5 ms·

In Python, you have pandas and then Python is great for text processing in general. Is there anything that R provides more for data munging?

minimaxir 10y ago
R has a robust package ecosystem for data analysis, even better than Python's suite. ETL is just one step of the pipeline.
eanzenberg 10y ago
R is generally better for data science, so it's nice to be able to use 1 ecosystem for a whole project. But yes, I too have in the past used python for data munging and R for machine learning.
nerdponx 10y ago
Purpose-built syntax. Pandas feels like a hack by comparison.
sgt101 10y ago
It's a bit lame, but I am so in love with rstudio... I can't think of a python IDE that's near for data science. Simple things like being able to see all the entities you've created in your session and being able to jump back and forth through plots are really nice.
- danielecook 10y ago
  Rodeo is a good start: http://blog.yhat.com/posts/rodeo-native.html http://blog.yhat.com/posts/rodeo-native.html
  - rz2k 10y ago
    Also a silly reason: for me the text rendering in Rodeo on a Mac without a retina display is a lot worse than the text rendering in RStudio.
- spdustin 10y ago
  Since the comment from the new account mentioning it may be modded away, I'll say (as a recent user, not affiliated with them in any way) that Rodeo is a fairly robust integrated analysis environment, a more immediate kind of Jupyter notebook. They recently added a native dataframe viz, too. http://blog.yhat.com/posts/rodeo-2.0.6-dataframes.html http://blog.yhat.com/posts/rodeo-2.0.6-dataframes.html
- resolaibohp 10y ago
  I am with you on this. While I feel like I should eventually move fully into python for data, with it being a more multipurpose language. I can never never seem to make the transition because python does not have a rstudio equivalent. I have tried yhat's rodeo and also yhat's port of ggplot for python but it just isn't there yet in my opinion.
- tyfon 10y ago
  For python you have something similar, though not quite as good with https://github.com/spyder-ide https://github.com/spyder-ide (Spyder). I use both heavily in my daily work.
nicolewhite 10y ago
One of the aspects of data munging is visualization, which is much easier in R with ggplot. Matplotlib is painful and always requires more than double the code to accomplish the same thing.
- blitzd 10y ago
  There's s lot more options in Python for data viz other than matplotlib. Pandas has several built ins as well, so in many cases simple plots on existing data frames can be a one liner.
akg_67 10y ago
I switched from Python/pandas to R because I was more interested in applying the algorithm/methods instead of developing them. Python is fine if you want to develop the algorithm or implement a particular method. But if you are more interested in application of algorithm/method, the availability of R packages to do so hands down the best. Anything you may want to do with R, most probably there is a R package already for it.
- kyzyl 10y ago
  This really strongly depends on your industry/focus, and what your end goal is. Some industries are completely dominated (library/community wise) by one or the other. I used to do finance work and strongly preferred python, so I tried hard to use it. (This was also before/right when pandas was out.) I was constantly plagued by needing some fitting routine e.g. for a vector GARCH model, and there was a package in R just sitting there. I was able to get very good mileage out of the RPy2 interface, though. On the flip side, I've done a fair swath of work in the machine learning arena, and in particular the deep learning topics before it was called 'deep learning', and it was nearly hopeless to use anything except Python or MATLAB (both strongly tied in with C++/CUDA libaries). I think this is still largely the case. As others have mentioned here, for me the biggest pull away from R was that it's not general purpose. Hard to ship someone R code. Hard to throw a web framework in front of your code. Hard to build a rich desktop GUI on top of it. I know you can do most of those things in R, but last time I dealt with it there was a massive ravine in usability/maturity. I'd also be insincere if I didn't admit that the pervasive R coding style just drove.me.fucking.crazy. That and I found that while there was a mindbogglingly large pile of libraries, documentations was usually very lacking. /rant
  - IndianAstronaut 10y ago
    One way to get R more production worthy is to use things like PMML to scale up models.
deleted 10y ago
[deleted]

minimaxir 10y ago

R has a robust package ecosystem for data analysis, even better than Python's suite. ETL is just one step of the pipeline.

eanzenberg 10y ago

R is generally better for data science, so it's nice to be able to use 1 ecosystem for a whole project. But yes, I too have in the past used python for data munging and R for machine learning.

nerdponx 10y ago

Purpose-built syntax. Pandas feels like a hack by comparison.

sgt101 10y ago

It's a bit lame, but I am so in love with rstudio... I can't think of a python IDE that's near for data science. Simple things like being able to see all the entities you've created in your session and being able to jump back and forth through plots are really nice.

danielecook 10y ago
Rodeo is a good start: http://blog.yhat.com/posts/rodeo-native.html http://blog.yhat.com/posts/rodeo-native.html
- rz2k 10y ago
  Also a silly reason: for me the text rendering in Rodeo on a Mac without a retina display is a lot worse than the text rendering in RStudio.
spdustin 10y ago
Since the comment from the new account mentioning it may be modded away, I'll say (as a recent user, not affiliated with them in any way) that Rodeo is a fairly robust integrated analysis environment, a more immediate kind of Jupyter notebook. They recently added a native dataframe viz, too. http://blog.yhat.com/posts/rodeo-2.0.6-dataframes.html http://blog.yhat.com/posts/rodeo-2.0.6-dataframes.html
resolaibohp 10y ago
I am with you on this. While I feel like I should eventually move fully into python for data, with it being a more multipurpose language. I can never never seem to make the transition because python does not have a rstudio equivalent. I have tried yhat's rodeo and also yhat's port of ggplot for python but it just isn't there yet in my opinion.
tyfon 10y ago
For python you have something similar, though not quite as good with https://github.com/spyder-ide https://github.com/spyder-ide (Spyder). I use both heavily in my daily work.

nicolewhite 10y ago

One of the aspects of data munging is visualization, which is much easier in R with ggplot. Matplotlib is painful and always requires more than double the code to accomplish the same thing.

blitzd 10y ago
There's s lot more options in Python for data viz other than matplotlib. Pandas has several built ins as well, so in many cases simple plots on existing data frames can be a one liner.

akg_67 10y ago

I switched from Python/pandas to R because I was more interested in applying the algorithm/methods instead of developing them. Python is fine if you want to develop the algorithm or implement a particular method. But if you are more interested in application of algorithm/method, the availability of R packages to do so hands down the best. Anything you may want to do with R, most probably there is a R package already for it.

kyzyl 10y ago
This really strongly depends on your industry/focus, and what your end goal is. Some industries are completely dominated (library/community wise) by one or the other. I used to do finance work and strongly preferred python, so I tried hard to use it. (This was also before/right when pandas was out.) I was constantly plagued by needing some fitting routine e.g. for a vector GARCH model, and there was a package in R just sitting there. I was able to get very good mileage out of the RPy2 interface, though. On the flip side, I've done a fair swath of work in the machine learning arena, and in particular the deep learning topics before it was called 'deep learning', and it was nearly hopeless to use anything except Python or MATLAB (both strongly tied in with C++/CUDA libaries). I think this is still largely the case. As others have mentioned here, for me the biggest pull away from R was that it's not general purpose. Hard to ship someone R code. Hard to throw a web framework in front of your code. Hard to build a rich desktop GUI on top of it. I know you can do most of those things in R, but last time I dealt with it there was a massive ravine in usability/maturity. I'd also be insincere if I didn't admit that the pervasive R coding style just drove.me.fucking.crazy. That and I found that while there was a mindbogglingly large pile of libraries, documentations was usually very lacking. /rant
- IndianAstronaut 10y ago
  One way to get R more production worthy is to use things like PMML to scale up models.

deleted 10y ago

[deleted]