Fringe
This page is experimental. Here we collect the fringe public comments related to data.table, in date order. Fringe in the sense of peripheral or extreme but also subtle. These can often go quietly viral and gently sway a community over time. For those for who English is not their first language, sarcasm and jest are powerful tools sometimes on display here. We have always added all articles we are aware of to the articles page if they mention data.table (whether positive or negative) and will continue to do so. Even so, the sentiment of the articles page is overwhelming positive. The goal of this fringe page is to collect public comments (anything that is not an article, since that belongs on the articles page) with a bias towards the negative to aid potential new users in their quest to build a full unbiased picture of the data.table package.
Or, in other words, a problem shared is a problem halved.
28 Aug 2017 Jose Manuel Vera on Twitter
Use data.table for wrangling data without the ugly data.table syntax https://cran.r-project.org/web/packages/dataPreparation … #rstats
It doesn't feel good for your work to be called "ugly". My only response was to retweet it.
25 April 2017 Adam T. Austin on Twitter
Collaborating with someone who uses data.table. My loyalty to the #tidyverse grows greater by the second... cc @hadleywickham #rstats
This was copied to #rstats, retweeted by Hadley and intended to be widely seen. It is not pleasant to be criticized publicly and to be denied the knowledge of why. Unfortunately this does work and it does sway a community. Perhaps the tweeter or his collaborator have misunderstood something - we will never know. Replying to ask risks escalating and taking even more time. Time I can't make.
My response was to retweet this one from earlier in the month. I hadn't felt it was appropriate to retweet that before. Silly, isn't it.
8 April 2017 Thọ Duy Nguyễn on Twitter
back to data.table after a long time with dplyr #rstats
24 Mar 2017 @statquant on Twitter
@MattDowle is up to another bloody benchmark, #python stocks are tumbling down
The word "bloody" is used in a negative connotation to convey that benchmarking is not appreciated by them. After the comma is sarcasm. It is a jibe. Anyway, if you ever need row-wise recursive updates within group, then take a look at the S.O. answer which shows how to reduce 19.2s to 0.27s using data.table together with Rcpp. My question was answered and here is how to do similar in Python.
25 Dec 2014 Hadley Wickham on Hacker News
Data tables are extremely fast but I think their concision makes it harder to learn and code that uses it is harder to read after you've written it. It's very reminiscent of APL.
Our response: See the hacker news item and comparing dplyr to data.table on Stack Overflow.
The word reminiscent was used to convey the notion of-the-past and is meant as criticism.
26 Jun 2014 Hadley Wickham on Stack Overflow
Also read.csv() reads everything into a big character matrix and then modifies that, does fread() do the same thing? In fastread we guess column types and then coerce as we go to avoid a complete copy of the df.
The Stack Overflow question is "Reason behind speed of fread in data.table package in R" and an implicit compliment to data.table. That's the context. The comment is a subtle way to i) create doubt about fread and ii) announce his new fastread package which had not been known before that. fastread subsequently became readr.
