[R] What don't I understand about sample()?

Sun Mar 16 20:52:07 CET 2025

Thanks for the clarification, Richard, as I clearly made the wrong guess of what you meant.

Your idea or objection was that you see the included read.csv function as adequate and see no incentive to use read_csv, and especially not if that is the only function being used. I only partially agree.

As usual, I look at things from multiple overlapping perspectives.

There are actually more ways to read in a CSV or other such data files including fread from data.table and another called feather and other base functions. Some people choose ONE and use it whenever possible and your choice might be the base version and mine would not be. 

So, one perspective is that the base version is in some sense pre-loaded and any other must be pre-downloaded and added with a library statement. I am not sure how much that costs or if the base version is also only partially preloaded and gotten only as needed. But it can be a valid concern, especially as some people write defensive code so that if it is not already installed, they first fetch it.

Another perspective, especially for larger files, is speed. One article I have suggests the base version is quite SLOW. 

https://www.r-bloggers.com/2017/04/fast-data-loading-from-files-to-r/

But that was in 2017, and using such concerns, you may be better off with data.table ...

Another issue is that some people have found it handy to deal with tibbles rather than unenhanced data.frames and if you read it in using the base, you may end up converting it later so the underscore version saves a small step. The OP clearly does not need this as no other tidyverse functions are used. Others may care.

But related to this are things like not converting strings to factors by default or play around with column names. It can be time consuming to read in data and then use multiple commands to change it to the way you want it, such as undoing the factors (albeit you can just set the default in the base too) or converting a column it guessed was integer to Boolean and so on.

And I note I have used other features that I like and base does not support. But, again, if the OP does not have any plans on using any such features or defaults and is reading fairly small amounts of data and running it once, there is no special reason to make it worth leaving the base. If they may later want to use additional tidyverse functionality, switching to use this by default may be wise. 

My philosophy is to keep thing as simple as reasonable but no simpler than reasonable. In programming languages, it is to use a simple consistent set of tools that gets me what I want with accuracy and thus it can be simpler to use the tidyverse a lot as my default. To each their own.

-----Original Message-----
From: Richard O'Keefe <raoknz using gmail.com> 
Sent: Sunday, March 16, 2025 7:53 AM
To: avi.e.gross using gmail.com
Cc: Kevin Zembower <kevin using zembower.org>; r-help using r-project.org
Subject: Re: [R] What don't I understand about sample()?

I think you think I mistook read_csv for read.csv.  Not so.  The point
was that base R with no additional packages loaded already contains a
CSV reader which is entirely adequate for the task at hand.  When you
are already struggling with the basics of a system (like how often and
when arguments are evaluated), I think it's wisests to stick with
basic tools.  When they taught me carpentry at school, they had me on
chisels before getting to lathes (and in fact never did get to lathes
at my school).

Sure, R isn't perfect.  But whenever I open the SAS manuals I remember
that things could be much worse.

On Sun, 16 Mar 2025 at 17:51, <avi.e.gross using gmail.com> wrote:
>
> Richard,
>
> The function with a period as a separator that you cite, read.csv, is part of normal base R.
>
> We have been discussing a different function named just a tad different that uses an underscore as a separator, read_csv that is similar but has some changes in how it works and the options supported and is considered part of the tidyverse grouping of packages and can also be gotten more compactly by importing package "readr" ...
>
> The OP, for reasons of their own, wanted to use read_csv and did not want or need anything else in the related packages.
>
> Of course, nobody is required to use other packages, albeit, as you noted, many packages you may choose to use have some dependencies on others you don't.
>
> Like many good things, added functionality available to you does add complexity and room for failures. But when a package is useful enough to be very useful, it can develop enough momentum that some functionality might well be a good idea to move into base R. As an example I already mentioned, of the various pipe implementations, a version has been added to base R and I suspect many older packages, including in the tidyverse, can adjust their code in new releases to use it but with CARE. Anyone still using older versions of R will experience failures in such a scenario.
>
> Luckily, many uses within a package are likely to be safe if done properly. Can anyone share if any such methods are in use?
>
> I mean, as an example, could a package early on check if the R version being used is later than the introduction, or some other way to check if a |> operation is supported? Could they then somehow introduce an operator that is either bound to |> or perhaps %>% and use that in any places in the code where both work the same, and only use the magrittr pipe when doing something it does differently such as needing to use a period to specify which argument in a function is receiving the pipelined data.
>
> There are programs people want to keep frozen so they only use the versions of R and packages that existed at some moment so you avoid some inevitable conflicts. So, I despair that older versions of R may stick around way too long and break with any newer packages.
>
> But languages cannot remain totally static or chances are people will move on to newer languages that offer things they want. Then again, there seem to still be COBOL programs out there.
>
> -----Original Message-----
> From: Richard O'Keefe <raoknz using gmail.com>
> Sent: Sunday, March 16, 2025 12:32 AM
> To: avi.e.gross using gmail.com
> Cc: Kevin Zembower <kevin using zembower.org>; r-help using r-project.org
> Subject: Re: [R] What don't I understand about sample()?
>
> Rgui 4.4.3 on Windows.  When I start it up, read.csv is just *there*.
> I don't need to load any package to get it.
>
> I have three reasons for being very sparing in the packages I use.
> 1. It took me long enough to get my head around R.  More packages =
> more things to learn.  I *still* have major trouble grasping
> tidyverse, and as far as I can see it doesn't solve any problem that
> *I* have.  I install a package only when I have a specific need for
> something it does, like spatial statistics.  (And yet I have hundreds
> of packages installed, because packages depend on other packages.)
> 2. Everything changes, and they don't all change coherently.  A
> package I've used for years may not be available in the next release.
> This is not a theoretical possibility; it has happened to me often.
> "If I don't use it I can't lose it."  Sometimes things break because
> something else on the system (tcl/tk, or the C or Fortran compiler)
> has changed.  I'm tired of things breaking because the C or Fortran compiler
> is now stricter.
> 3. The universe of R packages is vast and constantly expanding.  This
> makes it *impossible* for anyone to test every possible combination.  I
> used to teach software engineering, and we had a slogan "if it isn't
> tested it doesn't work".  Base R plus package X?  Probably tested.
> Base R plus package Y?  Probably tested.  Base R plus X plus Y?
> Not unless X requires Y or Y requires X.
>
> There is also the didactic point that the more you work with base R
> the better you will understand it, which you will need to understand
> other things like tidyverse.  It's like mastering the alphabet before you
> learn shorthand.
>
>
> On Sun, 16 Mar 2025 at 06:55, <avi.e.gross using gmail.com> wrote:
> >
> > Kevin & Richard, and of course everyone,
> >
> > As the main topic here is not the tidyverse, I will mention the perils of loading in more than needed in general.
> >
> > If you want to use one or a very few functions, it can be more efficient and safe to load exactly what is needed. In the case of wanting to use read_csv(), I think this suffices:
> >
> > library(readr)
> >
> > If you instead use:
> >
> > library(tidyverse)
> >
> > You load a varying number of packages (it may change) including some like lubridate or forcats or ggplot2 that you may not be even thinking of using or never heard of.
> >
> > The bigger problem is shadowing that happens. For example, you may be getting warning messages like:
> >
> > ✖ dplyr::filter() masks stats::filter()
> > ✖ dplyr::lag()    masks stats::lag()
> >
> > This can interfere with some other package you had already loaded unless it uses a notation like mypackage::filter(...) in their code to avoid being easily replaced but even then, if you yourself called what you though was filter() from base R or some package, you have a problem unless you invoke it like base::filter(...)
> >
> > The order packages like this load can matter as well as when you define a function of your own. So, it may be worth some effort to zoom in and call exactly what you need and only when you need it. I have seen code that only needs a package in rare conditions and only loads the package in one branch of an IF statement right before using in.
> > .
> > Packages can also be unloaded after use.
> >
> > From what you describe, none of this is crucially important as you are using R for your own purposes in your own RMarkDown file that you may not be distributing. And, when I write programs where I keep adjusting and adding things from the tidyverse, it is indeed much easier to just get the grouping on top and forget about it. That is, until I decide to do something with functional programming that uses reduce/filter/map... and have an odd error!
> >
> >
> > -----Original Message-----
> > From: R-help <r-help-bounces using r-project.org> On Behalf Of Kevin Zembower via R-help
> > Sent: Saturday, March 15, 2025 1:29 PM
> > To: r-help using r-project.org
> > Subject: Re: [R] What don't I understand about sample()?
> >
> > Hi, Richard, thanks for replying. I should have mentioned the third
> > edition, which we're using. The data file didn't change between the
> > second and third editions, and the data on Body Mass Gain was the same
> > as in the first edition, although the first edition data file contained
> > additional variables.
> >
> > According to my text, the BMGain was measured in grams. Thanks for
> > pointing out that my statement of the problem lacked crucial
> > information.
> >
> > The matrix in my example comes from an example in
> > https://pages.stat.wisc.edu/~larget/stat302/chap3.pdf, where the author
> > created a bootstrap example with a matrix that consisted of one row for
> > every sample in the bootstrap, and one column for each mean in the
> > original data. This allowed him to find the mean for each row to create
> > the bootstrap statistics.
> >
> > The only need for the tidyverse is to use the read_csv() function. I'm
> > regrettably lazy in not determining which of the multiple functions in
> > the tidyverse library loads read_csv(), and just using that one.
> >
> > Thanks, again, for helping me to further understand R and this problem.
> >
> > -Kevin
> >
> > On Sat, 2025-03-15 at 12:00 +0100, r-help-request using r-project.org wrote:
> > > Not having the book (and which of the three editions are you using?),
> > > I downloaded the data and played with it for a bit.
> > > dotchart() showed the Dark and Light conditions looked quite
> > > different, but also showed that there are not very many cases.
> > > After trying t.test, it occurred to me that I did not know whether
> > > "BMGain" means gain in *grams* or gain in *percent*.
> > > Reflection told me that for a growth experiment, percent made more
> > > sense, which reminded my of one of my first
> > > student advising experiences, where I said "never give the computer
> > > percentages; let IT calculate the percentages
> > > from the baseline and outcome, because once you've thrown away
> > > information, the computer can't magically get it back."
> > > In particular, in the real world I'd be worried about the possibility
> > > that there was some confounding going on, so I would
> > > much rather have initial weight and final weight as variables.
> > > If BMGain is an absolute measure, the p value for a t test is teeny
> > > tiny.
> > > If BMGain is a percentage, the p value for a sensible t test is about
> > > 0.03.
> > >
> > > A permutation test went like this.
> > > is.light <- d$Group == "Light"
> > > is.dark <- d$Group == "Dark"
> > > score <- function (g) mean(g[is.light]) - mean(g[is.dark])
> > > base.score <- score(d$BMGain)
> > > perm.scores <- sapply(1:997, function (i) score(sample(d$BMGain)))
> > > sum(perm.scores >= base.score) / length(perm.scores)
> > >
> > > I don't actually see where matrix() comes into it, still less
> > > anything
> > > in the tidyverse.
> > >
> >
> > ______________________________________________
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> > PLEASE do read the posting guide https://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
> >
> > ______________________________________________
> > R-help using r-project.org mailing list -- To UNSUBSCRIBE and more, see
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide https://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
>