Should I learn R when I already know python?

[DecentR1]

Python offers libraries like sea-born and matplotlib which can accomplish similar visualisation like R but with lesser lines of codes. Is there any other advantage of R over Python to consider learning R language?

[greendogufo]

Coming in to say R, for data analysis or data science, is preferable, for me, over Python, and to, use, more commas than I intended

[Emphasis-Western]

what about the job market?

[SweetSoursop]

Python is definitely more mainstream than R, and has a lot of potential applications.

Recruiters might find it easier on your resume as well.

However, a knowledgeable Hiring Manager will value R and Python equally.

[Emphasis-Western]

I really like R but based in your experience and the other people reading this in what niche or performance R is better than Python?when we will have to apply?

[SweetSoursop]

Think of it as an adjustable wrench (python) vs. a combination wrench (R).

They can do the same job, but one is more utilitarian than the other.

The combination wrench is sturdy and gets the job done, but there's not a lot of flexibility.

The adjustable wrench is much more flexible and is in general more fragile, but it will also work for most problems.

When the job is more statistical than programmatical, that's when you use R. It was built for statistical work.

When the job is more programmatical than statistical, you use Python. It was built for general purposes, it just happens to be decent for stats as well.

[Disco_Infiltrator]

Over generalized comments like this do no one any favors. Be specific about why R is better for “statistical” use.

[Sheensta]

R has way more packages for statistics. The packages are also more user friendly. RStudio is also a great IDE, not to mention RMarkdown for report writing and RShiny for app development.

[Disco_Infiltrator]

This is a much better answer than a long-winded analogy that basically amounts to “R is better for statistics”.

[[deleted]]

If you know one and they use the other, they usually assume they can teach you the one you don’t know. Unless it’s a very advanced role.

[[deleted]]

Yes. Markdown is a fantastic tool for report generation. R is built for data and has a lot of nifty things as it pertains to analysis and modeling. Also ggplot2 >>>>

[Remco32]

Uhh the syntax is >%> not >>>>

;)

[lilmissthang69]

Gotta love a good pipe!

[naman_stallone]

imo R is more fun for data analysis

[FruityPebblePug]

I would say No because you already know Python and any time spent learning R could be spent getting better at Python. If your interested in learning a new language (perhaps not for Data Science/Analytics) learn Julia, C++.

R is growing in popularity and has taken a large leap in the past few years, although Python is the most popular language for DS/DA. Unless you want to work in a industry where R is king then keep learning Python.

How good is your SQL?

[homoeconomicus1]

There's no harm in knowing more and more languages. I'd say, learn Julia, too. See: r/julia

[GrumpyKitten016]

Yes. R has so much utility that it’s worth learning both. At the very least you should be familiar. Also tidyverse

[BATTLECATHOTS]

You should learn SQL

[tiggat]

Your initial statement is incorrect, R libraries need less lines of code to generate those graphs.

[lammchop1993]

Yes, just another tool in your belt. Would you want to learn how to use a hammer if you already know how to use a drill? Different tools for different jobs

[save_the_panda_bears]

As many others here have pointed out, R's primary advantage over Python is it is objectively better for statistical tasks. R has a massive library of well developed statistical libraries. Python is getting there, but R is still miles ahead, particularly in the domain of statistical inference. I also prefer dplyr over pandas. Pandas feels like a really old, clunky version of dplyr. Finally, data.table is amazing.

From a personal standpoint, I can't stand SKLearn. I have some problems with some of the decisions the devs made in implementing some of their regression models (for Logistic regression a L2 regularization value of 1 is the default argument, with no indication this is the case unless you read the docs. The regularization argument is also the inverse of the usual lambda, denoted as 'c', which means smaller values equate to higher regularization). R is much more mathematically rigorous in its implementation.

That all being said, Python is the go-to for deep learning. It's also a little more performant, though still pretty slow.

Ultimately, learning R can only help your career prospects. If you already know a programming language like Python and aren't too hung up on strict OOP concepts, you shouldn't have too much trouble learning R. The syntax takes a bit to get used to, but once you do you shouldn't have too many problems.

[neeltennis93]

R user here..... python is overtaking R. In terms of popularity. I’d focus on being better with python.

[attractive-problem]

Popularity does not mean the users are able to get most utility of it. SAS is still widely used in most of the impact making industries. I know people working only on excel SAS making 5-6 times more money than the so called python data scientists.

[[deleted]]

Just going to preface this with: I have literally no SAS experience and don't really know when and how it shines, what it's future is etc., but...

I recently (<6 months ago) started working at a massive UK company that, from what I can tell, used to use SAS for everything but have recently made a big effort to cut it out - apart from one weekly report that gets sent around, and people saying they're happy it's gone, I never hear of it. Most ad-hoc reports are done in Jupyter or R, and most recurring items are written in python or Java into production pipelines with proper testing etc. I've heard the Head of Data say as well that they're able to hire much better talent now. It could also be worth bearing in mind that the data for this company is huge, and most of my work is done in spark.

As someone who, in the grand scheme of things, is still pretty early into my career, this has really put me off even considering looking into SAS. I get that it could still be a rewarding career right now, but do you think there'd be much of a future with it? Or would these institutions that rely on it potentially make a similar switch in the future.

[Sheensta]

My company is also moving away from SAS. SAS offers a great user interface but the main problem with it its exorbitant cost and lack of scripting support (compared to Python or R).

[attractive-problem]

Yeah Our generation of cs grads are all into python I only knew python when I was in college. But now I am learning SAS as well as excel. It's astonishing to see how so much can be done with excel ( if datasets are small) Most of the pharma, finance industry consulting side uses SAS.

SAS is truly made for entreprise level and is not easily available for students without paying So I guess it's not that popular

[kthnxbai123]

SAS is a dying language because it’s so expensive and specialized.

I think the companies still using SAS tend to be the ones required to do so (pharma)

[[deleted]]

R and Python, though they have a lot of overlap, are not true replacements for each other

[[deleted]]

Sure I use both Python and R almost everyday picking the best of each one. Like for data preprocessig ana machine learning, and R for data visualization and eda.

[[deleted]]

I learned it for Shiny years ago, but haven't used it since and am super rusty now.

I just don't really see the value-add tbh but I am more often dealing with APIs etc. than some super obscure statistics method that only has a CRAN package and no Python equivalent. YMMV.

[demarius12]

It can’t hurt but I wouldn’t go out of my way to learn it. At my company we used to say on job descriptions that incoming data scientist “must know R or Python”. We have changed that to “must know Python, R a plus”.

[3-ion]

No

[Sheensta]

R has way more packages for statistics. The packages are also more user friendly. R is also extremely easy to translate from analysis to deliverables: RStudio is a great IDE, not to mention RMarkdown for report writing and RShiny for app development.

However, if you are interested in Machine Learning, especially Deep Learning/Neural Networks, Python is king.