groupSums | R Documentation |
Compute summary statistics (sums, means) of data subsets.
groupSums(object, ...) ## S4 method for signature 'matrix' groupSums(object, MARGIN, by, na.rm = FALSE, ...) ## S4 method for signature 'sparseMatrix' groupSums(object, MARGIN, by, na.rm = FALSE, ...) ## S4 method for signature 'Mefa' groupSums(object, MARGIN, by, replace, na.rm = FALSE, ...) groupMeans(object, ...) ## S4 method for signature 'matrix' groupMeans(object, MARGIN, by, na.rm = FALSE, ...) ## S4 method for signature 'sparseMatrix' groupMeans(object, MARGIN, by, na.rm = FALSE, ...) ## S4 method for signature 'Mefa' groupMeans(object, MARGIN, by, replace, na.rm = FALSE, ...) sum_by(x, by)
object |
an object. |
x |
a vector. |
MARGIN |
numeric, |
by |
a vector of grouping elements corresponding to dimensions
of |
replace |
a data frame to be used when applying the method on a
|
na.rm |
logical. Should missing values be removed?
Sum is calculated by zeroing out |
... |
other argument, currently not implemented. |
The method sums/averages cells in a matrix.
The functions behind these methods use sparse matrices,
so calculations can be more efficient compared to using
aggregate
.
An object similar to the input one.
Peter Solymos <solymos@ualberta.ca>
rowSums
, rowMeans
,
colSums
, colMeans
Standard aggregate
in package stats
aggregate.mefa
in package
mefa for S3 "mefa"
objects.
x <- data.frame( sample = paste("Sample", c(1,1,2,2,3,4), sep="."), species = c(paste("Species", c(1,1,1,2,3), sep="."), "zero.pseudo"), count = c(1,2,10,3,4,0), stringsAsFactors = TRUE) samp <- data.frame(samples=levels(x$sample), var1=1:2, stringsAsFactors = TRUE) taxa <- data.frame(specnames=levels(x$species), var2=c("b","a"), stringsAsFactors = TRUE) rownames(samp) <- samp$samples rownames(taxa) <- taxa$specnames x2 <- Xtab(count ~ sample + species, x, cdrop=FALSE,rdrop=TRUE) x5 <- Mefa(x2, samp, taxa, join="inner") groupSums(as.matrix(x2), 1, c(1,1,2)) groupSums(as.matrix(x2), 2, c(1,1,2,2)) groupSums(x2, 1, c(1,1,2)) groupSums(x2, 2, c(1,1,2,2)) groupSums(x5, 1, c(1,1,2)) groupSums(x5, 2, c(1,1,2,2)) groupMeans(as.matrix(x2), 1, c(1,1,2)) groupMeans(as.matrix(x2), 2, c(1,1,2,2)) groupMeans(x2, 1, c(1,1,2)) groupMeans(x2, 2, c(1,1,2,2)) groupMeans(x5, 1, c(1,1,2)) groupMeans(x5, 2, c(1,1,2,2)) sum_by(runif(100, 0, 1), sample(LETTERS[1:4], 100, replace=TRUE))
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