Julia adoption keeps climbing

Post by evgen » Mon, Jan 18, 2021, 5:37 PM UTC

fnord123 wrote: post #167

alephnil wrote: post #11

Earlier quoted context omitted.

Julia has the focus on scientific and numerical computing, and is overtaking the python/numpy combo in that niche. In addition to being considerably faster than python, it also has quite some innovative libraries in the area. This can also extend into machine learning, where python has been the go to language, despite its limitations. For other areas, like web programming, there is no sign of Julia replacing Python i…

>Julia has the focus on scientific and numerical computing, and is overtaking the python/numpy combo in that niche\ I agree. In fact if Julia hasn't overtaken Python in numerical computing by January 2022 I will consider it a huge failure.

Post by evgen » Mon, Jan 18, 2021, 5:39 PM UTC

oxinabox wrote: post #161

superbcarrot wrote: post #4

Julia is a nice language, it's just tough to compete with Python. - The beginner experience in Julia is still much worse than it is in Python. Stuff that should work intuitively sometimes doesn't, and when you get a cryptic error message, it's difficult to find relevant help online. And when you do find help, some of it is out of date because the language has changed over the past few years. - You can squeeze a lot o…

I remember in ~2005 people said almost the exact same thing about Python and Perl.

Post by klaff » Mon, Jan 18, 2021, 5:45 PM UTC

tpoacher wrote: post #100

I don't want to disparage Julia, it's actually a very nice language, and I was very excited to learn it a couple of years ago. But, honestly, I think their adoption at this point is less "linux-like" driven and much more "apple-like". In that, the language is 'ok', but the company is going to INCREDIBLE lengths with respect to shrewd marketing and buzz-creation at this point. Which is admirable but also kinda worryin…

Post by laplacesdemon48 » Mon, Jan 18, 2021, 5:50 PM UTC

goatlover wrote: post #165

ryndbfsrw wrote: post #157

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If we removed dplyr, then R scripts would absolutely scream so I find the speed argument for 'why switch to X' unconvincing. If users cared so deeply about speed, almost no one would be using tidyverse instead we'd all be using base-R or data.table. Multiple dispatch? Hmm is this really a problem that I'm going to come across in the real-world when 90% of our time is spent ingesting a poorly-formatted csv, doing some…

> If the goal of Julia is to replace R/Python then their priorities feel way off the mark There's a lot more to scientific computing than wrangling tabular data. Julia is competing in that overall space with R/Python/Fortran/Java/C++. If R or Pandas is better at data wrangling, then Julia won't win out there. But so be it. No PL is best at everything.

  1) Read in badly formatted CSV data
  2) Wrangle the data into a useable form
  3) Do some basic exploratory analysis (including plots)
  4) Write several models in brms/raw Stan (via rstan)
  5) Simulate from the priors and reset them to more sensible values
  6) Run the model over the data to generate the posterior
  7) Plot/run posterior predictive checks, counterfactual analysis, outlier analysis (PSIS or WAIC), etc.

Post by enriquto » Mon, Jan 18, 2021, 6:04 PM UTC

Jugurtha wrote: post #206

enriquto wrote: post #198

Earlier quoted context omitted.

> How would that happen? At the very least, you could put the whole json stuff inside a comment. It's already plain text, isn't it?

> At the very least, you could put the whole json stuff inside a comment. It's already plain text, isn't it? So instead of having the whole file as JSON, which is lazy and not well thought-out, we'll put all content in JSON, then put that JSON inside a comment in a plain text file. Do I read you correctly? I feel we're making progress faster than these lazy Jupyter org bandits.