Teaching R to New Users: From tapply to Tidyverse

[@americanexile]

Outstanding blog post which led me here. Particularly valued your code example comparing "old school" and the Tidyverse on the same task, and the following analysis of each.

[@AranRyan]

Great talk. It improved my awareness and understanding of R as a organic, developing system. Thanks!

[@JakeTolbert]

Wickedly elegant answer about why R seems like a sloppy mess sometimes around 49:00 or so - Roger Peng in the best.

[@Standaardnaam]

I was promised some tapply, but all we got was aggregate.

[@nkristianschmidt]

22:50 where one realizes, TidyVerse will take away your freedom but also give you some tools to easily create some standard solutions.

[@martinbel]

It's a pity data.table is left out of this talk. It sorted out many of the problems mentioned and in a way has a deep relation to the "tidyverse", considering melt, dcast functions originally from reshape2 are in there.

[@nkristianschmidt]

data.table was one of the biggest advances R ever saw. TidyVerse is a new language and they push it a lot and try to ignore the rest or take it over as theirs.

[@nkristianschmidt]

learn Base-R first. Do not engage with TidyVerse until you absolutely have to. Otherwise, you will unable to document and debug your code. Then add data.table and packages that are relateable to base-R. And, though caret is fantastic, it is likely better to use the closer-to-base packages first and then learn caret. Otherwise you know too little about what is going on under the hood.

[@siqizhang3602]

Even from the very beginning, R has been only superficially similar to S, just as Julia is superficially similar to MATLAB.