Constructor function for creating an extensible time-series object.
xts is used to create an
xts object from raw data inputs.
xts(x = NULL, order.by = index(x), frequency = NULL, unique = TRUE, tzone = Sys.getenv("TZ"), ...) .xts(x = NULL, index, tclass = c("POSIXct", "POSIXt"), tzone = Sys.getenv("TZ"), check = TRUE, unique = FALSE, ...) is.xts(x)
an object containing the time series data
a corresponding vector of dates/times of a known time-based class. See Details.
a corresponding numeric vector specified as seconds since the UNIX epoch (1970-01-01 00:00:00.000)
numeric indicating frequency of
check the index for unique timestamps?
check that the index is ordered?
time class to use for the index. See codetclass.
time zone of the index (ignored indices without a time
component, e.g. Date, yearmon, yearqtr). See
additional attributes to be added. See Details.
xts object extends the S3 class
zoo from the package of the
xts() constructor is the preferred way to create xts objects. It
performs several checks to ensure it returns a well-formed xts object. The
.xts() constructor is mainly for internal use. It is more efficient than
xts() constructor because it doesn't perform as many
validity checks. Use it with caution.
Similar to zoo objects, xts objects must have an ordered index. While zoo
indexes cannot contain duplicate values, xts objects have optionally supported
duplicate index elements since version 0.5-0. The
xts class has one
additional requirement, the index must be a time-based class. Currently
supported classes include: ‘Date’, ‘POSIXct’, ‘timeDate’,
as well as ‘yearmon’ and ‘yearqtr’ where the index values
The uniqueness requirement was relaxed in version 0.5-0, but is still enforced
by default. Setting
unique = FALSE skips the uniqueness check and only
ensures that the index is ordered via the
As of version 0.10-0, xts no longer allows missing values in the index. This
is because many xts functions expect all index values to be finite. The most
important of these is
merge.xts, which is used ubiquitously. Missing
values in the index are usually the result of a date-time conversion error (e.g.
incorrect format, non-existent time due to daylight saving time, etc). Because
of how non-finite numbers are represented, a missing timestamp will always be
at the end of the index (except if it is
-Inf, which will be first).
Another difference from zoo is that xts object may carry additional attributes that may be desired in individual time-series handling. This includes the ability to augment the objects data with meta-data otherwise not cleanly attachable to a standard zoo object.
Examples of usage from finance may include the addition of data for keeping track of sources, last-update times, financial instrument descriptions or details, etc.
The idea behind
xts is to offer the user the ability to utilize
a standard zoo object, while providing an mechanism to customize
the object's meta-data, as well as create custom methods to handle
the object in a manner required by the user.
Many xts-specific methods have been written to better handle the unique aspects of xts. These include, ‘"["’, merge, cbind, rbind, c, Ops, lag, diff, coredata, head and tail. Additionally there are xts specific methods for converting to/from R's different time-series classes.
Subsetting via "[" methods offers the ability to specify dates by range, if they are enclosed in quotes. The style borrows from python by creating ranges with a double colon “"::"” or “"/"” operator. Each side of the operator may be left blank, which would then default to the beginning and end of the data, respectively. To specify a subset of times, it is only required that the time specified be in standard ISO format, with some form of separation between the elements. The time must be ‘left-filled’, that is to specify a full year one needs only to provide the year, a month would require the full year and the integer of the month requested - e.g. '1999-01'. This format would extend all the way down to seconds - e.g. '1999-01-01 08:35:23'. Leading zeros are not necessary. See the examples for more detail.
Users may also extend the
xts class to new classes to
allow for method overloading.
Additional benefits derive from the use of
reclass, which allow for lossless two-way conversion
between common R time-series classes and the
structure. See those functions for more detail.
An S3 object of class
xts. As it inherits and extends
the zoo class, all zoo methods remain valid. Additional
attributes may be assigned and extracted via
Most users will benefit the most by using the
reclass functions to
automagically handle all data objects
as one would handle a
Jeffrey A. Ryan and Joshua M. Ulrich
data(sample_matrix) sample.xts <- as.xts(sample_matrix, descr='my new xts object') class(sample.xts) str(sample.xts) head(sample.xts) # attribute 'descr' hidden from view attr(sample.xts,'descr') sample.xts['2007'] # all of 2007 sample.xts['2007-03/'] # March 2007 to the end of the data set sample.xts['2007-03/2007'] # March 2007 to the end of 2007 sample.xts['/'] # the whole data set sample.xts['/2007'] # the beginning of the data through 2007 sample.xts['2007-01-03'] # just the 3rd of January 2007
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.