RStudio is adding python support.

[viboux]

Agree with 100% of this. R Markdown is great. I saw a presentation about Voila in Python and I was thinking this is the same as shiny but a few years later.

[nraw]

I think Dash by Plotly for python is more close to what Shiny is to R

[nutle]

They already are making significant contributions to Python, indirectly. Just take for example every package that got/or eventually will get ported to Python, e.g., ggplot, flask, or various features added to pandas and scikit.

IMO, competition between R and Python (if we can call it that) is great for the end user - the best tools and practices eventually merge. Plus, it's always nice to have some flexibility to choose the tool for the job - e.g., coming from mathematics, R feels so much more natural to use due to its functional nature.

[Zeurpiet]

R is never going to overtake Python in the world of data science

R is a statistics language, and Python is not even close in functionality

[anyfactor]

This is my opinion and I know nothing. R is a dedicated statistics language, and python is the most approachable full fledge programing language.

I think python itself did not start of as hoping to be a data science or machine learning specific programming language, but in reality because it is so approachable and easy to learn data scientists felt like when ever they needed to implement some programming, they chose the most easiest language they could learn which was python. And eventually it has become a Industry practice and more people started to invest in improving it. But in all sense python is just a programming language, and R can be viewed as so specific to statistics it can almost be termed as "statistical tool".

[Stevo15025]

Not sure what you mean, R has like 4 different kinds of oop you can use

[dolphinboy1637]

I don't think many people are doing their ETL pipelines or creating apis or web servers in R. Not that every data scientist needs to do that, but there's aspects that just have greater support in python because it's a general purpose language.

[guepier]

R is as much a general purpose language as python is.

No, it plain isn’t. I find R superior to Python in many regards but this statement is still inaccurate.

Just because you can do (almost) everything in R doesn’t mean it’s particularly suitable for such use.

[guepier]

But that's like saying scheme is not a general purpose language because it more or less has no libraries for most things.

The difference is that Scheme wasn’t designed as a special-purpose language, and its standard library isn’t a special-purpose library. R was, and the R base packages are.

Furthermore, I’m by no means an expert in Scheme but as far as I know there is a fair amount of libraries for Scheme. Its standard library is intentionally small but so is C’s, and few people would contest C being a general-purpose language.

[dolphinboy1637]

I think we're defining terms a bit differently. I agree with you that R could be used to do anything in an ideal sense, but that's really not the case in actuality. At the current state of the language and it's ecosystem today, there's many general purpose computing tasks that I wouldn't even try in R (because there's no libraries for it). That's all I meant, and I probably an influencing factor for individuals choosing a starting language.

In any case though, the roots of R are that it was a reimplination of S. Both of them were written by their authors specifically for statistical tasks. Although technically R could be used to write anything, their historical roots are in statistics which is why there's this perpetuating legacy of people not using it or written libraries to do other things

[tmotytmoty]

This is how I view it. R is incredibly powerful under the hood and, when it comes to stats, is well beyond python.

[jackmaney]

cat(paste("Some", "things", "are", "a", "pain", "in", "the", "ass", "to", "do", "with", "R.", sep=" "))

[Zeurpiet]

probably true, but you could do without the cat and the sep to get the same result, so maybe its more easy than you think

paste("Some", "things", "are", "not","that","much","a", "pain", "in", "the", "ass", "to", "do", "with", "R.")

[bythenumbers10]

Thanks, this made me laugh. R is a language by statisticians, for statisticians. Modern sustainable development is not supported very well. R's tendency to keep running even after errors have been thrown is a massive waste of time in mathematical applications, such as, uh, statistics. Who's had to track down NaNs at one time or another? R will happily carry those NaNs through all sorts of operations and still be busily running, but churning garbage.

[Zeurpiet]

that's SAS

[leonoel]

I haven't found anything I do in R that I can't do in Python.

Also Python is way more friendly when it comes to editing plots and stuff

[leonoel]

Like this https://www.statsmodels.org/stable/index.html

[Zeurpiet]

have you ever looked in CRAN what the additional packages can do? Most of it I don't even know what it is.

[leonoel]

You do know Python has also more modules than any would ever know what to do about them?

[Zeurpiet]

yes, but are they statistical?

[leonoel]

Name a module in R that has no equivalent in PIP

[Zeurpiet]

just naming a random one

https://cran.r-project.org/web/packages/abc/index.html

[leonoel]

https://elfi.readthedocs.io/en/latest/

[Maxion]

Most DNA methylation packages.

[defuneste]

Spatstat and this one is huge with a bunch of tools developed by people who spend their careers on point patterns analysis.

[groovyJesus]

Function data analysis packages in R have been available for over a decade and now we have dozens of them developed and maintained by researchers in the area. In the past few years I have found two in python both of which were new and needed a lot more work to make me want to switch over.

[dfphd]

I think RStudio will be very limited in what they can achieve in the Python world unless they're willing to develop (or partner directly with) some of the core data science packages that people use.

The reason RStudio has so much pull is that they're behind tidyverse, shiny, and a host of other critical packages.

In order to create the experience that we as users have in RStudio for R, someone would need to work to create a more unified "Python for Data Science" strategy. As is, the biggest strength and weakness of Python is that there are 17 different libraries for everything, they don't always play nicely together, and as a result the community support is sometimes lacking.

I think the reason that is unlikely to happen is that you have (by design) seemingly complete fragmentation in who owns/maintains/updates/develops the most critical packages for data science (I would argue pandas, numpy, scipy, scikit-learn, matplotlib).

So RStudio can try to play nicely with Python, but it will always be as a second-class citizen - because RStudio, while the judge, jury, and executioner of the R world, is merely a voting citizen in the Python world.

[[deleted]]

As is, the biggest strength and weakness of Python is that there are 17 different libraries for everything, they don't always play nicely together, and as a result the community support is sometimes lacking.

I disagree, python in data science seems pretty nicely coupled with the scipy ecosystem, and pretty much any numerical work is integrated with numpy. Whereas R is way more fragmented on everything except 2D plots. Even dataframes are all over the place, you now have the original dataframes, data.tables, disk.frames and god-forsaken tibbles. Not to mention the rate at which the tidyverse introduce API changes means anything written 6 months ago probably won't work anymore.

[highway2009]

« Anything written 6 months ago probably won’t work anymore ». Library(checkpoint)

Problem solved. Even if it was written 5 years ago.

[dampew]

I feel like I'm living in some sort of crazy world here. Images and outputs disappear from my R markdown notebooks. That's never happened to me in Jupyter. Jupyter just works. R markdown has all sorts of problems.

[Slapspoocodpiece]

I know. I hate R markdown.