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Comment by minimaxir | Hacker News Reader

This tutorial is much more basic and has much less practical statistical applications than the R tutorial posted last week (https://news.ycombinator.com/item?id=12264360), which itself is out-of-date relative to the R for Data Science book (http://r4ds.had.co.nz/)

I really am curious why anything "R" and "Tutorial" gets massively upvoted to the Top 3 of HN like clockwork nowadays. I might have to restart my R tutorial screencasts since there appears to be a demand. :P

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Because it's exploding. Microsoft backing does wonders, for one. But it was even without them. Every analytics vendor standardizes on R.

> I really am curious why anything "R" and "Tutorial" gets massively upvoted to the Top 3 of HN like clockwork nowadays.

Same thing I was wondering. Is it simply that R is gaining popularity among a more mainstream audience, while it hasnt changed that much recently ?

Idiomatic R has changed enormously with the growth of Magrittr the ℅>℅ operator, dplyr and now broom. Code by expert users is unrecognisable from that of a few years ago.

Yeah but that tutorial does not even go into dplyr or tidyr.

Indeed - I think it's unclear how you should begin to teach R now. There is a lot of legacy stuff that you may need to recognise if you want to understand the help documentation. But if you wanted to seriously work in R it maybe better to skip straight to ggplot2, dplyr and broom without touching base R, the apply functions, and list in list in list hell.

That's pretty much what I did. I dip back down into the 'base' stuff when I need to, but in general I stick in the hadleyverse of tools. It's been great.

Indeed. 10-12 years ago there were probably no more than 50ish people in the world who knew R better than me. By 5 years ago the tide had heavily turned but I was still using it at least a few times a week.

If I had to use R as a daily driver now it'd be like learning the language all over again. I could write it using the old ways, and u do that on the few occasions I have to, but it's no longer idiomatic at all

R still looks pretty much the same to me when I look at the code on kaggle, stack exchange, etc. Can you share where you have seen this expert R use and/or what use cases?

Most modern R usage I've seen, even on Kaggle, has used margittr/dplyr since it's orders of magnitudes faster/easier than the base functions. (i.e. I almost quit R in favor for Python without those two)

A quick example of my own notebook which uses margittr/dplyr heavily to process Stack Overflow survey data: https://github.com/minimaxir/stack-overflow-survey/blob/mast...

Thanks for the example code, it actually has me wondering whether using the pipes has any impact on performance. They are much easier to read than the nested function calls that would be used instead.

However, I mean I just searched for an R script on kaggle and the first I found is this: https://www.kaggle.com/bpavlyshenko/grupo-bimbo-inventory-de...

From my experience that is a typical example (data.table is very common, but not so much dplyr and the pipes).

I looked through the recent/featured questions on stack overflow and cross validated and saw only "old school" R as well. For example: http://stats.stackexchange.com/questions/228800/

I would love R screencasts

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