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Comment by xiphias2 | Hacker News Reader

,,Text formatting and unit testing are two areas that should be relevant to almost any project''

This is just blatantly false.

I love Julia for statistical analysis.

I never needed speed when printing out stuff.

REPL should be much faster (especially importing libraries), and I'm not a fan of 1 based indexing, but everything else is awesome if you need great performance.

Also I'm missing a great Julia native graphing library with zooming support (Matlab's is far better).

Production binaries are very hard to make, but for statistical research I don't know any better software.

Also for unit testing asserts are good enough for me.

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> I never needed speed when printing out stuff.

> REPL should be much faster (especially importing libraries), and I'm not a fan of 1 based indexing, but everything else is awesome if you need great performance.

Then don't you want fast printing / string ops? REPLs heavily rely on that.

Speed on a repl is as much from not printing things as it is in printing them fast. At the least, always accept an interrupt to stop printing.

IO and strings in Julia are very fast. The comment here in the blog post is about the (currently) 130ms overhead in starting up Julia.

Yeah regarding that, I do think start-up speed is important, especially in a data-driven workflow where you run binaries on a dataset.

> run binaries on a dataset.

That's not how (barely) anyone actually works with Julia when they do data-analysis though.

The implicit assumption in much of the Julia world is that the work being done on the dataset will dwarf the startup time; if your workload takes less than 100ms, it’s probably not worth spending tens of milliseconds on compiling your code in the first place, and you should run it with an interpreter. (Incidentally Julia does support that, but it requires a command-line flag, and can come with a heavy performance penalty if you are running numérica-heavy code)

> but for statistical research I don't know any better software.

R is a hybrid of a very slow language and statistical algorithms written in C that can't be debugged. Also the libraries don't have name spaces, not strongly typed, and there are many other features and libraries that show their age.

My favourite examples of Julia's great type system are its automatic differentation libraries that take a native function and wouldn't be possible with R.

R has had namespaces for roughly 15 years now: http://r-pkgs.had.co.nz/namespace.html

not like python's though. The full name of the package have to be used. e.g. np and pd are not possible with R

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