R devel - Nov 2006 - data frame subscription operator

Hi all, 

   I was looking at the data frame subscription operator (attached in the end 
of this e-mail) and got puzzled by the following line:

    class(x) <- attr(x, "row.names") <- NULL

This appears to set the class and row.names attributes of the incoming data 
frame to NULL. So far I was not able to figure out why this is necessary - 
could anyone help ?

The reason I am looking at it is that changing attributes forces duplication 
of the data frame and this is the largest cause of slowness of data.frames in 
general.

                           thank you very much !

                                            Vladimir Dergachev

> `[.data.frame`
function (x, i, j, drop = if (missing(i)) TRUE else length(cols) =    1)
{
    mdrop <- missing(drop)
    Narg <- nargs() - (!mdrop)
    if (Narg < 3) {
        if (!mdrop)
            warning("drop argument will be ignored")
        if (missing(i))
            return(x)
        if (is.matrix(i))
            return(as.matrix(x)[i])
        y <- NextMethod("[")
        nm <- names(y)
        if (!is.null(nm) && any(is.na(nm)))
            stop("undefined columns selected")
        if (any(duplicated(nm)))
            names(y) <- make.unique(nm)
        return(structure(y, class = oldClass(x), row.names = attr(x,
            "row.names")))
    }
    rows <- attr(x, "row.names")
    cols <- names(x)
    cl <- oldClass(x)
    class(x) <- attr(x, "row.names") <- NULL
    if (missing(i)) {
        if (!missing(j))
            x <- x[j]
        cols <- names(x)
        if (any(is.na(cols)))
            stop("undefined columns selected")
    }
    else {
        if (is.character(i))
            i <- pmatch(i, as.character(rows), duplicates.ok = TRUE)
        rows <- rows[i]
        if (!missing(j)) {
            x <- x[j]
            cols <- names(x)
            if (any(is.na(cols)))
                stop("undefined columns selected")
        }
        for (j in seq_along(x)) {
            xj <- x[[j]]
            x[[j]] <- if (length(dim(xj)) != 2)
                xj[i]
            else xj[i, , drop = FALSE]
        }
    }
    if (drop) {
        drop <- FALSE
        n <- length(x)
        if (n == 1) {
            x <- x[[1]]
            drop <- TRUE
        }
        else if (n > 1) {
            xj <- x[[1]]
            nrow <- if (length(dim(xj)) == 2)
                dim(xj)[1]
            else length(xj)
            if (!mdrop && nrow == 1) {
                drop <- TRUE
                names(x) <- cols
                attr(x, "row.names") <- NULL
            }
        }
    }
    if (!drop) {
        names(x) <- cols
        if (any(is.na(rows) | duplicated(rows))) {
            rows[is.na(rows)] <- "NA"
            rows <- make.unique(rows)
        }
        if (any(duplicated(nm <- names(x))))
            names(x) <- make.unique(nm)
        attr(x, "row.names") <- rows
        class(x) <- cl
    }
    x
}
<environment: namespace:base>

'[' is the 'subscript' or 'extraction', not
'subscription' operator: this
is also called 'indexing', as in 'An Introduction to R'.

On Mon, 6 Nov 2006, Vladimir Dergachev wrote:
>   I was looking at the data frame subscription operator (attached in the
end
> of this e-mail) and got puzzled by the following line:
>
>    class(x) <- attr(x, "row.names") <- NULL
>
> This appears to set the class and row.names attributes of the incoming data
> frame to NULL.
Actually no, it removes them: see ?attr and ?class.
> So far I was not able to figure out why this is necessary -
> could anyone help ?
You need to remove the class to avoid recursion: a few lines later x[i]
needs to be a call to the primitive and not the data frame method.
> The reason I am looking at it is that changing attributes forces
duplication
> of the data frame and this is the largest cause of slowness of data.frames
in
> general.
Do you have evidence of that?  R has facilities to profile its code, and I 
have never seen  [.data.frame taking a significant proportion of the total 
time.  If it does for your application, consider if a data frame is an 
appropriate way to store your data.  I am not sure we would accept that
data frames do have 'slowness in general', but their generality does
make
them slower than alternatives where the generality is not needed.

[...]

-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595