Take a sequence of vector, matrix or data-frame arguments and combine
by *c*olumns or *r*ows, respectively. These are generic
functions with methods for other R classes.

```
cbind(…, deparse.level = 1)
rbind(…, deparse.level = 1)
# S3 method for data.frame
rbind(…, deparse.level = 1, make.row.names = TRUE,
stringsAsFactors = default.stringsAsFactors(), factor.exclude = NA)
```

…

(generalized) vectors or matrices. These can be given as named
arguments. Other R objects may be coerced as appropriate, or S4
methods may be used: see sections ‘Details’ and
‘Value’. (For the `"data.frame"`

method of `cbind`

these can be further arguments to `data.frame`

such as
`stringsAsFactors`

.)

deparse.level

integer controlling the construction of labels in
the case of non-matrix-like arguments (for the default method):
`deparse.level = 0`

constructs no labels; the default,
`deparse.level = 1 or 2`

constructs labels from the argument
names, see the ‘Value’ section below.

make.row.names

(only for data frame method:) logical
indicating if unique and valid `row.names`

should be
constructed from the arguments.

stringsAsFactors

logical, passed to `as.data.frame`

;
only has an effect when the `…`

arguments contain a
(non-`data.frame`

) `character`

.

factor.exclude

if the data frames contain factors,
`TRUE`

ensures that `NA`

levels of factors are kept, see
17562 and the ‘Data frame methods’. In R versions up
to 3.6.x, `factor.exclude = NA`

has been implicitly hardcoded
(R <= 3.6.0) or the default (R = 3.6.x, x >= 1).

For the default method, a matrix combining the `…`

arguments
column-wise or row-wise. (Exception: if there are no inputs or all
the inputs are `NULL`

, the value is `NULL`

.)

The type of a matrix result determined from the highest type of any of the inputs in the hierarchy raw < logical < integer < double < complex < character < list .

For `cbind`

(`rbind`

) the column (row) names are taken from
the `colnames`

(`rownames`

) of the arguments if these are
matrix-like. Otherwise from the names of the arguments or where those
are not supplied and `deparse.level > 0`

, by deparsing the
expressions given, for `deparse.level = 1`

only if that gives a
sensible name (a ‘symbol’, see `is.symbol`

).

For `cbind`

row names are taken from the first argument with
appropriate names: rownames for a matrix, or names for a vector of
length the number of rows of the result.

For `rbind`

column names are taken from the first argument with
appropriate names: colnames for a matrix, or names for a vector of
length the number of columns of the result.

The `cbind`

data frame method is just a wrapper for
`data.frame(..., check.names = FALSE)`

. This means that
it will split matrix columns in data frame arguments, and convert
character columns to factors unless `stringsAsFactors = FALSE`

is
specified.

The `rbind`

data frame method first drops all zero-column and
zero-row arguments. (If that leaves none, it returns the first
argument with columns otherwise a zero-column zero-row data frame.)
It then takes the classes of the columns from the
first data frame, and matches columns by name (rather than by
position). Factors have their levels expanded as necessary (in the
order of the levels of the level sets of the factors encountered) and
the result is an ordered factor if and only if all the components were
ordered factors. (The last point differs from S-PLUS.) Old-style
categories (integer vectors with levels) are promoted to factors.

Note that for result column `j`

, `factor(., exclude = X(j))`

is applied, where

X(j) := if(isTRUE(factor.exclude)) { if(!NA.lev[j]) NA # else NULL } else factor.exclude

where `NA.lev[j]`

is true iff any contributing data frame has had a
`factor`

in column `j`

with an explicit `NA`

level.

The method dispatching is *not* done via
`UseMethod()`

, but by C-internal dispatching.
Therefore there is no need for, e.g., `rbind.default`

.

The dispatch algorithm is described in the source file
(`.../src/main/bind.c`

) as

For each argument we get the list of possible class memberships from the class attribute.

We inspect each class in turn to see if there is an applicable method.

If we find an applicable method we make sure that it is identical to any method determined for prior arguments. If it is identical, we proceed, otherwise we immediately drop through to the default code.

If you want to combine other objects with data frames, it may be necessary to coerce them to data frames first. (Note that this algorithm can result in calling the data frame method if all the arguments are either data frames or vectors, and this will result in the coercion of character vectors to factors.)

The functions `cbind`

and `rbind`

are S3 generic, with
methods for data frames. The data frame method will be used if at
least one argument is a data frame and the rest are vectors or
matrices. There can be other methods; in particular, there is one for
time series objects. See the section on ‘Dispatch’ for how
the method to be used is selected. If some of the arguments are of an
S4 class, i.e., `isS4(.)`

is true, S4 methods are sought
also, and the hidden `cbind`

/ `rbind`

functions
from package methods maybe called, which in turn build on
`cbind2`

or `rbind2`

, respectively. In that
case, `deparse.level`

is obeyed, similarly to the default method.

In the default method, all the vectors/matrices must be atomic (see
`vector`

) or lists. Expressions are not allowed.
Language objects (such as formulae and calls) and pairlists will be
coerced to lists: other objects (such as names and external pointers)
will be included as elements in a list result. Any classes the inputs
might have are discarded (in particular, factors are replaced by their
internal codes).

If there are several matrix arguments, they must all have the same
number of columns (or rows) and this will be the number of columns (or
rows) of the result. If all the arguments are vectors, the number of
columns (rows) in the result is equal to the length of the longest
vector. Values in shorter arguments are recycled to achieve this
length (with a `warning`

if they are recycled only
*fractionally*).

When the arguments consist of a mix of matrices and vectors the number of columns (rows) of the result is determined by the number of columns (rows) of the matrix arguments. Any vectors have their values recycled or subsetted to achieve this length.

For `cbind`

(`rbind`

), vectors of zero length (including
`NULL`

) are ignored unless the result would have zero rows
(columns), for S compatibility.
(Zero-extent matrices do not occur in S3 and are not ignored in R.)

Matrices are restricted to less than \(2^{31}\) rows and columns even on 64-bit systems. So input vectors have the same length restriction: as from R 3.2.0 input matrices with more elements (but meeting the row and column restrictions) are allowed.

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988)
*The New S Language*.
Wadsworth & Brooks/Cole.

`c`

to combine vectors (and lists) as vectors,
`data.frame`

to combine vectors and matrices as a data
frame.

# NOT RUN { m <- cbind(1, 1:7) # the '1' (= shorter vector) is recycled m m <- cbind(m, 8:14)[, c(1, 3, 2)] # insert a column m cbind(1:7, diag(3)) # vector is subset -> warning cbind(0, rbind(1, 1:3)) cbind(I = 0, X = rbind(a = 1, b = 1:3)) # use some names xx <- data.frame(I = rep(0,2)) cbind(xx, X = rbind(a = 1, b = 1:3)) # named differently cbind(0, matrix(1, nrow = 0, ncol = 4)) #> Warning (making sense) dim(cbind(0, matrix(1, nrow = 2, ncol = 0))) #-> 2 x 1 ## deparse.level dd <- 10 rbind(1:4, c = 2, "a++" = 10, dd, deparse.level = 0) # middle 2 rownames rbind(1:4, c = 2, "a++" = 10, dd, deparse.level = 1) # 3 rownames (default) rbind(1:4, c = 2, "a++" = 10, dd, deparse.level = 2) # 4 rownames ## cheap row names: b0 <- gl(3,4, labels=letters[1:3]) bf <- setNames(b0, paste0("o", seq_along(b0))) df <- data.frame(a = 1, B = b0, f = gl(4,3)) df. <- data.frame(a = 1, B = bf, f = gl(4,3)) new <- data.frame(a = 8, B ="B", f = "1") (df1 <- rbind(df , new)) (df.1 <- rbind(df., new)) stopifnot(identical(df1, rbind(df, new, make.row.names=FALSE)), identical(df1, rbind(df., new, make.row.names=FALSE))) # }