Data science & kitchen gadgets

One of the things I really enjoy about my current job is chatting with other data science folks. Almost inevitably in the course of these conversations, the old “Python vs. R” debate comes up.

For those of you who aren’t familiar, Python and R are both programming languages often used by data scientists and other folks who work with data. Python is a general-purpose programming language (originally designed as a teaching language) that has some popular packages used for data analysis. R is a computer language specifically designed for doing statistics and visualization. They’re both useful languages, but R is much more specialized.

I use both Python & R, but I tend to prefer R for data analysis and vitalization. I also love kitchen gadgets. (I own and routinely use a melon baller, albeit only very rarely for actually balling melon.) My hypothesis is that my preference for R and love of kitchen gadgets share the same underlying cause: I really like specialized tools.

I was curious to see if there was a similar relationship for other people, so I reached out to my Twitter followers with a simple two-question poll:


Do you prefer Python or R?

  • Python
  • R

How do you feel about specialized kitchen gadgets (e.g. veggie peelers, egg slicers, specialized knives).

  • Hate ’em
  • Love ’em

185 people filled out the poll (if you were one of them, thanks!). Unfortunately for my hypothesis, a quick analysis of the results revealed no evidence that there was any relationship between whether someone prefers Python or R and if they like kitchen gadgets. You can check out the big ol’ null result for yourself:

Regardless of how poorly this experiment illustrates my point, however, it still stands: R is a specialized tool, while Python is general purpose one.

I like to think of R as a bread knife and Python as a pocket knife. It’s much easier to slice bread with a bread knife, but sometimes it’s more convenient to use a pocket knife if you already have it to hand.

If you spend a lot of time cleaning and analyzing data that’s already in a tabular format or doing statistical analysis, you might consider checking out R. It’s certainly saved me a lot of time. (Oh, and I juuuust so happen to have a couple of short R tutorials for folks with little to no programming background.)

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