colSums {base} R Documentation

Form Row and Column Sums and Means

Description

Form row and column sums and means for numeric arrays.

Usage

```colSums (x, na.rm = FALSE, dims = 1)
rowSums (x, na.rm = FALSE, dims = 1)
colMeans(x, na.rm = FALSE, dims = 1)
rowMeans(x, na.rm = FALSE, dims = 1)
```

Arguments

 `x` an array of two or more dimensions, containing numeric, complex, integer or logical values, or a numeric data frame. `na.rm` logical. Should missing values (including `NaN`) be omitted from the calculations? `dims` Which dimensions are regarded as “rows” or “columns” to sum over. For `row*`, the sum or mean is over dimensions `dims+1, ...`; for `col*` it is over dimensions `1:dims`.

Details

These functions are equivalent to use of `apply` with `FUN = mean` or `FUN = sum` with appropriate margins, but are a lot faster. As they are written for speed, they blur over some of the subtleties of `NaN` and `NA`. If ```na.rm = FALSE``` and either `NaN` or `NA` appears in a sum, the result will be one of `NaN` or `NA`, but which might be platform-dependent.

Value

A numeric or complex array of suitable size, or a vector if the result is one-dimensional. The `dimnames` (or `names` for a vector result) are taken from the original array.
If there are no values in a range to be summed over (after removing missing values with `na.rm = TRUE`), that component of the output is set to `0` (`*Sums`) or `NA` (`*Means`), consistent with `sum` and `mean`.

`apply`, `rowsum`

Examples

```## Compute row and column sums for a matrix:
x <- cbind(x1 = 3, x2 = c(4:1, 2:5))
rowSums(x); colSums(x)
dimnames(x)[[1]] <- letters[1:8]
rowSums(x); colSums(x); rowMeans(x); colMeans(x)
x[] <- as.integer(x)
rowSums(x); colSums(x)
x[] <- x < 3
rowSums(x); colSums(x)
x <- cbind(x1 = 3, x2 = c(4:1, 2:5))
x[3, ] <- NA; x[4, 2] <- NA
rowSums(x); colSums(x); rowMeans(x); colMeans(x)
rowSums(x, na.rm = TRUE); colSums(x, na.rm = TRUE)
rowMeans(x, na.rm = TRUE); colMeans(x, na.rm = TRUE)

## an array