Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

A newcomer’s (angry) guide to R

arrgh.tim-smith.us

Post reply on HN
Print view View original

81–90 of 232 posts

Re: A newcomer’s (angry) guide to R

#81

Post by asteriske »

ekianjo wrote: post #71
extr wrote: post #19

HN is predisposed to hate R because everyone here is coming from a "real" programming context. Their concerns are generally valid, but they should keep in mind a lot of people using do not have a software development background and do not care that the language is not elegantly designed: they just want to get analytical work done. In that respect, R is far, far superior to Python. Even something as simple as installi…

And there is litterally no equivalent to dplyr and ggplot2 in Python. Those alone can make a huge difference in how many lines you need to write to do something.

ggplot2 has plotnine (http://plotnine.readthedocs.io) which has a nearly identical API. I've found though it's not perfect, you can get closer to dplyr with JS-style method chaining on Pandas.

Re: A newcomer’s (angry) guide to R

#82

Post by durbatuluk »

extr wrote: post #19

HN is predisposed to hate R because everyone here is coming from a "real" programming context. Their concerns are generally valid, but they should keep in mind a lot of people using do not have a software development background and do not care that the language is not elegantly designed: they just want to get analytical work done. In that respect, R is far, far superior to Python. Even something as simple as installi…

As someone who taught R for students coming from Biology. Most of them just want to learn some_stat_test(), how to interpret it and who to plot().

These who tried to do modeling using vanilla R just dropped midway because of R performance and switched back to fortran

ggplot2 just should be baseline instead of default gfx.

Re: A newcomer’s (angry) guide to R

#83

Post by com2kid »

riskneutral wrote: post #45
bokstavkjeks wrote: post #32

Earlier quoted context omitted.

It's also worth noting that R becomes much more pleasurable with the Tidyverse libraries. The pipe alone makes everything more readable. I'm also coming from more of an office setting where everything is in Excel. I've used R to reorganize and tidy up Excel files a lot. Ggplot2 (part of the Tidyverse) is also fantastic for plotting, the grammar of graphics makes it really easy to make nice and slightly complex graphs…

Tidy features (like pipes) are detrimental to performance. The best things R has going for it are data.table, ggplot, stringr, RMarkdown, RStudio, and the massive, unmatched breadth and depth of special-purpose statistics libraries. Combined, this is a formidable and highly performant toolset for data analytics workflows, and I can say with some certainty that even though “base Python” might look prettier than “base…

> Tidy features (like pipes) are detrimental to performance.

But they are some absolutely amazing features to use. After helping my wife learn R, and learning about all the dypler features, going back to other languages sucked. C#'s LINQ is about as close as I can get to dypler like features in a main stream language.

Of course R's data tables and data frames are what enable dypler to do its magic, but wow what magic it is.

Re: A newcomer’s (angry) guide to R

#84

Post by kazinator »

riskneutral wrote: post #45
bokstavkjeks wrote: post #32

Earlier quoted context omitted.

It's also worth noting that R becomes much more pleasurable with the Tidyverse libraries. The pipe alone makes everything more readable. I'm also coming from more of an office setting where everything is in Excel. I've used R to reorganize and tidy up Excel files a lot. Ggplot2 (part of the Tidyverse) is also fantastic for plotting, the grammar of graphics makes it really easy to make nice and slightly complex graphs…

Tidy features (like pipes) are detrimental to performance. The best things R has going for it are data.table, ggplot, stringr, RMarkdown, RStudio, and the massive, unmatched breadth and depth of special-purpose statistics libraries. Combined, this is a formidable and highly performant toolset for data analytics workflows, and I can say with some certainty that even though “base Python” might look prettier than “base…

If you're in a situation where 80% of the time is spent in 20% of the code, you only have to use less expressive features in those hot-spots; you don't have to give up your pipes or whatever in places that don't contribute much to the run-time.

Re: A newcomer’s (angry) guide to R

#85

Post by crispyambulance »

curiousgal wrote: post #54
andrewla wrote: post #15

As a long-time R user, I agree with all of these complaints. The language itself is ugly and actively tries to get in your way. I'll add that concepts like data frames are not really intrinsic, and you get needless complexities like "length", "nrow", "dim", each of which does the wrong thing in 90% of the scenarios of interest. The confusion of lvalues is another strange quirk -- a If you discipline yourself to learn…

>split/lapply/aggregate/ave I always feel bad when I resort to a for loop in R. Do you have any recommended ressources about mastering those specific manipulations?

Really, it's not always worth the trouble.

Sometimes a loop is vastly more performant if you count the amount of time it takes to get the "idiomatic" way working and working in a way that allows easy troubleshooting.

Re: A newcomer’s (angry) guide to R

#86

Post by hobolord »

curiousgal wrote: post #49
riskneutral wrote: post #45

Earlier quoted context omitted.

Tidy features (like pipes) are detrimental to performance. The best things R has going for it are data.table, ggplot, stringr, RMarkdown, RStudio, and the massive, unmatched breadth and depth of special-purpose statistics libraries. Combined, this is a formidable and highly performant toolset for data analytics workflows, and I can say with some certainty that even though “base Python” might look prettier than “base…

From your experience what makes data.table so useful?

the grammar of data.table is a bit similar to SQL, and I find that it's easier to quickly write something vs piping in dplyr

Re: A newcomer’s (angry) guide to R

#87

Post by Giroflex »

CJefferson wrote: post #72
Giroflex wrote: post #44

Earlier quoted context omitted.

> Even something as simple as installing a library is a conceptual leap for these people (why wouldn't the software just come with everything needed to work?). > Have you ever tried explaining the various python package and environment management options to someone with a background in Excel/SQL? I don't understand the difficulty I've often seen voiced against this. Why would a newbie or someone who just wants to get…

Well pip install library needs root, which you probably don't have. So now you have to teach them about making, and acitvating, virtual environments. Also, they can't easily search through the packages in a nice GUI and just click on the one they want to install.

>pip install library needs root

Hmm, not really. It's actually advised against [1].

[1] - https://askubuntu.com/questions/802544/is-sudo-pip-install-s...

Re: A newcomer’s (angry) guide to R

#88

Post by extr »

vijucat wrote: post #68
curiousgal wrote: post #49

Earlier quoted context omitted.

From your experience what makes data.table so useful?

Answering questions in a rapid, interactive way (, while using C to be efficient enough that one can run it on millions of rows): # Given a dataset that looks like this… > head(dt, 3) mpg cyl disp hp drat wt qsec vs am gear carb name 1: 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4 2: 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 Mazda RX4 Wag 3: 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 Datsun 710 # What's the mean hp an…

Completely agree. dplyr is nice enough but the verbose style gets old fast when you're trying to use it in an interactive fashion. imo data.table is the fastest way to explore data across any language, period.

Re: A newcomer’s (angry) guide to R

#89

Post by Giroflex »

ekianjo wrote: post #76
CJefferson wrote: post #72

Earlier quoted context omitted.

Well pip install library needs root, which you probably don't have. So now you have to teach them about making, and acitvating, virtual environments. Also, they can't easily search through the packages in a nice GUI and just click on the one they want to install.

And pip s upgrade process for packages is almost non existent, while R supports that very, very easily.

Never had any trouble with `pip install --upgrade library`.

Not sure if there's support for upgrading all packages at once, though.

Re: A newcomer’s (angry) guide to R

#90

Post by trts »

andrewla wrote: post #18
fermienrico wrote: post #9

Having used Python, JSL, Julia, R and Matlab; I agree with most of the things in R. R is an extremely ugly language. It seems to be created by people who wear capris and uggs (both at the same time). But, R has incredible packages, especially the work done by Hadley Wickam. ggplot2 is beautiful. It is utterly gorgeous. It is what Ted Baker is to the capri guys that designed the language itself.

ggplot2 produces beautiful graphs. I don't think it's beautiful as a package -- the syntax is strange and reflects an earlier evolution of the ideas that went into the tidyverse. Notably the use of + instead of chaining operators, the use of a custom "ggproto" object system instead of S3 (which makes extensibility a nightmare), and the superfluous presence of the aes() function (rendered unnecessary by better lazy ev…

I seem to be in the extreme minority opinion that ggplot2 has facilitated the creation of millions of ugly charts. The default theming hurts my eyes. Much improvement can come from just adding + theme_bw() or + theme_minimal() as the last layer.

But it is flexible, and it's fun. I have enjoyed following other's examples of recreating chart themes mimicked from the Economist and FiveThirtyEight. I think the latter often does employ ggplot2 with heavy customization to create some nice looking visualizations.

Post reply on HN