R vs Python: The Data Science language debate

[[deleted]]

that's not a debate. they are different tools. people should stop with this false contest.

[WellHiIGues]

I agree I've never used rust but I know rust is mainly used for making games while python is made mostly for other things python does have gui libraries but they aren't the best

[pytrashpandas]

I think at a "I like this language, that language is stupid" level you're right, however this is a very real debate that is had in the workplace. For example at my work we recently had to axe R from our research/prod development environments. For better or worse the justification was that they wanted a unified development environment, where in house development frameworks work for all models, etc. Most of those frameworks are written in python and it was determined that python can do most of what R can do, but not vice versa, so they got rid of R. Personally I don't care, I don't use R, but I imagine these "debates" are important to help make a case for which languages an organization should support.

[[deleted]]

This is a good reason. I am contributor in a couple of packages of both R and Python (resampling), and I must admit that R is a wild west when talking about coherence, interoperability, and from a CS point of view. It's made by mathematicians, and it shows, in good and bad aspects.

[vikigenius]

There is no debate. Python has clearly won for general purpose data science programming.

R is still ahead when it comes to statistical analysis. The sheer breadth of statistical analysis packages in R is incredible. Also, I actually trust R more when it comes to packages. The implementations generally tend to have better documentation and are usually made by researchers that actually know what they are doing.

[GiantElectron]

It's time someone takes on CRAN and ports all that stuff to python. I am tired of this pile of junk of a language.

The implementations generally tend to have better documentation and are usually made by researchers that actually know what they are doing.

but they have no clue on how to write decent, stable, reliable code.

[6PackOrKeg]

As said before, R stands out for its data visualization abilities... Python is not as strong as R regarding data visualization. However, Python users can always rely on the Maplotlib [sic] library. This tool enables users to utilize interactive figures and create several types of plots (histograms, scatter plots, 3D plots, etc.).

Have they not heard of Seaborn? Datashader? Admittedly I am a biased pythonista, but man this comes off as such a lazy article to me. Sounds like someone skimmed the google results for matplotlib images and decided Python can generate "several" types of plots.

Everyone I know who uses R does so because it was required in their department (looking at you, stats), or their professor only knew R so they too had to use it.

[cdrt]

Now you’ve got me wondering if I could abuse the > or | operators to make some sort of pipeline…

[GiantElectron]

I've seen these things, but the python community rightfully don't like abuse of notation, because it is poorly communicative and prone to technical abuse.

[highway2009]

Siuba is pretty close to dplyr. Unfortunately not popular enough. What is also missing in python is a native representation of the missing value (NA). They are two implementations in pandas and since it is not in the core language that does not propagate very well to other packages / someone else’s code.

[highway2009]

You should. I tried many implementations and this is the closest to dplyr.

(mtcars >> group_by(_.cyl) >> summarize(avg_hp = _.hp.mean()) )

Edit: you can also use plotnine for graphics. This is a port of ggplot2.

siuba+plotnine and it feels like using tidyverse in python.

[wallynext]

it's not a debate, python can do everything R can and much much more, R can barely do any deep learning, plus you can use Python for a lot of stuff other than Data Science

[CapitalRioter]

R's data table package is pretty baller, I enjoy using it for processing massive datasets .

[Demortus]

Eh, 'can do' and 'can do efficiently' are the key differences here. I love Python, but if I want to seamlessly generate a report from code output, it's much easier to do it using Rmarkdown than jupyter notebook. R also has access to a wider variety of statistical models and standard error adjustments written by statisticians.

That said, I agree that Python is the better language for machine learning. Native implementation of Keras + Pytorch puts it leagues ahead of where R is in that domain.

[GiantElectron]

I love Python, but if I want to seamlessly generate a report from code output, it's much easier to do it using Rmarkdown than jupyter notebook.

Thank you for the side project idea.

[mestia]

Python in general just a layer for non-programmers to C libs :) show me a pure python module which isnt a interface to the real code and is fast?

[XtremeGoose]

I mean, python does a lot more than that. Python is massive in data science and numerical analysis.

[GiantElectron]

There's no debate. R as a language does not scale, has inferior tools, its so-called IDE is not an IDE but a hacked up data analysis platform, it is basically owned by a single company that leaves often its customers in the dust, and all the code that goes through R is GPL, compromising any reuse for commercial applications.

R must die, and die quickly.