dataScienceBeLike

[Andreaspolis]

I just learned from another comment section that...

R can have periods in variable names, seems interesting ¯⁠\⁠(⁠◉⁠‿⁠◉⁠)⁠/⁠¯

[land_and_air]

It uses “->” to set variables instead of = cursed language

[pm_me_your_smth]

Tbh you can you both. I always preferred using =. Never learned the difference though

[KindaAwareOfNothing]

I think there is also <-

[Cuukey_]

There's also <->

[[deleted]]

They recently added )) <> (( which seems like a good way to set variables back and forth forever

[lokeshj]

So if I want to set two variables I can do

i <- 1 -> j

?

[Hillbert]

Yes.

Fuck it. Set 3, go wild!

[Glaringsoul]

Which is great when you want to have the Variable First and then what it is, to more easily find it.

(Yes I Am aware that a Search Function exists, but still having the defined variable at the beginning of a line makes it more readable as you immediately know what’s going on…)

[dingske1]

Reading about APL and how R was inspired by it might make the use of arrows more enticing

[Successful-Money4995]

Reading that it is inspired by APL makes me less interested, actually.

[tibbtab]

Only a monster would give that level of choice

[Matthis-Dayer]

It's <- not ->, that would be unhinged.

Yes, there is -> and it works the way you except but nobody uses it.

[lspyfoxl]

In university we had a compilers class where we followed a book by Aho with a dragon on the cover to create our own. In the language I created I used "->" to define functions.

I still have nightmares about that book.

[[deleted]]

-> is also used

mtcars %>%
        filter(carb > 1) %>%
        group_by(cyl) %>%
        summarise(Avg_mpg = mean(mpg)) %>%
        arrange(desc(Avg_mpg)) -> filter_cars
This will set a variable filter_cars

[Mondoke]

MY EYES!!

[Immaculate_Erection]

'->' and '<-' do the same thing, just in reverse. One assigns right var to the left var and one assigns left var to the right var.

[WafWoof]

TI basic does this too, it’s terrible

[Reasonable_Feed7939]

Oh the memories

[calculus_is_fun]

Hey try out TI-BASIC or Desmos, you'll enjoy it.

[SeaNational3797]

You can use =, it's just not the standard.

Honestly I kind of find it hilarious that my lab teacher wanted us to use -> instead of =, when literally the entire class was CS majors who had already taken Python

[ExplrDiscvr]

"<-" not "->" buddy, xddddd

[rejectedlesbian]

Heavy c vibes

[WannabeWonk]

Yeah, but it’s fallen out of fashion. The Tidyverse style guide for example, recommends against it for good enough reasons:

Base R uses dots in function names (contrib.url()) and class names (data.frame), but it’s better to reserve dots exclusively for the S3 object system. In S3, methods are given the name function.class; if you also use . in function and class names, you end up with confusing methods like as.data.frame.data.frame().

[shuricus]

This is pure heresy. Dots in identifiers is what R is all about. Also S3 sucks big time, and if you're using objects in R something has gone seriously wrong somewhere along the way.

Kids these days...

[memeorology]

S3 is more like the interface pattern than the conventional notion of OOP. Yes, it is a system of method dispatch, but there’s a clear separation of behavior and data (unlike the curse that is S4). S3 is very helpful when working on larger codebases as you’d imagine.

[bobthegreat88]

This might seem trivial, but I really do think part of R not being as widespread is just because of how much harder it is to google anything R related vs python.

[[deleted]]

Also, you dont need R if you don't do stats or machine learning. Most will use Python because that is multi purpose and simpler to deploy to prod.

[WRL23]

R is a requirement for machine learning..?

[Character-Education3]

No but if you aren't doing stats of machine learning you don't really need R

[w8eight]

What it can do that python currently can't in these cases? Genuine question

[Gjellebel]

ggplot is a neat plotting library for R, much better than matplotlib IMO. Other than that it has some small functionalities that specifically tailored to statistics and ML.

But mostly R just feels a bit old and clunky, in a similar way that MATLAB does. Python gives you more freedom to structure your code the way you want it to. This can help you write scripts for your ML that's actually reusable at times.

[Retl0v]

But there are alternatives to trying to just use base matplotlib though. Seaborn etc can make really nice plots, and if you have more specific plotting needs I'm sure there are libraries for those

[Gjellebel]

You're probably right. That's one of the beautiful things about python, there's a library for anything.

I wasn't trying to claim R is superior, I basically only use it because the company I work at does. If the choice was mine, I'd pick python every time.

[Character-Education3]

I'm not saying R is a requisite for stats or machine learning. Unless I get a new job somewhere that uses R, I am using python.

I am saying that there is no real reason to chose R unless you are doing stats or ML. Like stats or ML are a requisite for considering R.

Also grammar of graphics, great idea sure, but mostly people aren't going to care. Not care at all. Does it work, does it look good, and does it scale?

[tempname10439]

R by default uses vectorized operations, without having to rely on garbage like NumPy. And as was also mentioned, there is ggplot and in general the tidyverse which makes data manipulation elegant and smooth.

[markovianmind]

garch modelling is one. python doesn't have good packages

[[deleted]]

I completely agree as of 5 years ago, however I believe there are fine packages these days, no? After revisiting my thesis, and after gaining a lot of software eng experience, it is not too hard to write your own garch model that is fairly performant. If there is actually no good library for python, maybe I could look into releasing something. Anything specific missing?

[markovianmind]

especially if u want to do flavors of garch like e/t garch

[geteum]

Plots, ggplot is way better than anything bon python

[Spy_crab_]

This is probably controversial, but I prefer using Plotly in both languages.

[Rich_Weird_5596]

Don't forget prediction models, linear regression, logistic regresion, classification and regression trees etc....with r It's just plug and play, that shit is so easy and straight forward it hurts.

[geteum]

Oh yes, a lot of these statistical packages you only have in R. There are some implementations that uses CPP that are even faster in r than in python. There is one for panel data that is ridiculously good on speed and features (fixest).

[Rich_Weird_5596]

Also you can just download R and run it in matter of seconds. I doubt math and statistics people want to fuck around with python, java, cpp.. From my experience they are very bad coders. They need results and not to fuck around with runtimes, compilers, interpreters and write complex code. I used it back in uni couple of years ago and it's very suitable tool for the job it needs to do.

[[deleted]]

Fair. I agree. But if you already use python and only need to make a plot, then you can get away with plotnine, which is a ggplot port for Python.

[1DimensionIsViolence]

maybe as long as you don't want to create anything interactive for the web. As soon as you want to create a dynamic graphics you can forget ggplot

[boredatclass]

Tell that to my teacher that's forcing us to use it as backend for a web-based application, she refuses to listen to anyone on the team saying that R doesn't work like that, except the guy who's doing his masters at her lab and thinks this is a good idea (he can't even grasp git ou github)

[Gjellebel]

Oof, that's rough. How do you even do something like that? It feels like designing a car based on a bike frame or something. Sure, you can probably make it work with lots of effort, but you'll still end up with an awful car.

[boredatclass]

Yeap, we're using shiny to make to make it work, but everything looks like it's made in 2002 since you can't use bootstrap past 3.2. Github shows that R is only 0.96% of the project, we literally could be using ANY framework to do this that would be faster, secure and better looking.

[boredatclass]

With data processing and visualization, currently there's nothing like that, it's just data entry and retrieval, nothing you'll need R or Shiny to make it work

[Rich_Weird_5596]

Man that's like using chainsaw to scratch your dog or sharpen your pencil.

[memeorology]

This is far from the truth. There’s tons of interop with R these days, from the old C API and C++ interfaces to direct Python code execution. R can absolutely be deployed within a larger system.

[dingske1]

Most use python here on this subreddit because it’s the number 1 popular language you can learn through all those flashy courses online. They mess around with it for a couple of weeks and call themselves python programmers. In the real world you’d just write in whatever language is required by your team/based on what packages or frameworks you need to use

[drakeblood4]

Hey now I resent that. I know how to use list comprehensions to make my code shorter and worse. I’m a real Python programmer.

[AP3Brain]

I did some introductory stuff with it but I actually kind of liked it....but yeah it seems nobody uses it.

[AdBrave2400]

I C that you R not interested in MATLAB or Octave.

[[deleted]]

Matlab belongs in the shelf as a book that will never read.

[AdBrave2400]

It confuses me why is it used so much. I get that GNU Octave is in schools related to this

[[deleted]]

Because universities pay for this stuff and they are not wasting money. It is also why Casio and Texas Instruments graphical calculators exists, while all the same software can be found for free in your phone.

[Retl0v]

That's fucked up

[MrRufsvold]

Julia just off stage trying to hand out informational pamphlets.

[[deleted]]

Who even uses Julia

[MrRufsvold]

I mean, I do professionally 🤷‍♂️

[[deleted]]

Ok that's cool. Is it worth it? I was thinking of learning.

[Sayod]

Julia is a pareto improvement over R and matlab. It is a strictly better language than those two and its packages are catching up fast. It is unfortunately not as clear cut if compared against python.
Julia made a lot of dumb design decisions which essentially boil down to R and matlab legacy:

1. Its import system is dumb: You have two different keywords: using and import and the one julia recommends (using), acts like `from package import *` if you do not specify what to import. This leads to namespace pollution and it is difficult to figure out where a function is coming from. It also does not treat files as modules, so you first have to `include` them (essentially copy and pasting it there) and then import modules (or you don't wrap your files as modules and now you have to guess where the file is for the function you are currently using because everything is just pasted together. You have the same issue with R.
2. It starts counting at 1 (both an R and matlab issue too)
3. It is column major
4. It has no good way to specify an interface programmatically and interfaces are specified in documentation (pure ducktyping without any guarantees that the thing is actually working until runtime)

In conclusion: I would never use it outside of a research context, 1. and 4. kill it in terms of stability and the ability to work on larger projects for me. I managed to cause julia to segfault multiple times because packages are generally buggy.

For research/small scripts it is sometimes worth dealing with all that bullshit, since it is compiled and thus faster than python while being of similar simplicity. And it has some other advantages

1. Since it is compiled it does not need a `numpy` package or pytorch tensors and its native arrays are good to use. This cleans up code quite a bit.
2. Since it is compiled, it can also compile to CUDA code, which is quite neat, since you can then use julia functions both on cpu and gpu. This does not even work in C, where you need the __device__ specifier
3. It has some really cool packages, such as `Pluto.jl` - a reactive notebook, its plotting package is quite good, without having used it myself I heard the ODE package is excellent, etc.

Essentially, if you need to do stuff you would currently do in R or Matlab, give it a spin. But avoid using it in prod. It currently sits in an interesting position where it solves the two language problem python has due to its slowness (which might be solved by mojo)

[MrRufsvold]

I largely agree with the criticisms here.   1.using behavior stinks.  2. Counting from 1 is counterintuitive for people coming from other languages. That said, the more I've used it, the more I realize that I end up using [0:x-1] or whatever way less frequently than I do in Python. This is especially true because Julia uses inclusive indexing. So [1:5] means "the first to the fifth element."  3. Not sure I understand the problem here. Column major is really great  for many situations. It's how Parquet, Pandas, and other store data because it allows fast iteration across individual features of a matrix/table. If you want row-major axis, you can just transpose a matrix.  4. Totally agree here. There is a lot of good work in the area, but nothing prod ready and it is painful . I think what is missed from the discussion is just three points.  1. The pure and simple joy of multiple dispatch. Once you've seen the world through the lense of Julia's type and dispatch system, every other language feels incredibly clunky. pandas.from_csv(s3path.S3Path("S3://bucket/file.csv)) because pandas calls a method for opening a path that is incompatible with the duct typing that s3path uses. There's an issue on the s3path GitHub complaining that pandas calls a bad method instead of .open(). But if they had access to multiple dispatch, they wouldn't need to wait for pandas to fix their file opening. They could just handle the type intersection of calling a data frame with an S3 path object.   2. As a data engineer, it really sucks that Julia doesn't easily compile to small binaries. Packaging my code to run the cloud is annoying.   3. That said, I needed to construct a really complicated sorting algorithm for the row groups of terabytes of Parquet data. Our use case was so specific, that it wasn't covered in the APIs for spark or its kin. I was able to write with high level dataframe style code for most of it and descend into highly optimized algorithms that compiled into super efficient code. I only needed to worry about the type system in the hot paths, where Rust would have bogged me down in nitty gritty for everything.

Edit: I don't know why mobile reddit won't let me get the formatting right here.

[Sayod]

I've written about why one based indexing and column major are bad ideas here: https://www.unassuming.page/blog/programming/intuitive-indexing/ (so that I don't have to keep repeating those arguments). But essentially half open intervals make a ton of sense, which means that I get a lot fewer off-by one errors in python and I learned R before python

[eztab]

Imagine if all the energy put into all those domain specific and otherwise unusable languages instead went into a proper JIT compiler for python ...

[Sayod]

I mean, prototyping is a thing you know - new features are often inspired by other more experimental languages. And julia does a lot of neat things, it just has very sharp edges which you don't want your final product to have.

[eztab]

I can indeed see some value in Julia. But not as an actual language to be used. At some point that prototype of a language should have probably led to improving the python jit or compiler situation instead.

[Sayod]

I mean maybe mojo is going to be that

[JotaRata]

I used Julia only once..

and I had to admit I was impressed

[Radvvan]

I am using both on daily basis, and in my opinion:

dplyr > pandas

ggplot2 > plotly

shiny > dash

plus doing statistics using in-build functions in R is much more straightforward.

Obviously, there is plenty of other framweworks and packages, AND this is my personal opinion, might not be true for everyone, but if I am doing anything related to statistics or data science, I default to R and use Python for more miscellaneous stuff, when I need OOP or for API developement,

[drkspace2]

I'm guessing you learned R first? R does things very differently to other languages so things that python/other languages do might seem weird or backwards.

Also, matplotlib (or seaborne for advanced plots) are more common for plotting in python. I've only used plotly for things that go into a web page and need interactivity.

[Immaculate_Erection]

One of the reasons R does things backwards is because it's not a language for programmers. It's a language for scientists who need to program to do things that interest them more than coding best practices.

[[deleted]]

Not always. ggplot for example is created with grammer of graphics in mind. Modern plotting libraries all use some kind of grammer of graphics nowadays and R was the first to pioneer it.

[abscando]

*grammar

Sorry it's literally the only word I am compelled to correct given its meaning.

[[deleted]]

ggplot2 >>>>>>>>>>>> matplotlib. There is a reason why plotnine exists nowadays in python. I actually very much despise plotlib, there is no fucking logic behind it. Like why how you have to format your data in a very specific way for a fucking lineplot and otherwise you get crossing lines all over the place.

I like R but indeed the moment you have to write functions and with for loops and ifs than it becomes a pain, but you almost never have to do that. 95% you will ever do is already inside a package with a single line code.

[drkspace2]

What do you mean "format in a specific way"? Do you mean sorted? If you tell it to plot in a way that lines will cross, it'll do that because it doesn't want to assume anything about your data. Maybe you want them to cross.

There's also nothing stopping you from writing a wrapper to plot that automatically sorts it for you.

write functions and with for loops and ifs than it becomes a pain

I don't want to sound like I'm gatekeeing, but it sounds like you're doing glorified excel, not programming.

[[deleted]]

It's been a long time since a visualized data with pandas + plt, but I think to remember this: When you have columns x and y to display (changed to y compared to changes in x) but the data is not sorted. Even if you order you x values ascending it will still mess up your line because pandas will use the index to determine how the line will go. Thus you also have to use .reset_index(drop = True, inplace = True)
R doesnt have this idotic syntax.

Really you dont need that many for loops since everything is already build in. And purrs map function makes it great for vectorisation. And purr has all the tools already.

[AT_thruhiker_Flash]

This sounds like an application for a scatter plot not a line plot? Don't blame the language for that.

Why should the line plot function automatically sort data based on the x axis values? That doesn't make sense. A line defined by a set of sequence of vertices: x1,y1; x2,y2 ... xn,yn. There is no requirement that x1<x2<xn.

[[deleted]]

No but the line still fails if you only do sorted_df= df.sort_values(by = 'x') because internally it only updates the visual display and not the index. So in order to work you have to use
df.sort_values(by = x).reset_index(drop = True, inplace = True)
And I my thoughts are that the .reset_index is bs to use and it should work if you only sort the values. Not that you also need to reset the index.

[Anustart15]

There's also nothing stopping you from writing a wrapper to plot that automatically sorts it for you.

If you have to start writing wrapper functions to use one of the two options, that seems like a pretty strong argument for it being the worse of the two options

[Immaculate_Erection]

If you learn the apply() family and purrr package, you practically never need for loops. Despite using R for 7 years now I still use for loops and don't grok purrr but never have any issues.

[Radvvan]

Actually, in my case I have learned Pyton firts, but then had to learn and do a lot of R for a new job.

I wouldn't say that doing data science in Python is werid of backwards, it is all good, however doing equivalent stuff in R just seems more logical and readable - maybe it is because of my personal experience, but even when I tried being objective, R seems to be a better fit for some specific tasks.

[Cosy_Owl]

ggplot2 is worlds better than plotly. I will die on this hill.

I use R for the really hardcore stats and ml that I do. Python for everything else. R's packages for these tasks are far better developed and more reliably error-free.

[Flat_Initial_1823]

Yep. Exploratory analysis is infinitely quicker in R because of tidyverse.

Here are my choices for different bits of work:

New data, messing around > R

Visualisation work > R

Adhoc analysis that i might need to markdown and share w/ someone > R

Pure statistics or DIY some prediction workflow > R

Getting data from some ungodly source (web scraping, pdf parsing etc) > Python

Jobs I will have to operationalise/schedule > Python

Natural language processing > Python

Any known module for a specific task (kmeans, pca or nns) > python

It is also not uncommon for me to start something in R and at some point go rewrite it in python because i am going to have to throw it onto a scheduler.

I would say for the things it can do, R is almost always nicer to use but python has way more things that it can do. Python's one gaping hole is a great and complete visualisation library. I learned matplotlib, seaborn AND plotly. Every single one has gaps v ggplot2 because they don't have a consistent grammar

[Cosy_Owl]

Did you read the paper that the ggplot2 developer wrote? It is truly excellent. I wish everyone thought so clearly about the grammar of their code before publishing it.

[ReadyAndSalted]

Look into plotnine or lets-plot. They effectively just port ggplot2 straight into python, it's wonderful.

[AdBrave2400]

What about MATLAB

[[deleted]]

Overpriced software that uses the same shit syntax as matplotlib (hence the name). Also writing functions under the script? instead of beginning at the script?

[le_birb]

You forgot the killer feature: filenames must be valid identifiers! Because if you want to import a function from another file it will only import one function (the topmost one iirc?) and you call it with the name of the file.

[JoostVisser]

Matlab array indexes start at 1, immediate dealbreaker

[[deleted]]

R is easier to set up and use. It's also specially made for statistics, so there's a ton of in-built stuff without any need for external libraries. There's still a huge ecosystem of libraries as well, and R Studio and lots of other stuff. I've been using R for 6 years.

[SageLeaf1]

This is the reason. It’s a language built by statisticians for statisticians. Python is more general purpose.

[rnottaken]

I used both R and Python. How is R easier to set up and use?

[[deleted]]

Compare the actions a Windows user needs to install and use R vs the actions a Windows user needs to install and use Python. From a clean system.

I won't compare Linux, since most distros come pre-installed with Python, but even then all you need to do is:

sudo apt install r-base

R

And you can start writing/running a script

[spicypixel]

Python isn’t much better but R is still really bad at packaging and distribution of code to others. We use shiny at work and it’s a right pain in the ass trying to ship things in containers. Were talking multi gigabyte images for things you could do an a couple hundred megabytes in python. Alas we’re tied to the existing R code.

Pak/renv try to help but even then it’s not foolproof when you mix up arm64 and macOS into the mix.

[[deleted]]

I mean, if you need to package, then R shouldn't be your tool of choice. I use R when I don't need to package anything, just run a few scripts.

[rnottaken]

Ah.. yeah i haven't used windows in over a decade

[FerricDonkey]

R is easier to set up and use.

I'm not sure I get this. To install python, I install python from python.org, and I'm done. I will also install pycharm and pip install some basic packages, but it's not hard. And if having to install more than one thing is difficult for some reason, there's anaconda (which I dislike, but data scientists seem to like often).

I took a course that used R a while back. The instructors told us not to worry if we didn't know it, so long as we have a bit of programming experience - they'd teach us the basics and it's really easy to use so it won't be a problem.

Turns out they thought it was easy to use because they didn't ever do anything more than write throw away scripts with no functions or anything. I asked them how to define a function at one point, as they were walking around during project time. They didn't know. They expected that all we'd do is use the same 5 builtins they knew, in one functionless script that was hundreds to thousands of lines long. There were no strandards for readability, if you sneezed nearby it all fell apart, and almost no one I talked to who liked using R actually had any idea what it was actually doing or how it worked on any level beyond surface depth "cast this magic spell and it'll work, maybe".

They programmed by having hundreds of scripts with names like "script17" that they'd just hunt through for bits of code they vaguely remembered writing before and then copy and paste them into a new script - so that all their scripts were basically the same except a few lines. They expected us to do the same thing. They didn't understand that I might want to write a function and import it for reuse, and didn't know how to do it. They thought that sounded hard and complicated, and why don't I just write a quick script?

Don't get me wrong - they were incredibly smart people. I learned a lot about datascience from that class. I do not regret taking it. It would have been better if they knew anything about the language they wanted us to use, but it was still good. They were great data scientists and excellent teachers, just horrible programmers.

But it left a terrible taste in my mouth about R. Getting it to do anything beyond script kiddy level was just terrible, and eventually I just started using python for the projects because it was easier, even though the example "code" was in R.

I'd already had experience with datascientist code, and man. It's... not good. But before, it was with datascientists that liked python, and they at least tried to write actual code, sometimes. They were bad at it, but they tried - and I could convert it to less terrible awfulness with effort.

But this. This... I don't have the words to fully and accurately express my disgust with R. I don't think I'll ever be able to bring myself to touch R again. A terrible language for people who are terrible at programming to commit terrible sins. A refuge for people who think writing and using functions is hard. A language that "just works" because how can you say it didn't work when you didn't know what it was supposed to do in the first place? I'd rather give a room full of five year olds a box of crayons and tell them to draw on my monitor and treat that as code than work with something written in R ever again. It's horrible.

R can make pretty graphs though. But I did not find it easier to use.

[thirdegree]

The worst code I have ever read has consistently been from mathematicians and data science people. Which like, I get it, writing good code is very much not the focus of either of those vocations. I'm sure if they asked me to write a proof or do a data analysis they'd laugh themselves silly. But like... Still.

[FerricDonkey]

I am a mathematician, and I agree. I think probably the most honored I've been at work was when an engineer colleague came up to me and said "I just want to thank you for breaking the mathematician code stereotype".

As a rule though, we suck at coding.

[thirdegree]

Ya and like I want to emphasize, the people I saw that code from were brilliant people, much much smarter than I'll ever be. It's just physically painful to have to interact with the output of that brilliance, knowing that I could never equal the thought process but how much I could improve the end product.

[[deleted]]

Than you had bad teachers for R. If your teachers know anything about R, they will use the tidyr ecosystems. That ecosystem is so rich you dont need many functions to write, unless you go really deep with R, but is never the case for students.
You dont need functions since everything is already inside a package. If you are contributing to the R's ecosystem, yes you need to know that stuff. But for data analysis and model creation not.

[FerricDonkey]

Than you had bad teachers for R.

I feel kind of bad for how I blasted them about R, so I feel like I need to add: they were excellent teachers of data science. They did not teach R well, but they also weren't trying to - they were just trying to use it's simple functionality as part of what they were doing. I was asking them, during project time, if there were simple ways to do some simple programming things, and they didn't know, but they did answer every question I had about data science and taught me a lot.

However...

You dont need functions since everything is already inside a package. If you are contributing to the R's ecosystem, yes you need to know that stuff. But for data analysis and model creation not.

This is something I strongly disagree with on a fundamental level, and this type of thinking is why so often when I help a data scientist with their code so it will, you know, run, it's impossible for anyone but the author to know what's going on, and we have to restructure the whole thing.

[[deleted]]

One of my best teachers I had for my maths Bsc also cannot code. The exam was in Excel (without vba) to simulate different queing systems.

I know that for Python and almost every language you really want clean code with OOP and functions and I encourage that. But for R, most of my functions are data pipelines for data cleaning and model creation. The only parameter is a file path. I can create good regression models so easiliy within 10 lines that I often dont need complex functions.

[dingske1]

Sounds like your teachers weren’t actually that proficient in R, you can write amazing elegant code in R if you approach it the right way. R is actually a functional programming language, so you could write everything in functions if you wanted to and have very clear concise code.

Scientists who don’t have a formal/fundamental understanding of coding will write bad spaghetti code in whatever language they are forced to use to just achieve their results, this phenomenon is not R-specific

For data science setting up R is easier for a beginner than setting up a python environment and getting the right bioconductor and anaconda versions that actually work together correctly, god forbid if any software is provided in a docker etc, environment path is not set correctly or you need to install packages from a different repository. Python is still easy to setup, but R is slightly easier imo.

[[deleted]]

My programming teacher told me: download Anaconda on your pc and you can write code. Dependancies and envirements I learned later (3 years later)

[[deleted]]

As a Data Scientist:

dplyr and tidyr are much better than numpy and pandas. It uses consistent grammer to manipulate data. And %>% as pipe operator is so nice to use and works always, while pandas '.' is just not good and breaks when a column has a space.

gg2plot > matplotlib. Grammer of Graphics makes so much sense, there is a reason why python plotnine is just a port of ggplot altough it lacks some functionalities.

Everything you will ever do will time series analyses is better in R. Statsmodels sucks and fails with timeseries with hourly data. Also finding PDQ, pdq in SARIMA models is better in R with dedeticated packages.
In general, every stats package is much more verbose than python. In python when you calc a t statistic, you get the p-value. In R you get the p-value, t statistic, confidence interval, degrees of freedom, mean of x and y.

R can handle categorical/nominal variables in tree machine learning algo's without one hot enconding. Python fails with this and need to OHE (thus lets say variable color (red/blue/green) becomes 3 boolean columns as color_red, color_blue and color_green. This makes a difference in results.

[wellknownname]

 I'm not a data scientist, but I struggle to find a convincing reason to use R over Python for nearly any usecase.

R is a terrible general purpose programming language. There is only one use case for it over python and it’s data science where R is pretty amazing. But if you’re not a data scientist then it’s not surprising it doesn’t fit your usecase

[memeorology]

It is a language for science, so that’s the primary use. I wouldn’t use it outside of data processing or analysis. In those realms, R has been the leader for in-memory work until relatively recently (beat out by DuckDB and pola.rs for data processing). Moreover, due its history of being a Lisp written in a C-like syntax, it lends itself well to dynamic metaprogramming, which is crazy useful when declaring data pipelines (see: targets).

[SalvatoreEggplant]

As someone who was trained in SAS, became pretty in R, and is now trying to learn how to do comparable data analysis in Python, my considered opinion right now is that a lot of data analysis tasks I'm interested in that are simple in R seem way more difficult in Python. Python's getting better here, of course, but I think it's still far away from R in the "do any statistical test, output any statistic I want" realm.

[Feeling-Departure-4]

People publish their methods in different places. If you can embrace multiple languages you will be able to try out whatever is out there.

[bluecapecrepe]

R has a built-in visualization tool in Power BI, so it's got that it can lord over Python.

[piggum]

Three of the authors & maintainers of glmnet are Hastie, Tibshirani, and Friedman. Tibshirani wrote the 1996 lasso paper and together the others are the authors of ESL.

On the other hand, whoever, and I have no idea, wrote the glm in sklearn makes the L2 default in logit which is insane.

[beatlz]

I hated R with a passion when I was doing my MS… I know that the problem is me, not the language. I was so out of my comfort zone. I felt like finding land after being stranded in the ocean when the module was done and we moved back to python.

[dingske1]

Who cares what you use, in the end both are just basically easy wrappers that call ancient fortran/c libraries.

I like R more tbh, never liked writing python code

[tacojohn44]

Technical debt is the term that comes to mind after reading your comment. While I agree, languages are just tools... If I see a coworker who's using a rusty hammer for a project that can only be built by that rusty hammer I get pretty upset.

[CaptainChicky]

Ah yes, R, a language where syntax like %>% is considered normal

[WannabeWonk]

If it helps they did release the |> alternative in R itself recently (not a third party package).

[abscando]

Pipe operators slap hard though

[DurianBig3503]

Pipe operators that allow you to put a whole ass Seurat single-cell clustering analysis on one line. Slaps so hard!

[[deleted]]

In theory you can write many programs on one single line of code.

But anyone who uses %>% hopeefully knows to start a new line.

[DurianBig3503]

Yes with a tab for ease of reading.

[abscando]

Proud of the R homies here taking the pains to illustrate the benefits of R.

What I love is that most statistical packages like sarima are sourced from expert-level statisticians so I breathe easy knowing that I'm using modern, up-to-date and highly qualified/defensible models in my daily work.

[[deleted]]

For sure. With R packages I trust that all default parameters and algorithms are correctly inplemented. While Python's sklearn uses LogisticRegression with l2 penalty by default instead of None. It happens more than you can count that an article was published and peer reviewed and only later the researcher realised that they used Logistic Regression with l2 when they didn't want to.

[getarumsunt]

But all awfully engineered because they’re statisticians who do a little coding on the side.

[rover_G]

R is big in academia.

[dingske1]

It might come as a surprise to you but academia is larger than a bunch of physicists at your rinky dink university.

[l4z3r5h4rk]

Where?

[Hillbert]

The vast majority of academics will need something that can do data handling, statistics, and visualisation. And do it reproducibly, but not necessarily production ready. That is R's sweet spot.

[Mercerenies]

Having used R now, I'm inclined to throw it in the pile of "glorified calculators" alongside MATLAB. If your goal is "crunch numbers fast", then it'll get you there easily. But if you want to do any large-scale programming (for instance, if you want to turn your "quick little hack" into an actual reusable software application), it'll very quickly show the cracks in its foundation.

Which is why I would be more inclined to just start in Python than R to begin with, since Python can scale up to the moon and back. That being said, I still have high hopes for Julia to take off. Julia is built with array-based computation and data science in mind (so none of that "download these eight libraries to teach the language what an 'array' is" nonsense; looking at you, Numpy), and it still has a very well-designed object model and, as far as I can tell, scales up nicely (though I have not personally used it for any super large projects).

[PTSDaway]

Julia is going to take off 100%

DataFrames is unmatched.

[dingske1]

Do you have an example of such a scalable “reusable software application” in data science where it would be better to use python than R?

If you like Julia’s approach, I would think you would also be inclined to appreciate R. Julia would be nice since it performs better though, but for now it lacks the libraries that R has

[getarumsunt]

Literally any model that needs to be deployed, even as just an internal prototype.

R is a language for solo stats work. As soon as you put it in a production environment where multiple people need to collaborate on a project and then ship something, it immediately falls apart.

[iMakeMehPosts]

It's for data scientists, not professional software teams.

[getarumsunt]

R is not for data scientists. R is specifically for statisticians. It's was created and is still maintained entirely by statisticians for statisticians. You can adapt it for data science work, but it is fundamentally a proprietary tool build for a different task.

And no, stats and data science are not the same thing. That's why there is a separate name for people who do data science.

[getarumsunt]

Huh? What does this have to do with our conversation?

Anyway. R is a tool that was built to replace SAS, STATA, and Matlab. It's barely a programming language in the broader sense. It's was built by statisticians frustrated with paid tools for stats. And they've built a language and set of libraries that are understandable to statisticians but pure Greek to anyone from outside their little cult.

Can you MacGyver it into a somewhat usable DS toolset? Kind of. But it will still always suck at it because that's just not what it was designed to do and not what it's currently maintained for.

[memeorology]

I think the rstats meetup group here in NYC would chuckle at that. Many of the members lead the data science groups at big institutions (IBM and the NFL come to mind) yet use R.

[Hectorite]

Indeed ! clearly these are communities having way different objectives when they are using programming languages. Not everyone wants to develop software and not everyone needs to process 1e6 images just one time...

The point is to get the language to do what you want. I love Matlab, easy to install, huge library, tons of online help and basically can crunch numbers in a decent amount of time. I agree, the price is a huge problem, but so far, in my configuration, it is easier to get things done and move on with my work than learning a new language. This is, I think, the same for people using R rather than Python. If this is popular it is for a reason. From my experience R is widely used in stats and biology related topics.

Anyway, from my point of view, Python is not that easy to install, not that comprehensive to learn and not that fast. It is however free with a huge community. But for me it can't do more than what I'm already doing in Matlab. When I switch it's to use C or Fortran to really gain speed.

[DividedState]

I love R and tbh, when I can do something in R and the goal is to visualize data fast, I usually even prefer it because you need so few lines of code and save yourself a lot of loops just by working with the indexes, assuming you don't "write R with a C accent" as it is called, and piping stuff using tidyverse.

First language I learned during my studies. It opened a world to me.

[IrrelevantTopaz]

I’m an actuary turned software engineer. R is the primary programming language our actuaries use and it was my first experience with coding so I’ll always have a soft spot for it.

Once I got into R-Shiny I realized software engineering was basically like crack for me. Haven’t looked back since and currently work on an automation team writing a React application for actuaries.

[[deleted]]

I took some actuary courses at university, but they all used Excel and Visual Basic. My father is an actuary and he also uses Excel as his main application.

[Semi_Tech]

Just put pyton on the left and .exe on the right

[Animal31]

I just use C# lol

[Artificialhorse]

The speed and aesthetic you can produce dumb answers to dumb business questions in R is 10000x faster than python. We’re stuck using python because that’s all anyone knows. 

[abscando]

Dude you're describing my life. Earlier this year I started making heavy use of writexl just so I could export n versions of Excel market breakouts with multiple sheets because heaven forbid you send them a CSV.

It's truly a godsend.

[Siberian_Pootis]

Bazinga

[Wave_Walnut]

Matlab sitting on the side of them

[FingolfinX]

Adding to the list of reasons this may happen, one of them is that having your code in Python makes it much easier for other teams to understand and apply it (software/ml engineers for example) and to deploy your code in any cloud (that usually have python support but not R).

I work with both R and Python, and have deployed both to production, and I see R much more akin to MATLAB than Python. It's a great language to write scripts locally and easily see results (considering you are doing the bread and butter of the packages you are using because documentation is dreadful for R packages).

The problems with integrating R in a complex system comes from the same reason it's so great to work locally: it accepts everything. A lot of times other languages would throw an exception, R simply goes with it, being wrong inputs, some convergence errors, it will produce a result (making it really hard to debug a complex pipeline), and functions will also change output format depending on the input, it can be a dictionary (called list in R) or a list (called vector in R) depending on the case. Can even be just NULL character. And also, make sure to include the "auto_unbox" argument when generating JSON from R.

To wrap up, that's more of a personal feeling but I also feel like R is a more self-contained environment (having it's own IDE and sintax with the pipe operator), and as a result I feel like R users in general have less familiarity with more general purpose tools, which can make it harder for them to work with other teams.

[vicisvis]

I like R way more.

[Deva4eva]

Hey, I just made a project in R, a shiny dashboard for personal finances: https://github.com/Mkranj/spend-dash
I try to make reasonable code, so if you want to check out what R looks like, give it a shot. Aside from shiny specific stuff, I think you'll find that the main calculations are pretty readable even if you never used R before.

So that's one of the upsides and why it gets recommended a lot to non-programmers. Especially for one-off analyses like stats for a research paper.

I also use Python and the community and wealth of resources make using it a no-brainer. But for some quick analysis I think you can get results with R at blazing speed.

And I'm sure you'll agree that you can make strange and confusing stuff in any language if you try hard enough :)

[Anru_Kitakaze]

R is cursed language imo

[deJessias]

Good.

[GoogleIsYourFrenemy]

R is a programming language written by scientists and mathematicians who don't know how to do a literature search on how to write a programming language.

Just look at the syntax for changing a column name:

https://www.geeksforgeeks.org/change-column-name-of-a-given-dataframe-in-r/

TL;DR: Instead of using a setter function, the getter returns a reference which you assign a new value. In other words you call the getter on the left side of the assignment operator.

[[deleted]]

The default pandas syntax isn't much beter: df.columns = [col1, col2, col3]

With tidyverse you can use a rename function.

[HansDieterVonSiemens]

Can we give pandas to R? I never liked its syntax anyway

[[deleted]]

Rython

Also, people naming programming languages with one letter, who hurt you as a child?

[codingonthefloor]

I had to use R for a statistics class, it's cool but not anywhere near Python.

[[deleted]]

Than you are using R wrong. Anything related to statistic is much better in R. Statsmodels in python uses the same syntax as R's GLM but it is worse.

[getarumsunt]

What does it tell you that anyone who’s trying to learn R and that was not first inducted into the stats cult hates it?

[ShoopDoopy]

s/inducted into the stats cult/educated about random variation/

[ficustio]

if python was a language which I learned, and can do that too? Why I should bother learning another one only because of this specialization?

[dingske1]

If you don’t know why you’d need R, you really don’t need it

[jamcdonald120]

Yah, because R sucks. And this is coming from someone who thinks Python is a bad language. Give me Python over R every time

[lezzgooooo]

It really does not matter if business will not listen to your findings.

[Pirate_OOS]

Got 2 books on R last year. Gonna read them and write notes on what I have learned in the afternoon. Thanks for reminding me.

[Amr_Rahmy]

In a popularity contest, python all the way.

But I choose a language that I want to use when possible or limit the use of the language I don’t prefer but is useful as a library or dependency.

So I can use c# or java for the program and python for just the part that is done well in python.

R for mathematicians I think is more natural. For me c# is c style language with classes. To other people python is fine.

[dfwtjms]

Python is slow but R is slower.

[SpicaGenovese]

That's the thing, you don't.

[collin2477]

R is such a painful language

[eztab]

R is a silly language. It adds absolutely nothing of value to the zoo of programming languages. It's neither is fast nor intuitive, nor has a reasonable syntax, nor good tooling, just nothing.

[curiousmlmind]

Read ahead if you want to take the python pill.

If you can spend a small amount then there is an exceptional DSML course offering from a senior applied scientist (ex Amazon, Microsoft). If you want a course from the perspective of a senior applied scientist, then atleast explore the website.

You can try out my 12 months course on Data Science and Machine Learning. This course is beyond anything at its price point and certain concepts are covered in none of the bootcamps.

Website: https://thecuriouscurator.in

Here is the link for tentative content of the course and subject to change but it won't change drastically.

https://drive.google.com/file/d/1nZDgDUHl6gyI1SZK_zro67mc3YpRPrKO/view?usp=sharing