I hope gwalkr can be added to this book, it's pretty intereting updates for visualizations in R recent years.
repo: https://github.com/Kanaries/GWalkR site: https://kanaries.net/gwalkr
Big Book of R
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Re: Big Book of R
#72Any Julians comment? 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/r…
For data analysis and visualization R is the lightsaber.
Re: Big Book of R
#73I hope gwalkr can be added to this book, it's pretty intereting updates for visualizations in R recent years. repo: https://github.com/Kanaries/GWalkR site: https://kanaries.net/gwalkr
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Re: Big Book of R
#74R 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.
As someone who is learning probability and statistics for recreation, I wholeheartedly agree. I wish I had come across R and dplyr/tidyverse/ggplot2 back in college while learning probability and stats. They were quite boring and drudgery to study because I wasn't aware of R to play around with data. Well, better late than never I guess.
R was the first thing we had in our syllabus for (shallow)Machine Learning.
the ease of doing `model <- lm(speed~dist, cars)` and then `predict(model, data.frame(dist = c(42)))` is unparalled.
Re: Big Book of R
#75Very nice, but instead of an owl, shouldn't the cover illustration be a pirate?
Totally agree. R is pure pirate energy. Half the functions are hidden on purpose, the other half only work if you chant the right incantation while facing the CRAN mirror at dawn.
unrelated to the post, but your comment history is very llm-like.
Re: Big Book of R
#76Any Julians comment? 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/r…
In my opinion, Julia has the best alternative to dplyr in its Dataframes.jl package [1]. The syntax is slightly more verbose than dplyr because it's more explicit, but in exchange you get data transformations that you can leave for 6 months and when you come back you can read and understand very quickly. When I used R, if I hadn't commented a pipeline properly I would have to focus for a few minutes to understand it.
In terms of performance, DF.jl seems to outperform dplyr in benchmarks, but for day to day use I haven't noticed much difference since switching to Julia.
There are also APIs built on top of DF.jl, but I prefer using the functions directly. The most promising seems to be Tidier.jl [2] which is a recreation of the Tidyverse in Julia.
In Python, Pandas is still the leader, but its API is a mess. I think most data scientists haven't used R, and so they don't know what they're missing out on. There was the Redframes project [3] to give Pandas a dplyr-esque API which I liked, but it's not being actively developed. I hope Polars can keep making progress in replacing Pandas, but it's still not quite as good as dplyr or even DF.jl.
For plotting, Julia's time to first plot has got a lot better in recent versions, from memory it's something like 20 seconds a few years ago down to 3 seconds now. It'll never be as fast as matplotlib, but if you leave your terminal window open you only pay that price once.
I actually think the best thing to come out of Julia recently is AlgebraOfGraphics.jl [4]. To me it's genuinely the biggest improvement to plotting since ggplot which is a high bar. It takes the ggplot concept of layers applied with the + operator and turns it into an equation, where + adds a layer on top of another, and the * operator has the distributive property, so you can write an expression like data * (layer_1 + layer_2) to visualise the same data with two visualisations. It's very powerful, but because it re-uses concepts from maths that you're already familiar with, it doesn't take a lot of brain space compared to other packages I've used.
[1] https://dataframes.juliadata.org/ [2] https://github.com/TidierOrg/Tidier.jl [3] https://github.com/maxhumber/redframes [4] https://aog.makie.org/
Re: Big Book of R
#77Earlier quoted context omitted.
Totally agree. R is pure pirate energy. Half the functions are hidden on purpose, the other half only work if you chant the right incantation while facing the CRAN mirror at dawn.
I've never used R before, why would functions be hidden on purpose? Sounds like a recipe for frustration.
Don't worry they're just a bot. R doesn't hide functions.
Re: Big Book of R
#78I will say, now after 15 years messing with this. With LLM I just do it all in Python. But, I still miss the elegance and simplicity of R for data manipulation and analysis. Especially the dplyr semantics. They really nailed it. I think they got crushed by the namespace / import system. There’s something about R that makes you so fluid and intuitive. But the engineering, the efficiency, I get with Python now, I can’t…
Funny you mention namespacing: R 4.5.0 was just released today with the new `use()` function, which allows you import just what you need instead of clobbering your global namespace, equivalent to python’s `from x import y` syntax.
e.g. avoid dplyr overriding base::filter
use(“dplyr”, c(“mutate”, “summarize”))
Re: Big Book of R
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Re: Big Book of R
#80R 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.
Why not mix R and Python in interactive analysis workflows: 1) Download positron: https://github.com/posit-dev/positron 2) Set up a quarto (.qmd) notebook 3) Set up R and Python code chunks in tour quarto document 4a) Use reticulate to spawn a Python session inside R and exchange objects beween both languages ( https://github.com/posit-dev/positron/pull/4603 ) 4b) Write a few helper functions that pass objects betwee…
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