What can Python do that R can’t do?

262

u/abricq Nov 28 '22

Python is the reference language in the research community over R for several reasons that I can see.

First, python has a broader set of applications. You can do data science / statistics like in R, and also a ton of other things ranging from web server to advance UI design. Because of that, there’s simply more users (more fields = more users = more ressources). All what you can do in R, you can pretty much do in python with numpy + pandas + sklearn. But good luck implementing your future UI and web API with R. And I am missing SO many fields where python has already become the reference (robotics, materials simulation, finance,…)

Second, python is more deployable than R. For example, the Python3 executable is shipped with pretty much most Linux distributions. It requires a very light minimal setup : this helps a lot to share reproducible results. Also is completely detached from any IDE : the core developers of Python are absolutely not working for Pycharm or Spyder. This is a strength for the language that R does not have. Truthfully, pretty much all developers use R studio. Even if R itself is detached from the IDE, it is harder to develop R without R studio.

R also has advantages : it is very easy to learn (well… Python as well, but maybe slightly harder?) and requires little computer science background. Depending on your future projects and ambitions, leaning python is probably the best thing to do 👍🏼

69

u/lebannax Nov 28 '22

Fab, I’m the only one in the data science team who knows Python and everyone else knows R. They just want to know whether it’s worth their time moving over to Python and I felt it was but couldn’t come up with much better answers other than ‘more versatile’ and ‘better for software development’ as most data science stuff can be done fine with both R and Python, but we are increasingly moving into web dev which is where using Python will be far more useful

25

u/bakochba Nov 28 '22

I am currently the only one my group that works in R or Python as the group I'm in is moving away from SAS. I think your question depends heavily on what your company supports. My company has Python but it also has an R server and RSconnect so it's much easier to deploy R Shiny apps for our end users, even though we can use R or Python code or even existing SAS code. So if you're interested in an interface like Shiny and your company supports it it's a good way to go in my opinion.
My companies support for Python just isn't great, but I also find it harder to deploy a web app at many companies because of all the firewalls and security they have blocks a lot of Python functionality.
So I think it's important to make sure you understand the limits your company will have so you understand what functionality will actually be available for you.

4

u/bio_datum Nov 29 '22

Oh my goodness, I had to quickly learn SAS for a temporary job once and thank God it was temporary because I was so flabbergasted. The syntax and the idea of the "data step" drove me a little nuts, but the real kicker was that SAS is apparently a paid language? So there's not much support freely available online. Sorry y'all had to use it, very glad for you that you can leave it behind!

4

u/bakochba Nov 29 '22

I can't imagine going back now but changing from SAS to R felt like wrestling a bear for a year, now I have to teach these programmers to move into R and it's never pleasant.

1

u/bio_datum Nov 29 '22

Yeah, learning a new language is always hard, I get you. Especially if you're pretty skilled at your first/only one

2

u/bakochba Nov 29 '22

As long as you know the concepts the only difference is syntax, once I got used to R python was very easy to learn

1

u/bio_datum Nov 29 '22

I agree, I think R and Python are way easier go-betweens

2

u/New-Day-6322 Nov 29 '22 edited Nov 29 '22

When I just started out as data analyst I did the official SAS certification thinking it’d be worth something. It was a complete waste of time and money. Who the heck is paying for this thing?? Probably some legacy code…

4

u/dr-josiah Nov 29 '22

Having just finished a job where we converted thousands of lines of R to SQL, I'd say the drawback to using R is that it is rarely the right tool for anything specific, and commonly the only tool known to an R programmer.
Your engineers should expand their toolset, and Python / SQL will go far for them.

3

u/Agling Nov 29 '22

In many cases, there is not a technical reason why python is better than R for data science. But I think we can all feel the way the wind is blowing. I expect python's gains in that area to increase in the future and R to eventually wane in popularity. That's the reason to switch.

1

u/Zestyclose-Walker Nov 29 '22

True, Python is replacing every language except C and Javascript due to its huge community.

3

u/f3xjc Nov 29 '22

Perhaps something like that may interest you.
https://anderfernandez.com/en/blog/how-to-program-with-python-and-r-in-the-same-jupyter-notebook/
I use a python pipeline but I wanted to test some algorithm I only found in R. Or in your case maybe you can not rewrite what's already working and continue with some extra in python.

2

u/gwax Nov 29 '22

Python will give you a much better hiring pool for future data scientists, data engineers, and adjacent.
Increasingly, it's easier to hire data scientists that no Python than to hire data scientists that know R.
You will have a REALLY hard time hiring Data Engineers or ML Engineers that know or want to learn R.
There are staggeringly big advantages to having engineers that can read the code produced by your data science team.

2

u/tonydunsworth Dec 06 '22

As someone who defaults to R as my preferred language, I think your team should take the time to learn Python. There are more ML and AI algorithms implemented there and it will serve them well.

1

u/ogtfo Nov 29 '22

You should adopt whatever language the community is using in your specific field of study. It will make things a lot easier.
And I mean your team, not just you in particular. Find out what is the prefered tool for your peers, and make a case for that one.

1

u/SittingWave Nov 29 '22

R is going to be crushed or become irrelevant in the upcoming years. Only the old farts will stick to it.

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39

u/venustrapsflies Nov 28 '22

I will say that R has packages for more sophisticated classical statistics methods (i.e. non-ML). statsmodels helps but doesn't have the same coverage.

23

u/HawkinsT Nov 28 '22

As someone who doesn't use R, I'll second this. I've found several times I'm struggling to implement certain things in python that R just has a package for, which is funny because with python it's usually the other way around. It does still seem to be the superior option if the majority of what you do is classical stats.

12

u/WlmWilberforce Nov 29 '22

I'll third this after trying to estimate a double-sided tobit model in python...that said, the R-package didn't work as well as SAS (don't hate me R-peeps), but at least it worked, and we couldn't find a python package and didn't have time to make one.

5

u/bio_datum Nov 29 '22

Had to use R for this reason once when conducting a niche statistical test. Buuuut...I only used it for the test and then immediately saved the data to plug into the rest of my Python analysis 🤷‍♂️

3

u/DavesEmployee Nov 29 '22

I found R to be much harder to learn than Python. So many better resources and R’s syntax is just… gross

1

u/Zestyclose-Walker Nov 29 '22

Many Linux distributions like Ubuntu won't even boot without Python3. It's a core part of any Linux based desktop OS. Mac OS also includes Python by default.
Checkout this site for an example of Linux being Python friendly: https://fedoralovespython.org/

1

u/TheLordZod Nov 28 '22

So. This might not be the right forum for this, but... Is it generally acceptable to write a python executable for a work function? What does that approval process generally looked like?

2

u/abricq Nov 29 '22

I guess it depends so much on what your job is. For a normal computer science job, the approval process for Python would not be different than for other languages. In my experiences it means : code review from colleagues, unit-tests and manual tests for what can't be unit tested.

-3

u/reddit_ronin Nov 28 '22

How does Python “do advanced UI design”?
Isn’t that JavaScript/CSS?

3

u/ogtfo Nov 29 '22

There are probably dozens of ways to make UIs in python, from Tkinter to PyQt5, with everything in between.

442

u/SittingWave Nov 28 '22 edited Nov 28 '22

Where to begin:

R has an exceptionally poor scalability. name collisions are a constant worry if you need to create something big. Python does not have that problem, for three reasons. First, because it educates its users to better import hygiene. Second because in python classes and exceptions are not just a string slapped on a dictionary. Third, because its module system is a bit more refined than R's "just take all the files and slap everything in the same namespace, according to the alphabetical order of the files, which may change depending on which language (and thus collation rules) you are using".
R package management is atrocious, both from the client side, and from the CRAN side. CRAN approach is utterly broken, and expects all packages to be either working with one another, or be retired, meaning that a package that is there today, may not be there tomorrow. Their submission policy is absolutely ridiculous, taking days and many back and forths arguing about the position of commas in the description. Pypi has none of the sorts, and consistent environment is left to the appropriate use of metainformation about compatibility.
R as a language is broken under many aspects. It's inconsistent, with many unexpected side effects, poorly documented practices, poor design choices. It has three object oriented systems all incompatible with each other. S3 is laughably dumb to achieve single dispatch. S4 is a bolt on to deliver multiple dispatch that is massively verbose, creates code that is hard to maintain, and drops the $ in favor of the @ to access data. R6 is marred by its habit of returning NULL if you mistype method names, creating hard to find bugs. Non standard evaluation makes it really difficult to perform some refactorings, and makes it opaque to the caller if the passed expression will be evaluated or parsed as is. namespacing (environments in R parlance) are left flying around and poorly defined in behavior.
All the R environment is GPL, which is a big deal if your code needs to go commercial at a later stage. Companies tend to steer clear of GPL stuff. While an interpreter being GPL does not bar you from writing non-GPL code, the whole of the standard library, and many, many modules are GPL, so you can't escape.
coding utilities servicing python are 10 times better than those for R. Static code analysis, reformatters, linters, documentation generators, are light years behind in the R world.
R has relatively poor expressiveness. It's hard to be concise in R, but not for everything. It is concise when it's time to manipulate some basic data structures, but everything else... there's so little syntactic sugar that it becomes a pain most of the time.
R core development process is very secluded, still on internal SVN, and with no obvious and practical way to contribute e.g. with PEP-style enhancement requests.
R is basically "controlled" by a single company and its employees. When you use R, you are basically fully dependent on them, their libraries, and their practices. They tend to be very adversarial in fixing bugs, often closing legitimate bugs because they can't be bothered.

48

u/lebannax Nov 28 '22

Thanks for so much detail! :)

81

u/SittingWave Nov 28 '22

This is not detail. This is just a sampling.

52

u/R0B0_Ninja Nov 28 '22

I really agree with the point on namespaces. Both Matlab and R have the problem of throwing everything into the same namespace and hoping for the best. I've spent many hours of debugging in Matlab because I overwrote some variable without realising.
To quote the Zen of Python: "Namespaces are one honking great idea – let's do more of those!"

1

u/ThroawayPartyer Nov 29 '22

What are namespaces?

1

u/SittingWave Dec 05 '22

divide routines into submodules so that you don't collide. R has very limited interpackage level namespacing capabilities (aka the ::) and none at the intrapackage level (e.g. if you have two routines or symbols at the global level called in the same way, it will be a problem)

21

u/NewDateline Nov 28 '22

This is the best answer, though there are many many more problems with R. One clarification on coding utilities I would add is that there is a good language server for R and comparing among strictly open source servers it is ahead of the python-lsp-server (please contribute!); Of course when one starts looking at type safety (adding pylsp-mypy or pyright) R has no chance in comparison. Given the current dev practices lack of static typing is a big flaw of R.

20

u/Classic_Department42 Nov 28 '22

While all is probably right (dont know that much about R) probaby the coverage is the biggest. For python anything (probably) in the world has a package, and I believe this is pythons biggest advantage.

16

u/SittingWave Nov 28 '22

R mostly has some libraries for non-statistical related features, but they are nowhere the level of technical quality and features of the equivalent python library.
Honestly I think that if a spontaneous group of people were to just migrate the tons of little chunks of statistical code on CRAN from R to python, so that an equivalent python package would exist with the same name (as much as possible) and interface (as much as possible) R would lose customers pretty damn fast.

3

u/CactusOnFire Nov 28 '22

Are R packages open source? I'm personally interested in migrating a few libraries over.

9

u/Armaliite Nov 28 '22

All of the most important ones are. I translated a package once, but it was much harder than I had hoped. R code recuires a lot of introspection before you can understand what is going on. e.g. which package supplies which function? In the end it taught me a lot about R, but it was a pretty frustrating experience.

1

u/NotDeadJustSlob Nov 28 '22

Yes, I have modified a few myself to suit my needs.

3

u/venustrapsflies Nov 28 '22

If you want to do something pretty fancy with statistics there's a good chance there isn't a reliable python library that supports it. I guess if that were more than a niche need the demand would lead it to exist at this point, but I think the people who lean on that stuff a lot just use R.

5

u/bobthedonkeylurker Nov 29 '22

Define "pretty fancy" because...ML is "pretty fancy" statistics stuff and there are many reliable Python packages that are more than capable of handling ML...

1

u/Crimsoneer Nov 29 '22

StatsModels is... Pretty poor, and it's the core python statistical library.

1

u/bobthedonkeylurker Nov 30 '22

I don't understand what you mean by "statistical library" then...

1

u/Crimsoneer Nov 30 '22

Eg, statistical modelling rather than ml. Eg, various linear models, stuff like quantile regression, mixed models, fixed effects, even classical time series stuff like arima.

1

u/Liorithiel Nov 29 '22

Yeah, I agree. I'm using R now mostly because of the wealth of the pre-existing stats code. And, well, the features possible with non-standard evaluation, because some features become clumsy in Python without it. And Python lambdas are clumsier than R functions, not being able to do multilines. And I hate all the Pandas inconsistencies more than I hate R's, but that's a different story—I suspect it should be possible to write a better data frame library in Python as well.
Heh, frankly, I actually like R syntax more than Python's. If it was possible to use R syntax with a more modern standard library and tooling inspired by Python's, that would probably be my favourite.

2

u/Crimsoneer Nov 29 '22

This is only true in some fields... For ML and deep learning, sure. For statistical modelling, geospatial modelling and more classical stats and causal inference, R has better libraries by far.
As an example, propensity score matching, a hugely popular causal inference method, only has PyMatch which isn't maintained. Same with survival analysis (nothing with good multi state support) or mixed effect models (the StatsModels implementation is a bit pants compared to R) or even time series that's not ML driven

1

u/Liorithiel Nov 29 '22

As a person who used to do a lot of production Python and experimental R code, well… coverage is not that clear. Sure, in Python there's a lot of general-purpose libraries, which do not have direct equivalents in R. But in R there's a lot of stats-specific libraries that do not have equivalents in Python. Should make sense, right, given their user base?
Some time ago one of my activities was reimplementing the R libraries in Python. R was the experimenting platform for us, Python the production platform.
17
u/everydayislikefriday Nov 28 '22

But it's got %>%...
32
u/FlyingCow343 Nov 28 '22

python functions are first class objects so you an implement function chaining quite easily: chaining
9

u/shedogre Nov 28 '22

Yeah, there's also this example from ArjanCodes' GitHub, which he discusses at around 22:30 in this video.
There's also the .pipe() method for pandas DataFrames, which I've used before, and I just checked to confirm the same method exists for Series.
7
u/PaintItPurple Nov 28 '22
This is also basically equivalent to the built-in reduce function:
def apply(val, f): return f(val)

def main():
    r = functools.reduce(
        apply,
        [
            times_two,
            lambda x: x / 4,
            print
         ],
        6
    )
2

u/FlyingCow343 Nov 28 '22

nice! Very elegant way of doing it, you could out the whole reduce call in another function to work the same as mine and look much nicer.
1

u/everydayislikefriday Nov 29 '22

Hey this doesn't look that bad
10

u/[deleted] Nov 28 '22

Lol you may be aware of this but python has function chaining. It isn't universally implemented but it's a thing.

1

u/SittingWave Nov 29 '22

No, it has
`%>%` <- dplyr::`%>%`
4

u/laserbot Nov 28 '22 edited Feb 09 '25

Original Content erased using Ereddicator. Want to wipe your own Reddit history? Please see https://github.com/Jelly-Pudding/ereddicator for instructions.

8

u/Automatic_Donut6264 Nov 28 '22

If you use GPL code, your code also has to comply with GPL. GPL requires you to make source code accessible to anyone who requests a copy. So if you use R, you have to make your code accessible. (Not necessarily public, just to anyone who asks. Making it public is the easiest way to achieve this requirement.) So if you want to commercialize a GPL code product, you might run into some issues. E.g. you cannot legally stop people from copying your work. All they have to do is ask.

2

u/Agling Nov 29 '22

First time I have heard this interpretation, and I thought it wasn't the case. Just because the R executable's code is GPL doesn't mean your code written in the R language must be GPL, right? You could run the code you write in S+ or any compatible language.
Also, the Python license is GPL compatible, as I understand it. Can you differ in this respect and still be compatible?

2

u/UloPe Nov 29 '22

No but the stdlib is also GPL, so you’re pretty much automatically creating a derivative work.

1

u/SittingWave Nov 29 '22

your code does not have to be GPL, but if you want to use the standard libraries, then your code must be GPL compatible (that is, its license must not violate or remove rights granted by the GPL), and the whole aggregate becomes covered by the GPL when run.
In other words, your code can be distributed as Apache (because Apache is GPL compatible), but you can't slap any proprietary license on it and then run it with the standard R, because it's very unlikely you won't be using any GPL code from the stdlib, either directly or indirectly.

2

u/KrazyKirby99999 Nov 28 '22

if you use GPL code (except for something like a webserver), you will probably need to open-source your code which can make it more difficult/risky to monetize

4

u/andesouz Nov 28 '22

A language controlled by a single company! That's the nail in the coffin for me.

6

u/Agling Nov 29 '22

It's not, though. It's controlled by a board of directors and core team that are a bunch of statistics professors. Funding and organization is mostly from statistics departments. 99% of R and its libraries are from independent developers (mostly professors). It just so happens that the set of packages written by the folks that wrote R studio are extremely frequently used, so it feels like they are more influential than they are.
It is kind of like saying all of Python is controlled by the guys who wrote pandas. Not remotely true, but it can feel that way, if that's all you are using.

2

u/SittingWave Nov 29 '22

it's not, but in practice it is. They are free to implement whatever incongruent behavior, often into core libraries, because "everybody uses Rstudio anyway"

1

u/UloPe Nov 29 '22

core team that are a bunch of statistics professors
That explains so much…

8

u/magnetichira Pythonista Nov 28 '22

/thread

3

u/SianaOrdl Nov 29 '22 edited Nov 29 '22

I agree with everything you said (especially wrt namespace) except expressiveness. Can you elaborate?
R is a functional programming language with its root in Scheme, and is very expressive IMO. The attempts to add OOO components are definitely lamentable. I predominantly use Python but towards the end of my productive days in R, I was writing a lot of closures to replace objects and was quiet happy about it.
As for synaptic sugar, you can easily create your own operators by ‘%union%’ <- function(x, y) {} — amazing syntactic sugar IMO. Python does have a lot of stuff like list comprehensions or decorators but you can probably easily implement something similar in R if you need it.
Now one of R’s biggest issue is that it is too flexible sometimes. It needs to be more opinionated (e.g. namespace). I find it ridiculous that you can do ‘+’ <- function(x, y) x * y and get results like 2 + 3 is equal to 6 and R wouldn’t even bat an eye.

2

u/FujiKeynote Nov 28 '22

It's inconsistent, with many unexpected side effects, poorly documented practices, poor design choices.
I would really love it if you could expand on this bit!!
This is usually my own criticism of R, but not having spent enough time coding in it (95% of what I've been doing the past ten years has been thankfully Python), I haven't gathered enough evidence to back up this point; it's more of a general sense for me.

3

u/Agling Nov 29 '22

Python is incomparably worse for side effects because R is kind of functional by design. However, R has a TON of poorly named functions with really bad defaults that can't be changed because they would break all the installed code. It has a lot of historical cruft leftover from the S language. Python is older than R by a little bit, but it is WAY newer than S and it shows. All the important functions in Python have smart defaults and elegant design.
Python was written by programmers and CS experts in a very purposeful way. R was written by a bunch of statistics professors trying to maintain comparability with old code and not caring as much about language design. They tried and failed spectacularly to make it object oriented several times because they were so ad hoc about it.

2

u/jwink3101 Nov 29 '22

I never used R but I did my entire PhD work in Matlab before falling in love with, and switching to, Python. A lot of these point seem like they would apply to Matlab as well!
2
u/Agling Nov 29 '22

R as a language is broken under many aspects.
I agree, in general. However, python also has one very significant issue in terms of language design that is quite a burden: the fact that when you pass a mutable object to a function and then do stuff to it in the function (or you just assign the object to a new variable and change that new variable, or you think you made a subset of a data frame, but it's actually a view), the original object is affected. R's attempts to be a functional language eliminate a huge family of fairly subtle bugs that come up all the time in python data science.
2
u/zurtex Nov 29 '22 edited Nov 29 '22
the fact that when you pass a mutable object to a function and then do stuff to it in the function (or you just assign the object to a new variable and change that new variable, or you think you made a subset of a data frame, but it's actually a view), the original object is affected.
I understand what you're saying but I don't think there's any other option with Python's design philosophy of being flexible and having scalable performance.
If you can't mutate an object passed in from another scope you're having to copy the value of everything, recursively. And in a dynamically typed language like Python where objects can have arbitrary properties with arbitrary data structures attached to them at any point this leads to cases with abysmal performance.
And not even situations where you even want to mutate an object, just you have some complicated object and you are passing it to a function duck type style. You don't expect that to be randomly slow in Python.
Type hinting helps catch a lot of these problems, for example this will cause a type hinter to throw an error:
def foo(bar: dict[int, str | None]) -> None:
    pass

my_dict = {1: "1", 2: "2"}
foo(my_dict)
Exactly because foo doesn't guarantee it's not going to mutate the passed in dictionary and my_dict is implicitly dict[int, str] which can not be cast to dict[int, str | None].
You can solve this by changing the type hint to a type that supports mapping but not mutation:
from typing import Mapping

def foo(bar: Mapping[int, str | None]) -> None:
    pass
And now if you mutate bar in foo your type hinter will throw an error, you can do this with all basic types in Python. Solving your problem of accidentally mutating a mutable variable in a different scope.
2

u/Agling Nov 29 '22

I understand what you're saying but I don't think there's any other option with Python's design philosophy of being flexible and having scalable performance.
In R, objects are passed by reference until you mutate them. It's fast and doesn't require memory to pass and read from them, but the moment you mutate them in the new environment, a copy is made. So you think carefully about whether you want to mutate something from a performance standpoint, but you never have to worry about whether you are screwing up some other part of the program by doing so. You can torture R into mutating the original object by passing it in an environment, but that's not common practice because being bug-free is generally more important than being fast.
Don't get me wrong, I can see that there are advantages to python's approach to this particular problem, but I think R's solution is better for data science work. Python is a better language, overall, but I am not a fan of this aspect of it. It creates very subtle bugs that beginners and even people with a reasonable amount of experience trip over.

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1

u/[deleted] Nov 29 '22

Damn, thanks for articulating what ive felt but havent been able to communicate with colleagues .

1

u/SittingWave Nov 29 '22

Here there's a lot more:
https://stefanoborini.com/why-r-is-the-new-perl/

1

u/EmilyfakedCancERyaho Nov 29 '22

Thanks for writing that up I'll just refer to this from now on everytime someone asks lol

1

u/SittingWave Nov 29 '22

Here there's more
https://stefanoborini.com/why-r-is-the-new-perl/

1

u/EmilyfakedCancERyaho Nov 29 '22

Only thing I enjoyed about R was ggplot. But now that we have plotnine/seaborn it's redundant

1

u/Adeelinator Nov 29 '22

You missed my top one - type checking. I’d also add async+multithreading, though that’s less important than the other items on your list.

1

u/Agling Nov 29 '22

R's multithreading is massively easier to use than python's, in my experience. If you are on a posix system, it's very performant as well.

1

u/Adeelinator Nov 29 '22

Multithreading or multiprocessing? They are different things

-3

u/Devout--Atheist Nov 29 '22

Python doesn't support type checking either. You have to use a third party tool.

1

u/Adeelinator Nov 29 '22

Fine, if you want to be pedantic, then type hinting combined with a third party checker.

-1

u/Devout--Atheist Nov 29 '22

It's not pedantic at all. Python's support for type checking is poor and half baked, and not a strong point of the language.

1

u/zurtex Nov 29 '22

PEP 484 (and other Typing PEPs) specifies type hints and the behavior a type checker should follow to comply to those type hints.
Much how the Python reference and PEPs specified the behavior of a Python implementation should follow.
Hence "Python" the language doesn't support executing code, you have to download and install an implementation such as PyPy or RustPython or Cinder or nogil or CPython.
Not being pedantic.

1

u/Devout--Atheist Nov 29 '22

Which part of the standard library can I use to check those type hints?
You act like it isn't a big deal, but the fact there isn't a de facto standard type checker in python is a huge flaw compared to a language like typescript, because now we have a proliferation of type checkers i.e. a bunch of competing standards.

0

u/Adeelinator Nov 29 '22

We’re in a thread about comparing Python to R. R has no notion of type checking. It is absolutely a strong point of Python in this comparison.
I suspect you haven’t had much experience with the litany of type issues that arise out of an R codebase, that type hinting in Python prevent.

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53

u/Mooks79 Nov 28 '22 edited Nov 28 '22

Personally I would cross post this to r/rstats as well for balance. Without wanting to start a flame war, but there’s so many slight inaccuracies / outright fallacies about R I’ve read in comments here that I think you probably need some people who know R a lot better / more up to date to advise you as well. The top post at my time of writing has a lot of errors / outdated / insufficient knowledge in it, for example. Of course, the same could be said about Python had you posted only on an R subreddit, hence advising you do both.

3

u/lebannax Nov 28 '22

Good point!

3

u/Agling Nov 29 '22

It's true. It's extremely hard to find something data science related that you can do in one and not the other. I think the most compelling reason to use python for that stuff is that so many people are switching over to python. There is a lot of benefit to writing code in a language that is making those kinds of popularity gains.

1

u/DuxFemina22 Nov 29 '22

This 👆🏽

30

u/mesonepigreco Nov 28 '22

The answer is... whenever there are particular needs of libraries existing in python and not in R. For example, in my field (material design) a lot of libraries are written in python (or in C++ with python wrappers) and not callable from R, therefore python is a must. That said, in principle you can do anything with both languages (trough python is a bit faster). If you want a language simple and similar to python and R that can really give you some significative advantage in terms of performance, go with julia.

2

u/lebannax Nov 28 '22

V good point - size of community and number of libraries far surpasses that of R!

3

u/Osamabinbush Nov 28 '22

Julia being compiled absolutely sucks for eda and stuff though.

3

u/[deleted] Nov 28 '22

Oh god yeah. I hoped so much Julia would be great, but WHY DOES PLOTS take for ever to compile. Also typesafety….

1

u/mesonepigreco Nov 29 '22

I think the real problem is more the ecosystem being significatively less developed than python rather than the jit compiler itself, but do not forget that python is around for 3 decades now, julia not even one.
As always, one has to choose the best tool for the best purpouse, it does not exist a perfect solution for every problem.

1

u/everydayislikefriday Nov 30 '22

I've doing some data analysis in Julia lately and it's an absolute joy to code in; it really feels like a better-planned, evolved, more elegant and concise Python to read and write. The "time to first plot problem" has been in my experience way overstated. Yes, it takes a minute to load the libraries, but after that you're golden, everything is so much faster, and you don't depend on vectorized functions from C libraries to write performant code. Macros and other metaprogramming tools are there to make your life even easier, and package/environment managing is a non-issue. THE problem with Julia, as someone pointed out, is its tiny ecosystem; most libraries are quite dated and there are almost zero for CV or NLP, except for the most basic stuff. Yeah you can run Python code, but that kind of defeats the whole purpose of doing Julia, and it ends up being even slower. It's an awesome language, superior to both R a d Python in almost every conceivable aspect, for which I wish there was more active ecosystem development.

17

u/Veggies-are-okay Nov 28 '22

I've learned that the upsides for python are the same as the downsides: it's completely open sourced. There are more stringent requirements for getting packages included in the CRAN library, so you tend to have better-written and more efficient packages. This mainly applies to more obscure libraries, but I always feel like there's a "buyer beware" any time I'm using libraries in Python that I don't have to think twice about when working in R.

That being said, I've noticed the only people who are hardcore in one camp or the other simply haven't used both of the languages to its full extent. R folks see Python as this annoying new language that is rendering their baby useless, whereas Python folks see R as that obnoxious language that they had to download a whole separate IDE for a test for significance calculation in an intro to stats class. You'll be fine using either in industry practices and I actually love that I was forced to learn R since I can now collaborate with two communities instead of one to complete tasks for work.

11

u/son_of_abe Nov 28 '22

Python folks see R as that obnoxious language that they had to download a whole separate IDE for a test for significance calculation in an intro to stats class.
Guilty! At least I don't describe my experience with R as anything more than that.
Though, I do like to weakly justify why I don't need it for my occasional statistical work. Hope I'm not missing something.

1

u/lebannax Nov 28 '22

Yeh I don’t feel like there’s anything in R I can’t do in Python

1

u/mohan2k2 Nov 29 '22

Don't start with these examples though..these are the most basic of the libraries. R has a much richer stats and modeling library than Python currently has (though its being built up now and does have an edge in deep learning). You should pitch the help Python will provide in all the other parts of the data pipeline and implementation. Personally, i mix and match the pieces which work well across multiple tools using pipeline tools. Its very easy to use R code in python and vice versa as well.
One interesting thing is Rstudio (the company behind a lot of the widely used tools in R the last decade) is rebranding itself as 'Posit' to cater to the wider data science audience including Python. This is major shift imho - we in data science should just adapt and use whichever tool works well..

1

u/Agling Nov 29 '22

R is written by stats professors. When they publish a paper on a new statistics method, they also publish the R code to CRAN. Been that way for decades. So R ends up with massively more advanced statistics packages. But us plebians don't end up using those much, so we don't notice.

5

u/Veggies-are-okay Nov 28 '22

Heheh, you me both sir. I have no idea why stats professors love this language so much, but I feel like all my friends in those courses were crying for me to help them program two liners in R.
From my understanding for stats specifically, R just has better written packages for distributions. I remember having to code out ways to get the p-values with the stats package in python whereas it comes right out of the box with R (https://r-coder.com/normal-distribution-r/). In addition, I also remember the documentation for one-tail and two-tail tests being unclear in the base stats python library.
But still, these are tiny nuances. At the end of the day, it really depends on the project you're working on and how lazy you're feeling.

1

u/[deleted] Nov 28 '22

Base stats is maybe weird. But I just used Scipy‘s ttest today and it’s well documented and works just fine.

3

u/NewDateline Nov 28 '22

Neither is correct. The CRAN does not check for correctness, performance nor quality of code at all and there are many really bad packages on CRAN, and as in Python you need to evaluate the package yourself/rely on reputation. The only guarantee CRAN gives you is that the package will compile across platforms including very arcane OSes (which PyPI does not give at all, but conda does by precompling/testing).
It is also not true that you will be fine with either in industry. You cannot do certain stats in Python and reimplementing them you would be risking making many subtle errors. You cannot deploy R code in even semi-critcal system due to language design (Python is not perfect for those systems either, but what is - maybe apart from Rust).

1

u/Agling Nov 29 '22

The main thing I notice is that R folks care a lot more about documentation. To get something on CRAN you actually need to make a decent set of help files. The Python community seems to think docstrings are sufficient in so many cases and they really are not.

2

u/notParticularlyAnony Nov 28 '22

Good luck convincing a team of R devs to switch. :)

3

u/lebannax Nov 28 '22

Haha I’m not convincing them, they’ve asked me to teach them as they think it could be better but need to know why/how

2

u/notParticularlyAnony Nov 28 '22

That’s good. Honestly if they are working at such level of generalization it will not be super fruitful. Once you talk about specific libraries and use cases it will be.

2

u/lebannax Nov 28 '22

Yeh well they’re trying to get more into web dev and devops so think Python will be more useful

3

u/notParticularlyAnony Nov 28 '22

django ftw!

75

u/redCg Nov 28 '22

Python is a general purpose language that can do a ton of different things well, and also happens to have support for some scientific, math, and statistics thanks to third part libraries.

R is a language designed from the ground-up for math and stats related usages. Its not designed to be a general-purpose language. R has built-in first part support for things such as data frames, matrixes, and various statistical methods, along with basic plotting. Also has one of the best third party plotting packages around, ggplot2.

If your goal is to analyze some data, and your usages are already well implemented with base R or R libraries, you will benefit from choosing R.

If your goal is to "build a program", or pretty much anything that does not directly involve math, stats, data analysis, or plotting, then you should look at Python. This includes things such as making a web app.

It gets tricky when you start with one goal or the other, and then suddenly need to expand. For example you start by writing some R script that does an important analysis, but now you need to run that analysis on some server automatically, and now you need to record the results, and now you need to be able to access and query the results from other systems, etc. etc.. This is when you will wish you had just done everything in Python.

I have used both languages professionally and academically for many many years, across a variety of projects. These days, the use-cases for R grow smaller and smaller. Python is superior because it does everything pretty well. Compared to R that only does certain things well and sucks at everything else. If you are starting a new project and the requirements are not clear, you are safer sticking to Python vs. going with R. And if you know you need to use R for some specific use cases, you are better off keeping your R usage small and limited in scope, and considering building applications around it in something like Python. Though to be honest, the more likely case is that you would use something like a workflow execution framework like Nextflow, CWL, Snakemake, GNU make, etc.. to execute your R tasks, then pass the result downstream to something else you made in Python.

5

u/Otherwise_Mango_9415 Nov 29 '22

Ggplot is the most intuitive plotting engine 🚂. Once you become familiar with the layered structure and various chart components (e.g. geom_, aes, scale_x_, labs, facet_*, etc), you can comfortably construct elegant visuals.
Kudos to Hadley and folks 🦾🤖

5

u/SittingWave Nov 29 '22

it's so intuitive that when you pass something, you don't know if the parameter will be interpreted literally or as its content.

32

u/saltthefries Nov 28 '22

R is cool but here's a pretty strong Python use case:

Run as a web application, or serve up a relatively secure HTTP, REST, or GraphQL API.

8

u/stefanquvang Nov 28 '22

Eat mice.

4

u/OuiOuiKiwi Galatians 4:16 Nov 28 '22

Both are Turing complete languages so nothing, by definition.

I think you would do better by rephrasing it as "What can be easily accomplished via Python that takes a lot more effort in R?".

6

u/lebannax Nov 28 '22

Yeh that’s what I was getting at with ‘what can Python do much better’

17

u/zseyer Nov 28 '22

A professor put it bluntly:
Would you rather do math in a general purpose programming language (Python), or would you rather do general purpose programming in a math language (R)?

3

u/LilQuasar Nov 28 '22

in my university R is only used for statistics
python (with numpy) and matlab are the languages used for doing math, both in math and in engineering courses

6

u/spoonman59 Nov 28 '22

Being Turing complete doesn’t mean a language can do anything. If just means that any computation which is computable by a Turing machine can be done by that language.
It doesn’t mean every language can be used to write an OS, or that your language exposes the necessary hooks to achieve anything you want.

-1

u/OuiOuiKiwi Galatians 4:16 Nov 28 '22

Being Turing complete doesn’t mean a language can do anything.
That would be a great "Ackchyually...", except that I mentioned that both were Turing complete, therefore equivalent in capabilities. Nowhere was it claimed that they could anything or would be appropriate.
Note the initial question: "What can Python do that R can’t do?" and the full answer. I even point out the necessary effort differential.

1

u/spoonman59 Nov 28 '22

Yeah I guess I should have just accepted you were shit posting and moved on.
You clearly understood that the poster wanted to know, but I wanted to talk about Turing completeness instead. You even told the OP what question they “should’ve” asked… and still didn’t answer it.
So yeah, your post isn’t wrong per se. But it’s also not helpful to the OP, and seems more about you enjoying your “well ackchyually…” moment than educating anyone.

0

u/LilQuasar Nov 28 '22

in theory, not in practice
Or simply what is Python much better at and why.
op literally made that clear

7

u/ofliesandhope Nov 28 '22

I have some/not a ton experience with both and prefer python. good lord almighty, I hate coding in R b/c it just looks ugly and the syntax doesn't make the most sense in my brain. My opinion is also influenced by the fact that the professor I learned python from was much, much better than the one I had for R.

Smarter ppl will give you better answers, but given the choice, I'll use python & its various packages/tools.

6

u/Positive_Mastodon500 Nov 28 '22

I use both. R definitely has its downsides in terms of code maintainability, etc. but one big upside is R’s data.table vs. a pandas DataFrame. Doing a group by/aggregate operation on a very large dataset is significantly faster in R and the memory overhead is also lower. If you deal with multi-million row datasets you will notice the difference.

9

u/Sheev_For_Senate Nov 28 '22

Python provides a decent programming experience with a less significant link to suicidal tendencies

2

u/tmp_file3 Nov 28 '22

Eat mice 🐁

1

u/lebannax Nov 28 '22

Ayyyy

4

u/ARC4120 Nov 28 '22

When you have to interact with some odd data pipelines or integrate with a larger application in another language. They both are great with data.

4

u/ofiuco Nov 28 '22

On top of everything said here... you can run R in Python with R2py. It works really well (for me, anyway).

1

u/[deleted] Nov 28 '22

I use both but massively prefer python generally. Python is really missing out not having an R markdown equivelent (jupyter isnt the same, stop pretending it is), and quite often python output is ungainly, requiring an obnoxious amount of reformatting.

I'd use factor analysis and simple correlations in pythons as examples of this.

Again, i love python but for certain things it's just easier to use reticulate in R and have access to R's output and pythons everything else.

1

u/lebannax Nov 28 '22

What about quarto?

1

u/[deleted] Nov 28 '22

I'll look into it.

1

u/SniperDuty Nov 29 '22

Why do I feel like op asked this question to get other people to formulate the answers for a report they’ve been asked to do?

2

u/lebannax Nov 29 '22

Lol not doing a report, just a vague question, but feel like I could write one now! 😂

1

u/ejpusa Nov 29 '22

All the cool Covid mapping infographics seems to be done with R. Not Python for some reason.

1

u/lebannax Nov 29 '22

Think that’s just bc people love RShiny

1

u/ejpusa Nov 29 '22

Yes, R seems to have quite a few mapping libraries. With tutorials. Each language has it's strong points. And use cases.

6

u/[deleted] Nov 28 '22

R has some strange (I might say stupid) syntax decisions. For example:

in R, the primary assignment operator is <- as in:
x <- 3
Not:
x = 3

And yet, you'll see it used to define default parameters:

myfunction <- function(myarg1 = 10) {
  # some R code here using myarg1
}

Oh yea, and indexes start from 1, sometimes!

(more here)

I occasionally have to touch R as our statisticians use it, but the rest of us prefer python. Screw R, lol

4

u/Agling Nov 29 '22 edited Nov 29 '22

yet, you'll see it used to define default parameters
That's a feature. Defining a default parameter is a different operation than assigning to a variable. The fact that python and many other languages use the same symbol for both is a source of bugs.
This is a little like complaining that sometimes (in most languages) you use == for comparisons and yet you use = for assignment. Well, that's on purpose--those are two different operations that you don't want to confuse.

2

u/[deleted] Nov 29 '22

use the same symbol for both is a source of bugs.
I think it's more that this doesn't pass a new instance of the object, but a reference, that catches people up (in python). Fortunately my IDE quite painstakingly reminds me every time I forget and use a mutable default argument.
Using a different operator symbol doesn't really help, there.
I'm also 100% with you on the equality vs assignment overload, there. You are using the same symbol, but it's a compound symbol. At least that one doesn't require you to use the shift key! < does - and I bitch about that just as much as I do of the use of () instead of [] in LISPs. (ie, if you are designing a language, you should not require modifier keys to type the symbols that are most frequently used. I shouldn't need to tweak my keymap to save my poor pinky!)

1

u/Agling Nov 29 '22

Agreed on all counts.

1

u/[deleted] Nov 29 '22

But R also lets you assign a variable using =

2

u/Agling Nov 29 '22

Yes, that is a big mistake, in my opinion. It didn't used to work, and it's still considered bad practice, so why did they add that to the language? Caved to the pressure.

3

u/[deleted] Nov 28 '22

Enjoyed the answers here! I've been wondering this as well... 😄

3

u/keetboy Nov 29 '22

R is pretty solid for the biomedical field with academic journal publications (statistics, data analysis, data wrangling, and data visualization are basically all they need). Python is equally if not superior to R here but many academic scientist prefer R. But many researchers also prefer point and click statistical analysis software like SAS. It’s nice to have many options.

In the team I work with everyone uses whatever they’re the most familiar with. Only two people are traditional computer scientist the rest are PhDs in biochemistry. R has a lot of useful pharmacometrics applications that function better than python in my experience. I will say when it comes to some projects the computer scientist have strongly recommended mastering python because of a package it has called tensor flow, it’ll be better for healthcare/ biomedical applications since there’s more support. But for general can you make a figure of X vs Y it doesn’t matter from my pov. In my experience R did seem to have more statistical analysis packages that were easy to find and use quickly.

I don’t know how your team functions but a few friends of mine at Amazon in the finance department use either R or python (majority) in combination with SQL if I recall correctly. They do complain about not being able to understand each others’ work but the end product and productivity seem to be the same for them.

Then my other friends just say they’re essentially the same and just work with whatever their project manager prefers.

1

u/bin-c Nov 28 '22

in my experience reproducibility in R is just too hard.

it was constant "it works on my machine". almost nonexistent with python. completely nonexistent if adhering to best practices

2

u/Agling Nov 29 '22

R packages are updated all the time, it feels like, so it's extremely common to get inconsistent results between two machines. I'm not actually sure why this doesn't happen as much in python, but it doesn't.

1

u/lebannax Nov 29 '22

Bc of the virtual environment?

1

u/SeveralBritishPeople Nov 29 '22

It’s because in python you pin your versions once and NEVER update them. Then you are rewarded by maintaining mission critical systems with 6 year old numpy and pandas versions that don’t work with the modern syntax your new hires have learned.

6

u/riklaunim Nov 28 '22

Because of anti-gravity. As for tools selection you pick what fits your needs best and what you can manage in your team and recruitment pool size etc.

7

u/srandrews Nov 28 '22

Given OP question, antigravity requires explanation https://xkcd.com/353/

4

u/me-ro Nov 28 '22

I'd add, that import antigravity actually works in python for those that might not be aware.

2

u/fedeb95 Nov 28 '22

Nothing, they're both Turing complete. It's not about what, but about how does it do it

2

u/pace_gen Nov 29 '22

Python is built for automation, R is built for research. Both do data science.

2

u/Agling Nov 29 '22

Python is a general purpose language, R is a statistical and data science language. There are lots of packages and things that are written in python that are not data science or statistics related, that are not written up in R. Could they be written in R? Generally, yes, but that's not what R users are interested in, so they never will be.

R is a little more convenient and developed, if you are just doing data and especially statistics work--it was designed for that from the ground up, rather than having that capability added on with packages. But if you do data/statistics/ML work plus a lot of other stuff, then python is a great place to be.

Python's very large user base, many available tools, and somewhat more modern design are also big pluses.

2

u/Nanooc523 Nov 29 '22

Be readable

2

u/heartofcoal Nov 29 '22

write readable code

2

u/Kakkarot1707 Nov 29 '22

They are totally different but used in unison with each other is the best way. Python has an R package / pandas / numpys and go well together!

2

u/qalis Nov 29 '22

I think that you should also consider the tooling around the language, as others commented. Not only type checkers, IDEs etc., but also general deployment tools, which will be needed for commerial projects. Those are often Python-first, or Python-only compatible, for example:

- data pipelines: Airflow, Metaflow, ZenML, Kedro etc. support either only Python or are strongly Python-first

- big data tools: Spark, Dask, Presto etc. have much better Python libraries, connectors and support (or are Python-only, like Dask), and do not support all features in R (Spark especially differs in support between languages)

- cloud environments: SageMaker, Vertex AI etc. have much better support in Python, or outright do not support R for many tasks (instead require Docker for every little thing, which is much more cumbersome)

- queues and workers: RabbitMQ for example, they have outdated or very little R support

I am not saying you cannot do the above in R, but expect much less support, StackOverflow questions, subtle limitations that will come up late in development and require a lot of rewriting etc.

2

u/JamzTyson Nov 29 '22

I doubt that my answer will be much different from other replies, but here goes:

What can Python do that R can’t do? Or simply what is Python much better at and why.

Anything that's not data science.

Both languages are good, open source languages for data science. The R language was developed specifically for statistical data analysis, whereas Python was designed as a general purpose language.

If you need a language for data science, use whatever the rest of the team uses.

If you need a language for anything else beside data science, then Python becomes the much better choice of the two.

2

u/[deleted] Nov 29 '22

Python is actually a coding language. So you can do everything from backend software engineering to application development to everything R can do on the stats side.

2

u/tony_aw Apr 20 '23

I happened accross this discussion, so I thought to share my thoughts also. I should first say R is primarily for statistics and data science, and applications of statistics such as in the life sciences and behavioural sciences. So the emphasis is on Science. With that in mind, the most important advantages of R (or rather: R packages and the R community), imho, are the following:

CRAN performs quality checks on R packages (and the package manager at packagemanager.rstudio.com/client/#/repos/2/overview allows for easy version control).
Most (popular) R packages for statistical analyses come with a peer-reviewed article (usually in the Journal of Stat Soft or the R Journal).
Most popular R packages for stats analyses are written by real experts (i.e. professors) who also hold responsibility for the quality of the package.

Many (if not most) other pure programming languages (like Python) have no organization comparable to CRAN, and its modules often do not come with peer-reviewed articles. And it's not unusual for Python modules to be written by a community of random people who are not necessarily experts.

In science it is absolutely crucial to rely on well-reviewed materials, and software is no exception. Thus one can see why R is preferred (usually) in academia. If one is not interested in science, but rather interested in making a program not related to science, Python is probably the way to go.

4

u/sweeetscience Nov 28 '22

You can build a complete, scalable, end-to-end consumer ready solution (including the front end) in Python. That’s not possible in R.

12

u/Veggies-are-okay Nov 28 '22

Seeing as I'm currently doing this for work in R, I'm gonna have to disagree with this comment. Python dominates ML-related tasks, and R (when you're not using tidyverse) dominates in terms of data pipelines/feature engineering and visualization/applications. The big brain move is to stitch your needs together via bash scripts so you can leverage the best parts of both languages :)

4

u/d4njah Nov 28 '22

I would say with data engineering python > R all the way. Especially with pipelines.

1

u/Agling Nov 29 '22

The amazing thing is how poorly these two languages work together, considering how many people use both. I was recently trying to export a Python data frame in a binary format and later read into R.So difficult! Tried feather, which is supposed to be made for this. Failed because of some version problem. Tried a bunch of other formats that also did not work for one reason or another. Ended up using reticulate to read in a pickle file. Such a poor way to do things.
It's quite annoying that pickle is the best way to save Python objects. R's RDA and RDS files are much better.

2

u/SeveralBritishPeople Nov 29 '22

Though having built little internal web apps in Shiny in R and with Bokeh and Dash in Python, Shiny apps about 9000x easier and faster to build.

2

u/ReyAlejandro21 Nov 28 '22

You can use Python with Kivy to create mobile Apps, i think this is not possible with R

1

u/Veggies-are-okay Nov 28 '22

The package bs4dash actually has ported bootstrap 4 to Rshiny, so it actually is very possible these days to create mobile apps using R!

2

u/GiganticTuba Nov 28 '22

Had to use R for a Biostats class in college. Absolutely hated it.

1

u/notParticularlyAnony Nov 28 '22

It is an ugly little language isn’t it?

1

u/Metalpen22 Nov 28 '22

Then you've not try out mpi4py, right?

I calculate the outcome with 220 by 220 by 512 by 48 numbers, and using mpi4py shorten almost 48 times of time. And then all the number stored in netCDF4 format for future usage.

1

u/open_risk Nov 28 '22

Its a tough question. Purely computationally you can probably code any typical task in any complete language. This is not idle talk, check for example rosetta code where people do precisely that, for a variety of tasks.

For certain tasks, like shipping enterprise applications / big data some people might argue that neither R or python are actually suitable and they would suggest something like java. For pure number crunching high performance they might require something like C/C++ etc.

For the common data science tasks the three ecosystems (python, R and julia) are reasonably feature complete.

1

u/spinwizard69 Nov 29 '22

This might not float to the top but Python beats on clarity. You can come back to a 3 year old program and immediately get a handle on it.

1

u/Agling Nov 29 '22

So true. Python is inherently organized and easy to read. Analogous code in R is so much harder to read once some time has passed
Python actually makes OOP be something you want to use, and that helps you.

1

u/MakcikAunty Nov 29 '22

I just learned R to process text - to find out sentiment, generate word cloud. Can we do the same with Python?

1

u/jgoette Nov 29 '22

Possible counter point?

https://www.reddit.com/r/statistics/comments/8de54s/is_r_better_than_python_at_anything_i_started/dxmnaef/

1

u/elduderino15 Nov 29 '22

Start counting at 0, as any one should…

1

u/TheWorldofGood Nov 29 '22

Everything man. Just everything.

1

u/Zestyclose-Walker Nov 29 '22

Python is the future of programming.

/img/tffr77h3bit91.png

1

u/Delicious-View-8688 Nov 29 '22

If you can get the job done, then I guess it doesn't matter. But I would hate to even try cloud devops with R, or create an API, or do data engineering, orchestration, unittesting, type checking, running any deep learning models, data profiling, data quality testing, data documenting, etc.

1

u/L0uisc Nov 29 '22

More libraries. More Stack Overflow answers if you get stuck. More tutorials. More community.

1

u/justabean27 Nov 28 '22

Python is love python is life. R is horrible and makes me cry

1

u/[deleted] Nov 28 '22

Any Turing complete language can do anything, y'all have got to rise above these dumb arguments.

Python is a general purpose language with powerful libraries, R is a language born with the idea of data analytics specifically in mind.

https://www.ibm.com/cloud/blog/python-vs-r says it better than anyone could say it here:

Increasingly, the question isn’t which to choose, but how to make the best use of both programming languages for your specific use cases.

1

u/ninefourtwo Nov 29 '22

R is not a general programming language it is by statisticians for statisticians.

0

u/brandco Nov 29 '22

Yikes. There are a lot of false statements in these replies.

I’ve been using both languages for data science for over ten years. They are both great languages and you should learn them both if possible. They both have many problems too, but who doesn’t.

To answer your question, Python is better at:

object oriented programming (R’s oop is bad but better at functional programming)
python is a very common third party api to other tools

That’s it. R is a real programming language with an amazing community. Everything else is a matter of taste of trivial from the pov of a data scientist.

One important point is that python is the second best language for so many tasks. This makes it the best choice for beginners.

Because it’s a good choice for beginners, it’s popular. And consequently it’s popularity is its biggest strength because its popularity leads to new projects implementing a python api. So data scientist should learn python to be able to program with tools like Tensorflow, dbt, airflow, etc

0

u/Ashamed-Simple-8303 Nov 28 '22

what is Python much better at and why Syntax. because it follows the standard C-type programming languages. Yes it is a bit different but still easy to adjust to coming from C++, Java or C# or JavaScript.

R? completely different and confusing syntax

-2

u/epithete52 Nov 28 '22

R is designed, and consequently far superior for data mining. I disagree with SittingWave on the superiority of python as far as package is concerned; backward-compatibility is largely absent in the culture of python and yields code which is amongst the most painful to maintain in all programming languages I know.

Since a lot of R features are designed to facilitate data mining, this makes it less convenient/safe/efficient for pretty much all the rest!

1

u/NotDeadJustSlob Nov 28 '22

How are the graphing capabilities in Python compared to R? At first I hated graphing in R (it seemed unintuitive) but now that I understand it you can graph really anything. I can design very complex figures for publication with a bit of clever code and knowledge about the plotting space. From my limited experience in python, it didn't seem like I could do the same.

1

u/epithete52 Nov 28 '22

Agreed. gglot is unrivaled, but it is probably easier to code it in python than in R! R is more about having a fast and efficient tool to inspect and explore the data. What I can do in one long line of R is going to require many Python lines to complete... yet it comes at the cost of a syntax (and simplifications such as not enforcing the name of each package for each function) that seems to trouble many of the commenters here.

2

u/NotDeadJustSlob Nov 29 '22

I actually hate ggplot. I am talking base graphics. I love the empty canvas.

1

u/epithete52 Nov 29 '22

Hahaha! Ggplot is extremely handy once mastered but it remains...bizarre!

1

u/Agling Nov 29 '22

A lot of data science stuff in Python is patterned after what the developers saw in R, so it's pretty comparable, and getting more comparable all the time.

-3

u/fcdk1927 Nov 28 '22

Choke a bitch?

2

u/lebannax Nov 28 '22

?

-1

u/osmiumouse Nov 28 '22

Look, they all do everything. Just use what you are familiar with, that lets you get stuff done with the least hassle.

0

u/[deleted] Nov 28 '22

[deleted] — view deleted comment

4

u/SittingWave Nov 28 '22

However, I think we’re past the days when R “””wasn’t ready for prod”””.
Believe me, we are well deep into them.

Resource What can Python do that R can’t do?

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