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Can Julia be a competitor to R? I love R in concept (interactive environment for statistical analysis) but the language just drives me crazy in its multitude o

Can Julia be a competitor to R? I love R in concept (interactive environment for statistical analysis) but the language just drives me crazy in its multitude of types and the loosey-goosey ways it converts between them. A friend of mine is really proficient with R; when I walked him through some of the R patterns that are very confusing/irregular to me, he sort of laughed: he could see what I was saying but he said "with R you can't worry about things too much, you kind of just have to just go with it." If Julia can serve some of the same use cases but in a better-designed way, sign me up!

xixi77 13y ago
I believe it has been one of the intentions for a while. How far it has advanced, I'm not sure, I haven't looked at it for almost a year, now time for a refresher. But R may be a bit tricky to compete with directly at this point: it has been designed as a statistical language from the ground up, and it would be a while for any language to catch up to R's library. But, I still see many people doing statistical computations in Matlab, and that can not be very hard to beat, especially how similar the syntax is, and how ugly Matlab is at statistics (from what I am reading in the comments here, vectorization still carries a performance penalty though, which is a real pity). Just curious -- what patterns bothered you the most with R btw?
- haberman 13y ago
  > Just curious -- what patterns bothered you the most with R btw? It's mostly around the multitude of subtly different types and the ways you convert between them. I think I also remember strange things like lists having named attributes in addition to list members that just seemed totally wrong and confusing to me. I wish I could give you better specifics but it's been several years since I've done anything with R.
Homunculiheaded 13y ago
The biggest thing that R has is just an incredible amount of really well documented packages, that are quite frequently cutting edge (unless you want to do any deep learning work). Not to mention that the base R has just a tremendous amount of useful stuff baked in. I've kept an eye on Julia and would love to use it in my everyday work, but also know that for now that's just not possible because of how many built-in functions and packages I rely on. However solving this is just a function of time and community (Julia just needs their Hadley Wickham). I remember when people scoffed at Python because it has nowhere near the ecosystem that Perl did.
- Fomite 13y ago
  This is, I think, the biggest hurdle for Julia. R's strength is not its language, it's the people. You need methods articles with supplements written in Julia, not R, for people to switch.
  - Toenex 13y ago
    Very true.
zhemao 13y ago
FWIW, there is a Julia library that allows you to call out to R (https://github.com/lgautier/Rif.jl https://github.com/lgautier/Rif.jl). I'm not sure how well-developed it is though. There are also a lot of R-inspired Julia libraries, such as Dataframes (https://github.com/JuliaStats/DataFrames.jl https://github.com/JuliaStats/DataFrames.jl). There is also a pretty good Julia-Python interface (https://github.com/stevengj/PyCall.jl https://github.com/stevengj/PyCall.jl) and bindings to Matplotlib (https://github.com/stevengj/PyPlot.jl https://github.com/stevengj/PyPlot.jl).
rainforest 13y ago
Patrick Burns' R inferno[1] enumerates a lot of R's eccentricities and workarounds for them. I think R gets a lot better once you switch to using the libraries Hadley maintains like plyr and ggplot. I still think proficiency in R is akin to a type of Stockholm syndrome. [1] : http://www.burns-stat.com/pages/Tutor/R_inferno.pdf http://www.burns-stat.com/pages/Tutor/R_inferno.pdf [PDF]
sampo 13y ago
Already in 2008 one of the R creators, Ross Ikaha, talked about how R is fundamentally slow and has inefficient memory use. And that a "next gen R" would be needed, and he was considering Common Lisp for the underlying language on compiler. See the "Back to the Future: Lisp as a Base for a Statistical Computing System". https://www.stat.auckland.ac.nz/~ihaka/?Papers_and_Talks https://www.stat.auckland.ac.nz/~ihaka/?Papers_and_Talks But now apparently Julia also fulfills Ihaka's requirements for the basis on the "new R" system, so I wonder is the need-for-speed part of the R community is considering a switch to Julia, instead of building a "new R" from scratch?
- otoburb 13y ago
  Lobbying Ross Ikaha to endorse Julia as the new R would hopefully bring more people over to the language. Instead of just the Matlab folks you would also renew interest with the slightly larger audience using R today.
- lispm 13y ago
  https://github.com/blindglobe/common-lisp-stat https://github.com/blindglobe/common-lisp-stat

xixi77 13y ago

I believe it has been one of the intentions for a while. How far it has advanced, I'm not sure, I haven't looked at it for almost a year, now time for a refresher. But R may be a bit tricky to compete with directly at this point: it has been designed as a statistical language from the ground up, and it would be a while for any language to catch up to R's library. But, I still see many people doing statistical computations in Matlab, and that can not be very hard to beat, especially how similar the syntax is, and how ugly Matlab is at statistics (from what I am reading in the comments here, vectorization still carries a performance penalty though, which is a real pity). Just curious -- what patterns bothered you the most with R btw?

haberman 13y ago
> Just curious -- what patterns bothered you the most with R btw? It's mostly around the multitude of subtly different types and the ways you convert between them. I think I also remember strange things like lists having named attributes in addition to list members that just seemed totally wrong and confusing to me. I wish I could give you better specifics but it's been several years since I've done anything with R.

Homunculiheaded 13y ago

The biggest thing that R has is just an incredible amount of really well documented packages, that are quite frequently cutting edge (unless you want to do any deep learning work). Not to mention that the base R has just a tremendous amount of useful stuff baked in. I've kept an eye on Julia and would love to use it in my everyday work, but also know that for now that's just not possible because of how many built-in functions and packages I rely on. However solving this is just a function of time and community (Julia just needs their Hadley Wickham). I remember when people scoffed at Python because it has nowhere near the ecosystem that Perl did.

Fomite 13y ago
This is, I think, the biggest hurdle for Julia. R's strength is not its language, it's the people. You need methods articles with supplements written in Julia, not R, for people to switch.
- Toenex 13y ago
  Very true.

zhemao 13y ago

FWIW, there is a Julia library that allows you to call out to R (https://github.com/lgautier/Rif.jl https://github.com/lgautier/Rif.jl). I'm not sure how well-developed it is though. There are also a lot of R-inspired Julia libraries, such as Dataframes (https://github.com/JuliaStats/DataFrames.jl https://github.com/JuliaStats/DataFrames.jl). There is also a pretty good Julia-Python interface (https://github.com/stevengj/PyCall.jl https://github.com/stevengj/PyCall.jl) and bindings to Matplotlib (https://github.com/stevengj/PyPlot.jl https://github.com/stevengj/PyPlot.jl).

rainforest 13y ago

Patrick Burns' R inferno[1] enumerates a lot of R's eccentricities and workarounds for them. I think R gets a lot better once you switch to using the libraries Hadley maintains like plyr and ggplot. I still think proficiency in R is akin to a type of Stockholm syndrome. [1] : http://www.burns-stat.com/pages/Tutor/R_inferno.pdf http://www.burns-stat.com/pages/Tutor/R_inferno.pdf [PDF]

sampo 13y ago

Already in 2008 one of the R creators, Ross Ikaha, talked about how R is fundamentally slow and has inefficient memory use. And that a "next gen R" would be needed, and he was considering Common Lisp for the underlying language on compiler. See the "Back to the Future: Lisp as a Base for a Statistical Computing System". https://www.stat.auckland.ac.nz/~ihaka/?Papers_and_Talks https://www.stat.auckland.ac.nz/~ihaka/?Papers_and_Talks But now apparently Julia also fulfills Ihaka's requirements for the basis on the "new R" system, so I wonder is the need-for-speed part of the R community is considering a switch to Julia, instead of building a "new R" from scratch?

otoburb 13y ago
Lobbying Ross Ikaha to endorse Julia as the new R would hopefully bring more people over to the language. Instead of just the Matlab folks you would also renew interest with the slightly larger audience using R today.
lispm 13y ago
https://github.com/blindglobe/common-lisp-stat https://github.com/blindglobe/common-lisp-stat