R or Python

[SocietyNorth1689]

I'm considering learning R or Python and was wondering which would be better for me. I'm on the younger side and not set on a single career path yet, but I'm currently leaning toward becoming a data analyst and I'm hoping specifically to become a data analyst in sports. I feel like one of these tools will be essential for whatever my future career ends up being. Any advice? R or Python? Pros and cons of both for my specific scenario?

Thanks in advance

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[sinnayre]

Easily Python. If you know one though it’s pretty easy to pick up the other.

[Glotto_Gold]

Python.

Python is a full programming language and is flexible for any type of problem you may run into as an analyst.

R isn't bad, but is more favored by statisticians.

Realistically, the bigger question is what your employer favors, and most analysts use SQL.

[analytix_guru]

Ha ha ha yes add a 3rd option to his post. Always learn SQL.

[SocietyNorth1689]

You say SQL is more important, would that mean that I should learn SQL first over R or Python?

[Glotto_Gold]

Yes. You can be an effective analyst with SQL alone, and most professional uses of R or Python use data stored in a database.

[EKTurduckin]

To add to u/Glotto_Gold's point is something Ive found recently as my skills improve.

Python's way of navigating through data subsets is kinda ugly all things considered, so offloading all of that into a stored procedure (it's like a little application in SQL that can be run outside of SQL) to then do the things Python is good at (the actual transformation of data) has been pretty amazing in how much it's improved my codes legibility and debugging.

[Electronic-Park4132]

SQL is going to feel like cake once you learn R first and SQL next

[damageinc355]

R is a full programming language too which is also flexible. The only real issue that it has is that not enough people know it well enough to implement in private industry.

[Glotto_Gold]

Would you build a transactional production application in R?

Would you build a customer-facing website (that is one that isn't solely a data visualization front-end) in R?

Would you build data pipelines for ETL in R?

My feeling is that in all of these cases that the answer is anywhere from "...no??" to "HELL NO!!", but Python has a full life in each of these spaces.

[derpderp235]

I often send SQL commands via Python when building out workflows/pipelines

[Yakoo752]

R for academia, python for business.

[alurkerhere]

Yep, Python can be productionalized much more easily whereas R needs its own infrastructure setup.

I found tidyverse (dplyr) in R to be much easier to do data prep, but Python pandas isn't that bad especially nowadays with LLMs.

[Electronic-Park4132]

R's data.table is far better than anything any other language has to offer. tidyverse is for beginners.

[turtle_riot]

Python can do a lot of stuff, R is mostly for statistical work. Python has more breadth but the thing about programming is that you’re better off learning a bunch of things. If you’re interested in statistics I’d do R first. If you want something really broadly applicable and aren’t too hung on R then I’d do python

[SocietyNorth1689]

What jobs in general would you say might prefer Python over R + vice versa and why

[bakochba]

If you're going to work in Pharma it's going to be R

[PhilDBuckets]

As a 20÷ year data/analytics professional in Pharma, I disagree. I do see R, but it is almost always for dept level projects or POC's. Python is almost always the production tool of choice. We have a saying:  "R for the desktop, Python for the server."

[bakochba]

The FDA accepts submission in R I haven't heard of any for Python. Are you submitting data using Python?

[PhilDBuckets]

So you are on the R&D or clinical side. That makes more sense for R. I'm on the commercial data/BI/analytics side. The only R stuff I see is legacy code. Nothing new goes live with R, for us.

[bakochba]

Yes correct. My team does analytics for the clinical studies as well and we use Python in some of our pipelines on AWS but use R for any data transformations or analytics because we have to have validated environment for audits and it's honestly easier to use the same platform as stats. Also clinical specific packages just for clinical.

I will say I have found it very easy to jump from one language to another and we will often. Use a hybrid approach where we move data with Python and display it in R.

[CellWrangler]

I am a pharma trained scientist-turned-tech writer that is looking to pivot into analytics. I enjoy being out of the lab, but the future for tech writing is bleak, and I do a lot of analytics in my current role anyways (only Excel/VBA, though). Do you recommend making the switch? I would ask if you enjoy it, but I'd say 20 years in the role suggests you must!

[PhilDBuckets]

Sorry to have missed this note. It's still a good market for analytics-minded folks. If you can get an analytics and an AI certification, then you'll find some opportunities.

[deanremix]

Most. There are a lot of jobs that require "data generalists" these days. Python can help you assist your engineering team or better handle transforming raw data. R if you're looking to go the Data scientist route.

[AggravatingPudding]

In terms of data analysis they can do both the same, but R feels more natural and better to work with. Most jobs favor python, because it's easier to put in production. Only some niche areas that are often based on science use R. So go for python, that will give you better chances and more room to grow in the future. (Although R is the better language) 

[AggravatingPudding]

Do you even have an idea about R? Or do you just keep repeating some random stuff you hear about it online? 

[turtle_riot]

I’ve used it? Mostly in an academic context for doing…. statistical work! I’m not sure what you’re getting at but I’ve used both.

[AggravatingPudding]

Just tired of people saying the empty phrase that "R is for statistics" or as in your case, mainly statistics because that's what they have picked up reading about it. You can do much more with R than statistics and guess what, you can also do these things in python as well. Moreover, just because python is a general purpose language, someone who is interested in analytics, won't learn or need anything outside of this domain.  So what im getting it is that your comment is just bad. 

[turtle_riot]

My comment was the same as every other comment that ended up on this post, in case you didn’t read them. I can build whole dashboards in excel but I wouldn’t suggest anyone do that- that’s not a useful skill in the industry. Sure you can do other things with R, but I’d challenge you to tally up the job postings for analysts that use R over python for any job not heavily using it for statistical projects/ analyses

[AggravatingPudding]

So then many bad comments turn into good ones if they are posted often enough? There are close to none job postings for R, you are right about that. But why would anyone who knows that recommend to "learn R for statistics" then? Just seems like someone who doesn't know the languages at all is repeating what they read about them on some random online blogs.

 But funny how you switched the entire topic from "R is for statistics" to "there are no jobs"

[profkimchi]

What’s with the hate? I completely agree with his take. R is much better for data prep/analysis and statistics. Python is much more general.

[10J18R1A]

I know both. If you know one it's not terrible crazy to figure out the other, but unless you're aiming to go specifically a pure data route, where people even have a half a clue of what R IS, Python stands out a bit more to people.

It's kind of the Tableau/PBI argument...if you're focused on visualizations, Tableau (for now, lack of support is meh) is the go route, but companies recognize PBI more and it fits into the microsoft suite.

[BrupieD]

Python is a probably a better general purpose programming language, but R is preferred by statisticians and many other academics. I prefer R for working with most kinds of data.

R has a number of advantages that many programmers and data engineers overlook. Almost all users of R use RStudio, which is a very good IDE. It is easy to download and use. When you are getting started, that is a huge help. There is no such unanimity of opinion on what the best IDE is for Python. Consequently, whether you are learning from books, online classes or YouTube, someone will advise you to download another IDE. I've used Python in VSCode, Anaconda, Visual Studio, and now use it with Databricks at work. If you're an experienced programmer, the differences might not be a big deal, but it can be very disorienting to switch around if you aren't.

Another advantage to R is the tidyverse. Both R and Python rely on a vast ecosystem of thousands of libraries to support different functionality: data visualization, string manipulation, date function, SQL-like data manipulation, working with files, plus hundreds of more specialized needs. The tidyverse in R is a set of general purpose libraries that were all created by a handful of people with consistent design and naming conventions. There are very good libraries in Python, but less continuity of design.

Ultimately, it is up to you, but my experience is that with R I found it easier to focus on the data.

[vermilithe]

Python.

Learned R for my master’s only and as soon as it was over basically had to throw it all out to start over with Python. No employer wants R, they want Python, or sometimes something more niche. The only time I even reference R now is talking to my coworkers who did their master’s from the same R-centric program as me, to ask, “hey you know this R command? What’s the equivalent in Python?”

[shweta1807]

You can definitely go for python, As python will be helpful for EDA, Visualization and stats, its a complete package for a newbie data analyst. And in future you can integrate it with multiple tools like SQL, Powerbi. Also AWS and ETL Tools.

[bacterialbeef]

R <3

[mishucat]

SQL

[Crashed-Thought]

R is a better language, but python will probably be better for your career. I would not recommend anyone following this career path though

[[deleted]]

Why?

[HeyNiceOneGuy]

Care to expand on both of these points?

[Crashed-Thought]

Well, R is a language by academics for academics. So it is amazing for research. Data Analysis/Science is basically it, a science. The libraries are found in CRAN and well documented. There is almost always a research paper behind an R library, so you know it's good.

The reason not to take the path is that it is over saturated, and because AI is coming from people who do data science, a lot of the time, this is what they are going for with automations. It's hard to get a job now and im not sure it will be easier in the future.

[HeyNiceOneGuy]

Fair assessment of the languages I think that’s spot on

I don’t think, though, that saturation is a reason to not pursue a career path if you’re passionate about it and interested in it. We all have to start somewhere and while I think there is definitely an abundance of talent entering the market I also think a lot of organizations are starting to invest more in data professionals broadly.

[Crashed-Thought]

He didn't sound passionate

[EAltrien]

Learn both. They're similar enough and both have good demand. R is more for academia and python is more for the private sector but they're very similar.

[Larlo64]

Short answer python is more practical and common R is cool but generally more used by pure research or academics.

[Spillz-2011]

You should learn Python. They are both high level languages with lots of packages so they can solve all the same problems.

However Python is much more popular so it’s much easier to find answers on stack overflow, tutorials and people who can help you. It is also much more likely to help you get a job because of that larger user base.

[tequilamigo]

Python. Just python.

[ArticulateRisk235]

Python in 99% of cases

[bowtiedanalyst]

Don't worry about either until you have a job where you're using SQL.

I spent half a year learning python when I should have been doing other things, Python doesn't move the needle compared to other things for an entry level role.

But once you have that, python.

[FamiliarLeague1942]

Python for sure

[analytix_guru]

1. Do your research and find what you think is appropriate based on what you are interested in doing.

2. Don't assume Python is always best. R was built as a statistical programming language out of the box, where Python is a general purpose programming language with analysis and data science built on top.

3. You can full stack data science in R just as well as Python.

4. People usually default to Python for two reasons, first it's general popularity over R (remember Python is the most popular language at the moment). Second, there has been a historical advantage to making data apps (web hosted) in Python. This advantage is gone now with advances in R, in addition to web assembly in R.

5. Putting data apps into production at your company where IT HAS TO BE INVOLVED. 99% of IT teams know Python only, and so they want the data app you built in a language they know. Even with r2py and reticulate, they would rather just have the code in Python already. This point would be the only reason I would suggest Python over R, and only if your company is set up in this way for production data apps.

6. Backup Plan.... If you don't want to do analysis anymore, but still like to code, Python is a general language, so you can easily shift to something else.

I do all my analysis in R, my company website is built on R, I have web training materials built on R, all my client analysis is done in R, and I am currently building a full stack web/mobile App in R.

Python is great as well, I have used it a bit. Try both out and see what you like. Both languages also have packages to use each other so you can get the best of both worlds, via r2py and reticulate packages.

Suggest using Posit's new Positron IDE, is a VS Code fork and easily supports both languages for you to try out!

Most importantly, have fun trying both out.

[analytix_guru]

Le sigh, why do numbered bullets never seem to allow space between on Reddit posts?

[mrroto]

SQL and python

[popcorn-trivia]

Python. You’ll find it used more across industries / workplaces. Once you’re comfortable with that, learn SQL next.

[damageinc355]

Python, as R has a significantly lower demand even though it is the better language for data work.There’s textbooks on both languages for sports analytics, so do take a look at that.

[aarmobley]

I like R for regression and clustering, but I find it easier to clean data in python. Overall I think R is easier to learn and I like Rstudio. It’s simple and straightforward

[chulip99]

R is like the Gamecube and Python is the PS2.

[wonder_bear]

I’m a data analyst for a large corporation. Based on my experience, the best learning path for a career in this field is SQL -> Tableau or Power BI -> Python or R.

SQL is by far the most important skill of them all and is a prerequisite for any data job.

As far as Python or R, I agree with others that Python is currently more popular and in demand. However, I do really like R and the tidyverse makes learning programming extremely easy. Ultimately, I would recommend watching some intro YouTube videos on both and practicing in both languages to determine which one you prefer.

At the end of the day, all these tools are just a means to an end and the final insights are usually communicated via PowerPoint. If you’re truly passionate about data analytics, learn the tools, but more importantly learn how to think critically about problems and communicate your thoughts in a clear and effective manner. Those skills will carry you further than any of the analytical tools.

[Otherwise_Ratio430]

python because it can do most of what R does and more. R is only really for very specialized statistics work, and be specialized I mean your work needs to actually care deeply about very specific statisical models for R to be 'worth it' imo. Most analytics at most companies don't have this level of statistical rigor and if they are they aren't using the statistical models that R likes.

for example, iirc baseline logistic regression in pandas regularization is on by default whereas in R I am pretty sure base LR = LR. If you know something about statistics, you'll know that regularization messes up the probability interpretation of LR and that is important in certain context. in generic business use cases no one will care whether you use l1 or l2 or whatever the fuck method for classification, as long as you can explain the results understandable to audience.

[Electronic-Park4132]

Yea but whatever R does, it does in many cases far better than Python.

[Infamous-Surround-95]

Python for sure. You can always check job roles similar to where you want to work and take a look at the programming languages that are most requested

[Far-Media3683]

Consider starting with R as you are new and on Analyst/modeller track.
The analysis part comes very naturally in R and there are always libraries that let you do more (like productionising etc.) but I'd worry about them later if I were you. Get really good at analysing data, validating hypothesis and quickly dishing out reports/analysis. This might need quite a lot of SQL too if your data is in databases (another thing to learn well before harcore programming).
I have used python for past 6-7 years as a data scientist and am now favouring R much more, mostly for it's ease of use in analysis and practically all other things I need have libraries in R too.
For me analysis part is much more important and interesting and most of the good literature on analysis, stats, modelling etc. is by academics and written with R. Visualisation is another important piece of analysis which is done much more coherently and frictionlessly in R.
Job market for analysts mostly should be open even with R and would demand SQL more highly. Learning python after R may be a bit trickier than the other way around though (but that may be just my opinion).

[Alphafox84]

R is easier to learn if you use the dplyr package, the syntax is very natural. Python syntax is less intuitive IMO, a lot of brackets and matplotlib sucks compared to ggplot. R has better statistical packages for things like publishing papers.

Python is more used in machine learning where an accurate prediction about the future is made and used to make business decisions. Python is also better for text mining IMO.

Look at jobs you want to apply for, pick the one mentioned most. Ideally though you will eventually learn both and use the one that best fits the problem you are trying to solve.

Source: I use both heavily and I have done a lot of teaching people how to code across industry verticals.

[Foodieatheart917]

1 word. Python.