Hacker News new | ask | show | jobs
by BadInformatics 1973 days ago
> 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 quick plots and perhaps building a model to test something out

Yes, multiple dispatch is not some highfalutin ivory tower concept that only comes up in specialized code. For example, the model in question could define custom plotting recipes[1] so that you can just call plot() and have it produce something useful.

Also, why shouldn't dplyr perform comparably against data.table? Seems like there would be no need for a fragmented library ecosystem here if the abstractions the tidyverse is built upon were lower-cost. Moreover, what if my data isn't CSV or in a table-like shape at all? "real world" does not mean the same thing across different domains.

[1] http://docs.juliaplots.org/latest/recipes/
2 comments
disgruntledphd2 1973 days ago
> Yes, multiple dispatch is not some highfalutin ivory tower concept that only comes up in specialized code. For example, the model in question could define custom plotting recipes[1] so that you can just call plot() and have it produce something useful.

This is literally the whole conception behind generic functions in R (print, plot, summary etc).

I agree it's great, but Julia is building on a lot of prior art here.

link
BadInformatics 1973 days ago
For sure, and one would be remiss not to mention Dylan, CL/CLOS and Clojure here as well. My quibble was with the claim that multiple dispatch rarely shows up in practice, which you've pretty clearly shown is not the case in R!
link
disgruntledphd2 1973 days ago
Yup, the R-FAQ specifically calls out Dylan and CL as influences.
link
ryndbfsrw 1973 days ago
'highfalutin ivory tower' is a great name for a band :D

Naturally you are correct and I am wrong to dismiss it as unimportant. What I'm saying is that the majority of R/Python users today are not looking for ultimate speed or sophisticated programming paradigms. Most users are doing the unsexy bread and butter of 'Take some tabular data' -> analyse -> report on it and I want to dismiss the argument of 'users will migrate to Julia because of these nifty features' because it ignores the very reasons the existing users use these tools in the first place. It would be as absurd as proclaiming Excel users will switch to Python because the accounts deparment suddenly cares about NLP.

link