# colSums: Form Row and Column Sums and Means

## Description

Form row and column sums and means for numeric arrays (or data frames).

## Usage

 ```1 2 3 4 5 6 7 8 9``` ```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) .colSums(x, m, n, na.rm = FALSE) .rowSums(x, m, n, na.rm = FALSE) .colMeans(x, m, n, na.rm = FALSE) .rowMeans(x, m, n, na.rm = FALSE) ```

## Arguments

 `x` an array of two or more dimensions, containing numeric, complex, integer or logical values, or a numeric data frame. For `.colSums()` etc, a numeric, integer or logical matrix (or vector of length `m * n`). `na.rm` logical. Should missing values (including `NaN`) be omitted from the calculations? `dims` integer: 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`. `m, n` the dimensions of the matrix `x` for `.colSums()` etc.

## 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.

Notice that omission of missing values is done on a per-column or per-row basis, so column means may not be over the same set of rows, and vice versa. To use only complete rows or columns, first select them with `na.omit` or `complete.cases` (possibly on the transpose of `x`).

The versions with an initial dot in the name (`.colSums()` etc) are ‘bare-bones’ versions for use in programming: they apply only to numeric (like) matrices and do not name the result.

## Value

A numeric or complex array of suitable size, or a vector if the result is one-dimensional. For the first four functions 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 `NaN` (`*Means`), consistent with `sum` and `mean`.

`apply`, `rowsum`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26``` ```## Compute row and column sums for a matrix: x <- cbind(x1 = 3, x2 = c(4:1, 2:5)) rowSums(x); colSums(x) dimnames(x)[] <- 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 dim(UCBAdmissions) rowSums(UCBAdmissions); rowSums(UCBAdmissions, dims = 2) colSums(UCBAdmissions); colSums(UCBAdmissions, dims = 2) ## complex case x <- cbind(x1 = 3 + 2i, x2 = c(4:1, 2:5) - 5i) 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) ```