aggregate.zoo {zoo} | R Documentation |
Splits a zoo object into subsets along a coarser index grid, computes summary statistics for each, and returns the reduced zoo object.
## S3 method for class 'zoo': aggregate(x, by, FUN, ...)
x |
an object of class "zoo" . |
by |
index vector the same length as index(x) which defines
aggregation groups and the new index to be associated with each group. |
FUN |
a scalar function to compute the summary statistics which can be applied to all subsets. |
... |
further arguments passed to FUN . |
An object of class "zoo"
.
## averaging over values in a month: # long series x.date <- as.Date(paste(2004, rep(1:4, 4:1), seq(1,20,2), sep = "-")) x <- zoo(rnorm(12), x.date) # coarser dates x.date2 <- as.Date(paste(2004, rep(1:4, 4:1), 1, sep = "-")) x2 <- aggregate(x, x.date2, mean) # compare time series plot(x) lines(x2, col = 2) ## aggregate on month and extend to monthly time series # test data if(require(chron)) { y <- zoo(matrix(11:15,nr=5,nc=2), chron(c(15,20,80,100,110))) colnames(y) <- c("A", "B") # aggregate by month using first of month as times for coarser series # replacing each such group with the first observation in that month. # Uses fact that chron dates format, by default, to mm/dd/yy firstofmonth <- function(x) chron(sub("/../", "/01/", format(x))) y2 <- aggregate(y, firstofmonth(time(y)), head, 1) # fill in missing months by merging with an empty series containing # a complete set of 1st of the months yrt2 <- range(time(y2)) y0 <- zoo(,seq(from = yrt2[1], to = yrt2[2], by = "month")) merge(y2, y0) } ## aggregate a daily time series to a quarterly series # create zoo series tt <- as.Date("2000-1-1") + 0:300 z.day <- zoo(0:300, tt) # function which returns corresponding first of quarter first.of.quarter <- function(tt) with(as.POSIXlt(tt), as.Date(paste(1900+year, 3*(mon%/%3) + 1, 1, sep = "-"))) # average z over quarters z.qtr <- aggregate(z.day, first.of.quarter(tt), mean)