decades. It is now one of the richest ecosystems to perform data analysis. Around 12000 packages are available in CRAN (open-source repository) now. A rich variety of libraries can be found for any analysis one needs to perform, making R the first choice for statistical analysis, especially for specialized analytical work.
One major difference between R and other statistical tools or languages is the output. Other than R, there are very good tools to communicate results and make presentation of findings easy. In R, Rstudio comes with the library knitr which helps with the same, but other than that it lacks the flexibility for presentation.
R and Python Comparison
|Objective||Data analysis and statistics||Deployment and production|
|Primary Users||Scholar and R&D||Programmers and developers|
|Flexibility||Easy to use available library||Easy to construct new models from scratch. I.e., matrix computation and optimization|
|Learning curve||Difficult at the beginning||Linear and smooth|
|Popularity of Programming Language. Percentage change||4.23% in 2018||21.69% in 2018|
|Integration||Run locally||Well-integrated with app|
|Task||Easy to get primary results||Good to deploy algorithm|
|Database size||Handle huge size||Handle huge size|
|IDE||Rstudio||Spyder, Ipthon Notebook|
|Important Packages and library||tydiverse, ggplot2, caret, zoo||pandas, scipy, scikit-learn, TensorFlow, caret|
|Disadvantages||Slow High Learning curve Dependencies between library||Not as many libraries as R|
As mentioned before, Python has influential libraries for math, statistics and Artificial Intelligence. While Python is the best tool for Machine Learning integration and deployment, the same cannot be said for business analytics.
R, on the other hand, is designed by experts to answer statistical problems. It can also solve problems on machine learning and data science. R is preferred for data science due to its powerful communication libraries. It is also equipped with numerous packages to perform time series analysis, panel data and data mining. But R is known to have a steep learning curve and therefore is not recommended for beginners.
As a beginner in data science with necessary statistical knowledge, it might be easier to use Python and to learn how to build a model from scratch and then switch to the functions from the machine learning libraries. R can be the first choice if the focus is going to be on statistics.
In conclusion, one needs to pick the programming language based on the requirements and available resources. The decision should be made based on what kind of problem is to be solved, or the kind of tools that are available in the field.
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