3. Packages
Both R and Python offer thousands of open-source packages you can readily use in your next project.
R puts forward a CRAN and hundreds of alternative packages to perform a single task, but they are less standardized. As a result, the API and its usage greatly varies, making it hard to learn and combine.
Additionally, the authors of highly specialized packages in R are often scientists and statisticians and not programmers. This means the outcome is simply a set of specialized tools designed for a specific purpose, such as DNA sequencing data analysis or even broadly defined statistical analysis.
However, R’s packages are less mix-and-match than Python’s. Currently, some attempts are being made to orchestrate suites of tools, like tidyverse, which gather packages working well together and using similar coding standards. When it comes to Python, its packages are more customizable and efficient, but they’re typically less specialized toward data analysis tasks.
Nevertheless, Python does feature some solid tools for data science like scikit-learn, Keras (ML), TensorFlow, pandas, NumPy (data manipulations), matplotlib, seaborn, and plotly (visualizations). R, on the other hand, has caret (ML), tidyverse (data manipulations), and ggplot2 (excellent for visualizations).
Furthermore, R has Shiny for rapid app deployment, while with Python, you will have to put in a bit more effort. Python also has better tools for integrations with databases than R, most importantly Dash.
In simple words, Python will be the ideal choice if you’re planning to build a full-fledged application, though both choices are good for a proof of concept. R comes with specialized packages for statistical purposes, and Python is not nearly as strong in this particular field. Additionally, R is very good at manipulating data from most popular data stores.
Another aspect worth mentioning here is maintainability. Python allows you to create, use, destroy, and duplicate a wild and vibrant menagerie of environments, each with different packages installed. With R, this happens to be a challenge, only exacerbated by package incompatibilities.
Experts often use Jupyter Notebook, a popular tool for scripting, rapid exploration, and sketch-like code development iterations. It supports kernels of both R and Python, but it’s worth mentioning that the tool itself was written and originated in the Python ecosystem.
