There is a question that comes up frequently in data analytics:
Should I use R or Python?
The short answer is: it depends on what you are trying to accomplish.
Both languages are powerful tools for working with data. Both can clean datasets, create visualizations, run statistical analyses, build models, and automate parts of a workflow.
The difference is less about what they can do and more about what they are particularly good at.
Instead of asking which language is better, I find it more useful to ask:
What does this particular task require?
R and Python were built around different strengths
R was created primarily for statistical computing and data analysis. It has a long history in statistics and is particularly strong when your work revolves around statistical methods, exploratory analysis, and visualization.
Python is a general-purpose programming language that became extremely popular in data science. Its data ecosystem is extensive, but its usefulness goes far beyond analytics.
That becomes important once your work starts moving outside the analysis itself.
Data Analyst Lead, land use planning practitioner, night-shift warrior, and currently someone navigating life with a brain arteriovenous malformation (AVM).
