R vs Python: A Comparison for Data Science and Programming
R or Python Usage
Python has been developed by Guido van Rossum, a computer guy, circa 1991. Python has influential libraries for math, statistic and Artificial Intelligence. You can think Python as a pure player in Machine Learning. However, Python is not entirely mature (yet) for econometrics and communication. Python is the best tool for Machine Learning integration and deployment but not for business analytics.
The good news is R is developed by academics and scientist. It is designed to answer statistical problems, machine learning, and data science. R is the right tool for data science because of its powerful communication libraries. Besides, R is equipped with many packages to perform time series analysis, panel data and data mining. On the top of that, there are not better tools compared to R.
In our opinion, if you are a beginner in data science with necessary statistical foundation, you need to ask yourself following two questions:
- Do I want to learn how the algorithm work?
- Do I want to deploy the model?
If your answer to both questions is yes, you'd probably begin to learn Python first. On the one hand, Python includes great libraries to manipulate matrix or to code the algorithms. As a beginner, it might be easier to learn how to build a model from scratch and then switch to the functions from the machine learning libraries. On the other hand, you already know the algorithm or want to go into the data analysis right away, then both R and Python are okay to begin with. One advantage for R if you're going to focus on statistical methods.
Secondly, if you want to do more than statistics, let's say deployment and reproducibility, Python is a better choice. R is more suitable for your work if you need to write a report and create a dashboard.
In a nutshell, the statistical gap between R and Python are getting closer. Most of the job can be done by both languages. You'd better choose the one that suits your needs but also the tool your colleagues are using. It is better when all of you speak the same language. After you know your first programming language, learning the second one is simpler.
Difference between R and Python
| Parameter | R | Python |
|---|---|---|
| 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 |
| Average Salary | $99.000 | $100.000 |
| 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 |
| Advantages |
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Conclusion
In the end, the choice between R or Python depends on:
- The objectives of your mission: Statistical analysis or deployment
- The amount of time you can invest
- Your company/industry most-used tool