zoo (version 1.3-2)

aggregate.zoo: Compute Summary Statistics of zoo Objects

Description

Splits a "zoo" object into subsets along a coarser index grid, computes summary statistics for each, and returns the reduced "zoo" object.

Usage

## S3 method for class 'zoo':
aggregate(x, by, FUN, \dots)

Arguments

x

an object of class "zoo".

by

index vector of the same length as index(x) which defines aggregation groups and the new index to be associated with each group. If by is a function, then it is applied to index(x) to obtain the aggregati

FUN

a scalar function to compute the summary statistics which can be applied to all subsets.

...

further arguments passed to FUN.

Value

• An object of class "zoo" or "zooreg" (if the resulting series is.regular).

See Also

zoo

Examples

## 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 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 "Date" of quarter
first.of.quarter <- function(tt) as.Date(as.yearqtr(tt))

# average z over quarters
# 1. via "yearqtr" index (regular)
# 2. via "Date" index (not regular)
z.qtr1 <- aggregate(z.day, as.yearqtr, mean)
z.qtr2 <- aggregate(z.day, first.of.quarter, mean)


## aggregate on month and extend to monthly time series
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 POSIXct seconds data every 10 minutes
tt <- seq(10, 2000, 10)
x <- zoo(tt, structure(tt, class = c("POSIXt", "POSIXct")))
aggregate(x, time(x) - as.numeric(time(x)) %% 600, mean)
}