Form Row and Column Sums and Means

Form row and column sums and means for objects, for sparseMatrix the result may optionally be sparse (sparseVector), too. Row or column names are kept respectively as for base matrices and colSums methods, when the result is numeric vector.

Usage

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

# S4 method for class 'CsparseMatrix'
colSums (x, na.rm = FALSE, dims = 1L,
         sparseResult = FALSE, ...)
# S4 method for class 'CsparseMatrix'
rowSums (x, na.rm = FALSE, dims = 1L,
         sparseResult = FALSE, ...)
# S4 method for class 'CsparseMatrix'
colMeans(x, na.rm = FALSE, dims = 1L,
         sparseResult = FALSE, ...)
# S4 method for class 'CsparseMatrix'
rowMeans(x, na.rm = FALSE, dims = 1L,
         sparseResult = FALSE, ...)

Arguments

x

a Matrix, i.e., inheriting from Matrix.

na.rm

logical. Should missing values (including NaN) be omitted from the calculations?

dims

completely ignored by the Matrix methods.

...

potentially further arguments, for method <-> generic compatibility.

sparseResult

logical indicating if the result should be sparse, i.e., inheriting from class sparseVector. Only applicable when x is inheriting from a sparseMatrix class.

Value

returns a numeric vector if sparseResult is FALSE as per default. Otherwise, returns a sparseVector.

dimnames(x) are only kept (as names(v)) when the resulting v is numeric, since sparseVectors do not have names.

See also

colSums and the sparseVector classes.

Examples

(M <- bdiag(Diagonal(2), matrix(1:3, 3,4), diag(3:2))) # 7 x 8
#> 7 x 8 sparse Matrix of class "dgCMatrix"
#>                     
#> [1,] 1 . . . . . . .
#> [2,] . 1 . . . . . .
#> [3,] . . 1 1 1 1 . .
#> [4,] . . 2 2 2 2 . .
#> [5,] . . 3 3 3 3 . .
#> [6,] . . . . . . 3 .
#> [7,] . . . . . . . 2
colSums(M)
#> [1] 1 1 6 6 6 6 3 2
d <- Diagonal(10, c(0,0,10,0,2,rep(0,5)))
MM <- kronecker(d, M)
dim(MM) # 70 80
#> [1] 70 80
length(MM@x) # 160, but many are '0' ; drop those:
#> [1] 160
MM <- drop0(MM)
length(MM@x) # 32
#> [1] 32
  cm <- colSums(MM)
(scm <- colSums(MM, sparseResult = TRUE))
#> sparse vector (nnz/length = 16/80) of class "dsparseVector"
#>  [1]  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 10 10 60 60 60 60 30 20  .
#> [26]  .  .  .  .  .  .  .  2  2 12 12 12 12  6  4  .  .  .  .  .  .  .  .  .  .
#> [51]  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .
#> [76]  .  .  .  .  .
stopifnot(is(scm, "sparseVector"),
          identical(cm, as.numeric(scm)))
rowSums (MM, sparseResult = TRUE) # 14 of 70 are not zero
#> sparse vector (nnz/length = 14/70) of class "dsparseVector"
#>  [1]   .   .   .   .   .   .   .   .   .   .   .   .   .   .  10  10  40  80 120
#> [20]  30  20   .   .   .   .   .   .   .   2   2   8  16  24   6   4   .   .   .
#> [39]   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .
#> [58]   .   .   .   .   .   .   .   .   .   .   .   .   .
colMeans(MM, sparseResult = TRUE) # 16 of 80 are not zero
#> sparse vector (nnz/length = 16/80) of class "dsparseVector"
#>  [1]          .          .          .          .          .          .
#>  [7]          .          .          .          .          .          .
#> [13]          .          .          .          . 0.14285714 0.14285714
#> [19] 0.85714286 0.85714286 0.85714286 0.85714286 0.42857143 0.28571429
#> [25]          .          .          .          .          .          .
#> [31]          .          . 0.02857143 0.02857143 0.17142857 0.17142857
#> [37] 0.17142857 0.17142857 0.08571429 0.05714286          .          .
#> [43]          .          .          .          .          .          .
#> [49]          .          .          .          .          .          .
#> [55]          .          .          .          .          .          .
#> [61]          .          .          .          .          .          .
#> [67]          .          .          .          .          .          .
#> [73]          .          .          .          .          .          .
#> [79]          .          .
## Since we have no 'NA's, these two are equivalent :
stopifnot(identical(rowMeans(MM, sparseResult = TRUE),
                    rowMeans(MM, sparseResult = TRUE, na.rm = TRUE)),
    rowMeans(Diagonal(16)) == 1/16,
    colSums(Diagonal(7)) == 1)

## dimnames(x) -->  names( <value> ) :
dimnames(M) <- list(paste0("r", 1:7), paste0("V",1:8))
M
#> 7 x 8 sparse Matrix of class "dgCMatrix"
#>    V1 V2 V3 V4 V5 V6 V7 V8
#> r1  1  .  .  .  .  .  .  .
#> r2  .  1  .  .  .  .  .  .
#> r3  .  .  1  1  1  1  .  .
#> r4  .  .  2  2  2  2  .  .
#> r5  .  .  3  3  3  3  .  .
#> r6  .  .  .  .  .  .  3  .
#> r7  .  .  .  .  .  .  .  2
colSums(M)
#> V1 V2 V3 V4 V5 V6 V7 V8 
#>  1  1  6  6  6  6  3  2 
rowMeans(M)
#>    r1    r2    r3    r4    r5    r6    r7 
#> 0.125 0.125 0.500 1.000 1.500 0.375 0.250 
## Assertions :
stopifnot(exprs = {
    all.equal(colSums(M),
              structure(c(1,1,6,6,6,6,3,2), names = colnames(M)))
    all.equal(rowMeans(M),
              structure(c(1,1,4,8,12,3,2)/8, names = paste0("r", 1:7)))
})