Description Usage Arguments Details Value References See Also Examples
zoo
is the creator for an S3 class of indexed
totally ordered observations which includes irregular
time series.
1 2 3 4 
x 
a numeric vector, matrix or a factor. 
order.by 
an index vector with unique entries by which the
observations in 
frequency 
numeric indicating frequency of 
calendar 
logical. If 
style 
a string specifying the printing style which can be

quote 
logical. Should characters be quoted? 
... 
further arguments passed to the print methods of the data and the index. 
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 nongeneric 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 nonmissing values in a "zoo"
object,
na.fill
which uses fixed specified values to replace NA
values,
na.locf
which
replaces NA
s by the last previous nonNA
,
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 NA
s 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/rdevel/2010August/058112.html.
A vector or matrix with an "index"
attribute of the same
dimension (NROW(x)
) by which x
is ordered.
Achim Zeileis and Gabor Grothendieck (2005).
zoo: S3 Infrastructure for Regular and Irregular Time Series.
Journal of Statistical Software, 14(6), 127.
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("zooquickref")
.
zooreg
, plot.zoo
, index
, merge.zoo
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154  suppressWarnings(RNGversion("3.5.0"))
set.seed(1)
## simple creation and plotting
x.Date < as.Date("20030201") + c(1, 3, 7, 9, 14)  1
x < zoo(rnorm(5), x.Date)
plot(x)
time(x)
## subsetting with numeric indexes
x[c(2, 4)]
## subsetting with index class
x[as.Date("20030201") + 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
plot(x)
## or POSIXct
y.POSIXct < ISOdatetime(2003, 02, c(1, 3, 7, 9, 14), 0, 0, 0)
y < zoo(rnorm(5), y.POSIXct)
plot(y)
## create a constant series
z < zoo(1, seq(4)[2])
## create a 0dimensional zoo series
z0 < zoo(, 1:4)
## create a 2dimensional zoo series
z2 < zoo(matrix(1:12, 4, 3), as.Date("20030101") + 0:3)
## create a factor zoo object
fz < zoo(gl(2,5), as.Date("20040101") + 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$foo
z$xyz < zoo(rnorm(3), 2:4)
z
## 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
x1
comment(x1)
# ifelse does not work with zoo but this works
# to create a zoo object which equals x1 at
# time i if x1[i] > x1[i1] 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))
plot(z3)
## 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.
## Not run:
# this gives a warning
# and then creates an illegal zoo object
z6 < zoo(11:15, c(1, 1, 2, 2, 5))
z6
# 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))]
z6
x1. < x1 < zoo (matrix (1:12, nrow = 3), as.Date("20080801") + 0:2)
colnames (x1) < c ("A", "B", "C", "D")
x2 < zoo (matrix (1:12, nrow = 3), as.Date("20080801") + 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)
# #4. restore x1 and show a different way
x1 < x1.
x1[time(x1)
## End(Not run)

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