I couldn't agree more. I'm fluent in languages like Julia, and MATLAB. I'm 90% fluent in R and prefer data.table over dplyr but working in both is easy enough. The past few months I've been fully transitioning to Python. And while base Python I find to be extremely elegant, typical data science and scientific computing workflows are a headache. There aren't just 1-2 packages to choose from for each use, every package has it's own syntax, keeping track of Pandas Series vs DataFrames is confusion. Want fast differentiable code? Then rewrite everything in numpy in JAX which requires its own tricks.
What Python desperately needs is a coordinated effort for a core data science /scientific computing stack with a unified framework.
In my opinion, if it weren't for Python's extensive use in Industry and package ecosystem, Julia would be the language of choice for nearly all data science and scientific computing uses.
hatmatrix · 1h ago
> And while base Python I find to be extremely elegant, typical data science and scientific computing workflows are a headache.
That's my impression as well. Going back to the topic of the original post, pandas only partially implements the idioms of the tidyverse so you have to mix in a lot of different forms of syntax (with lambdas to boot) go get things done.
Julia is much nicer, but I find myself using PythonCall more often than I'd like.
Scipy was originally supposed to provide the scientific computing stack, but then many offshoots in the direction of pandas / ibis / JAX, etc. happened. I guess that's what you get with a community-based language. MATLAB has its warts but MathWorks does manage to present a coherent stack on that end.
emehex · 1h ago
I haven't seriously used R in nearly a decade but I still miss (and think about) dpylr and the hadleyverse...
A few years ago I made a package called "redframes" that tried to "solve" all of my frustrations with pandas, make data wrangling feel more like R, while retaining all the best bits of Python...
There is so much hype and luck to widespread adoption, you never know with these things.
BDPW · 4h ago
I've had a similar experience from the opposite side. I've had quite a few years of experience in Python and had to work in R for an internship during my masters.
My impression was that it's pretty easy to do straightforward things like the examples described in the article. But when you have to do complicated or unusual things with your data I found it very frustrating to work with. Access to the underlying data was often opague and it was difficult to me at times to figure out what was happening under the hood.
Does anyone here know any research areas still using R?
vharuck · 3h ago
As an R user, I get what you mean. If you need to do things that don't fit well in the "tidyverse" model, you have three options:
1. Wrap the complicated bits in functions, then force it into the tidyverse model by abusing summarize and mutate.
2. Use data.table. It's very adaptable and handles arbitrary multiline expressions (returning a data.table if the last expression returns a list, otherwise returning the object as-is).
3. Use base R. It's not as bad as people make it out to be. You'll need to learn it to anyway, if you want to do anything beyond the basics.
Tomte · 4h ago
Everyone in statistics, and lots of people applying statistics in other disciplines (anthropology etc.).
j_bum · 4h ago
In addition to stats, R is widely used in computational biology and bioinformatics domains. It’s also widely used in the biopharma industry for a variety of other purposes.
mauritsd · 4h ago
IME (bioinformatics PhD in the netherlands a number of years ago) it's mostly still preferred in a (pre-)clinical context, not so much in academia itself
pteetor · 3h ago
R is used extensively in quant finance. The quant traders, portfolio managers, and risk managers with whom I work all use R.
kgwgk · 4h ago
> My impression was that it's pretty easy to do straightforward things like the examples described in the article. But when you have to do complicated or unusual things with your data I found it very frustrating to work with.
That's where I realised that the "modern" approach was taken in the article - which obviously I had not looked at.
tyfon · 3h ago
Not really research pr se, but it's used extensively in banking here in Norway for anything from statistical model development to basic analysis and reporting.
great_wubwub · 4h ago
I have no R experience but have been using Polars instead of Pandas for this sort of stuff and it feels less clunky. How does Polars compare to R?
j_bum · 4h ago
I strongly prefer `dplyr` and the R stack for table processing and visualization.
But, recently I’ve been working with much larger scale data than R can handle (thanks to R’s base int32 limitation) and have been needing to use Python instead.
Polars feels much more intuitive and similar to `dplyr` to me for table processing than Pandas does.
I often ask my LLM of choice to “translate this dplyr call to Polars” as I’ve been learning the Polars syntax.
aydyn · 3h ago
It blows my mind that in 2025 R is still limited to 2^31-1 rows. R needs a Python 3.0 moment, but that is unfortunately not going to happen for certain unfortunate but unnecessary reasons.
j_bum · 2h ago
Yep. I have a deep love/hate relationship with R.
This is one of those decisions that I just do not understand. In your mind, why do you imagine a set of improvements won’t be made?
Otherwise, for now, working with Python and R using the reticulate package in Quarto is perfect for my needs.
If the Positron IDE could get in-line plot visualization in Quarto documents like the RStudio IDE has, I’d be the happiest camper.
dkdcio · 4h ago
pandas* per the style guide (nobody follows it)
also I recommend trying Ibis. created by the creator of pandas originally and solves so many of the issues
Disclaimer: Never used Ibis before but I daily use polars and DuckDB.
It seems like Ibis uses DuckDB on its backend (by default) and has Polars support as well. Given this, maybe see if Ibis works better for you than polars. If you very specifically need polars, using that will for sure be better. DuckDB is faster than polars and it has great polars support, so depending on how Ibis is implemented it might be "better" than polars as data frame lib.
I tried really hard to use Ibis but I ran into issues where it was way easier to do some stuff in pandas/polars and had to keep coming out of Ibis to make it work so I gave up on it for the time being.
__mharrison__ · 1h ago
Lots of readers of my book, Effective Pandas, say it helps them feel more like they are used to with R...
(I've never used R myself, but certainly have some very strong opinions about Pandas after having written 3 books about it.)
wodenokoto · 2h ago
A really, really big part of this is thanks to RStudio, which, when you run a line and write a line will peek into memory to see what the columns in your dataframe is and understand the dplyr DSL to help you auto complete what essentially is non-existing variables.
smabie · 2h ago
The original sin of Pandas is row indices
hatmatrix · 1h ago
Actually I like that you can use it as a dictionary of tuples (i.e., rows).
Vaslo · 1h ago
One of the big benefits of polars over pandas is not dealing with the constant index nonsense. Can’t tell you all of the issues I had as a beginner with pandas trying to debug silly index errors.
What Python desperately needs is a coordinated effort for a core data science /scientific computing stack with a unified framework.
In my opinion, if it weren't for Python's extensive use in Industry and package ecosystem, Julia would be the language of choice for nearly all data science and scientific computing uses.
That's my impression as well. Going back to the topic of the original post, pandas only partially implements the idioms of the tidyverse so you have to mix in a lot of different forms of syntax (with lambdas to boot) go get things done. Julia is much nicer, but I find myself using PythonCall more often than I'd like.
Scipy was originally supposed to provide the scientific computing stack, but then many offshoots in the direction of pandas / ibis / JAX, etc. happened. I guess that's what you get with a community-based language. MATLAB has its warts but MathWorks does manage to present a coherent stack on that end.
A few years ago I made a package called "redframes" that tried to "solve" all of my frustrations with pandas, make data wrangling feel more like R, while retaining all the best bits of Python...
Alas, it never really took off. For those curious: https://github.com/maxhumber/redframes
There is so much hype and luck to widespread adoption, you never know with these things.
My impression was that it's pretty easy to do straightforward things like the examples described in the article. But when you have to do complicated or unusual things with your data I found it very frustrating to work with. Access to the underlying data was often opague and it was difficult to me at times to figure out what was happening under the hood.
Does anyone here know any research areas still using R?
1. Wrap the complicated bits in functions, then force it into the tidyverse model by abusing summarize and mutate.
2. Use data.table. It's very adaptable and handles arbitrary multiline expressions (returning a data.table if the last expression returns a list, otherwise returning the object as-is).
3. Use base R. It's not as bad as people make it out to be. You'll need to learn it to anyway, if you want to do anything beyond the basics.
That's where I realised that the "modern" approach was taken in the article - which obviously I had not looked at.
But, recently I’ve been working with much larger scale data than R can handle (thanks to R’s base int32 limitation) and have been needing to use Python instead.
Polars feels much more intuitive and similar to `dplyr` to me for table processing than Pandas does.
I often ask my LLM of choice to “translate this dplyr call to Polars” as I’ve been learning the Polars syntax.
This is one of those decisions that I just do not understand. In your mind, why do you imagine a set of improvements won’t be made?
Otherwise, for now, working with Python and R using the reticulate package in Quarto is perfect for my needs.
If the Positron IDE could get in-line plot visualization in Quarto documents like the RStudio IDE has, I’d be the happiest camper.
also I recommend trying Ibis. created by the creator of pandas originally and solves so many of the issues
https://ibis-project.org
It seems like Ibis uses DuckDB on its backend (by default) and has Polars support as well. Given this, maybe see if Ibis works better for you than polars. If you very specifically need polars, using that will for sure be better. DuckDB is faster than polars and it has great polars support, so depending on how Ibis is implemented it might be "better" than polars as data frame lib.
https://pypi.org/project/narwhals/#description
I tried really hard to use Ibis but I ran into issues where it was way easier to do some stuff in pandas/polars and had to keep coming out of Ibis to make it work so I gave up on it for the time being.
(I've never used R myself, but certainly have some very strong opinions about Pandas after having written 3 books about it.)