41–50 of 116 posts
I also like this fun though dated handbook, full of gotchas common among new R programmers: https://www.burns-stat.com/pages/Tutor/R_inferno.pdf
The invention of the Tidyverse freed new R programmers from 126 pages of gotchas.
Tell them to learn to use the tidyverse instead. For most of them, that will be all they ever need.
Very nice, but instead of an owl, shouldn't the cover illustration be a pirate?
Earlier quoted context omitted.
Yes but today I find little to no benefit over python
no plotting library available in python even comes close to ggplot2. just to give one major example. another would be the vast amount of statistics solutions. but ... python is good enough for everything and more - so, it doesn't really feel worth maintaining two separate code bases and R is lacking in too many areas for it to compete with python for most applications.
I so disagree. I've used R for plotting and a bit of data handling since 2014, I believe, to prove to a colleague I could do it (we were young). After all this time I still can't say I know how to do anything beyond plotting a simple function in R without looking up the syntax.
Last week I needed to create two figures, each with 16 subplots, and make sure all the subplot axis labels and titles are readable when the main text is readable (with the figure not more than half a page tall). On a whim I tried matplotlib, which I'd never tried before and... I got it to work.
I mean I had to make an effort and read the dox (OMG) and not just rummage around SO posts, but in like 60% of the time I could just use basic Python hacking skillz to intuit the right syntax. That is something that is completely impossible (for me anyway) to do in R, which just has no rhyme or reason, like someone came up with an ad-hoc new bit of syntax to do every different thing.
With Matplotlib I even managed to get a legend floating on the side of my plot. Each of my plots has lines connecting points in slightly different but overlapping scales (e.g. one plot has a scale 10, 20, 30,another 10, 20, 30, 40, 50) but they share some of the lines and markers automatically, so for the legend to make sense I had to create it manually. I also had to adjust some of the plot axis ticks manually.
No sweat. Not a problem! By that point I was getting the hang of it so it felt like a piece of cake.
And that's what kills me with R. No matter how long I use it, it never gets easier. Never.
I don't know what's wrong with that poor language and why it's such an arcane, indecipherable mess. But it's an arcane and indecipherable mess and I'm afraid to say I don't know if I'll ever go back to it again.
... gonna miss it a little though.
Edit: actually, I won't. Half of my repos are half R :|
Having seen Julia proposed as the nemesis of R (not python, that too political, non-lispy)
>the creator of the R programming language, Ross Ihaka, who provided benchmarks demonstrating that Lisp’s optional type declaration and machine-code compiler allow for code that is 380 times faster than R and 150 times faster than Python
(Would especially love an overview of the controversies in graphics/rendering)
R especially dplyr/tidyverse is so underrated. Working in ML engineering, I see a lot of my coworkers suffering through pandas (or occasionally polars or even base Python without dataframes) to do basic analytics or debugging, it takes eons and gets complex so quickly that only the most rudimentary checks get done. Anyone working in data-adjacent engineering work would benefit from R/dplyr in their toolkit.
what’s the story integrating R code into larger software systems (say, a saas product)? I’m sure part of Python’s success is sheer mindshare momentum from being a common computing denominator, but I’d guess the integration story is part of the margins. Your back end may well already be in python or have interop, reducing stack investment and systems tax.
I added the SQL query to the top of the R script to generate the input data.frame and my Python code reads the output CSV to do subsequent processing and storage into Django models.
I use a subprocess running Rscript to run the script.
It's not elegant but it is simple. This part of the system only has to run daily so efficiency isn't a big deal.
R especially dplyr/tidyverse is so underrated. Working in ML engineering, I see a lot of my coworkers suffering through pandas (or occasionally polars or even base Python without dataframes) to do basic analytics or debugging, it takes eons and gets complex so quickly that only the most rudimentary checks get done. Anyone working in data-adjacent engineering work would benefit from R/dplyr in their toolkit.
Earlier quoted context omitted.
what’s the story integrating R code into larger software systems (say, a saas product)? I’m sure part of Python’s success is sheer mindshare momentum from being a common computing denominator, but I’d guess the integration story is part of the margins. Your back end may well already be in python or have interop, reducing stack investment and systems tax.
I am working on a system at present where the data scientist has done the calculations in an R script. We agreed upon an input data.frame and an output csv as our 'interface'. I added the SQL query to the top of the R script to generate the input data.frame and my Python code reads the output CSV to do subsequent processing and storage into Django models. I use a subprocess running Rscript to run the script. It's not…
R especially dplyr/tidyverse is so underrated. Working in ML engineering, I see a lot of my coworkers suffering through pandas (or occasionally polars or even base Python without dataframes) to do basic analytics or debugging, it takes eons and gets complex so quickly that only the most rudimentary checks get done. Anyone working in data-adjacent engineering work would benefit from R/dplyr in their toolkit.
what’s the story integrating R code into larger software systems (say, a saas product)? I’m sure part of Python’s success is sheer mindshare momentum from being a common computing denominator, but I’d guess the integration story is part of the margins. Your back end may well already be in python or have interop, reducing stack investment and systems tax.
Earlier quoted context omitted.
I'm more excited about https://typst.app/
Quarto can output to Typst (as well as many other outputs simultaneously, e.g. .docx, HTML, PDF, PPT, etc) for it's typesetting capabilities. https://quarto.org/docs/output-formats/typst.html