Comment by geomark | Hacker News Reader

Comment by geomark | Hacker News Reader

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I just completed the Coursera data science track which took me from a complete R newbie to being at least somewhat proficient. Having previously used Python for a quite a bit of web programming, I disliked R at first except for its power in statistical programming. But I've since discovered a number of great R packages that make it a pleasure to use for things I would normally turn to Python for. Like I recently discovered the rvest package for webscraping.

Data visualizations with R seem vastly superior, unless I am missing something with Python (highly likely). And putting up a slick statistics app is easy with shiny or RStudio Presenter. But R can't really scale to a large production app, isn't that right?

So I feel I need to keep working with both Python and R.

Added: That's a nice list Lofkin. Thanks. Also, in the article he says that Python syntax feels more natural, which I also felt. But then I started to use things like the magrittr and dplyr packages in R which gives you nice things like pipes and that feeling starts to ebb.

For stats plotting in python: https://github.com/mwaskom/seaborn https://github.com/yhat/ggplot

For stats plotting and web apps in python: https://github.com/bokeh/bokeh

For calling r libraries in python: https://pypi.python.org/pypi/rpy2

For out of core datasets in python: https://github.com/blaze/dask https://github.com/blaze/blaze

Nice collection, let me add one more item to this list:

Seaborn: statistical data visualization: http://stanford.edu/~mwaskom/software/seaborn/

You bring up a good point in favor of R: Hadley Wickham and the rest of the RStudio people.

Packages like {ggplot2,rvest,dplyr,devtools, etc.} are basically creating a sub-language for R.

I use both at the moment, but I echo the OP's ideas that R's target audience is statisticians, where Python's target audience is broader and includes statisticians and computer scientists. And Python's syntax is nicer to work with. That's why it's become the primary glue language.

That said, the overhead for learning Python as your first data science language is a bit problematic for me, as you basically have to learn Python followed by Python's data science tools (pandas, matplotlib, etc). whereas with R, you're learning the language and the data science tools at the same time, even if they're a bit idiosyncratic.

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