base (version 3.0.3)

colSums: 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)
.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.

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.

X

a numeric matrix.

m, n

the dimensions of X.

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.

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 are ‘bare-bones’ versions for use in programming: they apply only to numeric matrices and do not name the result.

See Also

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
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)