How can I get a dataframe with columns temporarily removed by name?

Question

For example, with the data set mtcars

mtcars[ , "cyl"] 

and

mtcars[ , 2] 

both give me the same column. So, since I can get everything BUT the column 2 like this:

mtcars[ , -2] 

I don't expect this:

mtcars[ , -"cyl"] Error in -"cyl" : invalid argument to unary operator 

instead the best I can come up with is this:

mtcars[ , !colnames(mtcars)=="cyl"] 

Is there an easier solution?

EDIT: It seems logical that if the first two techniques work, so should the second two techniques. I hoped I was missing something. The help pages for ?"[" or ?subset don't explain this counterintuitive result. Does anybody know why this is?

Answer 1

[Edit:] Explanation of why negative string indices does not work:

-() is a function and the R developers say it can't be used on a character vector (and not just because negating a string doesn't make sense). Because you can't negate a character vector, you can't supply negative strings to drop columns. The problem is with - and is the source of the error message you quote. Hence the rule that negative indices only work for numerics. The source of the original error is:

> -"cyl" Error in -"cyl" : invalid argument to unary operator 

Note that in the comments to the Q, there was confusion that the negative version of "cyl" was "-cyl", which it isn't, it is just another string. The R snippet above shows what was happening in the subsetting tried in the Question.

Section 2.7 of the "An Introduction to R" manual describes the allowed methods of subsetting.

[Original:] The simplest way to remove a component is just to set that component to NULL:

> cars <- mtcars > cars[, "cyl"] <- NULL ## or cars$cyl <- NULL > names(cars)  [1] "mpg"  "disp" "hp"   "drat" "wt"   "qsec" "vs"   "am"   "gear" "carb" 

[Edit:] In light of the Edit to the Q indicating a temporary drop of the named column was desired, then:

subset(mtcars, select = -cyl) 

or

mtcars[, !names(mtcars) %in% "cyl"] 

are options, and the former cleaner than the latter.