The R Project SVN R

% File src/library/base/man/Extract.data.frame.Rd

% Part of the R package, http://www.R-project.org

% Copyright 1995-2013 R Core Team

% Distributed under GPL 2 or later

\name{Extract.data.frame}

\alias{[.data.frame}

\alias{[[.data.frame}

\alias{[<-.data.frame}

\alias{[[<-.data.frame}

\alias{$.data.frame}

\alias{$<-.data.frame}

\title{Extract or Replace Parts of a Data Frame}

\description{

  Extract or replace subsets of data frames.

}

\usage{

\method{[}{data.frame}(x, i, j, drop = )

\method{[}{data.frame}(x, i, j) <- value

\method{[[}{data.frame}(x, ..., exact = TRUE)

\method{[[}{data.frame}(x, i, j) <- value

\method{$}{data.frame}(x, name)

\method{$}{data.frame}(x, name) <- value

}

\arguments{

  \item{x}{data frame.}

  \item{i, j, ...}{elements to extract or replace.  For \code{[} and

    \code{[[}, these are \code{numeric} or \code{character} or, for

    \code{[} only, empty.  Numeric values are coerced to integer as if

    by \code{\link{as.integer}}.  For replacement by \code{[}, a logical

    matrix is allowed.}

  \item{name}{

    A literal character string or a \link{name} (possibly \link{backtick}

    quoted).}

  \item{drop}{logical.  If \code{TRUE} the result is coerced to the

    lowest possible dimension.  The default is to drop if only one

    column is left, but \bold{not} to drop if only one row is left.}

  \item{value}{A suitable replacement value: it will be repeated a whole

    number of times if necessary and it may be coerced: see the

    Coercion section.  If \code{NULL}, deletes the column if a single

    column is selected.}

   \item{exact}{logical: see \code{\link{[}}, and applies to column names.}

}

\details{

  Data frames can be indexed in several modes.  When \code{[} and

  \code{[[} are used with a single vector index (\code{x[i]} or

  \code{x[[i]]}), they index the data frame as if it were a list.  In

  this usage a \code{drop} argument is ignored, with a warning.

  The \code{data.frame} method for \code{$}, treats \code{x} as a

  list, except that (as of R-3.1.0) partial matching of \code{name} to

  the names of \code{x} will generate a warning; this may become an

  error in future versions.  The replacement method checks

  \code{value} for the correct number of rows, and replicates it if

  necessary.

  When \code{[} and \code{[[} are used with two indices (\code{x[i, j]}

  and \code{x[[i, j]]}) they act like indexing a matrix:  \code{[[} can

  only be used to select one element.  Note that for each selected

  column, \code{xj} say, typically (if it is not matrix-like), the

  resulting column will be \code{xj[i]}, and hence rely on the

  corresponding \code{[} method, see the examples section.

  If \code{[} returns a data frame it will have unique (and non-missing)

  row names, if necessary transforming the row names using

  \code{\link{make.unique}}.  Similarly, if columns are selected column

  names will be transformed to be unique if necessary (e.g. if columns

  are selected more than once, or if more than one column of a given

  name is selected if the data frame has duplicate column names).

  When \code{drop = TRUE}, this is applied to the subsetting of any

  matrices contained in the data frame as well as to the data frame itself.

  The replacement methods can be used to add whole column(s) by specifying

  non-existent column(s), in which case the column(s) are added at the

  right-hand edge of the data frame and numerical indices must be

  contiguous to existing indices.  On the other hand, rows can be added

  at any row after the current last row, and the columns will be

  in-filled with missing values.  Missing values in the indices are not

  allowed for replacement.

  For \code{[} the replacement value can be a list: each element of the

  list is used to replace (part of) one column, recycling the list as

  necessary.  If columns specified by number are created, the names

  (if any) of the corresponding list elements are used to name the

  columns.  If the replacement is not selecting rows, list values can

  contain \code{NULL} elements which will cause the corresponding

  columns to be deleted.  (See the Examples.)

  Matrix indexing (\code{x[i]} with a logical or a 2-column integer

  matrix \code{i}) using \code{[} is not recommended.  For extraction,

  \code{x} is first coerced to a matrix. For replacement, logical

  matrix indices must be of the same dimension as \code{x}.

  Replacements are done one column at a time, with multiple type

  coercions possibly taking place.

  Both \code{[} and \code{[[} extraction methods partially match row

  names.  By default neither partially match column names, but \code{[[}

  will if \code{exact = FALSE} (and with a warning if \code{exact =

  NA}).  If you want to exact matching on row names use

  \code{\link{match}}, as in the examples.

}

\section{Coercion}{

  The story over when replacement values are coerced is a complicated

  one, and one that has changed during \R's development.  This section

  is a guide only.

  When \code{[} and \code{[[} are used to add or replace a whole column,

  no coercion takes place but \code{value} will be

  replicated (by calling the generic function \code{\link{rep}}) to the

  right length if an exact number of repeats can be used.

  When \code{[} is used with a logical matrix, each value is coerced to

  the type of the column into which it is to be placed.

  When  \code{[} and \code{[[} are used with two indices, the

  column will be coerced as necessary to accommodate the value.

  Note that when the replacement value is an array (including a matrix)

  it is \emph{not} treated as a series of columns (as

  \code{\link{data.frame}} and \code{\link{as.data.frame}} do) but

  inserted as a single column.

}

\section{Warning}{

  The default behaviour when only one \emph{row} is left is equivalent to

  specifying \code{drop = FALSE}.  To drop from a data frame to a list,

  \code{drop = TRUE} has to be specified explicitly.

  Arguments other than \code{drop} and \code{exact} should not be named:

  there is a warning if they are and the behaviour differs from the

  description here.

}

\value{

  For \code{[} a data frame, list or a single column (the latter two

  only when dimensions have been dropped).  If matrix indexing is used for

  extraction a vector results.  If the result would be a data frame an

  error results if undefined columns are selected (as there is no general

  concept of a 'missing' column in a data frame).  Otherwise if a single

  column is selected and this is undefined the result is \code{NULL}.

  For \code{[[} a column of the data frame or \code{NULL}

  (extraction with one index)

  or a length-one vector (extraction with two indices).

  For \code{$}, a column of the data frame (or \code{NULL}).

  For \code{[<-}, \code{[[<-} and \code{$<-}, a data frame.

}

\seealso{

  \code{\link{subset}} which is often easier for extraction,

  \code{\link{data.frame}}, \code{\link{Extract}}.

}

\examples{

sw <- swiss[1:5, 1:4]  # select a manageable subset

sw[1:3]      # select columns

sw[, 1:3]    # same

sw[4:5, 1:3] # select rows and columns

sw[1]        # a one-column data frame

sw[, 1, drop = FALSE]  # the same

sw[, 1]      # a (unnamed) vector

sw[[1]]      # the same

sw[1,]       # a one-row data frame

sw[1,, drop = TRUE]  # a list

sw["C", ] # partially matches

sw[match("C", row.names(sw)), ] # no exact match

try(sw[, "Ferti"]) # column names must match exactly

\dontshow{

stopifnot(identical(sw[, 1], sw[[1]]),

          identical(sw[, 1][1], 80.2),

          identical(sw[, 1, drop = FALSE], sw[1]),

          is.data.frame(sw[1]), dim(sw[1] ) == c(5,1),

          is.data.frame(sw[1,]), dim(sw[1,]) == c(1,4),

          is.list(s1 <- sw[1, , drop = TRUE]), identical(s1$Fertility, 80.2))

}

swiss[ c(1, 1:2), ]   # duplicate row, unique row names are created

sw[sw <= 6] <- 6  # logical matrix indexing

sw

## adding a column

sw["new1"] <- LETTERS[1:5]   # adds a character column

sw[["new2"]] <- letters[1:5] # ditto

sw[, "new3"] <- LETTERS[1:5] # ditto

sw$new4 <- 1:5

sapply(sw, class)

sw$new4 <- NULL              # delete the column

sw

sw[6:8] <- list(letters[10:14], NULL, aa = 1:5)

# update col. 6, delete 7, append

sw

## matrices in a data frame

A <- data.frame(x = 1:3, y = I(matrix(4:6)), z = I(matrix(letters[1:9], 3, 3)))

A[1:3, "y"] # a matrix

A[1:3, "z"] # a matrix

A[, "y"]    # a matrix

## keeping special attributes: use a class with a

## "as.data.frame" and "[" method:

% "avector" := vector that keeps attributes and we could provide a constructor

%  avector <- function(x) { class(x) <- c("avector", class(x)); x }

as.data.frame.avector <- as.data.frame.vector

`[.avector` <- function(x,i,...) {

  r <- NextMethod("[")

  mostattributes(r) <- attributes(x)

  r

}

d <- data.frame(i = 0:7, f = gl(2,4),

                u = structure(11:18, unit = "kg", class = "avector"))

str(d[2:4, -1]) # 'u' keeps its "unit"

\dontshow{

stopifnot(identical(d[2:4,-1][,"u"],

                    structure(12:14, unit = "kg", class = "avector")))

}

}

\keyword{array}