Extract.data.frame {base}

R Documentation

Extract or Replace Parts of a Data Frame

Description

Extract or replace subsets of data frames.

Usage

## S3 method for class 'data.frame'
x[i, j, drop = ]
## S3 replacement method for class 'data.frame'
x[i, j] <- value
## S3 method for class 'data.frame'
x[[..., exact = TRUE]]
## S3 replacement method for class 'data.frame'
x[[i, j]] <- value
## S3 replacement method for class 'data.frame'
x$i <- value

Arguments

x	data frame.
i, j, ...	elements to extract or replace. For [ and [[, these 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. For replacement by $, i is a name or literal character string.
drop	logical. If TRUE the result is coerced to the lowest possible dimension. The default is to drop if only one column is left, but not to drop if only one row is left.
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.
exact	logical: see [, and applies to column names.

Details

Data frames can be indexed in several modes. When [ and [[ are used with a single index (x[i] or x[[i]]), 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 [[i, exact = FALSE]].

When [ and [[ are used with two indices (x[i, j] and x[[i, j]]) 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, 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 columns).

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. Missing values in the indices are not allowed for replacement.

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 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. (See the Examples.)

Matrix 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 way as for a matrix.

Both [ and [[ extraction methods partially match row names. By default neither partially match column names, but [[ will unless exact=TRUE. If you want to do exact matching on row names use match as in the examples.

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. 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 NULL.

For [[ a column of the data frame or NULL (extraction with one index) or a length-one vector (extraction with two indices).

For $, a column of the data frame (or NULL).

For [<-, [[<- and $<-, a data frame.

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 [ 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 into 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.

Warning

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 = TRUE has to be specified explicitly.

See Also

subset which is often easier for extraction, data.frame, 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



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

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