Checks if a value exists / does not exist in each row (column) of a matrix

Checks if a value exists / does not exist in each row (column) of a matrix.

rowAlls(x, rows = NULL, cols = NULL, value = TRUE, na.rm = FALSE,
  dim. = dim(x), ..., useNames = TRUE)

colAlls(x, rows = NULL, cols = NULL, value = TRUE, na.rm = FALSE,
  dim. = dim(x), ..., useNames = TRUE)

allValue(x, idxs = NULL, value = TRUE, na.rm = FALSE, ...)

rowAnys(x, rows = NULL, cols = NULL, value = TRUE, na.rm = FALSE,
  dim. = dim(x), ..., useNames = TRUE)

colAnys(x, rows = NULL, cols = NULL, value = TRUE, na.rm = FALSE,
  dim. = dim(x), ..., useNames = TRUE)

anyValue(x, idxs = NULL, value = TRUE, na.rm = FALSE, ...)

Arguments

x: An NxK matrix or, if dim. is specified, an N * K vector.
rows: A vector indicating subset of rows to operate over. If NULL, no subsetting is done.
cols: A vector indicating subset of columns to operate over. If NULL, no subsetting is done.
value: A value to search for.
na.rm: If TRUE, missing values are excluded.
dim.: An integer vector of length two specifying the dimension of x, also when not a matrix. Comment: The reason for this argument being named with a period at the end is purely technical (we get a run-time error if we try to name it dim).
...: Not used.
useNames: If TRUE (default), names attributes of the result are set, otherwise not.
idxs: A vector indicating subset of elements to operate over. If NULL, no subsetting is done.

Value

rowAlls() (colAlls()) returns an logical vector of length N (K). Analogously for rowAnys() (rowAlls()).

Details

These functions takes either a matrix or a vector as input. If a vector, then argument dim. must be specified and fulfill prod(dim.) == length(x). The result will be identical to the results obtained when passing matrix(x, nrow = dim.[1L], ncol = dim.[2L]), but avoids having to temporarily create/allocate a matrix, if only such is needed only for these calculations.

Logical `value`

When value is logical, the result is as if the function is applied on as.logical(x). More specifically, if x is numeric, then all zeros are treated as FALSE, non-zero values as TRUE, and all missing values as NA.

Author

Henrik Bengtsson

Examples

x <- matrix(FALSE, nrow = 10, ncol = 5)
x[3:7, c(2, 4)] <- TRUE
x[2:4, ] <- TRUE
x[, 1] <- TRUE
x[5, ] <- FALSE
x[, 5] <- FALSE
print(x)
#>        [,1]  [,2]  [,3]  [,4]  [,5]
#>  [1,]  TRUE FALSE FALSE FALSE FALSE
#>  [2,]  TRUE  TRUE  TRUE  TRUE FALSE
#>  [3,]  TRUE  TRUE  TRUE  TRUE FALSE
#>  [4,]  TRUE  TRUE  TRUE  TRUE FALSE
#>  [5,] FALSE FALSE FALSE FALSE FALSE
#>  [6,]  TRUE  TRUE FALSE  TRUE FALSE
#>  [7,]  TRUE  TRUE FALSE  TRUE FALSE
#>  [8,]  TRUE FALSE FALSE FALSE FALSE
#>  [9,]  TRUE FALSE FALSE FALSE FALSE
#> [10,]  TRUE FALSE FALSE FALSE FALSE

print(rowCounts(x))       # 1 4 4 4 0 3 3 1 1 1
#>  [1] 1 4 4 4 0 3 3 1 1 1
print(colCounts(x))       # 9 5 3 5 0
#> [1] 9 5 3 5 0

print(rowAnys(x))
#>  [1]  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
print(which(rowAnys(x)))  # 1  2  3  4  6  7  8  9 10
#> [1]  1  2  3  4  6  7  8  9 10
print(colAnys(x))
#> [1]  TRUE  TRUE  TRUE  TRUE FALSE
print(which(colAnys(x)))  # 1 2 3 4
#> [1] 1 2 3 4