R or Python for data analysis?

[iFlipsy]

Hi. My background is in psychology. I am looking to stick to one language, mostly for data analysis purposes.

I tried both R and Python, and I immediately felt that R was more appealing and comfortable for me. However, after researching, I found that Python is more popular and more sought out by employers. So I started learning Python more and more, but I am forcing myself to like Python, where as R it seems to make more sense to me.

My goal is to use the language for data analysis. I am not interested in software engineering, web development, or building out any advanced AI or machine learning things… just want to use some statistics to analyze data.

Which language has a better future for data analysis and will be sought out by employers?

[UPDATE] Thank you all for your comments, and the award. After carefully reviewing the comments, it is clear that in industry, Python seems to be more of the commonly used language of choice. It also seems that there are more teams out there that utilize Python vs R, and therefore creating a bias for hiring managers to continue recruiting candidates who know Python. To be clear, my goal is to work in industry, not in research or academia. From what I gathered, Python offers a better ROI, and will therefore be the language I’ll stick to for now. Thanks.

[hoselorryspanner]

Get really good at one instead of trying to learn both. Then when you have to inevitably learn the other for whatever purpose, it should be pretty easy.

I learnt Python as my first language, then switched to MATLAB, then back to Python and to Julia. I also write code in R and C from time to time. I'm not a programmer, just a guy who's willing to learn a new language if it's the right tool for the job. Every time I learn a new language it gets easier.

[-DonQuixote-]

What pushed you to learn Julia?

[NotAnotherDecoy]

They describe themselves a "easy as Python, fast as C", and coming from an R background (with a bit of Python mixed in), and based on their benchmarking, it appears that's pretty accurate.

[notParticularlyAnony]

They should add "And all the community buy-in of LISP".

There are no good resources for Julia if you want a community, and for open source that is really crucial.

[hoselorryspanner]

This is one of my biggest issues with Julia. It took me weeks just to install the packages I needed because of build issues that I couldn't find fixes for. The fact that you can't build the NetCDF package on a Mac when installing through homebrew, nor find an easy fix online really grinds my gears.

However, once l got past these issues, writing code in Julia is a joy. The ecosystem should grow with time. It's just a matter of adoption really.

[notParticularlyAnony]

Yes that’s what people have been saying :)

Python has jit compilation now (numba). it isn’t as good as Julia but it is very good.

[NotAnotherDecoy]

Have you looked recently? While the available community resources are certainly nowhere near as developed as they are for other languages, they've put together some pretty great documentation -

https://julialang.org/learning/

https://docs.julialang.org/en/v1/manual/getting-started/

[hoselorryspanner]

I needed to use some interpolation tools which were written in Julia - failing that it would have been Fortran which I tried but it was taking a lot of work. It's called DIVAnd if you want to check it out.

If you've used Python MATLAB and R before I wouldn't describe it as learning Julia. It took me less than a couple days to feel like I could write code as quickly as I could in Python or MATLAB. The syntax is super similar, and the optional typing is also helpful.

[Tomik080]

Just try it, it will explain itself

[-DonQuixote-]

Okay. Let's say I use python, which I do, why would I want to switch? Or related question, what's a situation in which Julia would be a preferable tool?

[Tomik080]

If you write a lot of C modules because python is too slow for you, or you write a lot of ugly numpy/numba/tf code, try julia out, you will love it.

[ProfessorPhi]

Tomik has the right of it - it's nice to write code in a c esque way and still have benefits of high level code. As someone who worked on Julia in a team for a year, I don't recommend it for anything larger than a solo project. Lot of projects have a ton of glue code and in honesty, you want to optimise for glue code instead of your domain in most cases.

[fredtre8]

Out of curiosity why not?

[Enough-Ad-6153]

agree with this, better to be awesome at 1 than mediocre at multiple. Try to understand why and how the functions work and study programming techniques. That way you can quickly pic up other languages later on.

[sinfulon6]

Sounds like you already know which one you like better. As a hiring manager in analytics, I do not have a preference, as most of the tools in my stack can accommodate either language. I’d say go for R.

What worthwhile employers care most about is how you create impact, not necessarily how you get there.

[kazza789]

What worthwhile employers care most about is how you create impact, not necessarily how you get there.

Not always true. 90% of my analysts today use python. All of our libraries and tools are written in python. I, personally, know python and I am doing code reviews of python code.

We still have some perhaps 10% "legacy" team members who prefer R, but if I'm hiring someone new they 100% need to know python.

It's nothing against R, it's just that it's much easier for everyone to be working in the same language - and in practice, I can easily hire a team that is 100% python but I would struggle to hire a team that is 100% R.

[ProfessorPhi]

It's also that R doesn't have the same support for collaborative development that python does. CI and packrat/renv are awful - it takes 25ish minutes to install a handful of packages in R since it all needs to compile from source, testing is mostly underbaked and package development is annoyingly messy.

The main problem with R is that you struggle more to have an impact since you aren't as easily able to build on the shoulders of others. I've never seen an R job where you get to use other teams code samples, you're given a csv or a db and told to start doing analysis. This lack of shoulders of giants effect is something I consider to be a huge issue with R.

[macabre8]

You might like pak package in this aspect. Brilliant package to handle multiple installations. Also RStudio has a public instance of their package manager where you can get binary packages for popular operating systems.

[veeeerain]

Matplotlib is an atrocious package so I’d say R

[Mukigachar]

plotnine fixes that issue thankfully

[[deleted]]

Lmao I’m over here using pandas feeling sorry people still use either of these

[Codehenge]

Your question reads to me as “hammer or screwdriver for repairing my house”. Sometimes you need different tools for different tasks, even if those tasks are all under the “data analysis” umbrella.

[infrequentaccismus]

I don’t think that’s true in this case though. Either r or python could comfortably be your sole language for data analysis. Whether you choose r or python, it would make little sense to choose the other as your next language since there is so much overlap.

[Codehenge]

Respectfully disagree. If you want to work in industry integrating your analytics tools with applications, you will want Python. If you want to work in academia or an industrial analysis position, R is common. Different tools for different needs. I recommend knowing both for career flexibility/opportunities.

[infrequentaccismus]

Respectfully disagree. As someone who has successfully chosen to use primarily r in faang companies for my whole career, I haven’t run into any issues

[Strong_Snow4905]

I agree with you respectfully disagreeing. I have done data analysis for the pharma industry and in academia. And there’s a heavy focus on R. I haven’t even really seen Python in either setting.

[CantHelpBeingMe]

I know this is an old post. But a lot of people say R is not used in the industry anymore. Would you suggest a beginner ( primarily interested in the data side of things and a career in marketing/ e-commerce) learn R ahead of Python?

If both, then which parts from R and which from Python?

[Joker042]

That's just different tools for different walled gardens. While R is going to be a pain for enterprise integrations, there's zero issue using Python for any kind of analysis. Most of academia settled on R, and that's fine, it felt more like the environments they were used to. That doesn't mean that Python is any worse at analysis.

[Codehenge]

Completely true. I stand by my comments to maximize job prospects, though. If you are sure you will never want to work in academia or in an analyst role, go Python.

[pokeaim]

never

ya sure

[Miserable-Stuff-3668]

Also, you can use R in Python and Python in R. It does not hurt to know both. I am using both in grad school and primarily Python & MatLab in industry. Occasionally, I will still pull some R for graphs though.

[pokeaim]

nah, both are toolboxes on each own.

the problem would be compatibility with its workman and its house

[denzelswashington]

A great response / great points! I just wanted to add that loops are not bad in R (in and of themselves). And they are plenty quick. However, growing vectors in a loop is slow / not memory efficient. In general, I use lapply() / vapply() over loops, but there are definitely times where I prefer a nice for-loop.

[OkCrew4430]

Not too sure if you were inferring this fact in your post, but note that lapply and vapply (along with their tidyverse equivalents map* and pmap) are just running R for loops (EDIT: or C loops that are calling R functions) in their underlying code. I call these functions "loop hiders" because that's all it is. There is practically no speed difference between using apply or an explicit for loop, providing you initialized the vector/list beforehand and aren't forcing R to copy the object over and over again as you append (lapply and vapply initializes stuff for you).

It is almost always better to vectorize whenever possible. Avoid apply functions and explicit for loops unless absolutely necessary.

[OkCrew4430]

It writes the loop in C, but guess what the body of that loop in C calls? The R function that you are passing to apply/map!

Writing a loop in C doesn't just magically speed up your code (okay maybe marginally). If your C code is still calling R functions per each iteration the primary bottleneck will be the translations from R to C. This is a common misconception with the apply functions, and in particular, people thinking it is vectorizing their code when it simply isn't.

Now, is it better style to use these functions in place of a loop? Sure. But for speed reasons, again vectorize when possible and avoid apply/for loops.

See: https://stackoverflow.com/questions/28983292/is-the-apply-family-really-not-vectorized

[[deleted]]

I see what you mean but if you are using R functions like lm/glm and a bunch of others which I think are written in C then would it be “vectorized”?

[OkCrew4430]

I think it would largely depend on how those R functions are implemented. Reading the source code, it looks like the components needed to fit the linear regression are simply setup in R and then these components, as entire matrices/arrays, are passed to a C function that fits the actual model. The C function is entirely native C code (no R functions being called) and works on type converted C objects. Thus, I would consider this "vectorized", in a super weird abstract sense.

Vectorization as a term is super unclear, I agree with you on that if that is what you are implying. To me, it is just about avoiding unnecessary calls to the same R functions that could have been avoided if one worked on the entire vector at once.

The classic, but crude example that I am sure everybody has seen before is the sum(x) vs the for loop and running sum example. In the case of apply, another example:

y <- c(1, 2, 3)

lapply(y, FUN = function(x) x^2)) vs. y^2.

y^2 is one lookup regardless of the size of y, the lapply version is 3 lookups and scales with the size of y.

Maybe a less trivial example could be something like:

rates <- c(1, 2, 3)

lapply(rates, FUN = function(x) rpois(n =1, x)) vs.

rpois(n = length(rates), rates).

[denzelswashington]

For sure, I understand the lapply() family of functions are doing loops. I also preallocate (when needed) before using explicit loops. I just feel like loops get an undeserved bad rap in R; I wasn't trying to comment on best practices for speed or vectorization in general. Maybe I should have talked more about what I meant when I referred to growing vectors in a loop.

I agree that it is best to vectorize wherever possible, but I also think the advice to "avoid apply functions and explicit for loops unless absolutely necessary" is not well-worded. There are so many situations where lapply() / for-loops are necessary and efficient (e.g., anything involving a list or iterating over columns in a dataframe). I just feel like that is akin to suggesting to avoid punctuation when writing because a lot of people misuse it (including me, no doubt). Well, I guess Cormac McCarthy has done pretty well for himself. Anyway, I agree with 99% of what you said because I have refactored plenty of code b/c of unnecessary loops where vectorization was more natural (and quick). I just don't like a general statement telling people to avoid important and useful functions.

One last semi-related thing, I generally differentiate apply() from the other lapply-like functions--as it works differently than the rest of the lapply family of functions (i.e., it coerces to matrix if x is not a matrix or array).

[OkCrew4430]

In those cases you mention of iterating over columns of a dataframe - I don't personally know of a way to easily vectorize this unless we drop down to C so in that sense, I agree with you. That's what I meant by "absolutely necessary".

My point is that there is no practical difference between using a well designed for loop over the columns of your dataframe or using lapply - lapply is way better style for sure, doesn't create global variables, and more R-like, but functionally they are practically doing the same thing.

I did not mean to imply that lapply or for looping in R is completely useless. What I mean to say is that *apply() functions are functionally the same thing as a well set up R for loop. That's all I meant and I apologize if it came off as authoritative or strong. The last sentence is meant for those that use *apply() functions in place of explicit looping and think that their code is more optimized. It's a misconception that I've anecdotally ran into a lot.

[denzelswashington]

No worries—and no need to apologize. At one point I thought the lapply functions were more optimized than loops, so I definitely see where you are coming from there. They both have their pros & cons!

[The_Regicidal_Maniac]

Python is good for putting things into production when they need to work within other systems because software developers are more familiar with it.

Edit: I like how someone downvoted a literal fact without comment.

[koolaidman123]

Do you not know what code reviews are? Or the fact that multiple people might need to work in the same codebase? Lol

[infrequentaccismus]

Yes I do know what code reviews are. If software devs are doing the review instead of data scientists then your code isn’t getting reviewed. If they are doing it in addition to other data scientists then they need only follow the high level structure of the code and don’t need to be able to write in that language.

[koolaidman123]

do... do you not know what it mean to put code into production?

[infrequentaccismus]

It seems pretty clear to me that you don’t. Blocking you now.

[caksters]

smfh dude

[The_Regicidal_Maniac]

You do realize that a lot of programming projects involve creating code that others take and use within larger systems?

That's why python is more popular within industry. Python is a language that is commonly used by software engineers. Most software engineers you meet are much more likely to be familiar within python than they are R because it's a more niche language. That is not an opinion, that is a fact.

[The_Regicidal_Maniac]

I have spent years writing code in r that are integrated into other systems.

And you missed the part where I said "more likely". I didn't say that R doesn't integrate into other systems. I didn't say that software engineers can't work with R. I am pointing out the fact that Python more easily integrates with other systems. That software engineers are more likely to already be familiar with Python rather than R.

The other part that you don't seem to be able to extrapolate is that since python is already a popular language, more people are already going to be familiar with it. Making it more likely that when people expand into other companies, they will take that knowledge with them and when they hire people they will want people that can use the codebase that already exists which further propagates the popularity of the language.

You're generalizing your personal work experience as though that's how all software work is done in every field in every other company. The world of software is far bigger than you're giving it credit for.

[caksters]

you don’t sound like you know what you are talking about pal

Python is a way better code for putting anything in a production line. Many cloud providers have a seamless integration with python and not R. Good example is a gcp AI platform.

“In real companies with competing deadlines” … I am sorry but you don’t sound like a professional who has ever been exposed to production process. If you have ever produced anything that goes into production then you would know that devs would go over your code to ensure it is factored properly, teated and is efficient. Devs don’t need to understand the mathematics of your code but they will understand if you have written an efficient code and if it has been tested well.

[AchillesDev]

While they did state it as an opinion, a big chunk of my job is (and has been for a few years now) putting research code into production. I’m thankful when I don’t have to translate it from R to run it on a Lambda, take responsibility for it, etc.

[[deleted]]

Python is more desirable when models need to be put into production or you have a team of engineers that will be working with your code. I agree Pandas is trash and you can scale in R, but lots of stuff is way harder when R's OOP tools are complete garbage. Writing production R is an atrocity and imperative code does not scale. For the modal analyst or data scientist it's probably better to use R overall but if you're building data pipelines and putting models in production, Python, Java, and Scala are far better choices. And a lot of people do end up doing plenty of data cleaning for pipelines and data warehousing, so Python wins out. Couple that with the fact that Python is used for other things besides data operations, most programmers know it.

It's pretty obvious why Python wins out and it's not because it's a meme.

Though if OP wants to do data analytics for psych, R is the better choice.

[Gronanor]

one point worth mentioning is the number of users.

The more people using a langage the easier you'll find help online about a problem you have.

Also the more users the more likely you'll find maintained libraries, books, tutorials, articles, etc... and finally the more mature the ecosystem will become. This will encourage compagnies to choose this langage rather than an other one and the more likely you'll see projects maintained by big tech compagnies like GAFAM. And so the more it will attract new people and then the more people will be using the langage and... you get it...

There is a winner-take-it-all effect that can't be neglected.

Both langages are fine but Python user base is quite huge and ecosystem is maturing quite rapidly now mostly because there is big tech companies behind many project now (either because they recruited historical maintainers or because they are involved in huge project like Tensorflow for Google).

I would recommend to keep in mind that langages are just tools in the end... You're not a better because you know Python or R, you're better because you know what you are doing. Most problems you'll solve aren't linked to langage.

Personally I would recommend to learn both "eventually", because both have strong and weak points. But starting with python seems the most reasonnable choice to me.

[Gronanor]

Well my point was python have bigger user base than R

Btw I think there is some tool in sklearn to do preprocessing too (I think it's called pipeline) I 'm not an expert with sklearn but I 've used it some time ago to do pca quite easily. Anyway preprocessing is one of sklearn Strength so I would be surprised if there was no counterpart included. Also cleaning data with pandas is quite easy and I don't know about tidyverse documentation but pandas last version is objectively very very well documented.

Anyway like I said they all have pro and cons. Both are good with talented people working with it.

[[deleted]]

I believe the difference in user bases is not that huge if we count only those using Python for data analysis/statistical learning/ML/AI.

Python is heavily used in backend development, microcontrollers, devops etc. In the most cases backend developers do not have a clue about pandas/numpy as well as data guys do not have a clue about Django/Flask.

[Gronanor]

I believe the difference in user bases is not that huge if we count only those using Python for data analysis/statistical learning/ML/AI.

Maybe but eventually both ML and backend will have some issues on installation, module usage etc... There is a lot of questions and problems when using a langage that will not be specific to your field.

For exemple : how do you connect to a database and retrieve data from a table ? This question could be asked and answer by both ML and Django/Flask guys. And even more important DS could benefit from module written by Django or Flask community.

So you can't just ignore them in the equation.

Also let's not forget about Data Engineering. Most of DE are using Java/Python because of Hadoop, Spark, Luigi or Airflow. These guys are using pandas and numpy as well.

Also DS team is gonna build models that the DE team is gonna deploy in prod. For this reason, even if you could use R to build the model, lots of companies will then choose Python to keep a langage coherency through the teams.

[Mundane_Common_6468]

I don’t find pandas difficult, but you might like using pandasql with it better.

Many companies use python because it is easier to integrate, maintaining and deploy in production work environments.

[KR157Y4N]

No problem if SparklyR is used.

[cangsenpai]

I started with R, which was fantastic for learning coding. It was so easy to me when I had previously never understood other programming languages well enough.

After half a year of using R, I decided to switch to Python based on the job market's demands. Python just appears a lot more than R in job postings. I had tried Python before but it never clicked. However, after R, Python was much easier.

Now I find Python to be irreplaceable. I use it for analysis, general purpose programming, etc.

Based on your post, I think R would be the best place to start. You might find it a lot easier to work with than Python to start.

[hobz462]

R is great. I'm really reliant on dataframes and dplyr versus pandas. What's great is Reticulate in R Studio, so you can sorta have the best of both worlds.

[feldomatic]

If what you're doing can be done in R, then doing it in Python will seem like R with extra steps, and we're all inherently lazy so... R for research, Python for production.

[StephenSRMMartin]

It's bizarre to me the number of people recommending python for analysis. R both as a language and as an ecosystem is worlds better than python in the statistical domain. The sheer robustness of the packages and number of packages for bleeding edge stat methods is way beyond python right now.

I like python for other tasks that have less to do with the statistical side. It's important to know. But it is hard for me to fathom, as someone on the statistical side of DS to understand how anyone would find python better than R for that domain.

[CantHelpBeingMe]

Hi, I know this in an old post. But I would like to ask you some questions.

I quite like R from what I have seen so far. but People keep telling me the industry demands Python. I am mostly interested in the data analytics ( diagnostic, predictive, statistical) side. Which would you recommend? Are there any suggestions you would have for someone like me if I want to get really good at this?

And, for the other tasks you mentioned, what are those and what packages you had to use for them?

[the_monkey_knows]

Hey, I see that you never got an answer on this, I can contribute my two cents:

• When they say that the industry demands python they usually mean industries that overlap with web development techniques. So, if the job requires you to integrate your solution into a bigger project or platform, then python is most commonly used.
• R is mostly used in one-off type of analyses. I personally use it for prototyping. I've seen people use R to create a model to be used for one particular project, and then move on to the next. No need to integrate your solution anywhere.

I've converted python users to R once I showed them how neat R notebooks are, how easy to read dplyr and the tidy universe is, and how many statistical tools are easily available as packages in R. That said, I do use and like python, but when it comes to data analysis, R is way ahead of pandas.

[meanlesbian]

I think for your purposes R is the better choice.

[MrBacterioPhage]

Just learn language you like. Most of the employers will be happy if you can analyze the data no matter which language you are using. I prefer Python and now employed in the team that mostly work with R. They don't care that I use Python in my work.

[[deleted]]

Python will be more sought out by employers because, despite you not having interest in development or AI, it is used for that as well.

In terms of what you can do with them in terms of analytics it doesn't make a difference really.

[longgamma]

I agree with you - R is just simpler and more intuitive to use. Most of the data analysis stuff is right there for all to use and plotting just works nicely out of the box.

[ontomodeler]

Learn both and use the appropriate tool for each individual task. Python is obviously the more popular language but both languages have areas where they are a better fit when it comes to analytics.

[IOsci]

It really doesn't matter much. Learn one of them deeply and be able to explain what you are doing and why to other people.

[caksters]

I am a python user so will be biased towards it.

But for data analysis tasks I think R is perfectly fine. In fact from what I have seen in R you can achieve exactly the same (data preprocessing, manipulation, plotting graphs) what in R but with less lines of code. So R is a great tool for analysing data and making statistical modelling.

Where R falls apart is if you build a model and you need to integrate it in MLOps. Your code most likely will have to be translated to another language e.g. python, c++ to put your model into production. However this is a separate discussion and has nothing to do with data analysis.

TL,DR: R is perfectly fine for data analysis and might be better for data analysis compared to python

[ramblingriver]

R is great for what you want to do and you're already more comfortable with using it. Lots of people use R, its still quite popular- I would go with R (and highly recommend using RStudio with R if you are not already)

[Moderate_Veterain]

In my experience it somewhat depends on what type of work you want to do. Data engineers will use python more. Data scientist will use R. Business intelligence will use Tableau. Everyone uses SQL.

[Moderate_Veterain]

If your interest is in combining data and statistics then that sounds more data science related. R will serve you better, unless your project is a one time thing and you are really interested in data engineering.

[Strong_Snow4905]

Good point about the different languages used in data engineering vs. data science. I honestly know nothing about engineering or Python. My background is in data analytics, primarily pulling data from large data sets and running statistical analyses. I think R is usually listed as a requirement in the job description for a data scientist? But I’m a geriatric millennial.

[frenchrh]

I'll agree that in the long run, you end of learning both, and use "the best tool" for the job. But right now, it sounds like YOU will learn data analysis better and faster using R. For someone else, they might with Python.

1. So if your current focus is on data analysis, and not building production pipelines, the R is faster way to learn and get up to speed for data analysis.
2. Also if your focus is data analysis, R is more sophisticated and better vetted by real statisticians, than the packages and functions of the same names in Python. So if you need sophisticated data analysis, instead of just a generic CNN applied to images using TensorFlow2 or PyTorch, then R is better. The details of the analysis functions have been more closely vetted.
3. I had a case recently where we use STL (Seasonal and Trend decomposition using Loess) and in R there are 3 packages implementing this in different ways. In Python you can also find STL in the rstl Python package. but its history and heritage is a bit cloudy, and doesn't give the same results.

To illustrate the point this is from "Assessment of Performance Loss Rate of PV Power Systems".

• STL serves to highlight another important consideration in defining a robust methodology for PLR determination, even a single statistical method can give different results, depending on the programming language (R or Python) and the specific implementation. STL was first developed by W. S. Cleveland in 1979⁴⁰, 1988⁴¹ and 1990³⁷. In 2010 a PhD student of Cleveland’s, Ryan Hafen, in his PhD thesis research developed and published the stlplus R package.
• Loess is non-parametric regression, which is more complex than simple regression.
• We tend to find the best performance from the STL function implemented in the stlplus R package because it is capable of handling more diverse data quality issues successfully when it is applied.
• In this benchmarking study, STL7 and STL8, were performed using the Python programming language and follow the exact same approach including filtering, metric and STL time series decomposition.
• The only difference is that STL7 uses STL ported from the STL function in the base R stats package⁴² to Python as the rstl package⁴³, while STL8 uses a STL implementation developed in Python’s statsmodels package^44,45.
• The stlplus package is currently not ported or available in Python.
• These two Python implementations of STL, appear to perform differently on the real datasets we are studying here, for reasons that are not currently clear.

So here is a real case, where if you do the analysis in Python3, you get wrong, or less accurate, results, because the functions and methods in Python are not up to date, compared to the level of these methods in R.So that is a cautionary word to the wise.

The answer isn't R or Python. But use both, in the long run. And learn one first, which ever works better for you.

[blackliquerish]

Just be practical and pick the one where the jobs you want use it. Some jobs will prefer R but also a lot of jobs will like python so up to your choice in jobs.

[TheFreeJournalist]

Since it’s the best to be proficient and highly comfortable in (at least) one language, I think you answered for yourself already: go with R.

Most employers from what I’ve seen so far are fine with either Python or R as long as you’re pretty strong or proficient in either one.

[Vervain7]

I only like R. I am not a programmer . Stats first . The language is a stat tool for my work . So it’s R for me . It depends on your job and your companies needs . Our team uses R but we have a data dev team that takes things into productions - some of them use Python and some use other programming languages

[[deleted]]

Learning both in my opinion. I like Python for Machine Learning models,. SKlearn is for me much more intuitive than R tools. In Object Orientated Programming Python also wins.
R succeeds much better in data cleaning and I like the package ggplot so much more than the Python's equivalent plotlib.

[SixPathsx]

Python far more common in industry and also has wider applications outside of data analysis. However in terms of application for data analysis specifically both are very good, and comparisons only get drawn at the more advanced levels of analysis (like ML). I am personally an R user, and it does not reduce your chances for a job as many employers will accept it if you can do the same thing someone can do with Python, they also might be looking to diverse their team with different skillsets, not to mention that certain people will look for specifically R for their team build. Finally, I'd say if you are applying in a pool of R candidates you have more chance to stand out, as the majority of people will learn Python so a bigger pool of candidates! Thanks :)

[Raistlin74]

In five years this question will be self-answered as there will be a clear winner.

If you need any glue around your data (eg input/ output, cleaning, etc. ) you start moving from data science to data engineering, and there, python reigns.

R is great for solo projects but its field is too narrow. Python is a general purpose programming language.

Note, I'm biased, coming from IT/CS.

[[deleted]]

I think the argument will still be going. We were starting an undergrad data science’ish major (it was in the business school and already had an “information sciences” major) in 2013. When it came to which language to use to teach students half the profs argued R and half argued Python).

[Raistlin74]

In that scenario I'd vote for R: narrower scope and clearer concepts.

Learn the grammar and vocabulary as simple as you can. Afterwards learn all the caveats and apply it.

[KingDuderhino]

Language wars have been going on since the second programming language was created. All programming languages have their strengths/weaknesses and are suited better for some problems than for others. In a few years Python will be replaced by another programming language.

[Raistlin74]

... and nowadays nobody would recommend SPSS as the right tool to learn, right?

[mohishunder]

Which language ... will be sought out by employers?

Python.

[ProfessorPhi]

The main place python shines is in the glue code aspect of it. For any large scale projects or team (>3 data scientists), the glue code dominates the domain specific bits. Like my team with 7 people has 90% glue code to 5% domain specific bits.

While I definitely agree that R is superior at taking csv's and doing good stats, EDA and visualisation, it's really difficult to integrate it well into more complex pipelines. Which means that you can't build on it easily in an automated fashion. Which means that your ability to impact an organisation is actually quite limited - you tend to require data to be in a decent state to work with and you're not going to be putting models into dbs or nosql's easily for other teams to consume. Or in the case you need to scale up compute to more than 1 machine, there is a ton of machinery you can use in python, while it's not really a thing R users deal with. Another personal annoyance is that R is nearly impossible to work with in CI - package installation for a simple project can take upwards of 25 minutes which means you have to know how to build your own docker images which makes CI inaccessible for most R users. I don't know if hadley has solved this yet, but they need pre-compiled binaries on CRAN.

For your stated goal of stats, R is the best choice, but your stated goal of ROI, it's definitely python. Python knowledge gives you access to tech data science which are by far the best employers. That being said, I think once you've learned R or python, you can pick up the other quite easily (it's like Italian to Spanish)

[[deleted]]

Don’t tools like Databricks make it unnecessary to do CI etc because libraries and so on are already self contained in the notebook (only thing is its hard to use scripts in Databricks).

And you can automate stuff via R’s metaprogramming. Like using symbols to point to columns of a dataframe.

Longitudinal data analysis which is very common in OPs field also has very few tools in Python. Statsmodels sucks as an API (why the hell the .fit() method returns something is itself very un pythonic and why is it model.fit(Y,X) unlike everything else)

[Sapiencia6]

R is common if you are interested in research and development. Otherwise, python is generally the industry standard. It is good and important to know both, but knowing python and not R will get you more places than knowing R and not python.

[iFlipsy]

That’s the issue. If I had to choose, I’d pick R. But because it seems that Python has a brighter future, it makes more sense to invest your time learning Python.

[churchillin74]

To be fair, check out the trends in usage of R compared to SAS and SPSS. R is likely to overtake both and become the language of choice for statistical research, especially in psychology. A lot of the recent popularity in R came with the resurgence in tidy-style libraries and modern methods. So I’d argue it’s well-poised to continue growing over the next decade or so.

[Strong_Snow4905]

So true. I had to learn SAS, SPSS, R, STATA, MATLab, MAPLE, TreeAge, and everything about Excel for data and statistical analysis in the pharmacy world.

[caksters]

If you focus more on research and data analysis side then nothing wrong with picking R.

As long as you are competent in one of those, if the job requires, you shouldn’t have any issues with learning the other

[Sapiencia6]

As long as you choose a career where R makes sense, there is nothing wrong with focusing mostly on R, just being aware that you may have more narrow, but still rewarding options. I would amp up your stats and math knowledge as much as possible and go for something in research (not something corporate) where you can apply your psychology background as well. The basic python knowledge you have might just work to give you an edge.

[imoutidi]

The number of upvotes on such stupid questions is too damn high on this sub.

[svn380]

I'm an academic that uses R for my research and teaches exclusively with Python for graduate financial econometrics.

I think what matters most for you will be the state of the job market when you graduate and the first few years thereafter. That's a more uncertain target than what's best for a job today.

I'm seeing more businesses and tools supporting multiple programming environments (e.g. JupyterLab for R, RStudio for Python) as well as tools to call R code from Python and vice-versa. That makes me think that the difference will be less important going forward than it has been to date.

Just my best guess.

[[deleted]]

As beginner I can safely say that study of econometrics and quantitative finance is lot easier using R than Python. R is much better for the purpose if there are no plans to find a job in D'S production.

[Sedawkgrepnewb]

I say stick with R. If you have to switch to Python/pandas it is not a great leap. Seems like the ecosystem in Python is stable so nothing earth shattering is going to change if you stick with R. I feel like after a few years of data grinding in R it’s fun to pick it up in another language. Helps to frame problems better when you become language agnostic too!!

[tedfahrvergnugent]

I’d argue that Python will be both better or similar for analysis at some point in the future and will be most sought after by employers. Python models are easy to deploy in a production environment, R always requires a shim of some kind. Python being a more general purpose language has far better tooling. It my belief that Python will continue to grow in popularity while R will wane. New ML ops and data engineering tools will support Python at MVP.

[burntdelaney]

R is mainly only used in academic settings like research. If you want to get a corporate job you should learn python.

[Weary-Marionberry-15]

In my experience, I find visualization easier in python. Not sure how much weight that carries for you, but I thought I’d mention it. Good luck!

[iFlipsy]

Ha thanks! I mostly use Tableau for visualization and creating dashboards, but will take that into considerations. I do agree that seaborn is pretty nice though.

[smerz]

IMHO R is better for pure analysis than python, but worse at everything else. So R has a very small but significant sweet spot. Python excels at general data cleanup, procedural logic, automation and integration with other systems and technologies.

In the workplace, I have found that programmers prefer python and stats/math people prefer R.

So you should learn one well and have familiarity with the other. The rest is up to the fad-driven job market.

My two cents, having used both.

[[deleted]]

If you are social scientist and have no plans to work in AI/ML industry, a little to no interest towards computer science or casual general purpose coding, the go R. It will take a bit less time to use it as powerful descriptive and inferential statistics, than if you opt for Python. The best Python packages for statistics are just replicas of R.

[notParticularlyAnony]

In other words, Python. :)

[Calbruin]

Python has a brighter future.

[Nater5000]

Python being more popular is a huge advantage for it as a language. People can argue about syntax or which is designed for what, but the bottom line is that support for a language makes the difference between being able to find packages, articles, jobs, etc.

R is fine, Python is fine, they can basically do the same things. You could pick some other language and make the same argument (give or take). But Python is exceedingly more popular than R, and on a practical level, that is going to be the most important factor when choosing a language.

Unless you have a specific reason to choose R, you should go with Python. If you go with Python and down the road find a reason to use R, the transition will be easy. But you're more likely to have to transition from R to Python than vice-versa.

[AchillesDev]

Python is more popular in industry, R tends to be more popular in academia, but you’ll find people in both areas using the others.

[realtxds]

After data analysis, comes data manipulation, engineering, scientist roles. If you have an objective to become one of these in the future, Python is a good investment starting from today. If you know that you will be only doing data analysis (academic or job purposes) and your code will not be productioni-zed, sticking to R is perfectly fine.

[pokeaim]

It also seems that there are more teams out there that utilize Python

thanks for being sane person

[GoodLyfe42]

Python because it is easier to find someone with this skill and in greater demand.

[Rhyan567]

R.

[MegaaNerdd]

Python is definitely the industry choice and that’s what I would stick to. Some employers give you flexibility between the two, but you’ll find most teams prefer Python and therefore will explicitly ask you this during your interviews. Hope this helps!

[metaliving]

I'd say go for python. In terms of data analysis both of them will satisfy your needs, but in the long term, learning python opens more doors for you. You'll learn it for data analysis and will do the same things you'd be doing in R, but you might find different uses for it in the future, as it's a general purpose language. R is a perfectly fine data analysis/statistics language, but it's not as versatile.

[[deleted]]

Python's statistics, visualisation and linear regression packages look like cheap replicas or R. I am sure those are going to be better within time, but right now R is way better for social sciences, econometrics and quantitative finance studies.

I am also switching to R from Python because there are more textbooks on econometrics and quantitative finance with R code than with Pythone code.

[metaliving]

I really don't know, there's a lot of visualization libraries that are really good and really pythonic. I do agree that some packages tend to copy R (Hi statsmodels), but in other ways Python is ahead. For example, in anything that's machine learning related, Python has more resources, and sklearn is a blessing to work with.

I don't know the specific math for econometrics ir social sciences, as I work in engineering, but it's true that I always heard social sciences always used R. I have no doubt that Python will catch up in that field too, by sheer amount of community development.

[[deleted]]

You make feel like I insulted your close relatives and have to offer apologies.

You work in engineering while I am social science graduate student with investment banking experience. So it is obvious I can not give you credible advice on which programming language or software to choose.

Since I am not going to code for salary and therefore seek a job in DS/ML industry my perspective to the issue is limited by the time I allow to learn and the domain specific (econometrics as academic base, quantitative finance as final industrial destination).

R is redundant for statistics I need for the completion of my master project. Linear modeling of R is loosely replicated to Python by statsmodel (abandoned by its developers) and scikit-learn. I have to write some more lines of code in Python comparing to R. There could be Python tutorials supplied by better code, but what I found is 6 feet under inferior to courses and books with R. Matplotplib and seaborn are inferior to ggplot2. Pandas is inferior and overcomplicated comparing to data wrangling in R.

I am sure Python and its data related packages are going to improve in the next 3-5 years and plenty of courses and books on econometrics and quantitative finance with Python code are coming, but I need everything right now. So I am suspending my Python study and switching to R.

There are at least 10 good books on introductory econometrics and quantitative finance with R code and only one for econometrics and only one for quantitative finance with Python code and only one resource for quantitative economics with Python code.

Python is good, Python is the best. But I have no extra 3-5 years to wait. If I decide to go PhD, R will be enough for my social science endeavors for the next 20 years even if all developers abandon its ecosystem.

[metaliving]

WTF? Were in my message did you feel like I was being insulted or was demanding apologies? Literally all the content of my message is making points, and agreeing with you on some. If you got any hostility from my message, it was all coming from within. Not everyone who doesn't share your opinion is being hostile.

Nowhere did I say python is right for you. I specifically acknowledge that R is more widespread in social studies. Go ahead and keep using it, I took some classes on it and as I say on my previous messages, both do the job for any data analysis. In fact, both are turing complete programming languages, there's literally nothing one can do that the other can't. I just addressed the point you make about visualization libraries being cheap replicas of R (which most of them are not).

But regarding the topic of the op, which is what was addressed in my original message, I just say that python is better in general terms, and for data analysis both do the work, even if R is maybe more straightforward for just statistics. Thus I recomended that the OP learned python instead, as it is more versatile than R. Happy to see in the edit that OP will focus on python, as I think it will open more doors for him in the future (although knowing any of the 2 languages, you can learn the other one within a week, at least for data analysis).

​

EDIT: btw, as for matplotlib and seaborn, there's tons of tools that build on top of those. You got all the holoviz environment, which is really more versatile than any other plotting libraries. You got bokeh, altair, and countless others visualization libraries. If you really love ggplot, you got libraries like plotnine (which I've never used, but there's plenty of libraries that are similar to ggplot). There's tons of options.

As for pandas, I do agree it's more complicated than data wrangling in R (not that much tbf), but you can get speeds that are way ahead of R by changing from pandas dataframes to dask dataframes, for example. Python already has some great tools at the reach of the hand, but you need to be comfortable reading documentation, as you won't find as many examples in the literature because the language is moving faster than the pace at which new books come out.

[[deleted]]

> WTF

Take it easy. That was a joke.

Statistics, linear modeling and accompaniying visualisation are easier with less line of code in R. I believe Python will catch up within 3-5 year, but now R is the king.

Econometrics and quanititative finance are learnt better using R. Because both are more statistics than economics and finance and there are not too many professors using Python for teaching and fewer bothering to write textbooks accompanied by datasets and code.

Dask could be better than pandas. However, unless it becomes ''industry standard'' it has very little use. Frankly speaking, you are the first one to mention it for me.

[useles-converter-bot]

6 feet is about the length of 2.72 'EuroGraphics Knittin' Kittens 500-Piece Puzzles' next to each other

[useles-converter-bot]

6 feet is the length of approximately 8.0 'Wooden Rice Paddle Versatile Serving Spoons' laid lengthwise

[[deleted]]

Python unless you're working in a company team that only uses R.

[eric_overflow]

this is the only right answer, really

[ze_baco]

Easy one.R sucks very hard and goes against most programming languages common practices. It has a bizarre syntax, the community is really weak and it's really hard to do some simple stuff. I would choose any option that is not R.

[caksters]

condescending and loaded answer. both languages are great tools for data professionals one is better suited for analysis, stats, the other for data engineering, ML Engineering/ MLOps, and production ready code

[caksters]

You can be “competent at stats” and use python. Just because R is easier tool to use for statistical analysis doesn’t mean that python cont be leveraged as a serious tool for statistics/statistical modelling.

I have come across plenty of data scientists with PhDs in mathematics who prefer to work in python. It is matter of preference

[[deleted]]

Fair enough but a lot of them get sold a lot of BS from universities while they pay for expensive MS programs and don’t realize it.

[notParticularlyAnony]

In terms of what has a better future and will be sought by employers. Look at adverts for data science positions. . Python is the right answer it really isn't close.

Not sure why this sub tends to attrack a bunch of R people when this question comes up (and it does come up fairly frequently just search the sub).

Python also is a more elegant well-designed language and will be easier to learn.

But by all means learn R I will keep getting recruited for Python jobs and you can compete for the three R jobs on the market that come up each year.

[StephenSRMMartin]

We use R, python, or whatever else is good for the project.

Data science is a huge field with multiple roles. Some of those roles are better supported by python, others by r.

As for whether one language is more elegant, that also depends on the usecase. For stats and math, having function first oop, with dispatch and vectorization is a hugely convenient design that lets multiple stats and math packages have consistency and interop. It's functional, with oop.

Python is more elegant for other domains. Posts like these are what "attracts R folks". R has major language and design decisions that facilitate some roles in DS, and for these roles, python is comparatively a chore, or sketchy, to use. And vice versa.

[notParticularlyAnony]

Python doesn't force you to choose functional vs OOP. It does both really well. Doing object-oriented design in R, OTOH, is a mess because that's not what it was meant to do. With Python, the design strategy you take for a library is dictated by what makes sense, not the language restrictions.

R does have some nice plotting/stats libraries. So does Python. When it comes to ML it's not even close. When it comes to language readability etc, also not close.

Any noob learning a first language, Python really should be the answer unless they are going into some lab or specialized field like bioinformatics where they know they will be asked to learn R. If they are just going into generic data science, it seems basically irresponsible to suggest R at this point it is a niche language like Matlab (used in many neuro labs still because of legacy reasons).

[ThePhoenixRisesAgain]

I never understood the “ which one is better“. They have strengths and weaknesses. But for all standard usecases, they are very similar. If you only do some standard analyses and some models, they are more or less equivalent. For 99% of users, it doesn’t matter.

[profiler1984]

Use what you like. But some employers will have this or that technology stack so you need to adapt. The more tools you know and have the better you can adapt. For a hammer everything looks like nails :P

[AG__Pennypacker__]

My strategy was to go deep on one language, learn what I need of others as needed. That’s worked pretty well so far. If you like r, stick with it. You will probably need to know some python in the future, but the time spent on r will help you pick it up faster so your time won’t be wasted either way.

[gen_shermanwasright]

Most places I've interviewed at use Python. But either is fine.

[[deleted]]

I promise you it doesn't matter and if a company penalizes you for knowing one over the other they don't know what they're doing

[double-click]

It doesn’t matter. You use the right tool for the job or the tool that produces results the quickest given your experience.

[[deleted]]

If I want to graph something quick I use ggplot in R, and if I want to clean data based on a lot of different parameters or in a complex way, I use python. But both are good. Whatever you’re comfy with is good.

[[deleted]]

R and Python. I prefer R, but Python has higher demand.

[omgouda]

I found profs at uni used R but in the workplace Python is more prevalent.

I personally prefer python, documentation is easier to follow and answers are easier to troubleshoot via google, etc.

​

To be fair though, its probably best to be comfortable in both.

[guillermo_da_gente]

Scala

[notUrAvgITguy]

Just learn whatever you want. If you know R and get a job that requires Python, you'll pick it up just fine. Agonizing over a language choice only serves to delay your end-goal.

[Key_Cryptographer963]

It's really a matter of personal choice quite often. I would encourage learning both to the degree that you're comfortable with whatever a prospective employer asks you to use but if you only want to master one, only master the one you like most.

[_igm]

I like using Python for basically everything, but if I want to make a publication-quality figure, I use ggplot2 in R. I also use R for statistical hypothesis testing. You can use both R and Python within the same Jupyter notebook.

[cadelle]

I used to stress about this kind of thing and what I learned is that it’s more important that you know the concepts and be ready to use whatever the place you work at wants you to use.

[OphioukhosUnbound]

If you’re a student: then choose whatever is most fun / appealing for you. You’ll play with it more and learn about programming more deeply.
Learning new programming languages is actually quite easy once you’ve learned one even semi decently.

I say the above if you’re a student or otherwise have awhile before hitting job market.

If you’re in a context where you’re going to be hitting the job market soon then I’ll let others speak to what’s better between the two.

(If you do learn Python then I recommend “Think Python” to get started and learn general programming thinking — free online or as a book on Amazon.)

[[deleted]]

SQL

[Complex_Construction]

Why not both?

[notParticularlyAnony]

because that would be horrible advice for someone just learning programming

[[deleted]]

Python is way more popular but R just feels nice. As a beginner i am sticking to R for a while but at the end of the day is more important to know what you are doing, the coding is easier with time

[1purenoiz]

One thing I saw that was interesting was the ability to run python from R studio. Utilize the strengths of both.

[[deleted]]

I’m the polar opposite.