R Vs. Python – When to Use What?
The conversation around data science only grows louder with every passing day. And with this grows the battle around the data tools.
The most common one being “which is better – R or Python”? A simple Google search shows you the rivalry between the two languages trying to establish the supremacy of one over the other. Without delving into this war, we’d like to assess which language is best suited to meet which need of data science.
But before we delve into this, we’d like to talk a little about both the languages to establish their premise in the data science story.
R and Python – The Backstory
Both R and Python are popular languages for data analysis.
- Python is hailed for being a general-purpose language and has an easy to understand syntax. It is used widely in many fields, such as web development. Python is a flexible language and focuses heavily on readability and simplicity and is also perhaps the programming language with the lowest learning curve
- R is a language that provides field-specific advantages since it was developed by keeping the statistician in mind. The purpose of developing R was to make data analysis, statistics, and graphical models building easier and more user-friendly.
- Despite being developed primarily for academics and research, today, R one of the fastest-growing statistical languages in the corporate world. Python is the flexible, general-purpose language that is focused on productivity, and has been the likely choice when you are working in an engineering environment.
- R has a huge community to provide support through a very active Stack Overflow group, user-contributed documentation, mailing lists, and a repository of curated and contributable R packages. Python, too, boasts of great community support, albeit it is a little scattered. Like R, Python also has package indexes (Python Package Index) and has many libraries that take user contributions.
R and Python – The Merits
Let’s now evaluate these languages based on their usage in the data pipeline that includes data collection, data exploration, data modeling, and data visualization.
When it comes to data collection, Python shows a good level of dexterity. It supports all data formats and allows you to create data sets easily. Do you want to include SQL tables into your code directly? Python will help you do that. Do you want to take data from different websites with one line of code? Do it easily with Python. Do you want to simplify HTTP requests into a line of code? It’s Python to the rescue.
R doesn’t fall behind in this context as R is not as versatile as Python when it comes to grabbing information from the web. But R can also easily handle data from your common sources. R allows you to easily import data from Excel, CSV, and text files. R data frames can accommodate files that are built in the Minitab or in the SPSS format. To navigate the challenge of grabbing information from the web, you have tools such as Rvest that allow web scraping. The tool magrittr cleans up the data and parses the information as well.
R gives you enough and more avenues to explore data as it was built with keeping the statistician in mind. With R, you can conduct a statistical and numerical analysis of large data sets. You can build probability distributions, employ standard machine learning and data mining techniques to it, and apply different statistical tests to your data. Analytics, statistical processing, random number generation, machine learning, optimization, and signal processing constitute basic R functionalities. But for heavier work, you’ll have to look at third-party libraries.
Pandas, the data analysis library for Python, helps you explore data to unearth insights from it. Unlike Excel, Pandas does not present any lags when working with huge data volumes. You can easily filter, sort, and arrange data, scan it, and clean it up when it doesn’t make much sense. Data cleaning can be executed easily as well by simply filling values like NAN with a value that makes sense for numerical analysis. Since Pandas is organized into data frames, it gives you the ability to define and redefine these frames many times during the course of the project.
R gives you a powerful environment that is well suited to make scientific visualizations. It provides many packages that specialize in graphical result displays. With a base R graphics module, you can easily make basic charts and plots from data matrices. These files can also be saved in different formats like jpg or can be saved as PDF’s. R also gives you the power to make advanced plots (think complex scatter plots with regression lines).
Python also scores on the data visualization parameter. With the IPython Notebook, you get powerful options for data visualization. You can generate basic graphs and charts from data that is embedded in Python with the Matplotlib library. Plot.ly works well for advanced graphs and designs. You can also access the intuitive Python API for data visualization and present well-designed graphs and dashboards. With the nbconvert function, you can also convert Python notebooks to HTML documents and also embed chunks of nicely formatted code into online portfolios and websites.
So, when should you use Python, and when should you favor R?
R lends itself well when you:
- Want to do exploratory work owing to the vast number of packages and readily usable tests
- Need to write statistical models using few lines of code
- Want to run the data analysis tasks and standalone computing/analysis on individual servers
- Need a robust tool library to get up and running fast with your data science initiatives
- Need a programming language that can be a part of a big data solution
- Need user-friendly data analysis mediums
- Need the same functionality to be written in different ways
- Want to do basic data analysis without package installation
Python works well when you:
- Want to implement algorithms for production use
- Need data analysis tasks to be integrated into web apps
- Need statistical code to be incorporated into a production base
- Need to do data analysis (Of course, you need NumPy and Pandas, amongst others, to make Python usable for data analysis)
- Want to get creative and power your data virtualization using heavy data
To cut a long story short, both R and Python are well-suited to meet the needs of data science. Some things work better with R and some with Python. While R might be a little head over the shoulders above Python for data analysis, Python is showing promise that it will play catch up very soon. The reality is that both the tools are excellent in their own right, and quite honestly, we don’t see why we have to form two separate and distinct camps around these programming languages. Funnily enough, this just shows that the divergence in thought does not lie with the programming languages as much as with the people who are using them.