It is straightforward to turn existing functions into functions that can deal with any ts-boxable object.
The ts_
function is a constructor function for tsbox
time series functions. It can be used to wrap any function that works
with time series. The default is set to R base "ts"
class,
so wrapping functions for "ts"
time series (or vectors or
matrices) is as simple as:
<- ts_(rowSums)
ts_rowsums ts_rowsums(ts_c(mdeaths, fdeaths))
Note that ts_
returns a function, which can be with or
without a name. Let’ have a closer look at how ts_rowsums
looks like:
ts_rowsums#> function (x, ...)
#> {
#> stopifnot(ts_boxable(x))
#> z <- rowSums(ts_ts(x), ...)
#> copy_class(z, x)
#> }
This is how most ts-functions work. They use a specific converter
function (here: ts_ts
) to convert a ts-boxable object to
the desired class. They then perform the main operation on the object
(here: rowSums
). Finally they convert the result back to
the original class, using copy_class
.
The resulting function has a ...
argument. You can use
it to pass arguments to the underlying functions. E.g.,
ts_rowsums(ts_c(mdeaths, fdeaths), na.rm = TRUE)
Here is a slightly more complex example, which uses a post processing function:
<- ts_(function(x) predict(prcomp(x, scale = TRUE)))
ts_prcomp ts_prcomp(ts_c(mdeaths, fdeaths))
It is easy to make functions from external packages ts-boxable, by
wrapping them into ts_
.
<- ts_(dygraphs::dygraph, class = "xts")
ts_dygraphs <- ts_(function(x, ...) forecast::forecast(x, ...)$mean, vectorize = TRUE)
ts_forecast <- ts_(function(x, ...) seasonal::final(seasonal::seas(x, ...)), vectorize = TRUE)
ts_seas
ts_dygraphs(ts_c(mdeaths, EuStockMarkets))
ts_forecast(ts_c(mdeaths, fdeaths))
ts_seas(ts_c(mdeaths, fdeaths))
If you are explicit about the namespace (e.g.,
dygraphs::dygraph
), ts_
recognized the package
in use and delivers a meaningful message if the package is not
installed.
Note that the ts_
function deals with the conversion
stuff, ‘vectorizes’ the function so that it can be used with multiple
time series.
Let’ have another look at ts_forecast
:
ts_forecast#> function (x, ...)
#> {
#> load_suggested("forecast")
#> ff <- function(x, ...) {
#> stopifnot(ts_boxable(x))
#> z <- (function(x, ...) forecast::forecast(ts_na_omit(x),
#> ...)$mean)(ts_ts(x), ...)
#> copy_class(z, x)
#> }
#> ts_apply(x, ff, ...)
#> }
There three differences to the ts_rowsum
example: First,
the function requires the forecast package. If it is not installed,
load_suggested
will ask the user to do so. Second, the
function in use is an anonymous function,
function(x) forecast::forecast(x, ...)$mean
, that also
extracts the $mean
component from the result. Third, the
function is ‘vectorized’, using ts_apply
. This causes the
process to be repeated for each time series in the object.