[R] Removing variables from data frame with a wile card

@vi@e@gross m@iii@g oii gm@ii@com @vi@e@gross m@iii@g oii gm@ii@com
Sun Jan 15 02:03:19 CET 2023 

Valentin,

You are correct that R does many things largely behind the scenes that make some operations fairly efficient.

>From a programming point of view, though, many people might make a data.frame and not think of it as a list of vectors of the same length that are kept that way.

So if they made a copy of the original data with fewer columns, they might be tempted to think the original item was completely copied and the original is either around or if the identifier was re-used, will be garbage collected. As you note, the only thinks collected are the columns you chose not to include.

For some it seems cleaner to set a list item to NULL, which seems to remove it immediately. 

The real point I hoped to make is that using base R, you can indeed approach removing (multiple) columns in two logical ways. One is to seemingly remove them in the original object, even if your point is valid. The other is to make a copy of just what you want and ignore the rest and it may be kept around or not.

If someone really wanted to get down to the basics, they could get a reference to all the columns they want to keep, as in col1 <- mydata[["col1"] ] and use those to make a new data.frame, or many other variants on these methods.  

Many programming languages have some qualms (I mean designers and programmers, and just plain purists) about when "pointers" of sorts are used and whether things should be mutable and so on so I prefer to avoid religious wars.

-----Original Message-----
From: Valentin Petzel <valentin using petzel.at> 
Sent: Saturday, January 14, 2023 1:21 PM
To: avi.e.gross using gmail.com
Cc: 'R-help Mailing List' <r-help using r-project.org>
Subject: Re: [R] Removing variables from data frame with a wile card

Hello Avi,

while something like d$something <- ... may seem like you're directly modifying the data it does not actually do so. Most R objects try to be immutable, that is, the object may not change after creation. This guarantees that if you have a binding for same object the object won't change sneakily.

There is a data structure that is in fact mutable which are environments. For example compare

L <- list()
local({L$a <- 3})
L$a

with

E <- new.env()
local({E$a <- 3})
E$a

The latter will in fact work, as the same Environment is modified, while in the first one a modified copy of the list is made.

Under the hood we have a parser trick: If R sees something like

f(a) <- ...

it will look for a function f<- and call

a <- f<-(a, ...)

(this also happens for example when you do names(x) <- ...)

So in fact in our case this is equivalent to creating a copy with removed columns and rebind the symbol in the current environment to the result.

The data.table package breaks with this convention and uses C based routines that allow changing of data without copying the object. Doing

d[, (cols_to_remove) := NULL]

will actually change the data.

Regards,
Valentin

14.01.2023 18:28:33 avi.e.gross using gmail.com:

> Steven,
> 
> Just want to add a few things to what people wrote.
> 
> In base R, the methods mentioned will let you make a copy of your original DF that is missing the items you are selecting that match your pattern.
> 
> That is fine.
> 
> For some purposes, you want to keep the original data.frame and remove a column within it. You can do that in several ways but the simplest is something where you sat the column to NULL as in:
> 
> mydata$NAME <- NULL
> 
> using the mydata["NAME"] notation can do that for you by using a loop of unctional programming method that does that with all components of your grep.
> 
> R does have optimizations that make this less useful as a partial copy of a data.frame retains common parts till things change.
> 
> For those who like to use the tidyverse, it comes with lots of tools that let you select columns that start with or end with or contain some pattern and I find that way easier.
> 
> 
> 
> -----Original Message-----
> From: R-help <r-help-bounces using r-project.org> On Behalf Of Steven Yen
> Sent: Saturday, January 14, 2023 7:49 AM
> To: Andrew Simmons <akwsimmo using gmail.com>
> Cc: R-help Mailing List <r-help using r-project.org>
> Subject: Re: [R] Removing variables from data frame with a wile card
> 
> Thanks to all. Very helpful.
> 
> Steven from iPhone
> 
>> On Jan 14, 2023, at 3:08 PM, Andrew Simmons <akwsimmo using gmail.com> wrote:
>> 
>> You'll want to use grep() or grepl(). By default, grep() uses 
>> extended regular expressions to find matches, but you can also use 
>> perl regular expressions and globbing (after converting to a regular expression).
>> For example:
>> 
>> grepl("^yr", colnames(mydata))
>> 
>> will tell you which 'colnames' start with "yr". If you'd rather you 
>> use globbing:
>> 
>> grepl(glob2rx("yr*"), colnames(mydata))
>> 
>> Then you might write something like this to remove the columns starting with yr:
>> 
>> mydata <- mydata[, !grepl("^yr", colnames(mydata)), drop = FALSE]
>> 
>>> On Sat, Jan 14, 2023 at 1:56 AM Steven T. Yen <styen using ntu.edu.tw> wrote:
>>> 
>>> I have a data frame containing variables "yr3",...,"yr28".
>>> 
>>> How do I remove them with a wild card----something similar to "del yr*"
>>> in Windows/doc? Thank you.
>>> 
>>>> colnames(mydata)
>>>   [1] "year"       "weight"     "confeduc"   "confothr" "college"
>>>   [6] ...
>>> [41] "yr3"        "yr4"        "yr5"        "yr6" "yr7"
>>> [46] "yr8"        "yr9"        "yr10"       "yr11" "yr12"
>>> [51] "yr13"       "yr14"       "yr15"       "yr16" "yr17"
>>> [56] "yr18"       "yr19"       "yr20"       "yr21" "yr22"
>>> [61] "yr23"       "yr24"       "yr25"       "yr26" "yr27"
>>> [66] "yr28"...
>>> 
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> 
> ______________________________________________
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> 
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> PLEASE do read the posting guide 
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