Type:	Package
Title:	Class-Agnostic Time Series
Version:	0.4.2
Description:	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.
Imports:	data.table (≥ 1.10.0), anytime
Suggests:	testthat, dplyr, tibble, tidyr, forecast, seasonal, dygraphs, xts, ggplot2, scales, knitr, rmarkdown, tsibble (≥ 0.8.2), tsibbledata, tibbletime, tseries, units, zoo, tis, timeSeries, nycflights13, imputeTS, spelling, covr
License:	GPL-3
Encoding:	UTF-8
URL:	https://docs.ropensci.org/tsbox/, https://github.com/ropensci/tsbox
BugReports:	https://github.com/ropensci/tsbox/issues
RoxygenNote:	7.3.2
VignetteBuilder:	knitr
Depends:	R (≥ 2.10)
Config/testthat/parallel:	true
Config/testthat/edition:	3
Language:	en-US
NeedsCompilation:	no
Packaged:	2024-10-22 17:22:37 UTC; christophsax
Author:	Christoph Sax [aut, cre], Cathy Chamberlin [rev], Nunes Matt [rev]
Maintainer:	Christoph Sax <christoph.sax@gmail.com>
Repository:	CRAN
Date/Publication:	2024-10-22 18:00:02 UTC

tsbox: Class-Agnostic Time Series

Description

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.

Details

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.

Author(s)

Christoph Sax christoph.sax@gmail.com

See Also

Useful links:

• https://docs.ropensci.org/tsbox/

• https://github.com/ropensci/tsbox

• Report bugs at https://github.com/ropensci/tsbox/issues

Re-Class ts-Boxable Object

Description

Copies class attributes from an existing ts-boxable series. Mainly used internally.

Usage

copy_class(
  x,
  template,
  preserve.mode = TRUE,
  preserve.names = FALSE,
  preserve.time = FALSE,
  preserve.attr = TRUE
)

Arguments

Details

Inspired by xts::reclass, which does something similar.

Value

a ts-boxable object of the same class as template, i.e., an object of class ts, xts, zoo, data.frame, data.table, tbl, tbl_ts, tbl_time, tis, irts or timeSeries.

Examples

copy_class(mdeaths, ts_tbl(fdeaths))

Extract Relevant Class

Description

Mainly used internally.

Usage

relevant_class(x)

Arguments

Value

character, the relevant class of ts-boxable object

Examples

relevant_class(AirPassengers)
x <- ts_df(AirPassengers)
relevant_class(x)

Constructing ts-Functions

Description

ts_ turns an existing function into a function that can deal with ts-boxable time series objects.

Usage

load_suggested(pkg)

ts_(fun, class = "ts", vectorize = FALSE, reclass = TRUE)

ts_apply(x, fun, ...)

Arguments

Details

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. ts_ deals with the conversion stuff, 'vectorizes' the function so that it can be used with multiple time series.

Value

A function that accepts ts-boxable time series as an input.

See Also

ts_examples, for a few useful examples of functions generated by ts_.

Vignette on how to make arbitrary functions ts-boxable.

Examples

ts_(rowSums)(ts_c(mdeaths, fdeaths))
ts_plot(mean = ts_(rowMeans)(ts_c(mdeaths, fdeaths)), mdeaths, fdeaths)
ts_(function(x) predict(prcomp(x)))(ts_c(mdeaths, fdeaths))
ts_(function(x) predict(prcomp(x, scale = TRUE)))(ts_c(mdeaths, fdeaths))
ts_(dygraphs::dygraph, class = "xts")

# attach series to serach path
ts_attach <- ts_(attach, class = "tslist", reclass = FALSE)
ts_attach(EuStockMarkets)
ts_plot(DAX, SMI)
detach()

Arithmetic Operators for ts-boxable objects

Description

Arithmetic Operators for ts-boxable objects

Usage

e1 %ts+% e2

e1 %ts-% e2

e1 %ts*% e2

e1 %ts/% e2

Arguments

Value

a ts-boxable time series, with the same class as the left input.

Examples

head(fdeaths - mdeaths)
head(fdeaths %ts-% mdeaths)
head(ts_df(fdeaths) %ts-% mdeaths)

Bind Time Series

Description

Combine time series to a new, single time series. ts_bind combines time series as they are, ts_chain chains them together, using percentage change rates.

Usage

ts_bind(...)

ts_chain(...)

Arguments

Details

In data frame objects, multiple time series are stored in a long data frame. In ts and xts objects, time series are combined horizontally.

Value

a ts-boxable object of the same class as the input, i.e., an object of class ts, xts, zoo, zooreg, data.frame, data.table, tbl, tbl_ts, tbl_time, tis, irts or timeSeries. If series of different classes are combined, the class of the first series is used (if possible).

See Also

ts_c to collect multiple time series

Examples

ts_bind(ts_span(mdeaths, end = "1975-12-01"), fdeaths)
ts_bind(mdeaths, c(2, 2))
ts_bind(mdeaths, 3, ts_bind(fdeaths, c(99, 2)))
ts_bind(ts_dt(mdeaths), AirPassengers)

# numeric vectors
ts_bind(12, AirPassengers, c(2, 3))
ts_chain(ts_span(mdeaths, end = "1975-12-01"), fdeaths)

ts_plot(ts_pc(ts_c(
  comb = ts_chain(ts_span(mdeaths, end = "1975-12-01"), fdeaths),
  fdeaths
)))

Test if an Object is ts-Boxable

Description

Mainly used internally.

Usage

ts_boxable(x)

check_ts_boxable(x)

Arguments

Value

logical, either TRUE or FALSE. check_ts_boxable() fails if not TRUE

Examples

ts_boxable(AirPassengers)
ts_boxable(lm)

Collect Time Series

Description

Collect time series as multiple time series.

Usage

ts_c(...)

Arguments

Details

In data frame objects, multiple time series are stored in a long data frame. In ts and xts objects, time series are combined horizontally.

Value

a ts-boxable object of the same class as the input, i.e., an object of class ts, xts, zoo, zooreg, data.frame, data.table, tbl, tbl_ts, tbl_time, tis, irts or timeSeries. If series of different classes are combined, the class of the first series is used (if possible).

See Also

ts_bind, to bind multiple time series to a single series.

Examples

ts_c(mdeaths, fdeaths)

ts_c(ts_df(EuStockMarkets), AirPassengers)

# labeling
x1 <- ts_c(
  `International Airline Passengers` = ts_xts(AirPassengers),
  `Deaths from Lung Diseases` = ldeaths
)
head(x1)

Default Column Names

Description

In data frame objects (data.frame, tibble, data.table), tsbox automatically detects the time and the value column. This function changes the column names to the defaults (time, value), so that auto-detection can be avoided in future operations.

Usage

ts_default(x)

Arguments

Value

a ts-boxable object of the same class as x, i.e., an object of class ts, xts, zoo, zooreg, data.frame, data.table, tbl, tbl_ts, tbl_time, tis, irts or timeSeries.

Examples

df <- ts_df(ts_c(mdeaths, fdeaths))
# non-default colnames
colnames(df) <- c("id", "date", "count")
# switch back to default colnames
ts_default(df)

Internal Time Series Class

Description

In data frame objects (data.frame, tibble, data.table), tsbox automatically detects the time and the value column. This function changes the column names to the defaults (time, value), so that auto-detection can be avoided in future operations.

Usage

ts_dts(x)

Arguments

Value

a ts-boxable object of the same class as x, i.e., an object of class ts, xts, zoo, zooreg, data.frame, data.table, tbl, tbl_ts, tbl_time, tis, irts or timeSeries.

Examples

df <- ts_df(ts_c(mdeaths, fdeaths))
# non-default colnames
colnames(df) <- c("id", "date", "count")
# switch back to default colnames
ts_default(df)

Principal Components, Dygraphs, Forecasts, Seasonal Adjustment

Description

Example Functions, Generated by ts_. ts_prcomp calculates the principal components of multiple time series, ts_dygraphs generates an interactive graphical visualization, ts_forecast return an univariate forecast, ts_seas the seasonally adjusted series. ts_na_interpolation imputes missing values.

Usage

ts_prcomp(x, ...)

ts_dygraphs(x, ...)

ts_forecast(x, ...)

ts_seas(x, ...)

ts_na_interpolation(x, ...)

Arguments

Details

With the exception of ts_prcomp, these functions depend on external packages.

Value

a ts-boxable object of the same class as x, i.e., an object of class ts, xts, zoo, zooreg, data.frame, data.table, tbl, tbl_ts, tbl_time, tis, irts or timeSeries.

See Also

Vignette on how to make arbitrary functions ts-boxable.

Examples

ts_plot(
  ts_scale(ts_c(
    Male = mdeaths,
    Female = fdeaths,
    `First principal compenent` = -ts_prcomp(ts_c(mdeaths, fdeaths))[, 1]
  )),
  title = "Deaths from lung diseases",
  subtitle = "Normalized values"
)

ts_plot(ts_c(
  male = mdeaths, female = fdeaths,
  ts_forecast(ts_c(`male (fct)` = mdeaths, `female (fct)` = fdeaths))
),
title = "Deaths from lung diseases",
subtitle = "Exponential smoothing forecast"
)

ts_plot(
  `Raw series` = AirPassengers,
  `Adjusted series` = ts_seas(AirPassengers),
  title = "Airline passengers",
  subtitle = "X-13 seasonal adjustment"
)


# See ?imputeTS::na_interpolation for options
dta <- ts_c(mdeaths, fdeaths)
dta[c(1, 3, 10), c(1, 2)] <- NA
head(ts_na_interpolation(dta, option = "spline"))

ts_dygraphs(ts_c(mdeaths, EuStockMarkets))

Use First Date of a Period

Description

Replace date or time values by the first of the period. tsbox usually relies on timestamps being the first value of a period.

Usage

ts_first_of_period(x)

Arguments

Value

a ts-boxable object of the same class as x, i.e., an object of class ts, xts, zoo, zooreg, data.frame, data.table, tbl, tbl_ts, tbl_time, tis, irts or timeSeries.

Examples

x <- ts_c(
  a = ts_lag(ts_df(mdeaths), "14 days"),
  b = ts_lag(ts_df(mdeaths), "-2 days")
)
ts_first_of_period(x)
ts_first_of_period(ts_lag(ts_df(austres), "14 days"))

Change Frequency

Description

Changes the frequency of a time series. By default, incomplete periods of regular series are omitted.

Usage

ts_frequency(
  x,
  to = c("year", "quarter", "month", "week", "day", "hour", "min", "sec"),
  aggregate = "mean",
  na.rm = FALSE
)

Arguments

Details

The tempdisagg package can convert low frequency to high frequency data and has support for ts-boxable objects. See vignette("hf-disagg", package = "tempdisagg").

Value

a ts-boxable time series, with the same class as the input.

Examples

ts_frequency(cbind(mdeaths, fdeaths), "year", "sum")
ts_frequency(cbind(mdeaths, fdeaths), "quarter", "last")

ts_frequency(AirPassengers, 4, "sum")

# Note that incomplete years are omited by default
ts_frequency(EuStockMarkets, "year")
ts_frequency(EuStockMarkets, "year", na.rm = TRUE)

Plot Time Series, Using ggplot2

Description

ts_ggplot() has the same syntax and produces a similar plot as ts_plot(), but uses the ggplot2 graphic system, and can be customized. With theme_tsbox() and scale_color_tsbox(), the output of ts_ggplot has a similar look and feel.

Usage

ts_ggplot(..., title, subtitle, ylab = "")

theme_tsbox(base_family = getOption("ts_font", ""), base_size = 12)

colors_tsbox()

scale_color_tsbox(...)

scale_fill_tsbox(...)

Arguments

Details

Both ts_plot() and ts_ggplot() combine multiple ID dimensions into a single dimension. To plot multiple dimensions in different shapes, facets, etc., use standard ggplot (see examples).

See Also

ts_plot(), for a simpler and faster plotting function. ts_dygraphs(), for interactive time series plots.

Examples

# using the ggplot2 graphic system
p <- ts_ggplot(total = ldeaths, female = fdeaths, male = mdeaths)
p

# with themes for the look and feel of ts_plot()
p + theme_tsbox() + scale_color_tsbox()

# also use themes with standard ggplot
suppressMessages(library(ggplot2))
df <- ts_df(ts_c(total = ldeaths, female = fdeaths, male = mdeaths))
ggplot(df, aes(x = time, y = value)) +
  facet_wrap("id") +
  geom_line() +
  theme_tsbox() +
  scale_color_tsbox()


## Not run: 
library(dataseries)
dta <- ds(c("GDP.PBRTT.A.R", "CCI.CCIIR"), "xts")
ts_ggplot(ts_scale(ts_span(
  ts_c(
    `GDP Growth` = ts_pc(dta[, "GDP.PBRTT.A.R"]),
    `Consumer Sentiment Index` = dta[, "CCI.CCIIR"]
  ),
  start = "1995-01-01"
))) +
  ggplot2::ggtitle("GDP and Consumer Sentiment", subtitle = "normalized") +
  theme_tsbox() +
  scale_color_tsbox()

## End(Not run)

Indices from Levels or Percentage Rates

Description

ts_index returns an indexed series, with value of 1 at the base date or range. ts_compound builds an index from percentage change rates, starting with 1 and compounding the rates.

Usage

ts_compound(x, denominator = 100)

ts_index(x, base = NULL)

Arguments

Value

a ts-boxable object of the same class as x, i.e., an object of class ts, xts, zoo, zooreg, data.frame, data.table, tbl, tbl_ts, tbl_time, tis, irts or timeSeries.

Examples

x <- ts_pc(ts_c(fdeaths, mdeaths))
ts_compound(x)
y <- ts_df(ts_c(fdeaths, mdeaths))
ts_index(y, "1974-02-01")

ts_plot(
  `My Expert Knowledge` = ts_chain(
    mdeaths,
    ts_compound(ts_bind(ts_pc(mdeaths), 15, 23, 33))
  ),
  `So Far` = mdeaths,
  title = "A Very Manual Forecast"
)

# mean of 1974 = 1
ts_index(mdeaths, c("1974-01-01", "1974-12-31"))

Lag or Lead of Time Series

Description

Shift time stamps in ts-boxable time series, either by a number of periods or by a fixed amount of time.

Usage

ts_lag(x, by = 1)

Arguments

Details

The lag order, by, is defined the opposite way as in R base. Thus, -1 is a lead and +1 a lag.

If by is integer, the time stamp is shifted by the number of periods. This requires the series to be regular.

If by is character, the time stamp is shifted by a specific amount of time. This can be one of one of "sec", "min", "hour", "day", "week", "month", ⁠"quarter" or ⁠"year", optionally preceded by a (positive or negative) integer and a space, or followed by plural "s". This is passed to base::seq.Date(). This does not require the series to be regular.

Value

a ts-boxable object of the same class as x, i.e., an object of class ts, xts, zoo, zooreg, data.frame, data.table, tbl, tbl_ts, tbl_time, tis, irts or timeSeries.

Examples

ts_plot(AirPassengers, ts_lag(AirPassengers), title = "The need for glasses")

ts_lag(fdeaths, "1 month")
ts_lag(fdeaths, "1 year")
x <- ts_df(fdeaths)
ts_lag(x, "2 day")
ts_lag(x, "2 min")
ts_lag(x, "-1 day")

Reshaping Multiple Time Series

Description

Functions to reshape multiple time series from 'wide' to 'long' and vice versa. Note that long format data frames are ts-boxable objects, where wide format data frames are not. ts_long automatically identifies a time column, and uses columns on the left as id columns.

Usage

ts_long(x)

ts_wide(x)

Arguments

Value

a ts-boxable object of the same class as x, i.e., an object of class ts, xts, zoo, zooreg, data.frame, data.table, tbl, tbl_ts, tbl_time, tis, irts or timeSeries.

Examples

x <- ts_df(ts_c(mdeaths, fdeaths))
df.wide <- ts_wide(x)
df.wide
ts_long(df.wide)

Omit NA values

Description

Remove NA values in ts-boxable objects, turning explicit into implicit missing values.

Usage

ts_na_omit(x)

Arguments

Details

Note that internal NAs in ts time series will not be removed, as this conflicts with the regular structure.

Value

a ts-boxable object of the same class as x, i.e., an object of class ts, xts, zoo, zooreg, data.frame, data.table, tbl, tbl_ts, tbl_time, tis, irts or timeSeries.

See Also

ts_regular, for the opposite, turning implicit into explicit missing values.

Examples

x <- AirPassengers
x[c(2, 4)] <- NA

# A ts object does only know explicit NAs
ts_na_omit(x)

# by default, NAs are implicit in data frames
ts_df(x)

# make NAs explicit
ts_regular(ts_df(x))

# and implicit again
ts_na_omit(ts_regular(ts_df(x)))

First Differences and Percentage Change Rates

Description

ts_pcy and ts_diffy calculate the percentage change rate and the difference compared to the previous period, ts_pcy and ts_diffy calculate the percentage change rate compared to the same period of the previous year. ts_pca calculates annualized percentage change rates compared to the previous period.

Usage

ts_pc(x)

ts_diff(x)

ts_pca(x)

ts_pcy(x)

ts_diffy(x)

Arguments

Value

a ts-boxable object of the same class as x, i.e., an object of class ts, xts, zoo, zooreg, data.frame, data.table, tbl, tbl_ts, tbl_time, tis, irts or timeSeries.

Examples

x <- ts_c(fdeaths, mdeaths)
ts_diff(x)
ts_pc(x)
ts_pca(x)
ts_pcy(x)
ts_diffy(x)

Pick Series (Experimental)

Description

Pick (and optionally rename) series from multiple time series.

Usage

ts_pick(x, ...)

Arguments

Value

a ts-boxable object of the same class as x, i.e., an object of class ts, xts, zoo, zooreg, data.frame, data.table, tbl, tbl_ts, tbl_time, tis, irts or timeSeries.

Examples

# Interactive use

ts_plot(ts_pick(
  EuStockMarkets,
  `My Dax` = "DAX",
  `My Smi` = "SMI"
))
ts_pick(EuStockMarkets, c(1, 2))
ts_pick(EuStockMarkets, `My Dax` = "DAX", `My Smi` = "SMI")

# Programming use
to.be.picked.and.renamed <- c(`My Dax` = "DAX", `My Smi` = "SMI")
ts_pick(EuStockMarkets, to.be.picked.and.renamed)

Plot Time Series

Description

ts_plot() is a fast and simple plotting function for ts-boxable time series, with limited customizability. For more theme options, use ts_ggplot().

Usage

ts_plot(..., title, subtitle, ylab = "", family = getOption("ts_font", "sans"))

Arguments

Details

Both ts_plot() and ts_ggplot() combine multiple ID dimensions into a single dimension. To plot multiple dimensions in different shapes, facets, etc., use standard ggplot.

Limited customizability of ts_plot is available via options. See examples.

See Also

ts_ggplot(), for a plotting function based on ggplot2. ts_dygraphs(), for interactive time series plots. ts_save() to save a plot to the file system.

Examples

ts_plot(
  AirPassengers,
  title = "Airline passengers",
  subtitle = "The classic Box & Jenkins airline data"
)

# naming arguments
ts_plot(total = ldeaths, female = fdeaths, male = mdeaths)

# using different ts-boxable objects
ts_plot(ts_scale(ts_c(
  ts_xts(airmiles),
  ts_tbl(co2),
  JohnsonJohnson,
  ts_df(discoveries)
)))

# customize ts_plot
op <- options(
  tsbox.lwd = 3,
  tsbox.col = c("gray51", "gray11"),
  tsbox.lty = "dashed"
)
ts_plot(
  "Female" = fdeaths,
  "Male" = mdeaths
)
options(op) # restore defaults

Enforce Regularity

Description

Enforces regularity in data frame and xts objects, by turning implicit NAs into explicit NAs. In ts objects, regularity is automatically enforced.

Usage

ts_regular(x, fill = NA)

Arguments

Value

a ts-boxable object of the same class as x, i.e., an object of class ts, xts, zoo, zooreg, data.frame, data.table, tbl, tbl_ts, tbl_time, tis, irts or timeSeries.

Examples

x0 <- AirPassengers
x0[c(10, 15)] <- NA
x <- ts_na_omit(ts_dts(x0))
ts_regular(x)
ts_regular(x, fill = 0)

m <- mdeaths
m[c(10, 69)] <- NA
f <- fdeaths
f[c(1, 3, 15)] <- NA

ts_regular(ts_na_omit(ts_dts(ts_c(f, m))))

Save Previous Plot

Description

Save Previous Plot

Usage

ts_save(
  filename = tempfile(fileext = ".pdf"),
  width = 10,
  height = 5,
  device = NULL,
  open = TRUE
)

Arguments

Value

invisible TRUE, if successful

Examples

ts_plot(AirPassengers)
tf <- tempfile(fileext = ".pdf")
ts_save(tf)
unlink(tf)

Scale and Center Time Series

Description

Subtract mean (sum(x)/n) and divide by standard deviation (sqrt(sum(x^2)/(n-1))). Based on base::scale().

Usage

ts_scale(x, center = TRUE, scale = TRUE)

Arguments

Value

a ts-boxable object of the same class as x, i.e., an object of class ts, xts, zoo, zooreg, data.frame, data.table, tbl, tbl_ts, tbl_time, tis, irts or timeSeries.

Examples

ts_plot(ts_scale((ts_c(airmiles, co2, JohnsonJohnson, discoveries))))
ts_plot(ts_scale(ts_c(AirPassengers, DAX = EuStockMarkets[, "DAX"])))

Limit Time Span

Description

Filter time series for a time span.

Usage

ts_span(x, start = NULL, end = NULL, template = NULL, extend = FALSE)

Arguments

Details

All date and times, when entered as character strings, are processed by anytime::anydate() or anytime::anytime(). Thus a wide range of inputs are possible. See examples.

start and end can be specified relative to each other, using one of "sec", "min", "hour", "day", "week", "month", ⁠"quarter" or ⁠"year", or an abbreviation. If the series are of the same frequency, the shift can be specified in periods. See examples.

Value

a ts-boxable object of the same class as x, i.e., an object of class ts, xts, zoo, zooreg, data.frame, data.table, tbl, tbl_ts, tbl_time, tis, irts or timeSeries.

Examples

# use 'anytime' shortcuts
ts_span(mdeaths, start = "1979")       # shortcut for 1979-01-01
ts_span(mdeaths, start = "1979-4")     # shortcut for 1979-04-01
ts_span(mdeaths, start = "197904")     # shortcut for 1979-04-01

# it's fine to use an to date outside of series span
ts_span(mdeaths, end = "2001-01-01")

# use strings to set start or end relative to each other

ts_span(mdeaths, start = "-7 month")   # last 7 months
ts_span(mdeaths, start = -7)           # last 7 periods
ts_span(mdeaths, start = -1)           # last single value
ts_span(mdeaths, end = "1e4 hours")    # first 10000 hours


ts_plot(
  ts_span(mdeaths, start = "-3 years"),
  title = "Three years ago",
  subtitle = "The last three years of available data"
)

ts_ggplot(
  ts_span(mdeaths, end = "28 weeks"),
  title = "28 weeks later",
  subtitle = "The first 28 weeks of available data"
) + theme_tsbox() + scale_color_tsbox()


# Limit span of 'discoveries' to the same span as 'AirPassengers'
ts_span(discoveries, template = AirPassengers)
ts_span(mdeaths, end = "19801201", extend = TRUE)

Time Series Properties

Description

Extract time series properties, such as the number of observations (obs), the time differences between observations (obs), the number of observations per year (freq), and the start time stamp (start) and the end time stamp (end) of the series.

Usage

ts_summary(x, spark = FALSE)

Arguments

Value

ts_summary returns a data.frame. Individual column can be accessed through the $ notation (see examples).

Examples

ts_summary(ts_c(mdeaths, austres))
ts_summary(ts_c(mdeaths, austres), spark = TRUE)
# Extracting specific properties
ts_summary(AirPassengers)$start
ts_summary(AirPassengers)$freq
ts_summary(AirPassengers)$obs

Loess Trend Estimation

Description

Trend estimation that uses stats::loess().

Usage

ts_trend(x, ...)

Arguments

Value

a ts-boxable object of the same class as x, i.e., an object of class ts, xts, zoo, zooreg, data.frame, data.table, tbl, tbl_ts, tbl_time, tis, irts or timeSeries.

References

Cleveland, William S., Eric Grosse, and William M. Shyu. "Local regression models." Statistical models in S. Routledge, 2017. 309-376.

Examples

ts_plot(
  `Raw series` = fdeaths,
  `Loess trend` = ts_trend(fdeaths),
  title = "Deaths from Lung Diseases",
  subtitle = "per month"
)

Convert Everything to Everything

Description

tsbox is built around a set of converters, which convert time series stored as ts, xts, zoo, zooreg, data.frame, data.table, tbl, tbl_ts, tbl_time, tis, irts or timeSeries to each other.

Usage

ts_data.frame(x)

ts_df(x)

ts_data.table(x)

ts_dt(x)

ts_tbl(x)

ts_tibbletime(x)

ts_timeSeries(x)

ts_tis(x)

ts_ts(x)

ts_irts(x)

ts_tsibble(x)

ts_tslist(x)

ts_xts(x)

ts_zoo(x)

ts_zooreg(x)

Arguments

Details

In data frames, multiple time series will be stored in a 'long' format. tsbox detects a value, a time and zero to several id columns. Column detection is done in the following order:

1. Starting on the right, the first first numeric or integer column is used as value column.

2. Using the remaining columns, and starting on the right again, the first Date, POSIXct, numeric or character column is used as time column. character strings are parsed by anytime::anytime(). The time stamp, time, indicates the beginning of a period.

3. All remaining columns are id columns. Each unique combination of id columns points to a time series.

Alternatively, the time column and the value column to be explicitly named as time and value. If explicit names are used, the column order will be ignored.

Whenever possible, tsbox relies on heuristic time conversion. When a monthly "ts" time series, e.g., AirPassengers, is converted to a data frame, each time stamp (of class "Date") is the first day of the month. In most circumstances, this reflects the actual meaning of the data stored in a "ts" object. Technically, of course, this is not correct: "ts" objects divide time in period of equal length, while in reality, February is shorter than January. Heuristic conversion is done for frequencies of 0.1 (decades), 1 (years), 4 (quarters) and 12 (month).

For other frequencies, e.g. 260, of EuStockMarkets, tsbox uses exact time conversion. The year is divided into 260 equally long units, and time stamp of a period will be a point in time (of class "POSIXct").

Value

ts-boxable time series of the desired class, i.e., an object of class ts, xts, zoo, zooreg, data.frame, data.table, tbl, tbl_ts, tbl_time, tis, irts or timeSeries.

Examples

x.ts <- ts_c(mdeaths, fdeaths)
x.ts
ts_df(x.ts)

suppressMessages(library(dplyr))
ts_tbl(x.ts)

suppressMessages(library(data.table))
ts_dt(x.ts)

suppressMessages(library(xts))
ts_xts(x.ts)

# heuristic time conversion
# 1 month: approx. 1/12 year
ts_df(AirPassengers)

# exact time conversion
# 1 trading day: exactly 1/260 year
ts_df(EuStockMarkets)

# multiple ids
a <- ts_df(ts_c(fdeaths, mdeaths))
a$type <- "level"
b <- ts_pc(a)
b$type <- "pc"
multi.id.df <- rbind(a, b)

ts_ts(multi.id.df)
ts_plot(multi.id.df)

Start and end of time series

Description

In data frame objects (data.frame, tibble, data.table), tsbox automatically detects the time and the value column. This function changes the column names to the defaults (time, value), so that auto-detection can be avoided in future operations.

Usage

ts_start(x)

ts_end(x)

Arguments

Value

a ts-boxable object of the same class as x, i.e., an object of class ts, xts, zoo, zooreg, data.frame, data.table, tbl, tbl_ts, tbl_time, tis, irts or timeSeries.

Examples

df <- ts_df(ts_c(mdeaths, fdeaths))
# non-default colnames
colnames(df) <- c("id", "date", "count")
# switch back to default colnames
ts_default(df)