rowsum {base} | R Documentation |

## Give Column Sums of a Matrix or Data Frame, Based on a Grouping Variable

### Description

Compute column sums across rows of a numeric matrix-like object for
each level of a grouping variable. `rowsum`

is generic, with a
method for data frames and a default method for vectors and matrices.

### Usage

```
rowsum(x, group, reorder = TRUE, ...)
## S3 method for class 'data.frame'
rowsum(x, group, reorder = TRUE, na.rm = FALSE, ...)
## Default S3 method:
rowsum(x, group, reorder = TRUE, na.rm = FALSE, ...)
```

### Arguments

`x` |
a matrix, data frame or vector of numeric data. Missing values are allowed. A numeric vector will be treated as a column vector. |

`group` |
a vector or factor giving the grouping, with one element
per row of |

`reorder` |
if |

`na.rm` |
logical ( |

`...` |
other arguments to be passed to or from methods. |

### Details

The default is to reorder the rows to agree with `tapply`

as in
the example below. Reordering should not add noticeably to the time
except when there are very many distinct values of `group`

and
`x`

has few columns.

The original function was written by Terry Therneau, but this is a new implementation using hashing that is much faster for large matrices.

To sum over all the rows of a matrix (i.e., a single `group`

) use
`colSums`

, which should be even faster.

For integer arguments, over/underflow in forming the sum results in
`NA`

.

### Value

A matrix or data frame containing the sums. There will be one row per
unique value of `group`

.

### See Also

### Examples

```
require(stats)
x <- matrix(runif(100), ncol = 5)
group <- sample(1:8, 20, TRUE)
(xsum <- rowsum(x, group))
## Slower versions
tapply(x, list(group[row(x)], col(x)), sum)
t(sapply(split(as.data.frame(x), group), colSums))
aggregate(x, list(group), sum)[-1]
```

*base*version 4.4.0 Index]