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One Year with R

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Re: One Year with R

#41

Post by em500 »

stewbrew wrote: post #25

> R has two types of empty string: character(0) and "". I understand it's frustrating trying to use a language you don't understand. And instead of reading the language manual you go on rambling. "" is an empty string (almost) as you know it from other languages. character(0) is an empty vector of type character (i.e. a vector with no elements). This vector doesn't even contain an empty string. R is a vectorized lang…

That could have been expressed more diplomatically, but I think you're right. IMHO, what people should try to understand about new languages first most thoroughly, are its native data types. This is more fundamental than the syntactical constructs.

R's data types are one of its most alien part, and that's why I think if you're coming from another language, chapter 20 of Hadley Wickham's book[1] is the most important one.

[1] https://r4ds.had.co.nz/vectors.html

Re: One Year with R

#42

Post by kgwgk »

> Here’s a challenge: Find the function that checks if "es" is in "test". You’ll be on for a while.

grepl(“es”, “test”)

Re: One Year with R

#43

Post by yarky »

pantulis wrote: post #24
em500 wrote: post #23

The common trope with R is that statisticians and love it and developers hate it. The the main reason that statisticians love it is that the libraries useful to them are much better in R than elsewhere (though Python keeps encroaching in that turf, and "real developers" dislike Python a lot less than they do R). The main reasons that developers hate it is that it is very unlike almost all other languages that they're…

I had some Matlab experience about 3 decades ago. What's you take in Matlab vs R as programming languages?

I've had to translate a lot of Matlab to R in college (physics and econometrics).

I rarely found an important difference between the languages besides having to transpose some matrices here and there.

Re: One Year with R

#44

Post by merlincorey »

kgwgk wrote: post #42

> Here’s a challenge: Find the function that checks if "es" is in "test". You’ll be on for a while. grepl(“es”, “test”)

I haven't used R at all in years and only used it a couple times in passing many years ago to try it out.

I searched "R string functions", saw "grep" and wondered if there was something I was missing in the author's challenge.

Is it because it's using regular expressions they don't consider it the correct answer or is it because they aren't as familiar with regular expressions as some other people are, I wonder?

Re: One Year with R

#45

Post by em500 »

probably_wrong wrote: post #35
stewbrew wrote: post #25

> R has two types of empty string: character(0) and "". I understand it's frustrating trying to use a language you don't understand. And instead of reading the language manual you go on rambling. "" is an empty string (almost) as you know it from other languages. character(0) is an empty vector of type character (i.e. a vector with no elements). This vector doesn't even contain an empty string. R is a vectorized lang…

I'm going to side with the author here: if he read "Advanced R", "R for data Science", "The R Inferno", "Rtips. Revival 2014!", the official "An Introduction to R", "R Language Definition", and "R FAQ", and yet he still has problems with the language , then maybe the language is to blame. And even if the author is the problem, I wouldn't accuse them of not reading enough.

Ok, but if someone claims to have read all the Python manuals and wrote something like

> Python has two types of empty string, array('u',) and ""

you'd probably conclude that hasn't really understood what he read.

Re: One Year with R

#46

Post by fn-mote »

AuthorizedCust wrote: post #13

I don’t like this. Much of this is: 1. pointing out that, like every other language, base R has idiosyncrasies 2. how use of R is more complex when you’re largely ignorant of the tidyverse, which is crucial for the vast majority of tissue today’s use of R 3. frustration because you’re using a language/ecosystem, that’s targeted for a few specific uses, as a general purpose programming language

> how use of R is more complex when you’re largely ignorant of the tidyverse

This.

I'm interested in non-flamewar non-religious reasons that the tidyverse is bad. He does give some. I think his complaints about inconsistency and a moving target have some validity. However, the price of not using tidyverse is (roughly) paid in the rest of the article. I would definitely not use R without it.

Read his Section 5 on the tidyverse... and see how absolutely minimal his complaints in that section are. E.g. to "purrr" his objections are "largely philosophical"... but he's complaining in the previous section about the annoyance of writing lambdas (which purrr makes even easier).

Yes, R has a big community and there's a lot of quirks in individual packages, especially less-used ones. Yes, there are packages presenting unified interfaces to other quirky outputs (e.g., broom). The necessity of this is not good. The existence of it is good.

HN readers - do you have an "up and coming" language that you think has better structured the fundamentals from R, that you hope will someday have enough capabilities you can use it instead of R? I've tried Julia, which is beautiful but the startup/compilation times were difficult to get over. Is it reasonable to hope Julia will be good for interactive usage someday? Is it already? Are there other candidates in this area?

Re: One Year with R

#47

Post by yarky »

em500 wrote: post #23

The common trope with R is that statisticians and love it and developers hate it. The the main reason that statisticians love it is that the libraries useful to them are much better in R than elsewhere (though Python keeps encroaching in that turf, and "real developers" dislike Python a lot less than they do R). The main reasons that developers hate it is that it is very unlike almost all other languages that they're…

> it doesn't help that almost nobody learned R as their first computer (as opposed to statistics) language.

Aside from two statisticians I had as professors, I am yet to meet someone with deep understanding of statistics who doesn't speak R as first language ...

I found it way easier to grasp the meaning of statistics by playing with R than by reading the maths.

Re: One Year with R

#48

Post by pmyteh »

stewbrew wrote: post #25

> R has two types of empty string: character(0) and "". I understand it's frustrating trying to use a language you don't understand. And instead of reading the language manual you go on rambling. "" is an empty string (almost) as you know it from other languages. character(0) is an empty vector of type character (i.e. a vector with no elements). This vector doesn't even contain an empty string. R is a vectorized lang…

The issue isn't so much that character(0) is a zero-length vector and "" is a length 1 vector containing the empty string, it's that you can't necessarily rely on other people's code returning one or the other: things that 'nearly' always return a one element vector (which may contain the empty string for 'nothing') can vary unexpectedly if it fails on an edge case. And, unless you catch it correctly, this can cause downstream failures with little in the way of warning (because a zero-length vector is obviously a 'sensible' thing to return from a function in the usual case).

In that sense, it's similar to the problem many languages have with NULL, but on steroids: you can have NULL, NA, character(0) (or anythingelse(0)), or '' as your null result and each of them are tested for in different ways.

Obviously this won't be a problem for the various battle-tested standard libraries, but a lot of my work in R at least is assembling somewhat-novel analysis pipelines based on quite new statistics code.

Re: One Year with R

#49

Post by ggrothendieck »

_Wintermute wrote: post #31
ggrothendieck wrote: post #21

What needs to be added is that before R the reproducibility problem in science was compounded by the fact that analyses were done with proprietary software limiting communication and replication of those analyses. This was and continues to be a major problem, particular in some fields, but at least now there is a common widely used language that can be used to overcome this. I wouldn't focus on idiosyncrasies but rat…

I don't see how R specifically addresses the reproducibility problem, It's been around for almost 30 years and before its recent rise in popularity, lots of science was done in C, perl, fortran etc. Not to mention that actual dependency versioning is pretty poor. I struggle to run other people's R code after about 6 months (especially if they used the tidyverse as it pulls in hundreds of unstable dependencies) and no…

1. Before R commercial statistical packages were mainly used. You can, in principle, just use assembler too and develop everything yourself but it isn't practical. Regarding C/C++ and Fortran, many R packages are, in fact, wrappers around code in those or other languages making it easier to access them. From that point of view R can be regarded as a glue language. 2. Regarding keeping versions straight, all past versions of packages in the CRAN repository are kept on CRAN. Microsoft MRAN repository also maintains histories of packages that can be accessed via the checkpoint package which will install packages as they existed on a given date. Furthermore, install_version in the remotes and devtools packages can install specific versions. 3. Regarding tidyverse dependencies you can reduce the number of packages you load by not using library(tidyverse) and instead load the specific packages you need. This will result in fewer packages being loaded.

Re: One Year with R

#50

Post by popcube »

_Wintermute wrote: post #31
ggrothendieck wrote: post #21

What needs to be added is that before R the reproducibility problem in science was compounded by the fact that analyses were done with proprietary software limiting communication and replication of those analyses. This was and continues to be a major problem, particular in some fields, but at least now there is a common widely used language that can be used to overcome this. I wouldn't focus on idiosyncrasies but rat…

I don't see how R specifically addresses the reproducibility problem, It's been around for almost 30 years and before its recent rise in popularity, lots of science was done in C, perl, fortran etc. Not to mention that actual dependency versioning is pretty poor. I struggle to run other people's R code after about 6 months (especially if they used the tidyverse as it pulls in hundreds of unstable dependencies) and no…

um... this is about statistics, before R people should finish analysis in MATLAB
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