Wondering whether you should use R or Python for your next data analysis?
For a number of people, data analysis is a central part of their job. If you are new to data analysis looking for the right language to start with can be difficult. R and Python are the two most popular programming languages used by data analysts and data scientists. They are both free and open source. Learning these languages is a time investment. If you are trying to decide which one to learn or which one to use in your next data analysis project. We are going to take a look at both of them to help you decide.
R was created by Ross Ihaka and Robert Gentleman in 1995. It focuses on better user friendly data analysis, statistics and graphical model. R is now one of the richest ecosystems to perform data analysis. The output is what makes R different and stand out from other statistical products. It has fantastic tools to communicate results such as knitr.
Named after the “Monty Python’s Flying Circus” comedy series, Python was created by Guido Van Rossum in 1991. It emphasises productivity and code readability. Python is a tool to deploy and implement machine learning and can do pretty much the same tasks as R. However unlike R, Python makes replicability and accessibility easier.
Data Analysis Battlefield
DataCamp created this useful infographic that highlights the key differences between the two languages.
The Pros and Cons
Check out the full infographic here!
In the end, the choice is up to you and what fits your needs.
Before making the decision you should consider…
- The objectives of your mission
- The amount of time you want to invest and the net costs of learning a language
- Your company/industry most used tool.