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A newcomer’s (angry) guide to R

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Re: A newcomer’s (angry) guide to R

#61

Post by ggm »

The first time I dived into R, I was confused.

The second time, I had a purpose and found enough code to copy to achieve it. I was rewarded.

The third time, I had a more complex problem demanding use of JSON from Elastic Search, and found that the two packages out there in git are basically orphanware, use dplyr in extremely confusing ways, and offer little or no advantage to simplistic HTTP fetching and direct to JSON parsing. Which is a huge shame, because the idea of an elastic search abstraction is very attractive. But. "it just didn't work out of the box"

I am very clear I am an R "consumer" not an R developer. But, at this point, absent Shiny and a gui, I think that Python and Numpy has as much to offer me basically.

Some people say the syntax is FP friendly. I have been trying to learn FP in Haskell and I think R is about the worst notation you could invent to sell FP.

Re: A newcomer’s (angry) guide to R

#62

Post by vorg »

wdkrnls wrote: post #50

I don't understand why HN hates R. HN loves lisp, and R as a language shares a much greater affinity with lisp languages than python or Go do. The language was born out of the original authors reading SICP (as statisticians). Sure, many of the users of R molded it to look like what they were used to (S), but that just highlights the powerful metaprogramming capabilities of the language.

> HN hates R. HN loves lisp, and R as a language shares a much greater affinity with lisp languages than python or Go do

HN was started by someone who wrote a book on, and flipped a startup using, Lisp. Python and Go are both used a lot by a buyer of startups, and HN exists to deliver startups, or their IP, to those buyers. R is more of a language for helping its users do data analysis, perhaps in corporate offices, and hence doesn't have as much use for HN's business purpose. Submissions on Python and Go are more likely to stick to HN's front page.

Re: A newcomer’s (angry) guide to R

#63

Post by thousandautumns »

This is a stupendous example of someone going overboard on their criticisms in order to grandstand.

R may not be the most "beautiful" language in a general perspective, but it certainly is more beautiful than Python when it comes to actual data analysis. There is nothing in R that is as ugly as even the best implemented pandas, numpy, and matplotlib code. All of the options in Python, which is generally pointed to as the "superior" language to R, feel tacked on and hackish.

The real story behind most of the complaints is that they come from software developers who only rarely need to do data analysis that would require R, and therefore use it infrequently and mistake their unfamiliarity with the language with the language being bad.

I also groaned at the part where the author struggled to google questions about R because of its "stupid name". I have literally never, ever had issues Googling anything about R they same way I haven't ever had issues finding answers to my questions about "Python". The author is grasping at straws, and this is a programming blog's equivalent of clickbait.

Re: A newcomer’s (angry) guide to R

#64

Post by CharlesMerriam2 »

Some reading this will claim any criticism is the fault of the critic. Others will jump in claiming it is all revealing the emperor's clothing and promoting an alternate religion.

A few will deconstruct the criticisms and look for the small documentation or even language changes to solve something.

I applaud the final group.

Re: A newcomer’s (angry) guide to R

#65

Post by crispyambulance »

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…

I use R at least a couple times a week. It gets the job done and I will be forever grateful for the tidyverse.

That said, R can be goddamn frustrating at times because of the way the documentation is written. It would be nice to simply be able to query about a function and get a cogent help file that explains THE BASICS of how to use the function for the most common use-case(s). Instead, the help files try to be "canonical" and front-load a bunch useless technical detail-- like that something is an "S3" object. Still haven't figured out what that really means and, I expect, that knowing something is "S3" will NEVER help me out when I am in a jam and need a little help to do something simple because I forgot some data manipulation detail.

Instead, I end up googling all the time connecting the dots all over the internet to get very simple stuffs done. At least now we have stackoverflow which, as vicious as it is, seems like Mister Rogers' Neighborhood compared to the old R mailing list.

Re: A newcomer’s (angry) guide to R

#66

Post by jandrese »

thousandautumns wrote: post #63

This is a stupendous example of someone going overboard on their criticisms in order to grandstand. R may not be the most "beautiful" language in a general perspective, but it certainly is more beautiful than Python when it comes to actual data analysis. There is nothing in R that is as ugly as even the best implemented pandas, numpy, and matplotlib code. All of the options in Python, which is generally pointed to as…

Some of it is undoubtedly hyperbole, but:

> Index vectors like a[1] … a[4]. All indexing in R is base-one. Note that no error is thrown if you try to access a[0]; it always returns an atomic vector of the same type but of length zero, written like numeric(0)

That's serious WTF right there.

In general a lot of the complaints revolve around the language making error handling unnecessarily difficult which is something that will drive me up the wall with a language. I'm a fairly defensive programmer and if your language is fighting me when I'm trying to do error checking I'm not going to be happy. I can kind of understand the thinking of "just write a perl script to verify/reformat your data before passing it to R", but that doesn't help me find my own errors.

Re: A newcomer’s (angry) guide to R

#67

Post by haberman »

thousandautumns wrote: post #63

This is a stupendous example of someone going overboard on their criticisms in order to grandstand. R may not be the most "beautiful" language in a general perspective, but it certainly is more beautiful than Python when it comes to actual data analysis. There is nothing in R that is as ugly as even the best implemented pandas, numpy, and matplotlib code. All of the options in Python, which is generally pointed to as…

One time I was struggling with some odd R behavior of the sort described by the author. I asked my local R expert. He told me how to fix my program, but I protested that none of it made any sense, even when explained. He didn't disagree, he just laughed and said "don't worry about it."

That works great for him, he can "not worry about it" and things work because he knows all the quirks.

If I just "don't worry about it" my programs don't work for mysterious reasons.

It seems likely that R could have been designed to have the same strengths without having so many weird and arbitrary quirks.

Re: A newcomer’s (angry) guide to R

#68

Post by vijucat »

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?

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 and wt by number of carburettors?
  > dt[, list(mean(hp), mean(wt)), by=carb]
     carb    V1     V2
  1:    4 187.0 3.8974
  2:    1  86.0 2.4900
  3:    2 117.2 2.8628
  4:    3 180.0 3.8600
  5:    6 175.0 2.7700
  6:    8 335.0 3.5700
  
  # How many Mercs are there and what's their median hp?
  
  > dt[grepl('Merc', name), list(.N, median(hp))]
     N  V2
  1: 7 123

  # Non-Mercs?
  > dt[!grepl('Merc', name), list(.N, median(hp))]
      N  V2
  1: 25 113

  # N observations and avg hp and wt per {num. cylinders and num. carburettors}

  > dcast(dt, cyl + carb ~ ., value.var=c("hp", "wt"), fun.aggregate=list(mean, length))
     cyl carb hp_mean  wt_mean hp_length wt_length
  1:   4    1    77.4 2.151000         5         5
  2:   4    2    87.0 2.398000         6         6
  3:   6    1   107.5 3.337500         2         2
  4:   6    4   116.5 3.093750         4         4
  5:   6    6   175.0 2.770000         1         1
  6:   8    2   162.5 3.560000         4         4
  7:   8    3   180.0 3.860000         3         3
  8:   8    4   234.0 4.433167         6         6
  9:   8    8   335.0 3.570000         1         1
I used slightly verbose syntax so that it is (hopefully) clear even to non-R users.

You can see that the interactivity is great at helping you compose answers step-by-step, molding the data as you go, especially when you combine with tools like plot.ly to also visualize results.

Re: A newcomer’s (angry) guide to R

#69

Post by killjoywashere »

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…

> though Anaconda is getting better with this

Anaconda is becoming to python what Chrome is to browsers, particularly as Jupyter matures. Drop it in, and a huge amount of what you want to do is ready to go. Sure, there's lots of libraries/extensions available, but most of the time you can do real work with a default userland, non-privileged install.

Re: A newcomer’s (angry) guide to R

#70

Post by claytonjy »

_Wintermute wrote: post #21
huac wrote: post #2

I think many of the gotchas and annoying parts of base R are solved by using tools from the tidyverse: http://github.com/tidyverse . For example, the pain of needing to specify `stringsAsFactors=FALSE` is solved in the tibble package by setting a sensible default. At any rate, at least it's not Pandas and matplotlib...

I can't stand the non-standard evaluation of the tidyverse. It works great for writing one-off scripts, but as soon as you start trying to put it into functions or your own package it's just not worth the pain of quosures and the tidyeval nonsense that changes every 6 months.

I used to feel similarly, but I think it's much more stable than it was even a year ago; `!!`, `enquo`, and `:=` is good enough for the vast majority of users who want to write their own NSE functions now.
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