[R] Removing variables from data frame with a wile card
Rui Barradas
ru|pb@rr@d@@ @end|ng |rom @@po@pt
Sun Jan 15 21:59:04 CET 2023
Às 16:54 de 15/01/2023, Sorkin, John escreveu:
> I am new to this thread. At the risk of presenting something that has been shown before, below I demonstrate how a column in a data frame can be dropped using a wild card, i.e. a column whose name starts with "th" using nothing more than base r functions and base R syntax. While additions to R such as tidyverse can be very helpful, many things that they do can be accomplished simply using base R.
>
> # Create data frame with three columns
> one <- rep(1,10)
> one
> two <- rep(2,10)
> two
> three <- rep(3,10)
> three
> mydata <- data.frame(one=one, two=two, three=three)
> cat("Data frame with three columns\n")
> mydata
>
> # Drop the column whose name starts with th, i.e. column three
> # Find the location of the column
> ColumToDelete <- grep("th",colnames((mydata)))
> cat("The colomumn to be dropped is the column called three, which is column",ColumToDelete,"\n")
> ColumToDelete
>
> # Drop the column whose name starts with "th"
> newdata2 <- mydata[,-ColumnToDelete]
> cat("Data frame after droping column whose name is three\n")
> newdata2
>
> I hope this helps.
> John
>
>
> ________________________________________
> From: R-help <r-help-bounces using r-project.org> on behalf of 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'
> 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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> and provide commented, minimal, self-contained, reproducible code.
Hello,
Actually, Bill had addressed this in his post yesterday [1].
With your example,
one <- rep(1,10)
two <- rep(2,10)
three <- rep(3,10)
mydata <- data.frame(one=one, two=two, three=three)
ColumToDelete <- grep("fo",colnames((mydata)))
ColumToDelete
#> integer(0)
ColumToDeleteLogical <- grepl("fo",colnames((mydata)))
ColumToDeleteLogical
#> [1] FALSE FALSE FALSE
# Drop the column whose name starts with "fo"
# empty data.frame
mydata[, -ColumToDelete]
#> data frame with 0 columns and 10 rows
# nothing is deleted
mydata[, !ColumToDeleteLogical]
#> one two three
#> 1 1 2 3
#> 2 1 2 3
#> 3 1 2 3
#> 4 1 2 3
#> 5 1 2 3
#> 6 1 2 3
#> 7 1 2 3
#> 8 1 2 3
#> 9 1 2 3
#> 10 1 2 3
[1] https://stat.ethz.ch/pipermail/r-help/2023-January/476682.html
Hope this helps,
Rui Barradas
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