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Any 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, Ro

Any 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/rendering) https://news.ycombinator.com/item?id=42785785 https://news.ycombinator.com/item?id=42785785

barrenko 2y ago
For data analysis and visualization R is the lightsaber.
Hasnep 2y ago
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/ https://dataframes.juliadata.org/ [2] https://github.com/TidierOrg/Tidier.jl https://github.com/TidierOrg/Tidier.jl [3] https://github.com/maxhumber/redframes https://github.com/maxhumber/redframes [4] https://aog.makie.org/ https://aog.makie.org/
- staplung 2y ago
  Thanks for the links. FWIW, the link for 4 (aog) is currently 404'd, which is amusing because the site is still up. They just seem to have deleted their own top level index.html file. Anyway, this works: https://aog.makie.org/v0.10.3/ https://aog.makie.org/v0.10.3/
CreRecombinase 2y ago
The comment you linked is a response to my comment where I tried (and failed) to articulate the world in which R is situated. I finally "RTFA" and the benchmark I think perfectly deomonstrates why conversations about R tend not to be very productive. The benchmark is of a hypothetical "sum" function. In R, if you pass a vector of numbers to the sum function, it will call a C function sum. That's it. In R when you want to do lispy tricky metaprogramming stuff you do that in R, when you want stuff to go fast you write C/C++/Rust extensions. These extensions are easy to write in a really performant way because R objects are often thinly wrapped contiguous arrays. I think in other programming language communitues, the existence of library code written in another language is some kind of sign of failure. R programmers just do not see the world that way.
fithisux 2y ago
Julia is what I mostly use. I used R in the past, but I was all the time puzzled from the documentation. It did not work for me. Sometimes I fire the REPL for some interpolation, but I limit myself to what I understand. BTW I am a senior Java / Python developer

barrenko 2y ago

For data analysis and visualization R is the lightsaber.

Hasnep 2y ago

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/ https://dataframes.juliadata.org/ [2] https://github.com/TidierOrg/Tidier.jl https://github.com/TidierOrg/Tidier.jl [3] https://github.com/maxhumber/redframes https://github.com/maxhumber/redframes [4] https://aog.makie.org/ https://aog.makie.org/

staplung 2y ago
Thanks for the links. FWIW, the link for 4 (aog) is currently 404'd, which is amusing because the site is still up. They just seem to have deleted their own top level index.html file. Anyway, this works: https://aog.makie.org/v0.10.3/ https://aog.makie.org/v0.10.3/

CreRecombinase 2y ago

The comment you linked is a response to my comment where I tried (and failed) to articulate the world in which R is situated. I finally "RTFA" and the benchmark I think perfectly deomonstrates why conversations about R tend not to be very productive. The benchmark is of a hypothetical "sum" function. In R, if you pass a vector of numbers to the sum function, it will call a C function sum. That's it. In R when you want to do lispy tricky metaprogramming stuff you do that in R, when you want stuff to go fast you write C/C++/Rust extensions. These extensions are easy to write in a really performant way because R objects are often thinly wrapped contiguous arrays. I think in other programming language communitues, the existence of library code written in another language is some kind of sign of failure. R programmers just do not see the world that way.

fithisux 2y ago

Julia is what I mostly use. I used R in the past, but I was all the time puzzled from the documentation. It did not work for me. Sometimes I fire the REPL for some interpolation, but I limit myself to what I understand. BTW I am a senior Java / Python developer