Package: tsbox 0.4.2
Christoph Sax
tsbox: Class-Agnostic Time Series
Time series toolkit with identical behavior for all time series classes: 'ts','xts', 'data.frame', 'data.table', 'tibble', 'zoo', 'timeSeries', 'tsibble', 'tis' or 'irts'. Also converts reliably between these classes.
Authors:
tsbox_0.4.2.tar.gz
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tsbox.pdf |tsbox.html✨
tsbox/json (API)
NEWS
| # Install 'tsbox' in R: |
| install.packages('tsbox', repos = c('https://packages.ropensci.org', 'https://cloud.r-project.org')) |
Reviews:rOpenSci Software Review #464
Bug tracker:https://github.com/ropensci/tsbox/issues
Pkgdown site:https://docs.ropensci.org
On CRAN:tsbox-0.4.2(2024-10-22)
Last updated 5 months agofrom:0a80208f0f (on main). Checks:9 OK. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 14 2025 |
| R-4.5-win | OK | Mar 14 2025 |
| R-4.5-mac | OK | Mar 14 2025 |
| R-4.5-linux | OK | Mar 14 2025 |
| R-4.4-win | OK | Mar 14 2025 |
| R-4.4-mac | OK | Mar 14 2025 |
| R-4.4-linux | OK | Mar 14 2025 |
| R-4.3-win | OK | Mar 14 2025 |
| R-4.3-mac | OK | Mar 14 2025 |
Exports:%ts-%%ts*%%ts/%%ts+%check_ts_boxablecolors_tsboxcopy_classload_suggestedrelevant_classscale_color_tsboxscale_fill_tsboxtheme_tsboxts_ts_applyts_bindts_boxablets_cts_chaints_compoundts_data.framets_data.tablets_defaultts_dfts_diffts_diffyts_dtts_dtsts_dygraphsts_endts_first_of_periodts_forecastts_frequencyts_ggplotts_indexts_irtsts_lagts_longts_na_interpolationts_na_omitts_pcts_pcats_pcyts_pickts_plotts_prcompts_regularts_savets_scalets_seasts_spants_startts_summaryts_tblts_tibbletimets_timeSeriests_tists_trendts_tsts_tsibblets_tslistts_widets_xtsts_zoots_zooreg
Dependencies:anytimeBHdata.tableRcpp
Introduction to tsbox
Rendered fromtsbox.Rmdusingknitr::rmarkdownon Mar 14 2025.Last update: 2023-03-07
Started: 2018-02-18
Time conversion and regularization
Rendered fromconvert.Rmdusingknitr::rmarkdownon Mar 14 2025.Last update: 2021-07-26
Started: 2018-02-25
User defined ts-functions
Rendered fromts-functions.Rmdusingknitr::rmarkdownon Mar 14 2025.Last update: 2021-07-26
Started: 2018-02-25
Citation
To cite tsbox in publications use:
Sax C (2021). tsbox: Class-Agnostic Time Series in in R. R package, https://docs.ropensci.org/tsbox/.
Corresponding BibTeX entry:
@Manual{,
title = {tsbox: Class-Agnostic Time Series in in {R}},
author = {Christoph Sax},
note = {{R package}},
url = {https://docs.ropensci.org/tsbox/},
year = {2021},
}
Readme and manuals
tsbox: Class-Agnostic Time Series in R
The R ecosystem knows a vast number of time series standards. Instead of creating the ultimate 15th time series class, tsbox provides a set of tools that are agnostic towards the existing standards. The tools also allow you to handle time series as plain data frames, thus making it easy to deal with time series in a dplyr or data.table workflow.
See tsbox.help for the full documentation of the package.
To install the stable version from CRAN:
install.packages("tsbox")
To install the development version:
# install.packages("remotes")
# remotes::install_github("ropensci/tsbox")
install.packages("tsbox", repos = c('https://ropensci.r-universe.dev', 'https://cloud.r-project.org'))
install.packages("ropensci/tsbox", repos = "https://ropensci.r-universe.dev")
Convert everything to everything
tsbox is built around a set of converters, which convert time series stored as ts, xts, data.frame, data.table, tibble, zoo, zooreg, tsibble, tibbletime, timeSeries, irts or tis to each other:
library(tsbox)
x.ts <- ts_c(fdeaths, mdeaths)
x.xts <- ts_xts(x.ts)
x.df <- ts_df(x.xts)
x.dt <- ts_dt(x.df)
x.tbl <- ts_tbl(x.dt)
x.zoo <- ts_zoo(x.tbl)
x.zooreg <- ts_zoo(x.zoo)
x.tsibble <- ts_tsibble(x.zooreg)
x.tibbletime <- ts_tibbletime(x.tsibble)
x.timeSeries <- ts_timeSeries(x.tibbletime)
x.irts <- ts_irts(x.tibbletime)
x.tis <- ts_tis(x.irts)
all.equal(ts_ts(x.tis), x.ts)
#> [1] TRUE
Use same functions for time series classes
Because this works reliably, it is easy to write functions that work for all classes. So whether we want to smooth, scale, differentiate, chain, forecast, regularize or seasonally adjust a time series, we can use the same commands to whatever time series class at hand:
ts_trend(x.ts)
ts_pc(x.xts)
ts_pcy(x.df)
ts_lag(x.dt)
Time series of the world, unite!
A set of helper functions makes it easy to combine or align multiple time series of all classes:
# collect time series as multiple time series
ts_c(ts_dt(EuStockMarkets), AirPassengers)
ts_c(EuStockMarkets, mdeaths)
# combine time series to a new, single time series
ts_bind(ts_dt(mdeaths), AirPassengers)
ts_bind(ts_xts(AirPassengers), ts_tbl(mdeaths))
And plot just about everything
Plotting all kinds of classes and frequencies is as simple as it should be. And we finally get a legend!
ts_plot(ts_scale(ts_c(mdeaths, austres, AirPassengers, DAX = EuStockMarkets[ ,'DAX'])))
Cheatsheet
