zoo (version 1.8-6)

zoo: Z's Ordered Observations


zoo is the creator for an S3 class of indexed totally ordered observations which includes irregular time series.


zoo(x = NULL, order.by = index(x), frequency = NULL,
  calendar = getOption("zoo.calendar", TRUE))
# S3 method for zoo
print(x, style = , quote = FALSE, …)



a numeric vector, matrix or a factor.


an index vector with unique entries by which the observations in x are ordered. See the details for support of non-unique indexes.


numeric indicating frequency of order.by. If specified, it is checked whether order.by and frequency comply. If so, a regular "zoo" series is returned, i.e., an object of class c("zooreg", "zoo"). See below and zooreg for more details.


logical. If frequency is specified and is 4 or 12: Should yearqtr or yearmon be used for a numeric index order.by?


a string specifying the printing style which can be "horizontal" (the default for vectors), "vertical" (the default for matrices) or "plain" (which first prints the data and then the index).


logical. Should characters be quoted?

further arguments passed to the print methods of the data and the index.


A vector or matrix with an "index" attribute of the same dimension (NROW(x)) by which x is ordered.


zoo provides infrastructure for ordered observations which are stored internally in a vector or matrix with an index attribute (of arbitrary class, see below). The index must have the same length as NROW(x) except in the case of a zero length numeric vector in which case the index length can be any length. Emphasis has been given to make all methods independent of the index/time class (given in order.by). In principle, the data x could also be arbitrary, but currently there is only support for vectors and matrices and partial support for factors.

zoo is particularly aimed at irregular time series of numeric vectors/matrices, but it also supports regular time series (i.e., series with a certain frequency). zoo's key design goals are independence of a particular index/date/time class and consistency with ts and base R by providing methods to standard generics. Therefore, standard functions can be used to work with "zoo" objects and memorization of new commands is reduced.

When creating a "zoo" object with the function zoo, the vector of indexes order.by can be of (a single) arbitrary class (if x is shorter or longer than order.by it is expanded accordingly), but it is essential that ORDER(order.by) works. For other functions it is assumed that c(), length(), MATCH() and subsetting [, work. If this is not the case for a particular index/date/time class, then methods for these generic functions should be created by the user. Note, that to achieve this, new generic functions ORDER and MATCH are created in the zoo package with default methods corresponding to the non-generic base functions order and match. Note that the order and hence the default ORDER typically work if there is a xtfrm method. Furthermore, for certain (but not for all) operations the index class should have an as.numeric method (in particular for regular series) and an as.character method might improve printed output (see also below).

The index observations order.by should typically be unique, such that the observations can be totally ordered. Nevertheless, zoo() is able to create "zoo" objects with duplicated indexes (with a warning) and simple methods such as plot() or summary() will typically work for such objects. However, this is not formally supported as the bulk of functionality provided in zoo requires unique index observations/time stamps. See below for an example how to remove duplicated indexes.

If a frequency is specified when creating a series via zoo, the object returned is actually of class "zooreg" which inherits from "zoo". This is a subclass of "zoo" which relies on having a "zoo" series with an additional "frequency" attribute (which has to comply with the index of that series). Regular "zooreg" series can also be created by zooreg, the zoo analogue of ts. See the respective help page and is.regular for further details.

Methods to standard generics for "zoo" objects currently include: print (see above), summary, str, head, tail, [ (subsetting), rbind, cbind, merge (see merge.zoo), aggregate (see aggregate.zoo), rev, split (see aggregate.zoo), barplot, plot and lines (see plot.zoo). For multivariate "zoo" series with column names the $ extractor is available, behaving similar as for "data.frame" objects. Methods are also available for median and quantile.

ifelse.zoo is not a method (because ifelse is not a generic) but must be written out including the .zoo suffix.

To “prettify” printed output of "zoo" series the generic function index2char is used for turning index values into character values. It defaults to using as.character but can be customized if a different printed display should be used (although this should not be necessary, usually).

The subsetting method [ work essentially like the corresponding functions for vectors or matrices respectively, i.e., takes indexes of type "numeric", "integer" or "logical". But additionally, it can be used to index with observations from the index class of the series. If the index class of the series is one of the three classes above, the corresponding index has to be encapsulated in I() to enforce usage of the index class (see examples). Subscripting by a zoo object whose data contains logical values is undefined.

Additionally, zoo provides several generic functions and methods to work (a) on the data contained in a "zoo" object, (b) the index (or time) attribute associated to it, and (c) on both data and index:

(a) The data contained in "zoo" objects can be extracted by coredata (strips off all "zoo"-specific attributes) and modified using coredata<-. Both are new generic functions with methods for "zoo" objects, see coredata.

(b) The index associated with a "zoo" object can be extracted by index and modified by index<-. As the interpretation of the index as “time” in time series applications is more natural, there are also synonymous methods time and time<-. The start and the end of the index/time vector can be queried by start and end. See index.

(c) To work on both data and index/time, zoo provides methods lag, diff (see lag.zoo) and window, window<- (see window.zoo).

In addition to standard group generic function (see Ops), the following mathematical operations are available as methods for "zoo" objects: transpose t which coerces to a matrix first, and cumsum, cumprod, cummin, cummax which are applied column wise.

Coercion to and from "zoo" objects is available for objects of various classes, in particular "ts", "irts" and "its" objects can be coerced to "zoo", the reverse is available for "its" and for "irts" (the latter in package tseries). Furthermore, "zoo" objects can be coerced to vectors, matrices and lists and data frames (dropping the index/time attribute). See as.zoo.

Several methods are available for NA handling in the data of "zoo" objects: na.aggregate which uses group means to fill in NA values, na.approx which uses linear interpolation to fill in NA values. na.contiguous which extracts the longest consecutive stretch of non-missing values in a "zoo" object, na.fill which uses fixed specified values to replace NA values, na.locf which replaces NAs by the last previous non-NA, na.omit which returns a "zoo" object with incomplete observations removed, na.spline which uses spline interpolation to fill in NA values and na.StructTS which uses a seasonal Kalman filter to fill in NA values, na.trim which trims runs of NAs off the beginning and end but not in the interior. Yet another NA routine can be found in the stinepack package where na.stinterp performs Stineman interpolation.

A typical task to be performed on ordered observations is to evaluate some function, e.g., computing the mean, in a window of observations that is moved over the full sample period. The generic function rollapply provides this functionality for arbitrary functions and more efficient versions rollmean, rollmax, rollmedian are available for the mean, maximum and median respectively.

The zoo package has an as.Date numeric method which is similar to the one in the core of R except that the origin argument defaults to January 1, 1970 (whereas the one in the core of R has no default).

Note that since zoo uses date/time classes from base R and other packages, it may inherit bugs or problems with those date/time classes. Currently, there is one such known problem with the c method for the POSIXct class in base R: If x and y are POSIXct objects with tzone attributes, the attribute will always be dropped in c(x, y), even if it is the same across both x and y. Although this is documented at c.POSIXct, one may want to employ a workaround as shown at https://stat.ethz.ch/pipermail/r-devel/2010-August/058112.html.


Achim Zeileis and Gabor Grothendieck (2005). zoo: S3 Infrastructure for Regular and Irregular Time Series. Journal of Statistical Software, 14(6), 1-27. URL http://www.jstatsoft.org/v14/i06/ and available as vignette("zoo").

Ajay Shah, Achim Zeileis and Gabor Grothendieck (2005). zoo Quick Reference. Package vignette available as vignette("zoo-quickref").

See Also

zooreg, plot.zoo, index, merge.zoo


Run this code

## simple creation and plotting
x.Date <- as.Date("2003-02-01") + c(1, 3, 7, 9, 14) - 1
x <- zoo(rnorm(5), x.Date)

## subsetting with numeric indexes
x[c(2, 4)]
## subsetting with index class
x[as.Date("2003-02-01") + c(2, 8)]

## different classes of indexes/times can be used, e.g. numeric vector
x <- zoo(rnorm(5), c(1, 3, 7, 9, 14))
## subsetting with numeric indexes then uses observation numbers
x[c(2, 4)]
## subsetting with index class can be enforced by I()
x[I(c(3, 9))]

## visualization
## or POSIXct
y.POSIXct <- ISOdatetime(2003, 02, c(1, 3, 7, 9, 14), 0, 0, 0)
y <- zoo(rnorm(5), y.POSIXct)

## create a constant series
z <- zoo(1, seq(4)[-2])

## create a 0-dimensional zoo series
z0 <- zoo(, 1:4)

## create a 2-dimensional zoo series
z2 <- zoo(matrix(1:12, 4, 3), as.Date("2003-01-01") + 0:3)

## create a factor zoo object
fz <- zoo(gl(2,5), as.Date("2004-01-01") + 0:9)

## create a zoo series with 0 columns
z20 <- zoo(matrix(nrow = 4, ncol = 0), 1:4)

## arithmetic on zoo objects intersects them first
x1 <- zoo(1:5, 1:5)
x2 <- zoo(2:6, 2:6)
10 * x1 + x2

## $ extractor for multivariate zoo series with column names
z <- zoo(cbind(foo = rnorm(5), bar = rnorm(5)))
z$xyz <- zoo(rnorm(3), 2:4)

## add comments to a zoo object
comment(x1) <- c("This is a very simple example of a zoo object.",
  "It can be recreated using this R code: example(zoo)")
## comments are not output by default but are still there

# ifelse does not work with zoo but this works
# to create a zoo object which equals x1 at
# time i if x1[i] > x1[i-1] and 0 otherwise
(diff(x1) > 0) * x1

## zoo series with duplicated indexes
z3 <- zoo(1:8, c(1, 2, 2, 2, 3, 4, 5, 5))
## remove duplicated indexes by averaging
lines(aggregate(z3, index(z3), mean), col = 2)
## or by using the last observation
lines(aggregate(z3, index(z3), tail, 1), col = 4)

## x1[x1 > 3] is not officially supported since
## x1 > 3 is of class "zoo", not "logical".
## Use one of these instead:
x1[which(x1 > 3)]
x1[coredata(x1 > 3)]
x1[as.logical(x1 > 3)]
subset(x1, x1 > 3)

## any class supporting the methods discussed can be used
## as an index class. Here are examples using complex numbers
## and letters as the time class.

z4 <- zoo(11:15, complex(real = c(1, 3, 4, 5, 6), imag = c(0, 1, 0, 0, 1)))
merge(z4, lag(z4))

z5 <- zoo(11:15, letters[1:5])
merge(z5, lag(z5))

# index values relative to 2001Q1
zz <- zooreg(cbind(a = 1:10, b = 11:20), start = as.yearqtr(2000), freq = 4)
zz[] <- mapply("/", as.data.frame(zz), coredata(zz[as.yearqtr("2001Q1")]))

## even though time index must be unique zoo (and read.zoo)
## will both allow creation of such illegal objects with
## a warning (rather than ana error) to give the user a 
## chance to fix them up.  Extracting and replacing times
## and aggregate.zoo will still work.
# }
# this gives a warning
# and then creates an illegal zoo object
z6 <- zoo(11:15, c(1, 1, 2, 2, 5))

# fix it up by averaging duplicates
aggregate(z6, identity, mean)

# or, fix it up by taking last in each set of duplicates
aggregate(z6, identity, tail, 1)

# fix it up via interpolation of duplicate times
time(z6) <- na.approx(ifelse(duplicated(time(z6)), NA, time(z6)), na.rm = FALSE)
# if there is a run of equal times at end they
# wind up as NAs and we cannot have NA times
z6 <- z6[!is.na(time(z6))]

x1. <- x1 <- zoo (matrix (1:12, nrow = 3), as.Date("2008-08-01") + 0:2)
colnames (x1) <- c ("A", "B", "C", "D")
x2 <- zoo (matrix (1:12, nrow = 3), as.Date("2008-08-01") + 1:3)
colnames (x2) <- c ("B", "C", "D", "E")

both.dates = as.Date (intersect (index (t1), index (t2)))
both.cols = intersect (colnames (t1), colnames (t2))

x1[both.dates, both.cols]
## there is "[.zoo" but no "[<-.zoo" however four of the following
## five examples work

## wrong
## x1[both.dates, both.cols] <- x2[both.dates, both.cols]

# 4 correct alternatives
# #1
window(x1, both.dates)[, both.cols] <- x2[both.dates, both.cols]

# #2. restore x1 and show a different way
x1 <- x1.
window(x1, both.dates)[, both.cols] <- window(x2, both.dates)[, both.cols]

# #3. restore x1 and show a different way
x1 <- x1.
x1[time(x1) <!-- %in% both.dates, both.cols] <- x2[both.dates, both.cols] -->

# #4. restore x1 and show a different way
x1 <- x1.
x1[time(x1) <!-- %in% both.dates, both.cols] <- x2[time(t2) %in% both.dates, both.cols] -->

# }
# }

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