Gadfly.jl

I agree, and I think it's worse than Rust because Rust at least mostly lives up to the hype, and the main caveats (compile time, borrow checker complexity, etc.) are well known at this point. How many people know that loading the graph plotting library takes 30 seconds, and that is working as intended? https://github.com/GiovineItalia/Gadfly.jl/issues/251 I've tried to use Julia a number of times and the experience is always worse than Matlab.

R is nice for stats stuff, even if performance can be iffy for certain tasks. The existing libraries for basic ML tasks are largely fantastic. The syntax rubs many people (including me) the wrong way. But I don't like the paradigm for basic ML tasks in scikit-learn (python) much better. Anything involving matrix computation is great to do in python because of existing libraries (numpy). I think most people who are actually writing code (i.e. grad students) don't mind the syntax because they learned it in school starting in undergrad. Octave's main appeal to me (and most people I work with) is being able to run existing MATLAB code. However, making it work for nontrivial MATLAB projects, is, well, nontrivial. It's also very slow. Julia is the language I'm most excited about. It's fast [1] even though it has a slow startup time, and that's

In regards to graphics, there is really nice work being done by Daniel Jones. His Gadfly.jl (https://github.com/dcjones/Gadfly.jl) package implements the grammar of graphics and it can already produce high quality plots similar to ggplot2. I will also disagree with your statement about Julia not catering to statisticians. Especially as datasets grow, performance keeps becoming more and more important. And this essentially means that statisticians implementing new methods in R packages have to drop down to C++ via Rcpp anyway. And this is exactly the problem Julia tackles. Of course, you could argue that practitioners do not care about the trouble the method developer went through in order to optimize the code via Rcpp. Yet, the practitioner will just use the language which offers the statistical tools he needs, thus Julia will draw him in

For plotting you'll need a plotting package – I recommend Gadfly [1]. I should definitely make the docs clearer for people who haven't used Julia before. That should be notes.html, I'll go ahead and fix that. [1] https://github.com/dcjones/Gadfly.jl

It depends on what you need. DataFrames [1] is a pretty good replacement for R's data frames. Distributions [2] is pretty amazing. Gadfly.jl [3] is a great ggplot-style plotting library that integrates nicely with IJulia [4]. If you have questions about specific things you need to do and want pointers, post on the julia-users list [5] and you should get some good answers. [1] http://harlanh.github.io/DataFrames.jl/ [2] http://distributionsjl.readthedocs.org/en/latest/ [3] https://github.com/dcjones/Gadfly.jl [4] https://github.com/JuliaLang/IJulia.jl [5] https://groups.google.com/forum/#!forum/julia-users

It is open-source software, not a walled garden - there are going to be multiple options, and they are not going to all be named ["PLOTTINGISHERE$i" for i in 1:4]. If you like MATLAB/pyplot/matplotlib style, PyPlot.jl is for you. If you like ggplot2, Gadfly.jl is for you. If you like gnuplot, Gaston.jl is for you. Finally, but not least, is Winston.jl, which is its own thing altogether. I think in many cases its good for packages that overlap to come together/one to become dominant, but I don't think that applies here. https://github.com/stevengj/PyPlot.jl https://github.com/dcjones/Gadfly.jl https://github.com/mbaz/Gaston.jl https://github.com/nolta/Winston.jl

For those interested in this sort of thing, Julia's Gadfly can also use D3 as a backend: https://github.com/dcjones/Gadfly.jl (although to be fair gadfly has more in common with ggplot than matplotlib)

Agreed. If you are worried about the syntax for plotting, take a look at Gadfly, which aims to implements the Grammar of Graphics language for julia. https://github.com/dcjones/Gadfly.jl