Academics and statisticians have developed R over two decades. R has now one of the richest ecosystems to perform data analysis. There are around 12000 packages available in CRAN (open-source repository). It is possible to find a library for whatever the analysis you want to perform. The rich variety of library makes R the first choice for statistical analysis, especially for specialized analytical work.
The cutting-edge difference between R and the other statistical products is the output. R has fantastic tools to communicate the results. Rstudio comes with the library knitr. Xie Yihui wrote this package. He made reporting trivial and elegant. Communicating the findings with a presentation or a document is easy.
Python can pretty much do the same tasks as R: data wrangling, engineering, feature selection web scrapping, app and so on. Python is a tool to deploy and implement machine learning at a large-scale. Python codes are easier to maintain and more robust than R. Years ago; Python didn’t have many data analysis and machine learning libraries. Recently, Python is catching up and provides cutting-edge API for machine learning or Artificial Intelligence. Most of the data science job can be done with five Python libraries: Numpy, Pandas, Scipy, Scikit-learn and Seaborn.
Python, on the other hand, makes replicability and accessibility easier than R. In fact, if you need to use the results of your analysis in an application or website, Python is the best choice.
|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, Ipython Notebook|
|Important Packages and library||tidyverse, ggplot2, caret, zoo||pandas, scipy, scikit-learn, TensorFlow, caret|
High Learning curve
Dependencies between library
|Not as many libraries as R|
|Advantages||Graphs are made to talk. R makes it beautiful |
Large catalog for data analysis
|Jupyter notebook: Notebooks help to share data with colleagues |
Function in Python