| Title: | eXtensible Time Series |
|---|---|
| Description: | Provide for uniform handling of R's different time-based data classes by extending zoo, maximizing native format information preservation and allowing for user level customization and extension, while simplifying cross-class interoperability. |
| Authors: | Jeffrey A. Ryan [aut, cph], Joshua M. Ulrich [cre, aut], Ross Bennett [ctb], Corwin Joy [ctb] |
| Maintainer: | Joshua M. Ulrich <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.14.2 |
| Built: | 2024-11-19 06:11:30 UTC |
| Source: | https://github.com/joshuaulrich/xts |
Extensible time series class and methods, extending and behaving like zoo.
Easily convert one of R's many time-series (and non-time-series) classes to a true time-based object which inherits all of zoo's methods, while allowing for new time-based tools where appropriate.
Additionally, one may use xts to create new objects which can contain arbitrary attributes named during creation as name=value pairs.
Jeffrey A. Ryan and Joshua M. Ulrich
Maintainer: Joshua M. Ulrich [email protected]
xts(), as.xts(), reclass(), zoo()
This function replicates most of the ISO standard for parsing times and time-based ranges in a universally accepted way. The best documentation is the official ISO page as well as the Wikipedia entry for ISO 8601:2004.
.parseISO8601(x, start, end, tz = "").parseISO8601(x, start, end, tz = "")
x |
A character string conforming to the ISO 8601:2004(e) rules. |
start |
Lower constraint on range. |
end |
Upper constraint of range |
tz |
Timezone (tzone) to use internally. |
The basic idea is to create the endpoints of a range, given a string representation. These endpoints are aligned in POSIXct time to the zero second of the day at the beginning, and the 59.9999th second of the 59th minute of the 23rd hour of the final day.
For dates prior to the epoch (1970-01-01) the ending time is aligned to the
59.0000 second. This is due to a bug/feature in the R implementation of
as.POSIXct() and mktime0() at the C-source level. This limits the
precision of ranges prior to 1970 to 1 minute granularity with the current
xts workaround.
Recurring times over multiple days may be specified using the "T" notation. See the examples for details.
A two element list with an entry named ‘first.time’ and one named ‘last.time’.
There is no checking done to test for a properly constructed ISO format string. This must be correctly entered by the user.
When using durations, it is important to note that the time of the duration specified is not necessarily the same as the realized periods that may be returned when applied to an irregular time series. This is not a bug, it is a standards and implementation gotcha.
Jeffrey A. Ryan
https://en.wikipedia.org/wiki/ISO_8601
https://www.iso.org/iso-8601-date-and-time-format.html
# the start and end of 2000 .parseISO8601('2000') # the start of 2000 and end of 2001 .parseISO8601('2000/2001') # May 1, 2000 to Dec 31, 2001 .parseISO8601('2000-05/2001') # May 1, 2000 to end of Feb 2001 .parseISO8601('2000-05/2001-02') # Jan 1, 2000 to Feb 29, 2000; note the truncated time on the LHS .parseISO8601('2000-01/02') # 8:30 to 15:00 (used in xts subsetting to extract recurring times) .parseISO8601('T08:30/T15:00')# the start and end of 2000 .parseISO8601('2000') # the start of 2000 and end of 2001 .parseISO8601('2000/2001') # May 1, 2000 to Dec 31, 2001 .parseISO8601('2000-05/2001') # May 1, 2000 to end of Feb 2001 .parseISO8601('2000-05/2001-02') # Jan 1, 2000 to Feb 29, 2000; note the truncated time on the LHS .parseISO8601('2000-01/02') # 8:30 to 15:00 (used in xts subsetting to extract recurring times) .parseISO8601('T08:30/T15:00')
Details on efficient subsetting of xts objects for maximum performance and compatibility.
## S3 method for class 'xts' x[i, j, drop = FALSE, which.i = FALSE, ...]## S3 method for class 'xts' x[i, j, drop = FALSE, which.i = FALSE, ...]
x |
An xts object. |
i |
The rows to extract. Can be a numeric vector, time-based vector, or an ISO-8601 style range string (see details). |
j |
The columns to extract, either a numeric vector of column locations or a character vector of column names. |
drop |
Should dimension be dropped, if possible? See notes section. |
which.i |
Logical value that determines whether a subset xts object is
returned (the default), or the locations of the matching rows (when
|
... |
Additional arguments (currently unused). |
One of the primary motivations and key points of differentiation of xts is the ability to subset rows by specifying ISO-8601 compatible range strings. This allows for natural range-based time queries without requiring prior knowledge of the underlying class used for the time index.
When i is a character string, it is processed as an ISO-8601 formatted
datetime or time range using .parseISO8601(). A single datetime is
parsed from left to to right, according to the following specification:
CCYYMMDD HH:MM:SS.ss+
A time range can be specified by two datetimes separated by a forward slash or double-colon. For example:
CCYYMMDD HH:MM:SS.ss+/CCYYMMDD HH:MM:SS.ss
The ISO8601 time range subsetting uses a custom binary search algorithm to
efficiently find the beginning and end of the time range. i can also be a
vector of ISO8601 time ranges, which enables subsetting by multiple
non-contiguous time ranges in one subset call.
The above parsing, both for single datetimes and time ranges, will be done
on each element when i is a character vector. This is very inefficient,
especially for long vectors. In this case, it's recommened to use I(i) so
the xts subset function can process the vector more efficiently. Another
alternative is to convert i to POSIXct before passing it to the subset
function. See the examples for an illustration of using I(i).
The xts index is stored as POSIXct internally, regardless of the value of
its tclass attribute. So the fastest time-based subsetting is always when
i is a POSIXct vector.
An xts object containing the subset of x. When which.i = TRUE,
the corresponding integer locations of the matching rows is returned.
By design, xts objects always have two dimensions. They cannot be
vectors like zoo objects. Therefore drop = FALSE by default in order to
preserve the xts object's dimensions. This is different from both matrix and
zoo, which use drop = TRUE by default. Explicitly setting drop = TRUE
may be needed when performing certain matrix operations.
Jeffrey A. Ryan
ISO 8601: Date elements and interchange formats - Information interchange - Representation of dates and time https://www.iso.org
xts(), .parseISO8601(), .index()
x <- xts(1:3, Sys.Date()+1:3) xx <- cbind(x,x) # drop = FALSE for xts, differs from zoo and matrix z <- as.zoo(xx) z/z[,1] m <- as.matrix(xx) m/m[,1] # this will fail with non-conformable arrays (both retain dim) tryCatch( xx/x[,1], error = function(e) print("need to set drop = TRUE") ) # correct way xx/xx[,1,drop = TRUE] # or less efficiently xx/drop(xx[,1]) # likewise xx/coredata(xx)[,1] x <- xts(1:1000, as.Date("2000-01-01")+1:1000) y <- xts(1:1000, as.POSIXct(format(as.Date("2000-01-01")+1:1000))) x.subset <- index(x)[1:20] x[x.subset] # by original index type system.time(x[x.subset]) x[as.character(x.subset)] # by character string. Beware! system.time(x[as.character(x.subset)]) # slow! system.time(x[I(as.character(x.subset))]) # wrapped with I(), faster! x['200001'] # January 2000 x['1999/2000'] # All of 2000 (note there is no need to use the exact start) x['1999/200001'] # January 2000 x['2000/200005'] # 2000-01 to 2000-05 x['2000/2000-04-01'] # through April 01, 2000 y['2000/2000-04-01'] # through April 01, 2000 (using POSIXct series) ### Time of day subsetting i <- 0:60000 focal_date <- as.numeric(as.POSIXct("2018-02-01", tz = "UTC")) x <- .xts(i, c(focal_date + i * 15), tz = "UTC", dimnames = list(NULL, "value")) # Select all observations between 9am and 15:59:59.99999: w1 <- x["T09/T15"] # or x["T9/T15"] head(w1) # timestring is of the form THH:MM:SS.ss/THH:MM:SS.ss # Select all observations between 13:00:00 and 13:59:59.9999 in two ways: y1 <- x["T13/T13"] head(y1) x[.indexhour(x) == 13] # Select all observations between 9:30am and 30 seconds, and 4.10pm: x["T09:30:30/T16:10"] # It is possible to subset time of day overnight. # e.g. This is useful for subsetting FX time series which trade 24 hours on week days # Select all observations between 23:50 and 00:15 the following day, in the xts time zone z <- x["T23:50/T00:14"] z["2018-02-10 12:00/"] # check the last day # Select all observations between 7pm and 8.30am the following day: z2 <- x["T19:00/T08:29:59"] head(z2); tail(z2)x <- xts(1:3, Sys.Date()+1:3) xx <- cbind(x,x) # drop = FALSE for xts, differs from zoo and matrix z <- as.zoo(xx) z/z[,1] m <- as.matrix(xx) m/m[,1] # this will fail with non-conformable arrays (both retain dim) tryCatch( xx/x[,1], error = function(e) print("need to set drop = TRUE") ) # correct way xx/xx[,1,drop = TRUE] # or less efficiently xx/drop(xx[,1]) # likewise xx/coredata(xx)[,1] x <- xts(1:1000, as.Date("2000-01-01")+1:1000) y <- xts(1:1000, as.POSIXct(format(as.Date("2000-01-01")+1:1000))) x.subset <- index(x)[1:20] x[x.subset] # by original index type system.time(x[x.subset]) x[as.character(x.subset)] # by character string. Beware! system.time(x[as.character(x.subset)]) # slow! system.time(x[I(as.character(x.subset))]) # wrapped with I(), faster! x['200001'] # January 2000 x['1999/2000'] # All of 2000 (note there is no need to use the exact start) x['1999/200001'] # January 2000 x['2000/200005'] # 2000-01 to 2000-05 x['2000/2000-04-01'] # through April 01, 2000 y['2000/2000-04-01'] # through April 01, 2000 (using POSIXct series) ### Time of day subsetting i <- 0:60000 focal_date <- as.numeric(as.POSIXct("2018-02-01", tz = "UTC")) x <- .xts(i, c(focal_date + i * 15), tz = "UTC", dimnames = list(NULL, "value")) # Select all observations between 9am and 15:59:59.99999: w1 <- x["T09/T15"] # or x["T9/T15"] head(w1) # timestring is of the form THH:MM:SS.ss/THH:MM:SS.ss # Select all observations between 13:00:00 and 13:59:59.9999 in two ways: y1 <- x["T13/T13"] head(y1) x[.indexhour(x) == 13] # Select all observations between 9:30am and 30 seconds, and 4.10pm: x["T09:30:30/T16:10"] # It is possible to subset time of day overnight. # e.g. This is useful for subsetting FX time series which trade 24 hours on week days # Select all observations between 23:50 and 00:15 the following day, in the xts time zone z <- x["T23:50/T00:14"] z["2018-02-10 12:00/"] # check the last day # Select all observations between 7pm and 8.30am the following day: z2 <- x["T19:00/T08:29:59"] head(z2); tail(z2)
Add vertical lines and labels to an existing xts plot.
addEventLines(events, main = "", on = 0, lty = 1, lwd = 1, col = 1, ...)addEventLines(events, main = "", on = 0, lty = 1, lwd = 1, col = 1, ...)
events |
An xts object of events and their associated labels. It is
ensured that the first column of |
main |
Main title for a new panel, if drawn. |
on |
Panel number to draw on. A new panel will be drawn if |
lty |
Set the line type, same as in |
lwd |
Set the line width, same as in |
col |
Color palette to use, set by default to rational choices. |
... |
Any other passthrough parameters to |
Ross Bennett
## Not run: library(xts) data(sample_matrix) sample.xts <- as.xts(sample_matrix) events <- xts(letters[1:3], as.Date(c("2007-01-12", "2007-04-22", "2007-06-13"))) plot(sample.xts[,4]) addEventLines(events, srt = 90, pos = 2) ## End(Not run)## Not run: library(xts) data(sample_matrix) sample.xts <- as.xts(sample_matrix) events <- xts(letters[1:3], as.Date(c("2007-01-12", "2007-04-22", "2007-06-13"))) plot(sample.xts[,4]) addEventLines(events, srt = 90, pos = 2) ## End(Not run)
Add a legend to an existing panel.
addLegend( legend.loc = "topright", legend.names = NULL, col = NULL, ncol = 1, on = 0, ... )addLegend( legend.loc = "topright", legend.names = NULL, col = NULL, ncol = 1, on = 0, ... )
legend.loc |
One of nine locations: bottomright, bottom, bottomleft, left, topleft, top, topright, right, or center. |
legend.names |
Character vector of names for the legend. When |
col |
Fill colors for the legend. When |
ncol |
Number of columns for the legend. |
on |
Panel number to draw on. A new panel will be drawn if |
... |
Any other passthrough parameters to |
Ross Bennett
Apply a function to the data of an existing xts plot object and plot the
result on an existing or new panel. FUN should have arguments x or R
for the data of the existing xts plot object to be passed to. All other
additional arguments for FUN are passed through ....
addPanel( FUN, main = "", on = NA, type = "l", col = NULL, lty = 1, lwd = 1, pch = 1, ... )addPanel( FUN, main = "", on = NA, type = "l", col = NULL, lty = 1, lwd = 1, pch = 1, ... )
FUN |
An xts object to plot. |
main |
Main title for a new panel if drawn. |
on |
Panel number to draw on. A new panel will be drawn if |
type |
The type of plot to be drawn, same as in |
col |
Color palette to use, set by default to rational choices. |
lty |
Set the line type, same as in |
lwd |
Set the line width, same as in |
pch |
The type of plot to be drawn, same as in |
... |
Additional named arguments passed through to |
Ross Bennett
library(xts) data(sample_matrix) sample.xts <- as.xts(sample_matrix) calcReturns <- function(price, method = c("discrete", "log")){ px <- try.xts(price) method <- match.arg(method)[1L] returns <- switch(method, simple = , discrete = px / lag(px) - 1, compound = , log = diff(log(px))) reclass(returns, px) } # plot the Close plot(sample.xts[,"Close"]) # calculate returns addPanel(calcReturns, method = "discrete", type = "h") # Add simple moving average to panel 1 addPanel(rollmean, k = 20, on = 1) addPanel(rollmean, k = 40, col = "blue", on = 1)library(xts) data(sample_matrix) sample.xts <- as.xts(sample_matrix) calcReturns <- function(price, method = c("discrete", "log")){ px <- try.xts(price) method <- match.arg(method)[1L] returns <- switch(method, simple = , discrete = px / lag(px) - 1, compound = , log = diff(log(px))) reclass(returns, px) } # plot the Close plot(sample.xts[,"Close"]) # calculate returns addPanel(calcReturns, method = "discrete", type = "h") # Add simple moving average to panel 1 addPanel(rollmean, k = 20, on = 1) addPanel(rollmean, k = 40, col = "blue", on = 1)
Draw a polygon on an existing xts plot by specifying a time series of y coordinates. The xts index is used for the x coordinates and the first two columns are the upper and lower y coordinates, respectively.
addPolygon(x, y = NULL, main = "", on = NA, col = NULL, ...)addPolygon(x, y = NULL, main = "", on = NA, col = NULL, ...)
x |
An xts object to plot. Must contain 2 columns for the upper and the lower y coordinates for the polygon. The first column is interpreted as upper y coordinates and the second column as the lower y coordinates. |
y |
|
main |
Main title for a new panel, if drawn. |
on |
Panel number to draw on. A new panel will be drawn if |
col |
Color palette to use, set by default to rational choices. |
... |
Any other passthrough parameters to |
Ross Bennett
Based on code by Dirk Eddelbuettel from http://dirk.eddelbuettel.com/blog/2011/01/16/
## Not run: library(xts) data(sample_matrix) x <- as.xts(sample_matrix)[,1] ix <- index(x["2007-02"]) shade <- xts(matrix(rep(range(x), each = length(ix)), ncol = 2), ix) plot(x) # set on = -1 to draw the shaded region *behind* the main series addPolygon(shade, on = -1, col = "lightgrey") ## End(Not run)## Not run: library(xts) data(sample_matrix) x <- as.xts(sample_matrix)[,1] ix <- index(x["2007-02"]) shade <- xts(matrix(rep(range(x), each = length(ix)), ncol = 2), ix) plot(x) # set on = -1 to draw the shaded region *behind* the main series addPolygon(shade, on = -1, col = "lightgrey") ## End(Not run)
Add a time series to an existing xts plot
addSeries( x, main = "", on = NA, type = "l", col = NULL, lty = 1, lwd = 1, pch = 1, ... )addSeries( x, main = "", on = NA, type = "l", col = NULL, lty = 1, lwd = 1, pch = 1, ... )
x |
An xts object to add to the plot. |
main |
Main title for a new panel if drawn. |
on |
Panel number to draw on. A new panel will be drawn if |
type |
The type of plot to be drawn, same as in |
col |
Color palette to use, set by default to rational choices. |
lty |
Set the line type, same as in |
lwd |
Set the line width, same as in |
pch |
The type of plot to be drawn, same as in |
... |
Any other passthrough graphical parameters. |
Ross Bennett
Change timestamps to the start of the next period, specified in multiples of seconds.
adj.time(x, ...) align.time(x, ...) ## S3 method for class 'xts' align.time(x, n = 60, ...) shift.time(x, n = 60, ...)adj.time(x, ...) align.time(x, ...) ## S3 method for class 'xts' align.time(x, n = 60, ...) shift.time(x, n = 60, ...)
x |
Object containing timestamps to align. |
... |
Additional arguments. See details. |
n |
Number of seconds to adjust by. |
This function is an S3 generic. The result is to round up to the next period determined by 'n modulo x'.
A new object with the same class as x.
Jeffrey A. Ryan with input from Brian Peterson
x <- Sys.time() + 1:1000 # every 10 seconds align.time(x, 10) # align to next whole minute align.time(x, 60) # align to next whole 10 min interval align.time(x, 10 * 60)x <- Sys.time() + 1:1000 # every 10 seconds align.time(x, 10) # align to next whole minute align.time(x, 60) # align to next whole 10 min interval align.time(x, 10 * 60)
Apply a specified function to each distinct period in a given time series object.
apply.daily(x, FUN, ...) apply.weekly(x, FUN, ...) apply.monthly(x, FUN, ...) apply.quarterly(x, FUN, ...) apply.yearly(x, FUN, ...)apply.daily(x, FUN, ...) apply.weekly(x, FUN, ...) apply.monthly(x, FUN, ...) apply.quarterly(x, FUN, ...) apply.yearly(x, FUN, ...)
x |
A time-series object coercible to xts. |
FUN |
A function to apply to each period. |
... |
Additional arguments to |
Simple mechanism to apply a function to non-overlapping time periods, e.g. weekly, monthly, etc. Different from rolling functions in that this will subset the data based on the specified time period (implicit in the call), and return a vector of values for each period in the original data.
Essentially a wrapper to the xts functions endpoints() and
period.apply(), mainly as a convenience.
A vector of results produced by FUN, corresponding to the
appropriate periods.
When FUN = mean the results will contain one column for every
column in the input, which is different from other math functions (e.g.
median, sum, prod, sd, etc.).
FUN = mean works by column because the default method stats::mean
previously worked by column for matrices and data.frames. R Core changed the
behavior of mean to always return one column in order to be consistent
with the other math functions. This broke some xts dependencies and
mean.xts() was created to maintain the original behavior.
Using FUN = mean will print a message that describes this inconsistency.
To avoid the message and confusion, use FUN = colMeans to calculate means
by column and use FUN = function(x) mean to calculate one mean for all the
data. Set options(xts.message.period.apply.mean = FALSE) to suppress this
message.
Jeffrey A. Ryan
endpoints(), period.apply(), to.monthly()
xts.ts <- xts(rnorm(231),as.Date(13514:13744,origin="1970-01-01")) start(xts.ts) end(xts.ts) apply.monthly(xts.ts,colMeans) apply.monthly(xts.ts,function(x) var(x))xts.ts <- xts(rnorm(231),as.Date(13514:13744,origin="1970-01-01")) start(xts.ts) end(xts.ts) apply.monthly(xts.ts,colMeans) apply.monthly(xts.ts,function(x) var(x))
Method to automatically convert an xts object to an environment containing vectors representing each column of the original xts object. The name of each object in the resulting environment corresponds to the name of the column of the xts object.
## S3 method for class 'xts' as.environment(x)## S3 method for class 'xts' as.environment(x)
x |
An xts object. |
An environment containing ncol(x) vectors extracted by
column from x.
Environments do not preserve (or have knowledge) of column order and cannot be subset by an integer index.
Jeffrey A. Ryan
x <- xts(1:10, Sys.Date()+1:10) colnames(x) <- "X" y <- xts(1:10, Sys.Date()+1:10) colnames(x) <- "Y" xy <- cbind(x,y) colnames(xy) e <- as.environment(xy) # currently using xts-style positive k ls(xy) ls.str(xy)x <- xts(1:10, Sys.Date()+1:10) colnames(x) <- "X" y <- xts(1:10, Sys.Date()+1:10) colnames(x) <- "Y" xy <- cbind(x,y) colnames(xy) e <- as.environment(xy) # currently using xts-style positive k ls(xy) ls.str(xy)
Conversion S3 methods to coerce data objects of arbitrary classes to xts and back, without losing any attributes of the original format.
## S3 method for class 'Date' as.xts(x, ...) ## S3 method for class 'POSIXt' as.xts(x, ...) ## S3 method for class 'data.frame' as.xts( x, order.by, dateFormat = "POSIXct", frequency = NULL, ..., .RECLASS = FALSE ) ## S3 method for class 'irts' as.xts(x, order.by, frequency = NULL, ..., .RECLASS = FALSE) ## S3 method for class 'matrix' as.xts( x, order.by, dateFormat = "POSIXct", frequency = NULL, ..., .RECLASS = FALSE ) ## S3 method for class 'timeDate' as.xts(x, ...) ## S3 method for class 'timeSeries' as.xts( x, dateFormat = "POSIXct", FinCenter, recordIDs, title, documentation, ..., .RECLASS = FALSE ) ## S3 method for class 'ts' as.xts(x, dateFormat, ..., .RECLASS = FALSE) as.xts(x, ...) xtsible(x) ## S3 method for class 'yearmon' as.xts(x, ...) ## S3 method for class 'yearqtr' as.xts(x, ...) ## S3 method for class 'zoo' as.xts(x, order.by = index(x), frequency = NULL, ..., .RECLASS = FALSE)## S3 method for class 'Date' as.xts(x, ...) ## S3 method for class 'POSIXt' as.xts(x, ...) ## S3 method for class 'data.frame' as.xts( x, order.by, dateFormat = "POSIXct", frequency = NULL, ..., .RECLASS = FALSE ) ## S3 method for class 'irts' as.xts(x, order.by, frequency = NULL, ..., .RECLASS = FALSE) ## S3 method for class 'matrix' as.xts( x, order.by, dateFormat = "POSIXct", frequency = NULL, ..., .RECLASS = FALSE ) ## S3 method for class 'timeDate' as.xts(x, ...) ## S3 method for class 'timeSeries' as.xts( x, dateFormat = "POSIXct", FinCenter, recordIDs, title, documentation, ..., .RECLASS = FALSE ) ## S3 method for class 'ts' as.xts(x, dateFormat, ..., .RECLASS = FALSE) as.xts(x, ...) xtsible(x) ## S3 method for class 'yearmon' as.xts(x, ...) ## S3 method for class 'yearqtr' as.xts(x, ...) ## S3 method for class 'zoo' as.xts(x, order.by = index(x), frequency = NULL, ..., .RECLASS = FALSE)
x |
Data object to convert. See details for supported types. |
... |
Additional parameters or attributes. |
order.by, frequency
|
See zoo help. |
dateFormat |
What class should the dates be converted to? |
.RECLASS |
Should the conversion be reversible via |
FinCenter, recordIDs, title, documentation
|
See timeSeries help. |
A simple and reliable way to convert many different objects into a uniform format for use within R.
as.xts() can convert objects of the following classes into an xts object:
object: timeSeries, ts, matrix, data.frame,
and zoo. xtsible() safely checks whether an object can be converted to
an xts object.
Additional name = value pairs may be passed to the function to be added to
the new object. A special print.xts() method ensures the attributes are
hidden from view, but will be available via R's standard attr() function,
as well as the xtsAttributes() function.
When .RECLASS = TRUE, the returned xts object internally preserves all
relevant attribute/slot data from the input x. This allows for temporary
conversion to xts in order to use zoo and xts compatible methods. See
reclass() for details.
An S3 object of class xts.
Jeffrey A. Ryan
## Not run: # timeSeries library(timeSeries) x <- timeSeries(1:10, 1:10) str(as.xts(x)) str(reclass(as.xts(x))) str(try.xts(x)) str(reclass(try.xts(x))) ## End(Not run)## Not run: # timeSeries library(timeSeries) x <- timeSeries(1:10, 1:10) str(as.xts(x)) str(reclass(as.xts(x))) str(try.xts(x)) str(reclass(try.xts(x))) ## End(Not run)
Compute x-axis tickmarks like axTicks() in base but with respect to
time. This function is written for internal use, and documented for those
wishing to use it for customized plots.
axTicksByTime( x, ticks.on = "auto", k = 1, labels = TRUE, format.labels = TRUE, ends = TRUE, gt = 2, lt = 30 )axTicksByTime( x, ticks.on = "auto", k = 1, labels = TRUE, format.labels = TRUE, ends = TRUE, gt = 2, lt = 30 )
x |
An object indexed by time or a vector of times/dates. |
ticks.on |
Time unit for tick locations. |
k |
Frequency of tick locations. |
labels |
Should a labeled vector be returned? |
format.labels |
Either a logical value specifying whether labels should be formatted, or a character string specifying the format to use. |
ends |
Should the ends be adjusted? |
gt |
Lower bound on number of tick locations. |
lt |
Upper bound on number of tick locations. |
The default ticks.on = "auto" uses heuristics to compute sensible tick
locations. Use a combination of ticks.on and k to create tick locations
at specific intervals. For example, ticks.on = "days" and k = 7 will
create tick marks every 7 days.
When format.labels is a character string the possible values are the same
as those listed in the Details section of strptime().
A numeric vector of index element locations where tick marks should be drawn. These are locations (e.g. 1, 2, 3, ...), not the index timestamps.
If possible, the result will be named using formatted values from the index timestamps. The names will be used for the tick mark labels.
Jeffrey A. Ryan
data(sample_matrix) axTicksByTime(as.xts(sample_matrix),'auto') axTicksByTime(as.xts(sample_matrix),'weeks') axTicksByTime(as.xts(sample_matrix),'months',7)data(sample_matrix) axTicksByTime(as.xts(sample_matrix),'auto') axTicksByTime(as.xts(sample_matrix),'weeks') axTicksByTime(as.xts(sample_matrix),'months',7)
Concatenate or bind by row two or more xts objects along a time-based index. All objects must have the same number of columns and be xts objects or coercible to xts.
## S3 method for class 'xts' c(...) ## S3 method for class 'xts' rbind(..., deparse.level = 1)## S3 method for class 'xts' c(...) ## S3 method for class 'xts' rbind(..., deparse.level = 1)
... |
Objects to bind by row. |
deparse.level |
Not implemented. |
Duplicate index values are supported. When one or more input has the same
index value, the duplicated index values in the result are in the same order
the objects are passed to rbind(). See examples.
c() is an alias for rbind() for xts objects.
See merge.xts() for traditional merge operations.
An xts object with one row per row for each object concatenated.
rbind() is a '.Primitive' function in R, which means method dispatch
occurs at the C-level, and may not be consistent with normal S3 method
dispatch (see rbind() for details). Call rbind.xts() directly to
avoid potential dispatch ambiguity.
Jeffrey A. Ryan
x <- xts(1:10, Sys.Date()+1:10) str(x) merge(x,x) rbind(x,x) rbind(x[1:5],x[6:10]) c(x,x) # this also works on non-unique index values x <- xts(rep(1,5), Sys.Date()+c(1,2,2,2,3)) y <- xts(rep(2,3), Sys.Date()+c(1,2,3)) # overlapping indexes are appended rbind(x,y) rbind(y,x)x <- xts(1:10, Sys.Date()+1:10) str(x) merge(x,x) rbind(x,x) rbind(x[1:5],x[6:10]) c(x,x) # this also works on non-unique index values x <- xts(rep(1,5), Sys.Date()+c(1,2,2,2,3)) y <- xts(rep(2,3), Sys.Date()+c(1,2,3)) # overlapping indexes are appended rbind(x,y) rbind(y,x)
Extraction and replacement functions to access the xts '.CLASS' attribute.
The '.CLASS' attribute is used by reclass() to transform an xts object
back to its original class.
CLASS(x) CLASS(x) <- valueCLASS(x) CLASS(x) <- value
x |
An xts object. |
value |
The new value to assign to the '.CLASS' attribute. |
This is meant for use in conjunction with try.xts() and reclass() and is
is not intended for daily use. While it's possible to interactively coerce
objects to other classes than originally derived from, it's likely to cause
unexpected behavior. It is best to use the usual as.xts() and other
classes' as methods.
Called for its side-effect of changing the '.CLASS' attribute.
Jeffrey A. Ryan
Mechanism to extract and replace the core data of an xts object.
## S3 method for class 'xts' coredata(x, fmt = FALSE, ...) xcoredata(x, ...) xcoredata(x) <- value## S3 method for class 'xts' coredata(x, fmt = FALSE, ...) xcoredata(x, ...) xcoredata(x) <- value
x |
An xts object. |
fmt |
Should the rownames be formated using |
... |
Unused. |
value |
Non-core attributes to assign. |
Extract coredata of an xts object - removing all attributes except
dim and dimnames and returning a matrix object with rownames
converted from the index of the xts object.
The rownames of the result use the format specified by tformat(x) when
fmt = TRUE. When fmt is a character string to be passed to format().
See strptime() for valid format strings. Setting fmt = FALSE will
return the row names by simply coercing the index class to a character
string in the default manner.
xcoredata() is the complement to coredata(). It returns all of the
attributes normally removed by coredata(). Its purpose, along with the
the replacement function xcoredata<- is primarily for developers using
xts' try.xts() and reclass() functionality inside functions
so the functions can take any time series class as an input and return the
same time series class.
Returns either a matrix object for coredata, or a list of named attributes.
The replacement functions are called for their side-effects.
Jeffrey A. Ryan
data(sample_matrix) x <- as.xts(sample_matrix, myattr=100) coredata(x) xcoredata(x)data(sample_matrix) x <- as.xts(sample_matrix, myattr=100) coredata(x) xcoredata(x)
Get or set dimnames of an xts object.
## S3 method for class 'xts' dimnames(x) ## S3 replacement method for class 'xts' dimnames(x) <- value## S3 method for class 'xts' dimnames(x) ## S3 replacement method for class 'xts' dimnames(x) <- value
x |
An xts object. |
value |
A two element list. See Details. |
For efficienty, xts objects do not have rownames (unlike zoo objects).
Attempts to set rownames on an xts object will silently set them to NULL.
This is done for internal compatibility reasons, as well as to provide
consistency in performance regardless of object use.
A list or character string containing coerced row names and/or actual column names.
Attempts to set rownames on xts objects via rownames or dimnames will silently fail.
Unlike zoo, all xts objects have dimensions. xts objects cannot be plain vectors.
Jeffrey A. Ryan
x <- xts(1:10, Sys.Date()+1:10) dimnames(x) rownames(x) rownames(x) <- 1:10 rownames(x) str(x)x <- xts(1:10, Sys.Date()+1:10) dimnames(x) rownames(x) rownames(x) <- 1:10 rownames(x) str(x)
Extract index locations for an xts object that correspond to the last
observation in each period specified by on and k.
endpoints(x, on = "months", k = 1)endpoints(x, on = "months", k = 1)
x |
An xts object. |
on |
A character string specifying the period. |
k |
The number of periods each endpoint should cover. |
endpoints() returns a numeric vector that always begins with zero and ends
with the number of observations in x.
Periods are always based on the distance from the UNIX epoch (midnight
1970-01-01 UTC), not the first observation in x. See the examples.
Valid values for the on argument are: “us” (microseconds),
“microseconds”, “ms” (milliseconds), “milliseconds”,
“secs” (seconds), “seconds”, “mins” (minutes),
“minutes”, “hours”, “days”, “weeks”,
“months”, “quarters”, and “years”.
A numeric vector of beginning with 0 and ending with the number of
of observations in x.
Jeffrey A. Ryan
data(sample_matrix) endpoints(sample_matrix) endpoints(sample_matrix, "weeks") ### example of how periods are based on the UNIX epoch, ### *not* the first observation of the data series x <- xts(1:38, yearmon(seq(2018 - 1/12, 2021, 1/12))) # endpoints for the end of every other year ep <- endpoints(x, "years", k = 2) # Dec-2017 is the end of the *first* year in the data. But when you start from # Jan-1970 and use every second year end as your endpoints, the endpoints are # always December of every odd year. x[ep, ]data(sample_matrix) endpoints(sample_matrix) endpoints(sample_matrix, "weeks") ### example of how periods are based on the UNIX epoch, ### *not* the first observation of the data series x <- xts(1:38, yearmon(seq(2018 - 1/12, 2021, 1/12))) # endpoints for the end of every other year ep <- endpoints(x, "years", k = 2) # Dec-2017 is the end of the *first* year in the data. But when you start from # Jan-1970 and use every second year end as your endpoints, the endpoints are # always December of every odd year. x[ep, ]
Generic functions to return the first or last elements or rows of a vector or two-dimensional data object.
first(x, ...) ## Default S3 method: first(x, n = 1, keep = FALSE, ...) ## S3 method for class 'xts' first(x, n = 1, keep = FALSE, ...) last(x, ...) ## Default S3 method: last(x, n = 1, keep = FALSE, ...) ## S3 method for class 'xts' last(x, n = 1, keep = FALSE, ...)first(x, ...) ## Default S3 method: first(x, n = 1, keep = FALSE, ...) ## S3 method for class 'xts' first(x, n = 1, keep = FALSE, ...) last(x, ...) ## Default S3 method: last(x, n = 1, keep = FALSE, ...) ## S3 method for class 'xts' last(x, n = 1, keep = FALSE, ...)
x |
An object. |
... |
Arguments passed to other methods. |
n |
Number of observations to return. |
keep |
Should removed values be kept as an attribute on the result? |
A more advanced subsetting is available for zoo objects with indexes inheriting from POSIXt or Date classes.
Quickly and easily extract the first or last n observations of an object.
When n is a number, these functions are similar to head() and
tail(), but only return the first or last observation by default.
n can be a character string if x is an xts object or coerceable to xts.
It must be of the form ‘n period’, where 'n' is a numeric value
(1 if not provided) describing the number of periods to return. Valid
periods are: secs, seconds, mins, minutes, hours, days, weeks, months,
quarters, and years.
The 'period' portion can be any frequency greater than or equal to the
frequency of the object's time index. For example, first(x, "2 months")
will return the first 2 months of data even if x is hourly frequency.
Attempts to set 'period' to a frequency less than the object's frequency
will throw an error.
n may be positive or negative, whether it's a number or character string.
When n is positive, the functions return the obvious result. For example,
first(x, "1 month") returns the first month's data. When n is negative,
all data except first month's is returned.
Requesting more data than is in x will throw a warning and simply return
x.
A subset of elements/rows of the original data.
Jeffrey A. Ryan
first(1:100) last(1:100) data(LakeHuron) first(LakeHuron,10) last(LakeHuron) x <- xts(1:100, Sys.Date()+1:100) first(x, 10) first(x, '1 day') first(x, '4 days') first(x, 'month') last(x, '2 months') last(x, '6 weeks')first(1:100) last(1:100) data(LakeHuron) first(LakeHuron,10) last(LakeHuron) x <- xts(1:100, Sys.Date()+1:100) first(x, 10) first(x, '1 day') first(x, '4 days') first(x, 'month') last(x, '2 months') last(x, '6 weeks')
Easily create of time stamps corresponding to the first or last observation in a specified time period.
firstof(year = 1970, month = 1, day = 1, hour = 0, min = 0, sec = 0, tz = "") lastof( year = 1970, month = 12, day = 31, hour = 23, min = 59, sec = 59, subsec = 0.99999, tz = "" )firstof(year = 1970, month = 1, day = 1, hour = 0, min = 0, sec = 0, tz = "") lastof( year = 1970, month = 12, day = 31, hour = 23, min = 59, sec = 59, subsec = 0.99999, tz = "" )
year, month, day
|
Numeric values to specify a day. |
hour, min, sec
|
Numeric vaues to specify time within a day. |
tz |
Timezone used for conversion. |
subsec |
Number of sub-seconds. |
This is a wrapper to ISOdatetime() with defaults corresponding to the
first or last possible time in a given period.
An POSIXct object.
Jeffrey A. Ryan
firstof(2000) firstof(2005,01,01) lastof(2007) lastof(2007,10)firstof(2000) firstof(2005,01,01) lastof(2007) lastof(2007,10)
Functions to get and replace an xts object's index values and it's components.
## S3 method for class 'xts' index(x, ...) ## S3 replacement method for class 'xts' index(x) <- value ## S3 replacement method for class 'xts' time(x) <- value ## S3 method for class 'xts' time(x, ...) .index(x, ...) .index(x) <- value .indexsec(x) .indexmin(x) .indexhour(x) .indexmday(x) .indexmon(x) .indexyear(x) .indexwday(x) .indexbday(x) .indexyday(x) .indexisdst(x) .indexDate(x) .indexday(x) .indexweek(x) .indexyweek(x) convertIndex(x, value)## S3 method for class 'xts' index(x, ...) ## S3 replacement method for class 'xts' index(x) <- value ## S3 replacement method for class 'xts' time(x) <- value ## S3 method for class 'xts' time(x, ...) .index(x, ...) .index(x) <- value .indexsec(x) .indexmin(x) .indexhour(x) .indexmday(x) .indexmon(x) .indexyear(x) .indexwday(x) .indexbday(x) .indexyday(x) .indexisdst(x) .indexDate(x) .indexday(x) .indexweek(x) .indexyweek(x) convertIndex(x, value)
x |
An xts object. |
... |
Arguments passed to other methods. |
value |
A new time index value. |
An xts object's index is stored internally as the number of seconds since
UNIX epoch in the UTC timezone. The .index() and .index<- functions get
and replace the internal numeric value of the index, respectively. These
functions are primarily for internal use, but are exported because they may
be useful for users.
The replacement method also updates the tclass() and tzone() of the
index to match the class and timezone of the new index, respectively. The
index() method converts the internal numeric index to the class specified
by the 'tclass' attribute and with the timezone specified by the 'tzone'
attribute before returning the index values to the user.
The .indexXXX() functions below extract time components from the internal
time index. They return values like the values of POSIXlt components.
.indexsec0 - 61: seconds of the minute (local time)
.indexmin0 - 59: minutes of the hour (local time)
.indexhour0 - 23: hours of the day (local time)
.indexDatedate as seconds since the epoch (UTC not local time
.indexdaydate as seconds since the epoch (UTC not local time
.indexwday0 - 6: day of the week (Sunday - Saturday, local time)
.indexmday1 - 31: day of the month (local time)
.indexweekweeks since the epoch (UTC not local time
.indexmon0 - 11: month of the year (local time)
.indexyearyears since 1900 (local time)
.indexyday0 - 365: day of the year (local time, 365 only in leap years)
.indexisdst1, 0, -1: Daylight Saving Time flag. Positive if Daylight Saving Time is in effect, zero if not, negative if unknown.
Changes in timezone, index class, and index format internal structure, by xts version:
The .indexTZ, .indexCLASS and .indexFORMAT
attributes are no longer stored on xts objects, only on the index itself.
The indexTZ(), indexClass(), and indexFormat() functions (and
their respective replacement methods) are deprecated in favor of their
respective tzone(), tclass(), and tformat() versions. The previous
versions throw a warning that they're deprecated, but they will continue
to work. They will never be removed or throw an error. Ever.
The new versions are careful to look for the old attributes on the xts
object, in case they're ever called on an xts object that was created prior
to the attributes being added to the index itself.
You can set options(xts.warn.index.missing.tzone = TRUE) and
options(xts.warn.index.missing.tclass = TRUE) to identify xts objects
that do not have a 'tzone' or 'tclass' attribute on the index, even if
there is a 'tzone' or 'tclass' attribute on the xts object itself. The
warnings will be thrown when the object is printed.
Use x <- as.xts(x) to update these objects to the new structure.
The index timezone is now set to "UTC" for time classes that do not have any intra-day component (e.g. days, months, quarters). Previously the timezone was blank, which meant "local time" as determined by R and the OS.
There are new get/set methods for the timezone, index
class, and index format attributes: tzone() and, tzone<-, tclass()
and tclass<-, and tformat() and tformat<-. These new functions are
aliases to their indexTZ(), indexClass(), and indexFormat()
counterparts.
The timezone, index class, and index format were added
as attributes to the index itself, as 'tzone', 'tclass', and 'tformat',
respectively. This is in order to remove those three attributes from the xts
object, so they're only on the index itself.
The indexTZ(), indexClass(), and indexFormat() functions (and their
respective replacement methods) will continue to work as in prior xts
versions. The attributes on the index take priority over their respective
counterparts that may be on the xts object.
Objects track their timezone and index class in their '.indexTZ' and '.indexCLASS' attributes, respectively.
Jeffrey A. Ryan
tformat() describes how the index values are formatted when
printed, tclass() documents how xts handles the index class, and
tzone() has more information about index timezone settings.
x <- timeBasedSeq('2010-01-01/2010-01-01 12:00/H') x <- xts(seq_along(x), x) # the index values, converted to 'tclass' (POSIXct in this case) index(x) class(index(x)) # POSIXct tclass(x) # POSIXct # the internal numeric index .index(x) # add 1 hour (3600 seconds) to the numeric index .index(x) <- index(x) + 3600 index(x) y <- timeBasedSeq('2010-01-01/2010-01-02 12:00') y <- xts(seq_along(y), y) # Select all observations in the first 6 and last 3 minutes of the # 8th and 15th hours on each day y[.indexhour(y) %in% c(8, 15) & .indexmin(y) %in% c(0:5, 57:59)] i <- 0:60000 focal_date <- as.numeric(as.POSIXct("2018-02-01", tz = "UTC")) y <- .xts(i, c(focal_date + i * 15), tz = "UTC", dimnames = list(NULL, "value")) # Select all observations for the first minute of each hour y[.indexmin(y) == 0] # Select all observations on Monday mon <- y[.indexwday(y) == 1] head(mon) tail(mon) unique(weekdays(index(mon))) # check # Disjoint time of day selections # Select all observations between 08:30 and 08:59:59.9999 or between 12:00 and 12:14:59.99999: y[.indexhour(y) == 8 & .indexmin(y) >= 30 | .indexhour(y) == 12 & .indexmin(x) %in% 0:14] ### Compound selections # Select all observations for Wednesdays or Fridays between 9am and 4pm (exclusive of 4pm): y[.indexwday(y) %in% c(3, 5) & (.indexhour(y) %in% c(9:15))] # Select all observations on Monday between 8:59:45 and 09:04:30: y[.indexwday(y) == 1 & (.indexhour(y) == 8 & .indexmin(y) == 59 & .indexsec(y) >= 45 | .indexhour(y) == 9 & (.indexmin(y) < 4 | .indexmin(y) == 4 & .indexsec(y) <= 30))] i <- 0:30000 u <- .xts(i, c(focal_date + i * 1800), tz = "UTC", dimnames = list(NULL, "value")) # Select all observations for January or February: u[.indexmon(u) %in% c(0, 1)] # Select all data for the 28th to 31st of each month, excluding any Fridays: u[.indexmday(u) %in% 28:31 & .indexwday(u) != 5] # Subset by week since origin unique(.indexweek(u)) origin <- xts(1, as.POSIXct("1970-01-01")) unique(.indexweek(origin)) # Select all observations in weeks 2515 to 2517. u2 <- u[.indexweek(u) %in% 2515:2517] head(u2); tail(u2) # Select all observations after 12pm for day 50 and 51 in each year u[.indexyday(u) %in% 50:51 & .indexhour(u) >= 12]x <- timeBasedSeq('2010-01-01/2010-01-01 12:00/H') x <- xts(seq_along(x), x) # the index values, converted to 'tclass' (POSIXct in this case) index(x) class(index(x)) # POSIXct tclass(x) # POSIXct # the internal numeric index .index(x) # add 1 hour (3600 seconds) to the numeric index .index(x) <- index(x) + 3600 index(x) y <- timeBasedSeq('2010-01-01/2010-01-02 12:00') y <- xts(seq_along(y), y) # Select all observations in the first 6 and last 3 minutes of the # 8th and 15th hours on each day y[.indexhour(y) %in% c(8, 15) & .indexmin(y) %in% c(0:5, 57:59)] i <- 0:60000 focal_date <- as.numeric(as.POSIXct("2018-02-01", tz = "UTC")) y <- .xts(i, c(focal_date + i * 15), tz = "UTC", dimnames = list(NULL, "value")) # Select all observations for the first minute of each hour y[.indexmin(y) == 0] # Select all observations on Monday mon <- y[.indexwday(y) == 1] head(mon) tail(mon) unique(weekdays(index(mon))) # check # Disjoint time of day selections # Select all observations between 08:30 and 08:59:59.9999 or between 12:00 and 12:14:59.99999: y[.indexhour(y) == 8 & .indexmin(y) >= 30 | .indexhour(y) == 12 & .indexmin(x) %in% 0:14] ### Compound selections # Select all observations for Wednesdays or Fridays between 9am and 4pm (exclusive of 4pm): y[.indexwday(y) %in% c(3, 5) & (.indexhour(y) %in% c(9:15))] # Select all observations on Monday between 8:59:45 and 09:04:30: y[.indexwday(y) == 1 & (.indexhour(y) == 8 & .indexmin(y) == 59 & .indexsec(y) >= 45 | .indexhour(y) == 9 & (.indexmin(y) < 4 | .indexmin(y) == 4 & .indexsec(y) <= 30))] i <- 0:30000 u <- .xts(i, c(focal_date + i * 1800), tz = "UTC", dimnames = list(NULL, "value")) # Select all observations for January or February: u[.indexmon(u) %in% c(0, 1)] # Select all data for the 28th to 31st of each month, excluding any Fridays: u[.indexmday(u) %in% 28:31 & .indexwday(u) != 5] # Subset by week since origin unique(.indexweek(u)) origin <- xts(1, as.POSIXct("1970-01-01")) unique(.indexweek(origin)) # Select all observations in weeks 2515 to 2517. u2 <- u[.indexweek(u) %in% 2515:2517] head(u2); tail(u2) # Select all observations after 12pm for day 50 and 51 in each year u[.indexyday(u) %in% 50:51 & .indexhour(u) >= 12]
Generic functions to get or replace the timezone of an xts object's index.
indexTZ(x, ...) tzone(x, ...) indexTZ(x) <- value tzone(x) <- valueindexTZ(x, ...) tzone(x, ...) indexTZ(x) <- value tzone(x) <- value
x |
An xts object. |
... |
Arguments passed to other methods. |
value |
A valid timezone value (see |
Internally, an xts object's index is a numeric value corresponding to
seconds since the epoch in the UTC timezone. When an xts object is created,
all time index values are converted internally to POSIXct()
(which is also in seconds since the UNIX epoch), using the underlying OS
conventions and the TZ environment variable. The xts() function
manages timezone information as transparently as possible.
The tzone<- function does not change the internal index values
(i.e. the index will remain the same time in the UTC timezone).
A one element named vector containing the timezone of the object's index.
Both indexTZ() and indexTZ<- are deprecated in favor of
tzone() and tzone<-, respectively.
Problems may arise when an object that had been created under one timezone
are used in a session using another timezone. This isn't usually a issue,
but when it is a warning is given upon printing or subsetting. This warning
may be suppressed by setting options(xts_check_TZ = FALSE).
Jeffrey A. Ryan
index() has more information on the xts index, tformat()
describes how the index values are formatted when printed, and tclass()
provides details how xts handles the class of the index.
# Date indexes always have a "UTC" timezone x <- xts(1, Sys.Date()) tzone(x) str(x) print(x) # The default 'tzone' is blank -- your machine's local timezone, # determined by the 'TZ' environment variable. x <- xts(1, Sys.time()) tzone(x) str(x) # now set 'tzone' to different values tzone(x) <- "UTC" str(x) tzone(x) <- "America/Chicago" str(x) y <- timeBasedSeq('2010-01-01/2010-01-03 12:00/H') y <- xts(seq_along(y), y, tzone = "America/New_York") # Changing the tzone does not change the internal index values, but it # does change how the index is printed! head(y) head(.index(y)) tzone(y) <- "Europe/London" head(y) # the index prints with hours, but head(.index(y)) # the internal index is not changed!# Date indexes always have a "UTC" timezone x <- xts(1, Sys.Date()) tzone(x) str(x) print(x) # The default 'tzone' is blank -- your machine's local timezone, # determined by the 'TZ' environment variable. x <- xts(1, Sys.time()) tzone(x) str(x) # now set 'tzone' to different values tzone(x) <- "UTC" str(x) tzone(x) <- "America/Chicago" str(x) y <- timeBasedSeq('2010-01-01/2010-01-03 12:00/H') y <- xts(seq_along(y), y, tzone = "America/New_York") # Changing the tzone does not change the internal index values, but it # does change how the index is printed! head(y) head(.index(y)) tzone(y) <- "Europe/London" head(y) # the index prints with hours, but head(.index(y)) # the internal index is not changed!
A generic function to force sorted time vectors to be unique. Useful for
high-frequency time-series where original time-stamps may have identical
values. For the case of xts objects, the default eps is set to ten
microseconds. In practice this advances each subsequent identical time by
eps over the previous (possibly also advanced) value.
is.index.unique(x) is.time.unique(x) make.index.unique(x, eps = 1e-06, drop = FALSE, fromLast = FALSE, ...) make.time.unique(x, eps = 1e-06, drop = FALSE, fromLast = FALSE, ...)is.index.unique(x) is.time.unique(x) make.index.unique(x, eps = 1e-06, drop = FALSE, fromLast = FALSE, ...) make.time.unique(x, eps = 1e-06, drop = FALSE, fromLast = FALSE, ...)
x |
An xts object, or POSIXct vector. |
eps |
A value to add to force uniqueness. |
drop |
Should duplicates be dropped instead of adjusted by |
fromLast |
When |
... |
Unused. |
The returned time-series object will have new time-stamps so that
isOrdered(.index(x)) evaluates to TRUE.
A modified version of x with unique timestamps.
Incoming values must be pre-sorted, and no check is done to make sure
that this is the case. ‘integer’ index value will be coerced to
‘double’ when drop = FALSE.
Jeffrey A. Ryan
ds <- options(digits.secs=6) # so we can see the change x <- xts(1:10, as.POSIXct("2011-01-21") + c(1,1,1,2:8)/1e3) x make.index.unique(x) options(ds)ds <- options(digits.secs=6) # so we can see the change x <- xts(1:10, as.POSIXct("2011-01-21") + c(1,1,1,2:8)/1e3) x make.index.unique(x) options(ds)
Used to verify that the object is one of the known time-based classes in R.
Current time-based objects supported are Date, POSIXct, chron,
yearmon, yearqtr, and timeDate.
is.timeBased(x) timeBased(x)is.timeBased(x) timeBased(x)
x |
Object to test. |
A logical scalar.
Jeffrey A. Ryan
timeBased(Sys.time()) timeBased(Sys.Date()) timeBased(200701)timeBased(Sys.time()) timeBased(Sys.Date()) timeBased(200701)
Check if a vector is strictly increasing, strictly decreasing, not decreasing, or not increasing.
isOrdered(x, increasing = TRUE, strictly = TRUE)isOrdered(x, increasing = TRUE, strictly = TRUE)
x |
A numeric vector. |
increasing |
Test for increasing ( |
strictly |
When |
Designed for internal use with xts, this provides highly optimized tests for ordering.
A logical scalar indicating whether or not x is ordered.
Jeffrey A. Ryan
# strictly increasing isOrdered(1:10, increasing=TRUE) isOrdered(1:10, increasing=FALSE) isOrdered(c(1,1:10), increasing=TRUE) isOrdered(c(1,1:10), increasing=TRUE, strictly=FALSE) # decreasing isOrdered(10:1, increasing=TRUE) isOrdered(10:1, increasing=FALSE)# strictly increasing isOrdered(1:10, increasing=TRUE) isOrdered(1:10, increasing=FALSE) isOrdered(c(1,1:10), increasing=TRUE) isOrdered(c(1,1:10), increasing=TRUE, strictly=FALSE) # decreasing isOrdered(10:1, increasing=TRUE) isOrdered(10:1, increasing=FALSE)
Methods for computing lags and differences on xts objects. This provides similar functionality as the zoo counterparts, but with some different defaults.
## S3 method for class 'xts' lag(x, k = 1, na.pad = TRUE, ...) ## S3 method for class 'xts' diff( x, lag = 1, differences = 1, arithmetic = TRUE, log = FALSE, na.pad = TRUE, ... )## S3 method for class 'xts' lag(x, k = 1, na.pad = TRUE, ...) ## S3 method for class 'xts' diff( x, lag = 1, differences = 1, arithmetic = TRUE, log = FALSE, na.pad = TRUE, ... )
x |
An xts object. |
k |
Number of periods to shift. |
na.pad |
Should |
... |
Additional arguments. |
lag |
Period to difference over. |
differences |
Order of differencing. |
arithmetic |
Should arithmetic or geometric differencing be used? |
log |
Should (geometric) log differences be returned? |
The primary motivation for these methods was to take advantage of a faster
C-level implementation. Another motivation was to make lag() behave using
standard sign for k. Both zoo's lag() method and lag.default() require a
negative value for k in order to shift a series backward. So k = 1,
shifts the series forward one observation. This is especially confusing
because k = 1 is the default for those functions. When x is an xts
object, lag(x, 1) returns an object where the value at time 't' is the
value at time 't-1' in the original object.
Another difference is that na.pad = TRUE by default, to better reflect the
transformation visually and for functions the require positional alignment
of data.
Set options(xts.compat.zoo.lag = TRUE) to use make lag.xts() consistent
with lag.zoo() by reversing the sign of k and setting na.pad = FALSE.
An xts object with the desired lag and/or differencing.
Jeffrey A. Ryan
https://en.wikipedia.org/wiki/Lag
x <- xts(1:10, Sys.Date()+1:10) lag(x) # currently using xts-style positive k lag(x, k=2) lag(x, k=-1, na.pad=FALSE) # matches lag.zoo(x, k=1) diff(x) diff(x, lag=1) diff(x, diff=2) diff(diff(x))x <- xts(1:10, Sys.Date()+1:10) lag(x) # currently using xts-style positive k lag(x, k=2) lag(x, k=-1, na.pad=FALSE) # matches lag.zoo(x, k=1) diff(x) diff(x, lag=1) diff(x, diff=2) diff(diff(x))
Perform merge operations on xts objects by time index.
## S3 method for class 'xts' merge( ..., all = TRUE, fill = NA, suffixes = NULL, join = "outer", retside = TRUE, retclass = "xts", tzone = NULL, drop = NULL, check.names = NULL ) ## S3 method for class 'xts' cbind(..., all = TRUE, fill = NA, suffixes = NULL)## S3 method for class 'xts' merge( ..., all = TRUE, fill = NA, suffixes = NULL, join = "outer", retside = TRUE, retclass = "xts", tzone = NULL, drop = NULL, check.names = NULL ) ## S3 method for class 'xts' cbind(..., all = TRUE, fill = NA, suffixes = NULL)
... |
One or more xts objects, or objects coercible to class xts. |
all |
A logical vector indicating merge type. |
fill |
Values to be used for missing elements. |
suffixes |
Suffix to be added to merged column names. |
join |
Type of database join. One of 'outer', 'inner', 'left', or 'right'. |
retside |
Which side of the merged object should be returned (2-case only)? |
retclass |
Either a logical value indicating whether the result should have a 'class' attribute, or the name of the desired class for the result. |
tzone |
Time zone to use for the merged result. |
drop |
Not currently used. |
check.names |
Use |
This xts method is compatible with zoo's merge() method but implemented almost entirely in C-level code for efficiency.
The function can perform all common database join operations along the time index by setting 'join' to one of the values below. Note that 'left' and 'right' are only implemented for two objects.
outer: full outer (all rows in all objects)
inner: only rows with common indexes in all objects
left: all rows in the first object, and rows from the second object that have the same index as the first object
right: all rows in the second object, and rows from the first object that have the same index as the second object
The above join types can also be accomplished by setting 'all' to one of the values below.
outer: all = TRUE or all = c(TRUE, TRUE)
inner: all = FALSE or all = c(FALSE, FALSE)
left: all = c(TRUE, FALSE)
right: all = c(FALSE, TRUE)
The result will have the timezone of the leftmost argument if available. Use the 'tzone' argument to override the default behavior.
When retclass = NULL the joined objects will be split and reassigned
silently back to the original environment they are called from. This is for
backward compatibility with zoo, but unused by xts. When retclass = FALSE
the object will be stripped of its class attribute. This is for internal use.
See the examples in order to join using an 'all' argument that is the same
arguments to join, like you can do with merge.zoo().
A new xts object containing the appropriate elements of the objects passed in to be merged.
This is a highly optimized merge, specifically designed for ordered data. The only supported merging is based on the underlying time index.
Jeffrey A. Ryan
Merge Join Discussion: https://learn.microsoft.com/en-us/archive/blogs/craigfr/merge-join
(x <- xts(4:10, Sys.Date()+4:10)) (y <- xts(1:6, Sys.Date()+1:6)) merge(x,y) merge(x,y, join='inner') merge(x,y, join='left') merge(x,y, join='right') merge.zoo(zoo(x),zoo(y),zoo(x), all=c(TRUE, FALSE, TRUE)) merge(merge(x,x),y,join='left')[,c(1,3,2)] # zero-width objects (only index values) can be used xi <- xts( , index(x)) merge(y, xi)(x <- xts(4:10, Sys.Date()+4:10)) (y <- xts(1:6, Sys.Date()+1:6)) merge(x,y) merge(x,y, join='inner') merge(x,y, join='left') merge(x,y, join='right') merge.zoo(zoo(x),zoo(y),zoo(x), all=c(TRUE, FALSE, TRUE)) merge(merge(x,x),y,join='left')[,c(1,3,2)] # zero-width objects (only index values) can be used xi <- xts( , index(x)) merge(y, xi)
xts method replace NA with most recent non-NA
## S3 method for class 'xts' na.locf(object, na.rm = FALSE, fromLast = FALSE, maxgap = Inf, ...)## S3 method for class 'xts' na.locf(object, na.rm = FALSE, fromLast = FALSE, maxgap = Inf, ...)
object |
An xts object. |
na.rm |
Logical indicating whether leading/trailing |
fromLast |
Logical indicating whether observations should be carried
backward rather than forward. Default is |
maxgap |
Consecutive runs of observations more than 'maxgap' will
remain |
... |
Unused. |
This is the xts method for the S3 generic na.locf(). The primary
difference to note is that after the NA fill action is carried out, the
default it to leave trailing or leading NA's in place. This is different
than zoo behavior.
An object where each NA in object is replaced by the most recent
non-NA prior to it. See na.locf() for details.
Jeffrey A. Ryan
x <- xts(1:10, Sys.Date()+1:10) x[c(1,2,5,9,10)] <- NA x na.locf(x) na.locf(x, fromLast=TRUE) na.locf(x, na.rm=TRUE, fromLast=TRUE)x <- xts(1:10, Sys.Date()+1:10) x[c(1,2,5,9,10)] <- NA x na.locf(x) na.locf(x, fromLast=TRUE) na.locf(x, na.rm=TRUE, fromLast=TRUE)
Calculate the number of specified periods in a given time series like data object.
nseconds(x) nminutes(x) nhours(x) ndays(x) nweeks(x) nmonths(x) nquarters(x) nyears(x)nseconds(x) nminutes(x) nhours(x) ndays(x) nweeks(x) nmonths(x) nquarters(x) nyears(x)
x |
A time-based object. |
Essentially a wrapper to endpoints() with the appropriate period
specified. The result is the number of endpoints found.
As a compromise between simplicity and accuracy, the results will always round up to the nearest complete period. Subtract 1 from the result to get the completed periods.
For finer grain detail one should call the higher frequency functions.
An alternative summary can be found with periodicity(x) and
unclass(periodicity(x)).
The number of respective periods in x.
Jeffrey A. Ryan
## Not run: getSymbols("QQQQ") ndays(QQQQ) nweeks(QQQQ) ## End(Not run)## Not run: getSymbols("QQQQ") ndays(QQQQ) nweeks(QQQQ) ## End(Not run)
Apply a specified function to data over intervals specified by INDEX. The
intervals are defined as the observations from INDEX[k]+1 to INDEX[k+1],
for k = 1:(length(INDEX)-1).
period.apply(x, INDEX, FUN, ...)period.apply(x, INDEX, FUN, ...)
x |
The data that |
INDEX |
A numeric vector of index breakpoint locations. The vector
should begin with 0 and end with |
FUN |
A function to apply to each interval in |
... |
Additional arguments for |
Similar to the rest of the apply family, period.apply() calculates the
specified function's value over a subset of data. The primary difference is
that period.apply() applies the function to non-overlapping intervals of a
vector or matrix.
Useful for applying functions over an entire data object by any
non-overlapping intervals. For example, when INDEX is the result of a
call to endpoints().
period.apply() checks that INDEX is sorted, unique, starts with 0, and
ends with nrow(x). All those conditions are true of vectors returned by
endpoints().
An object with length(INDEX) - 1 observations, assuming INDEX
starts with 0 and ends with nrow(x).
When FUN = mean the results will contain one column for every
column in the input, which is different from other math functions (e.g.
median, sum, prod, sd, etc.).
FUN = mean works by column because the default method stats::mean
previously worked by column for matrices and data.frames. R Core changed the
behavior of mean to always return one column in order to be consistent
with the other math functions. This broke some xts dependencies and
mean.xts() was created to maintain the original behavior.
Using FUN = mean will print a message that describes this inconsistency.
To avoid the message and confusion, use FUN = colMeans to calculate means
by column and use FUN = function(x) mean to calculate one mean for all the
data. Set options(xts.message.period.apply.mean = FALSE) to suppress this
message.
Jeffrey A. Ryan, Joshua M. Ulrich
zoo.data <- zoo(rnorm(31)+10,as.Date(13514:13744,origin="1970-01-01")) ep <- endpoints(zoo.data,'weeks') period.apply(zoo.data, INDEX=ep, FUN=function(x) colMeans(x)) period.apply(zoo.data, INDEX=ep, FUN=colMeans) #same period.apply(letters,c(0,5,7,26), paste0)zoo.data <- zoo(rnorm(31)+10,as.Date(13514:13744,origin="1970-01-01")) ep <- endpoints(zoo.data,'weeks') period.apply(zoo.data, INDEX=ep, FUN=function(x) colMeans(x)) period.apply(zoo.data, INDEX=ep, FUN=colMeans) #same period.apply(letters,c(0,5,7,26), paste0)
Calculate a sum, product, minimum, or maximum for each non-overlapping
period specified by INDEX.
period.sum(x, INDEX) period.prod(x, INDEX) period.max(x, INDEX) period.min(x, INDEX)period.sum(x, INDEX) period.prod(x, INDEX) period.max(x, INDEX) period.min(x, INDEX)
x |
A univariate data object. |
INDEX |
A numeric vector of endpoints for each period. |
These functions are similar to calling period.apply() with the same
endpoints and function. There may be slight differences in the results due
to numerical accuracy.
For xts-coercible objects, an appropriate INDEX can be created by a call
to endpoints().
An xts or zoo object containing the sum, product, minimum, or
maximum for each endpoint in INDEX.
Jeffrey A. Ryan
x <- c(1, 1, 4, 2, 2, 6, 7, 8, -1, 20) i <- c(0, 3, 5, 8, 10) period.sum(x, i) period.prod(x, i) period.min(x, i) period.max(x, i) data(sample_matrix) y <- sample_matrix[, 1] ep <- endpoints(sample_matrix) period.sum(y, ep) period.sum(as.xts(y), ep) period.prod(y, ep) period.prod(as.xts(y), ep) period.min(y, ep) period.min(as.xts(y), ep) period.max(y, ep) period.max(as.xts(y), ep)x <- c(1, 1, 4, 2, 2, 6, 7, 8, -1, 20) i <- c(0, 3, 5, 8, 10) period.sum(x, i) period.prod(x, i) period.min(x, i) period.max(x, i) data(sample_matrix) y <- sample_matrix[, 1] ep <- endpoints(sample_matrix) period.sum(y, ep) period.sum(as.xts(y), ep) period.prod(y, ep) period.prod(as.xts(y), ep) period.min(y, ep) period.min(as.xts(y), ep) period.max(y, ep) period.max(as.xts(y), ep)
Estimate the periodicity of a time-series-like object by calculating the median time between observations in days.
periodicity(x, ...)periodicity(x, ...)
x |
A time-series-like object. |
... |
Unused. |
A simple wrapper to quickly estimate the periodicity of a given data.
Returning an object of type periodicity.
This calculates the median time difference between observations as a difftime object, the numerical difference, the units of measurement, and the derived scale of the data as a string.
The time index currently must be of either a 'Date' or 'POSIXct' class, or or coercible to one of them.
The 'scale' component of the result is an estimate of the periodicity of the data in common terms - e.g. 7 day daily data is best described as 'weekly', and would be returned as such.
A 'periodicity' object with the following elements:
the difftime object,
frequency: the median time difference between observations
start: the first observation
end: the last observation
units: one of secs, mins, hours, or days
scale: one of seconds, minute, hourly, daily, weekly, monthly, quarterly, or yearly
label: one of second, minute, hour, day, week, month, quarter, year
Possible scale values are: ‘minute’, ‘hourly’, ‘daily’,
‘weekly’, ‘monthly’, ‘quarterly’, and ‘yearly’.
This function only attempts to be a good estimate for the underlying periodicity. If the series is too short, or has highly irregular periodicity, the return values will not be accurate. That said, it is quite robust and used internally within xts.
Jeffrey A. Ryan
zoo.ts <- zoo(rnorm(231),as.Date(13514:13744,origin="1970-01-01")) periodicity(zoo.ts)zoo.ts <- zoo(rnorm(231),as.Date(13514:13744,origin="1970-01-01")) periodicity(zoo.ts)
Plotting for xts objects.
## S3 method for class 'xts' plot( x, y = NULL, ..., subset = "", panels = NULL, multi.panel = FALSE, col = 1:8, up.col = NULL, dn.col = NULL, bg = "#FFFFFF", type = "l", lty = 1, lwd = 2, lend = 1, main = deparse(substitute(x)), main.timespan = TRUE, observation.based = FALSE, log = FALSE, ylim = NULL, yaxis.same = TRUE, yaxis.left = TRUE, yaxis.right = TRUE, yaxis.ticks = 5, major.ticks = "auto", minor.ticks = NULL, grid.ticks.on = "auto", grid.ticks.lwd = 1, grid.ticks.lty = 1, grid.col = "darkgray", labels.col = "#333333", format.labels = TRUE, grid2 = "#F5F5F5", legend.loc = NULL, extend.xaxis = FALSE ) ## S3 method for class 'xts' lines( x, ..., main = "", on = 0, col = NULL, type = "l", lty = 1, lwd = 1, pch = 1 ) ## S3 method for class 'xts' points(x, ..., main = "", on = 0, col = NULL, pch = 1)## S3 method for class 'xts' plot( x, y = NULL, ..., subset = "", panels = NULL, multi.panel = FALSE, col = 1:8, up.col = NULL, dn.col = NULL, bg = "#FFFFFF", type = "l", lty = 1, lwd = 2, lend = 1, main = deparse(substitute(x)), main.timespan = TRUE, observation.based = FALSE, log = FALSE, ylim = NULL, yaxis.same = TRUE, yaxis.left = TRUE, yaxis.right = TRUE, yaxis.ticks = 5, major.ticks = "auto", minor.ticks = NULL, grid.ticks.on = "auto", grid.ticks.lwd = 1, grid.ticks.lty = 1, grid.col = "darkgray", labels.col = "#333333", format.labels = TRUE, grid2 = "#F5F5F5", legend.loc = NULL, extend.xaxis = FALSE ) ## S3 method for class 'xts' lines( x, ..., main = "", on = 0, col = NULL, type = "l", lty = 1, lwd = 1, pch = 1 ) ## S3 method for class 'xts' points(x, ..., main = "", on = 0, col = NULL, pch = 1)
x |
A xts object. |
y |
Not used, always |
... |
Any passthrough arguments for |
subset |
An ISO8601-style subset string. |
panels |
Character vector of expressions to plot as panels. |
multi.panel |
Either |
col |
Color palette to use. |
up.col |
Color for positive bars when |
dn.col |
Color for negative bars when |
bg |
Background color of plotting area, same as in |
type |
The type of plot to be drawn, same as in |
lty |
Set the line type, same as in |
lwd |
Set the line width, same as in |
lend |
Set the line end style, same as in |
main |
Main plot title. |
main.timespan |
Should the timespan of the series be shown in the top right corner of the plot? |
observation.based |
When |
log |
Should the y-axis be in log scale? Default |
ylim |
The range of the y axis. |
yaxis.same |
Should 'ylim' be the same for every panel? Default |
yaxis.left |
Add y-axis labels to the left side of the plot? |
yaxis.right |
Add y-axis labels to the right side of the plot? |
yaxis.ticks |
Desired number of y-axis grid lines. The actual number of
grid lines is determined by the |
major.ticks |
Period specifying locations for major tick marks and labels on the x-axis. See Details for possible values. |
minor.ticks |
Period specifying locations for minor tick marks on the
x-axis. When |
grid.ticks.on |
Period specifying locations for vertical grid lines. See details for possible values. |
grid.ticks.lwd |
Line width of the grid. |
grid.ticks.lty |
Line type of the grid. |
grid.col |
Color of the grid. |
labels.col |
Color of the axis labels. |
format.labels |
Label format to draw lower frequency x-axis ticks and
labels passed to |
grid2 |
Color for secondary x-axis grid. |
legend.loc |
Places a legend into one of nine locations on the chart:
bottomright, bottom, bottomleft, left, topleft, top, topright, right, or
center. Default |
extend.xaxis |
When |
on |
Panel number to draw on. A new panel will be drawn if |
pch |
the plotting character to use, same as in 'par' |
Possible values for arguments major.ticks, minor.ticks, and
grid.ticks.on include ‘auto’, ‘minute’, ‘hours’,
‘days’, ‘weeks’, ‘months’, ‘quarters’, and
‘years’. The default is ‘auto’, which attempts to determine
sensible locations from the periodicity and locations of observations. The
other values are based on the possible values for the ticks.on
argument of axTicksByTime().
Ross Bennett
based on chart_Series() in quantmod
written by Jeffrey A. Ryan
## Not run: data(sample_matrix) sample.xts <- as.xts(sample_matrix) # plot the Close plot(sample.xts[,"Close"]) # plot a subset of the data plot(sample.xts[,"Close"], subset = "2007-04-01/2007-06-31") # function to compute simple returns simple.ret <- function(x, col.name){ x[,col.name] / lag(x[,col.name]) - 1 } # plot the close and add a panel with the simple returns plot(sample.xts[,"Close"]) R <- simple.ret(sample.xts, "Close") lines(R, type = "h", on = NA) # add the 50 period simple moving average to panel 1 of the plot library(TTR) lines(SMA(sample.xts[,"Close"], n = 50), on = 1, col = "blue") # add month end points to the chart points(sample.xts[endpoints(sample.xts[,"Close"], on = "months"), "Close"], col = "red", pch = 17, on = 1) # add legend to panel 1 addLegend("topright", on = 1, legend.names = c("Close", "SMA(50)"), lty = c(1, 1), lwd = c(2, 1), col = c("black", "blue", "red")) ## End(Not run)## Not run: data(sample_matrix) sample.xts <- as.xts(sample_matrix) # plot the Close plot(sample.xts[,"Close"]) # plot a subset of the data plot(sample.xts[,"Close"], subset = "2007-04-01/2007-06-31") # function to compute simple returns simple.ret <- function(x, col.name){ x[,col.name] / lag(x[,col.name]) - 1 } # plot the close and add a panel with the simple returns plot(sample.xts[,"Close"]) R <- simple.ret(sample.xts, "Close") lines(R, type = "h", on = NA) # add the 50 period simple moving average to panel 1 of the plot library(TTR) lines(SMA(sample.xts[,"Close"], n = 50), on = 1, col = "blue") # add month end points to the chart points(sample.xts[endpoints(sample.xts[,"Close"], on = "months"), "Close"], col = "red", pch = 17, on = 1) # add legend to panel 1 addLegend("topright", on = 1, legend.names = c("Close", "SMA(50)"), lty = c(1, 1), lwd = c(2, 1), col = c("black", "blue", "red")) ## End(Not run)
Method for printing an extensible time-series object.
## S3 method for class 'xts' print(x, fmt, ..., show.rows = 10, max.rows = 100)## S3 method for class 'xts' print(x, fmt, ..., show.rows = 10, max.rows = 100)
x |
An xts object. |
fmt |
Passed to |
... |
Arguments passed to other methods. |
show.rows |
The number of first and last rows to print if the number of
rows is truncated (default 10, or |
max.rows |
The output will contain at most |
Returns x invisibly.
Joshua M. Ulrich
data(sample_matrix) sample.xts <- as.xts(sample_matrix) # output is truncated and shows first and last 10 observations print(sample.xts) # show the first and last 5 observations print(sample.xts, show.rows = 5)data(sample_matrix) sample.xts <- as.xts(sample_matrix) # output is truncated and shows first and last 10 observations print(sample.xts) # show the first and last 5 observations print(sample.xts, show.rows = 5)
Simulated 180 observations on 4 variables.
data(sample_matrix)data(sample_matrix)
The format is:
num [1:180, 1:4] 50.0 50.2 50.4 50.4 50.2 ...
- attr(*, "dimnames")=List of 2
..$ : chr [1:180] "2007-01-02" "2007-01-03" "2007-01-04" "2007-01-05" ...
..$ : chr [1:4] "Open" "High" "Low" "Close"
data(sample_matrix)data(sample_matrix)
Creates a list of xts objects split along time periods.
## S3 method for class 'xts' split(x, f = "months", drop = FALSE, k = 1, ...)## S3 method for class 'xts' split(x, f = "months", drop = FALSE, k = 1, ...)
x |
An xts object. |
f |
A character vector describing the period to split by. |
drop |
Ignored by |
k |
Number of periods to aggregate into each split. See details. |
... |
Further arguments passed to other methods. |
A quick way to break up a large xts object by standard time periods; e.g. 'months', 'quarters', etc.
endpoints() is used to find the start and end of each period (or
k-periods). See that function for valid arguments.
The inputs are passed to split.zoo() when f is not a character vector.
A list of xts objects.
aggregate.zoo() is more flexible, though not
as fast for xts objects.
Jeffrey A. Ryan
endpoints(), split.zoo(),
aggregate.zoo()
data(sample_matrix) x <- as.xts(sample_matrix) split(x) split(x, f="weeks") split(x, f="weeks", k=4)data(sample_matrix) x <- as.xts(sample_matrix) split(x) split(x, f="weeks") split(x, f="weeks", k=4)
Generic functions to get or replace the class of an xts object's index.
tclass(x, ...) ## Default S3 method: tclass(x, ...) ## S3 method for class 'xts' tclass(x, ...) tclass(x) <- value ## Default S3 replacement method: tclass(x) <- value indexClass(x) indexClass(x) <- value ## S3 replacement method for class 'xts' tclass(x) <- valuetclass(x, ...) ## Default S3 method: tclass(x, ...) ## S3 method for class 'xts' tclass(x, ...) tclass(x) <- value ## Default S3 replacement method: tclass(x) <- value indexClass(x) indexClass(x) <- value ## S3 replacement method for class 'xts' tclass(x) <- value
x |
An xts object. |
... |
Arguments passed to other methods. |
value |
The new index class (see Details for valid values). |
Internally, an xts object's index is a numeric value corresponding to
seconds since the epoch in the UTC timezone. The index class is stored as
the tclass attribute on the internal index. This is used to convert
the internal index values to the desired class when the index
function is called.
The tclass function retrieves the class of the internal index, and
the tclass<- function sets it. The specified value for
tclass<- must be one of the following character strings:
"Date", "POSIXct", "chron", "yearmon",
"yearqtr", or "timeDate".
A vector containing the class of the object's index.
Both indexClass and indexClass<- are deprecated in favor
of tclass and tclass<-, respectively.
Replacing the tclass can potentially change the values of the internal
index. For example, changing the 'tclass' from POSIXct to Date will
truncate the POSIXct value and convert the timezone to UTC (since the Date
class doesn't have a timezone). See the examples.
Jeffrey A. Ryan
index() has more information on the xts index, tformat()
details how the index values are formatted when printed, and tzone()
has more information about the index timezone settings.
The following help pages describe the characteristics of the valid index
classes: POSIXct(), Date(), chron(),
yearmon(), yearqtr(),
timeDate()
x <- timeBasedSeq('2010-01-01/2010-01-02 12:00') x <- xts(seq_along(x), x) y <- timeBasedSeq('2010-01-01/2010-01-03 12:00/H') y <- xts(seq_along(y), y, tzone = "America/New_York") # Changing the tclass *changes* the internal index values head(y) # the index has times head(.index(y)) tclass(y) <- "Date" head(y) # the index prints as a Date head(.index(y)) # the internal index is truncatedx <- timeBasedSeq('2010-01-01/2010-01-02 12:00') x <- xts(seq_along(x), x) y <- timeBasedSeq('2010-01-01/2010-01-03 12:00/H') y <- xts(seq_along(y), y, tzone = "America/New_York") # Changing the tclass *changes* the internal index values head(y) # the index has times head(.index(y)) tclass(y) <- "Date" head(y) # the index prints as a Date head(.index(y)) # the internal index is truncated
Generic functions to get or replace the format that determines how an xts object's index is printed.
tformat(x, ...) tformat(x) <- value indexFormat(x) indexFormat(x) <- valuetformat(x, ...) tformat(x) <- value indexFormat(x) indexFormat(x) <- value
x |
An xts object. |
... |
Arguments passed to other methods. |
value |
New index format string (see |
Valid values for the value argument are the same as specified in the
Details section of strptime().
An xts object's tformat is NULL by default, so the index will be
be formatted according to its tclass() (e.g. Date, POSIXct, timeDate,
yearmon, etc.).
The tformat only changes how the index is printed and how the row names
are formatted when xts objects are converted to other classes (e.g. matrix
or data.frame). It does not affect the internal index in any way.
A vector containing the format for the object's index.
Both indexFormat() and indexFormat<- are deprecated in
favor of tformat() and tformat<-, respectively.
Jeffrey A. Ryan
index() has more information on the xts index, tclass()
details how xts handles the class of the index, tzone() has more
information about the index timezone settings.
x <- timeBasedSeq('2010-01-01/2010-01-02 12:00') x <- xts(seq_along(x), x) # set a custom index format head(x) tformat(x) <- "%Y-%b-%d %H:%M:%OS3" head(x)x <- timeBasedSeq('2010-01-01/2010-01-02 12:00') x <- xts(seq_along(x), x) # set a custom index format head(x) tformat(x) <- "%Y-%b-%d %H:%M:%OS3" head(x)
A function to create a vector of time-based objects suitable for indexing an xts object, given a string conforming to the ISO-8601 time and date standard for range-based specification. The resulting series can be of any class supported by xts, including POSIXct, Date, chron, timeDate, yearmon, and yearqtr.
timeBasedRange(x, ...) timeBasedSeq(x, retclass = NULL, length.out = NULL)timeBasedRange(x, ...) timeBasedSeq(x, retclass = NULL, length.out = NULL)
x |
An ISO-8601 time-date range string. |
... |
Unused. |
retclass |
The return class desired. |
length.out |
Passed to |
timeBasedRange() creates a one or two element numeric vector representing
the start and end number of seconds since epoch (1970-01-01). For internal
use.
timeBasedSeq() creates sequences of time-based observations using strings
formatted according to the ISO-8601 specification. The general format is
from/to/by or from::to::by, where to and by are optional when the
'length.out' argument is specified.
The from and to elements of the string must be left-specified with respect to the standard CCYYMMDD HHMMSS form. All dates/times specified will be set to either the earliest point (from) or the latest (to), to the given the level of specificity. For example, ‘1999’ in the from field would set the start to the beginning of 1999. ‘1999’ in the to field would set the end to the end of 1999.
The amount of resolution in the result is determined by the resolution of the from and to component, unless the optional by component is specified.
For example, timeBasedSeq("1999/2008") returns a vector of Dates for
January 1st of each year. timeBasedSeq("199501/1996") returns a yearmon
vector of 24 months in 1995 and 1996. And timeBasedSeq("19950101/1996")
creates a Date vector for all the days in those two years.
The optional by field (the third delimited element to the string), will the resolution heuristic described above and will use the specified by resolution. The possible values for by are: 'Y' (years), 'm' (months), 'd' (days), 'H' (hours), 'M' (minutes), 'S' (seconds). Sub-second resolutions are not supported.
timeBasedSeq() returns a vector of time-based observations.
timeBasedRange() returns a one or two element numeric vector representing
the start and end number of seconds since epoch (1970-01-01).
When retclass = NULL, the result of timeBasedSeq() is a named list
containing elements "from", "to", "by" and "length.out".
Jeffrey A. Ryan
International Organization for Standardization: ISO 8601 https://www.iso.org
timeBasedSeq('1999/2008') timeBasedSeq('199901/2008') timeBasedSeq('199901/2008/d') timeBasedSeq('20080101 0830',length=100) # 100 minutes timeBasedSeq('20080101 083000',length=100) # 100 secondstimeBasedSeq('1999/2008') timeBasedSeq('199901/2008') timeBasedSeq('199901/2008/d') timeBasedSeq('20080101 0830',length=100) # 100 minutes timeBasedSeq('20080101 083000',length=100) # 100 seconds
Convert an OHLC or univariate object to a specified periodicity lower than the given data object. For example, convert a daily series to a monthly series, or a monthly series to a yearly one, or a one minute series to an hourly series.
to.period( x, period = "months", k = 1, indexAt = NULL, name = NULL, OHLC = TRUE, ... ) to.minutes(x, k, name, ...) to.minutes3(x, name, ...) to.minutes5(x, name, ...) to.minutes10(x, name, ...) to.minutes15(x, name, ...) to.minutes30(x, name, ...) to.hourly(x, name, ...) to.daily(x, drop.time = TRUE, name, ...) to.weekly(x, drop.time = TRUE, name, ...) to.monthly(x, indexAt = "yearmon", drop.time = TRUE, name, ...) to.quarterly(x, indexAt = "yearqtr", drop.time = TRUE, name, ...) to.yearly(x, drop.time = TRUE, name, ...)to.period( x, period = "months", k = 1, indexAt = NULL, name = NULL, OHLC = TRUE, ... ) to.minutes(x, k, name, ...) to.minutes3(x, name, ...) to.minutes5(x, name, ...) to.minutes10(x, name, ...) to.minutes15(x, name, ...) to.minutes30(x, name, ...) to.hourly(x, name, ...) to.daily(x, drop.time = TRUE, name, ...) to.weekly(x, drop.time = TRUE, name, ...) to.monthly(x, indexAt = "yearmon", drop.time = TRUE, name, ...) to.quarterly(x, indexAt = "yearqtr", drop.time = TRUE, name, ...) to.yearly(x, drop.time = TRUE, name, ...)
x |
A univariate or OHLC type time-series object. |
period |
Period to convert to. See details. |
k |
Number of sub periods to aggregate on (only for minutes and seconds). |
indexAt |
Convert final index to new class or date. See details. |
name |
Override column names? |
OHLC |
Should an OHLC object be returned? (only |
... |
Additional arguments. |
drop.time |
Remove time component of POSIX datestamp (if any)? |
The result will contain the open and close for the given period, as well as the maximum and minimum over the new period, reflected in the new high and low, respectively. Aggregate volume will also be calculated if applicable.
An easy and reliable way to convert one periodicity of data into any new
periodicity. It is important to note that all dates will be aligned to the
end of each period by default - with the exception of to.monthly() and
to.quarterly(), which use the zoo package's yearmon and
yearqtr classes, respectively.
Valid period character strings include: "seconds", "minutes", "hours",
"days", "weeks", "months", "quarters", and "years". These are
calculated internally via endpoints(). See that function's help page for
further details.
To adjust the final indexing style, it is possible to set indexAt to one
of the following: ‘yearmon’, ‘yearqtr’, ‘firstof’,
‘lastof’, ‘startof’, or ‘endof’. The final index will
then be yearmon, yearqtr, the first time of the period, the last time
of the period, the starting time in the data for that period, or the ending
time in the data for that period, respectively.
It is also possible to pass a single time series, such as a univariate exchange rate, and return an OHLC object of lower frequency - e.g. the weekly OHLC of the daily series.
Setting drop.time = TRUE (the default) will convert a series that includes
a time component into one with just a date index, since the time component
is often of little value in lower frequency series.
An object of the original type, with new periodicity.
In order for this function to work properly on OHLC data, it is
necessary that the Open, High, Low and Close columns be names as such;
including the first letter capitalized and the full spelling found.
Internally a call is made to reorder the data into the correct column order,
and then a verification step to make sure that this ordering and naming has
succeeded. All other data formats must be aggregated with functions such as
aggregate() and period.apply().
This method should work on almost all time-series-like objects. Including ‘timeSeries’, ‘zoo’, ‘ts’, and ‘irts’. It is even likely to work well for other data structures - including ‘data.frames’ and ‘matrix’ objects.
Internally a call to as.xts() converts the original x into the
universal xts format, and then re-converts back to the original type.
A special note with respect to ‘ts’ objects. As these are strictly
regular they may include NA values. These are removed before aggregation,
though replaced before returning the result. This inevitably leads to many
additional NA values in the result. Consider using an xts object or
converting to xts using as.xts().
Jeffrey A. Ryan
data(sample_matrix) samplexts <- as.xts(sample_matrix) to.monthly(samplexts) to.monthly(sample_matrix) str(to.monthly(samplexts)) str(to.monthly(sample_matrix))data(sample_matrix) samplexts <- as.xts(sample_matrix) to.monthly(samplexts) to.monthly(sample_matrix) str(to.monthly(samplexts)) str(to.monthly(sample_matrix))
Functions to convert objects of arbitrary classes to xts and then back to the original class, without losing any attributes of the original class.
try.xts(x, ..., error = TRUE) reclass(x, match.to, error = FALSE, ...) Reclass(x)try.xts(x, ..., error = TRUE) reclass(x, match.to, error = FALSE, ...) Reclass(x)
x |
Data object to convert. See details for supported types. |
... |
Additional parameters or attributes. |
error |
Error handling option. See Details. |
match.to |
An xts object whose attributes will be copied to the result. |
A simple and reliable way to convert many different objects into a uniform format for use within R.
try.xts() and reclass() are functions that enable external developers
access to the reclassing tools within xts to help speed development of
time-aware functions, as well as provide a more robust and seemless end-user
experience, regardless of the end-user's choice of data-classes.
try.xts() calls as.xts() internally. See as.xts() for available xts
methods and arguments for each coercible class. Since it calls as.xts(),
you can add custom attributes as name = value pairs in the same way. But
these custom attributes will not be copied back to the original object when
reclass() is called.
The error argument can be a logical value indicating whether an error
should be thrown (or fail silently), a character string allowing for custom
error error messages, or a function of the form f(x, ...) that will be
called if the conversion fails.
reclass() converts an object created by try.xts() back to its original
class with all the original attributes intact (unless they were changed
after the object was converted to xts). The match.to argument allows you
copy the index attributes (tclass, tformat, and tzone) and
xtsAttributes() from another xts object to the result. match.to must
be an xts object with an index value for every observation in x.
Reclass() is designed for top-level use, where it is desirable to have
the object returned from an arbitrary function in the same class as the
object passed in. Most functions in R are not designed to return objects
matching the original object's class. It attempts to handle conversion and
reconversion transparently but it requires the original object must be
coercible to xts, the result of the function must have the same number of
rows as the input, and the object to be converted/reclassed must be the
first argument to the function being wrapped. Note that this function
hasn't been tested for robustness.
See the accompanying vignette for more details on the above usage.
try.xts() returns an xts object when conversion is successful.
The error argument controls the function's behavior when conversion fails.
Reclass() and reclass() return the object as its original class, as
specified by the 'CLASS' attribute.
Jeffrey A. Ryan
a <- 1:10 # fails silently, the result is still an integer vector try.xts(a, error = FALSE) # control the result with a function try.xts(a, error = function(x, ...) { "I'm afraid I can't do that." }) z <- zoo(1:10, timeBasedSeq("2020-01-01/2020-01-10")) x <- try.xts(z) # zoo to xts str(x) str(reclass(x)) # reclass back to zooa <- 1:10 # fails silently, the result is still an integer vector try.xts(a, error = FALSE) # control the result with a function try.xts(a, error = function(x, ...) { "I'm afraid I can't do that." }) z <- zoo(1:10, timeBasedSeq("2020-01-01/2020-01-10")) x <- try.xts(z) # zoo to xts str(x) str(reclass(x)) # reclass back to zoo
Method for extracting time windows from xts objects.
## S3 method for class 'xts' window(x, index. = NULL, start = NULL, end = NULL, ...)## S3 method for class 'xts' window(x, index. = NULL, start = NULL, end = NULL, ...)
x |
An xts object. |
index. |
A user defined time index (default |
start |
A start time coercible to POSIXct. |
end |
An end time coercible to POSIXct. |
... |
Unused. |
The xts window() method provides an efficient way to subset an xts object
between a start and end date using a binary search algorithm. Specifically,
it converts start and end to POSIXct and then does a binary search of
the index to quickly return a subset of x between start and end.
Both start and end may be any class that is convertible to POSIXct, such
as a character string in the format ‘yyyy-mm-dd’. When start = NULL
the returned subset will begin at the first value of index.. When
end = NULL the returned subset will end with the last value of index..
Otherwise the subset will contain all timestamps where index. is between
start and end, inclusive.
When index. is specified, findInterval() is used to quickly retrieve
large sets of sorted timestamps. For the best performance, index. must be
a sorted POSIXct vector or a numeric vector of seconds since the epoch.
index. is typically a subset of the timestamps in x.
The subset of x that matches the time window.
Corwin Joy
subset.xts(), findInterval(), xts()
## xts example x.date <- as.Date(paste(2003, rep(1:4, 4:1), seq(1,19,2), sep = "-")) x <- xts(matrix(rnorm(20), ncol = 2), x.date) x window(x, start = "2003-02-01", end = "2003-03-01") window(x, start = as.Date("2003-02-01"), end = as.Date("2003-03-01")) window(x, index. = x.date[1:6], start = as.Date("2003-02-01")) window(x, index. = x.date[c(4, 8, 10)]) ## Assign to subset window(x, index. = x.date[c(4, 8, 10)]) <- matrix(1:6, ncol = 2) x## xts example x.date <- as.Date(paste(2003, rep(1:4, 4:1), seq(1,19,2), sep = "-")) x <- xts(matrix(rnorm(20), ncol = 2), x.date) x window(x, start = "2003-02-01", end = "2003-03-01") window(x, start = as.Date("2003-02-01"), end = as.Date("2003-03-01")) window(x, index. = x.date[1:6], start = as.Date("2003-02-01")) window(x, index. = x.date[c(4, 8, 10)]) ## Assign to subset window(x, index. = x.date[c(4, 8, 10)]) <- matrix(1:6, ncol = 2) x
Constructor function for creating an extensible time-series object.
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)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)
x |
An object containing the underlying data. |
order.by |
A corresponding vector of dates/times of a known time-based class. See Details. |
frequency |
Numeric value indicating the frequency of |
unique |
Can the index only include unique timestamps? Ignored when
|
tzone |
Time zone of the index (ignored for indices without a time
component, e.g. Date, yearmon, yearqtr). See |
... |
Additional attributes to be added. See details. |
index |
A corresponding numeric vector specified as seconds since the UNIX epoch (1970-01-01 00:00:00.000). |
tclass |
Time class to use for the index. See |
check |
Must the index be ordered? The index cannot contain duplicates
when |
xts() is used to create an xts object from raw data inputs. The xts class
inherits from and extends the zoo class, which means most zoo functions can
be used on xts objects.
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 then
the regular 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 remain unique.
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 isOrdered() function.
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. These attributes may be
assigned and extracted via xtsAttributes() and xtsAttributes<-,
respectively.
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, subsetting ([), merge(), cbind(),
rbind(), c(), math and logical operations, lag(), diff(),
coredata(), head(), and tail(). There are also 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 separated by a double colon “"::"” or “"/"”. Each side
of the range may be left blank, which would then default to the start 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 requires
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 as.xts() and reclass(),
which allow for lossless two-way conversion between common R time-series
classes and the xts object structure. See those functions for more detail.
An S3 object of class xts.
Jeffrey A. Ryan and Joshua M. Ulrich
zoo
as.xts(), index(), tclass(), tformat(), tzone(),
xtsAttributes()
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 2007data(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
This help file is to help in development of xts, as well as provide some clarity and insight into its purpose and implementation.
Last modified: 2008-08-06 by Jeffrey A. Ryan Version: 0.5-0 and above
The xts package xts designed as a drop-in replacement for the very popular zoo package. Most all functionality of zoo has been extended or carries into the xts package.
Notable changes in direction include the use of time-based indexing, at first explicitely, now implicitely.
An xts object consists of data in the form of a matrix, an index - ordered and increasing, either numeric or integer, and additional attributes for use internally, or for end-user purposes.
The current implementation enforces two major rules on the object. One is
that the index must be coercible to numeric, by way of as.POSIXct.
There are defined types that meet this criteria. See timeBased for
details.
The second requirement is that the object cannot have rownames. The
motivation from this comes in part from the work Matthew Doyle has done in
his data.table class, in the package of the same name. Rownames in must be
character vectors, and as such are inefficient in both storage and
conversion. By eliminating the rownames, and providing a numeric index of
internal type REAL or INTEGER, it is possible to maintain a
connection to standard date and time classes via the POSIXct functions,
while at at the same time maximizing efficiencies in data handling.
User level functions index, as well as conversion to other classes
proceeds as if there were rownames. The code for index automatically
converts time to numeric in both extraction and replacement functionality.
This provides a level of abstraction to facilitate internal, and external
package use and inter-operability.
There is also new work on providing a C-level API to some of the xts functionality to facilitate external package developers to utilize the fast utility routines such as subsetting and merges, without having to call only from . Obviously this places far more burden on the developer to not only understand the internal xts implementation, but also to understand all of what is documented for R-internals (and much that isn't). At present the functions and macros available can be found in the ‘xts.h’ file in the src directory.
There is no current documentation for this API. The adventure starts here. Future documentation is planned, not implemented.
Jeffrey A. Ryan
This help file is to help in development of xts, as well as provide some clarity and insight into its purpose and implementation.
By Jeffrey A. Ryan, Dirk Eddelbuettel, and Joshua M. Ulrich Last modified: 2018-05-02 Version: 0.10-3 and above
At present the xts API has publicly available interfaces to the
following functions (as defined in xtsAPI.h):
Callable from other R packages: SEXP xtsIsOrdered(SEXP x, SEXP increasing, SEXP strictly) SEXP xtsNaCheck(SEXP x, SEXP check) SEXP xtsTry(SEXP x) SEXP xtsRbind(SEXP x, SEXP y, SEXP dup) SEXP xtsCoredata(SEXP x) SEXP xtsLag(SEXP x, SEXP k, SEXP pad) Internal use functions: SEXP isXts(SEXP x) void copy_xtsAttributes(SEXP x, SEXP y) void copy_xtsCoreAttributes(SEXP x, SEXP y) Internal use macros: xts_ATTRIB(x) xts_COREATTRIB(x) GET_xtsIndex(x) SET_xtsIndex(x,value) GET_xtsIndexFormat(x) SET_xtsIndexFormat(x,value) GET_xtsCLASS(x) SET_xtsCLASS(x,value) Internal use SYMBOLS: xts_IndexSymbol xts_ClassSymbol xts_IndexFormatSymbol Callable from R: SEXP mergeXts(SEXP args) SEXP rbindXts(SEXP args) SEXP tryXts(SEXP x)
Jeffrey A. Ryan
## Not run: # some example code to look at file.show(system.file('api_example/README', package="xts")) file.show(system.file('api_example/src/checkOrder.c', package="xts")) ## End(Not run)## Not run: # some example code to look at file.show(system.file('api_example/README', package="xts")) file.show(system.file('api_example/src/checkOrder.c', package="xts")) ## End(Not run)
Extract and replace non-core xts attributes.
xtsAttributes(x, user = NULL) xtsAttributes(x) <- valuextsAttributes(x, user = NULL) xtsAttributes(x) <- value
x |
An xts object. |
user |
Should user-defined attributes be returned? The default of
|
value |
A list of new |
This function allows users to assign custom attributes to the xts objects, without altering core xts attributes (i.e. tclass, tzone, and tformat).
attributes() returns all attributes, including core attributes of the
xts class.
A named list of user-defined attributes.
Jeffrey A. Ryan
x <- xts(matrix(1:(9*6),nc=6), order.by=as.Date(13000,origin="1970-01-01")+1:9, a1='my attribute') xtsAttributes(x) xtsAttributes(x) <- list(a2=2020) xtsAttributes(x) xtsAttributes(x) <- list(a1=NULL) xtsAttributes(x)x <- xts(matrix(1:(9*6),nc=6), order.by=as.Date(13000,origin="1970-01-01")+1:9, a1='my attribute') xtsAttributes(x) xtsAttributes(x) <- list(a2=2020) xtsAttributes(x) xtsAttributes(x) <- list(a1=NULL) xtsAttributes(x)