The R ecosystem knows a vast number of time series classes: ts, xts, zoo, tsibble, tibbletime, tis, or timeSeries. The plethora of standards causes confusion. As different packages rely on different classes, it is hard to use them in the same analysis. tsbox provides a set of tools that make it easy to switch between these classes. It also allows the user to treat time series as plain data frames, facilitating the use with tools that assume rectangular data.
Christoph Sax christoph.sax@gmail.com
The package is built around a set of functions that convert time series of different classes to each other. They are frequency-agnostic, and allow the user to combine multiple non-standard and irregular frequencies. Because coercion works reliably, it is easy to write functions that work identically for all classes. So whether we want to smooth, scale, differentiate, chain-link, forecast, regularize or seasonally adjust a time series, we can use the same tsbox-command for any time series classes.
The best way to start is to check out the package website.
In the ropensci classification, this package is An improvement on other
implementations of similar algorithms in R. Many time series packages,
e.g., zoo or
tsibble contain converter
functions from one class to another. They often convert from their class
to ts objects and back, but lack converters to other time series class.
In most cases, tsbox transforms an object into an augmented data.table. And
uses the data.table infrastructure for efficient joining and reshaping. After
computation, it restores the original input class. This restoring feature is
was also used in the xts::reclass() function of the
xts package.