| Extract.data.frame {base} | R Documentation |
Extract or replace subsets of data frames.
x[i] x[i] <- value x[i, j, drop = TRUE] x[i, j] <- value x[[i]] x[[i]] <- value x[[i, j]] x[[i, j]] <- value x$name x$name <- value
x |
data frame. |
i, j |
elements to extract or replace. i, j are
numeric or character or, for [ only, empty.
Numeric values are coerced to integer as if by as.integer.
For replacement by [, a logical matrix is allowed.
|
drop |
logical. If TRUE the result is coerced to the
lowest possible dimension: however, see the Warning below. |
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 NULL, deletes the column if a single
column is selected. |
name |
name or literal character string. |
Data frames can be indexed in several modes. When [ and
[[ are used with a single index, they index the data frame
as if it were a list. In this usage a drop argument is
ignored, with a warning. Using $ is equivalent to using
[[ with a single index.
When [ and [[ are used with two indices they act
like indexing a matrix: [[ can only be used to select one element.
If [ returns a data frame it will have unique (and non-missing)
row names, if necessary transforming the row names using
make.unique. Similarly, column names
will be transformed (if columns are selected more than once).
When 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.
For [ 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 the 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 NULL elements which will cause the corresponding
columns to be deleted.
Matrixing indexing using [ is not recommended, and barely
supported. For extraction, x is first coerced to a matrix.
For replacement a logical matrix (only) can be used to select the
elements to be replaced in the same ways as for a matrix.
Missing values in the matrix are treated as false, unlike S which
does not replace them but uses up the corresponding values in value.
For [ 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 matrix results.
For [[ a column of the data frame (extraction with one index)
or a length-one vector (extraction with two indices).
For [<-, [[<- and $<-, a data frame.
The story over when replacement values are coerced is a complicated one, and one that has changed during R's development. This section is a guide only.
When [ and [[ are used to add or replace a whole column,
no coercion takes place but value will be
replicated (by calling the generic function rep) to the
right length if an exact number of repeats can be used.
When [ is used with a logical matrix, each value is coerced to
the type of the column in which it is to be placed.
When [ and [[ are used with two indices, the
column will be coerced as necessary to accommodate the value.
Note that when the replacement value is an array (including a matrix)
it is not treated as a series of columns (as
data.frame and as.data.frame do) but
inserted as a single column.
Although the default for drop is TRUE, the default
behaviour when only one row is left is equivalent to
specifying drop = FALSE. To drop from a data frame to a list,
drop = FALSE has to specified explicitly.
subset which is often easier for extraction,
data.frame, Extract.
data(swiss)
sw <- swiss[1:5, 1:4] # select a manageable subset
sw[1:3] # select columns
sw[, 1:3] # same
sw[4:5, 1:3] # select rows and columns
sw[1] # a one-column data frame
sw[, 1, drop = FALSE] # the same
sw[, 1] # a (unnamed) vector
sw[[1]] # the same
sw[1,] # a one-row data frame
sw[1,, drop=TRUE] # a list
swiss[ c(1, 1:2), ] # duplicate row, unique row names are created
sw[sw <= 6] <- 6 # logical matrix indexing
sw
## adding a column
sw["new1"] <- LETTERS[1:5] # adds a character column
sw[["new2"]] <- letters[1:5] # ditto
sw[, "new3"] <- LETTERS[1:5] # ditto
# but this got converted to a factor in 1.7.x
sw$new4 <- 1:5
sapply(sw, class)
sw$new4 <- NULL # delete the column
sw
sw[6:8] <- list(letters[10:14], NULL, aa=1:5) # delete col7, update 6, 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, was a vector prior to 1.8.0
A[1:3, "z"] # a matrix
A[, "y"] # a matrix