as_tsibble {tsibble}

Coerce to a tsibble object

Description

Usage

as_tsibble(
  x,
  key = NULL,
  index,
  regular = TRUE,
  validate = TRUE,
  .drop = TRUE,
  ...
)

## S3 method for class 'ts'
as_tsibble(x, ..., tz = "UTC")

## S3 method for class 'mts'
as_tsibble(x, ..., tz = "UTC", pivot_longer = TRUE)

Arguments

x

Other objects to be coerced to a tsibble (tbl_ts).

key

Variable(s) that uniquely determine time indices. NULL for empty key, and c() for multiple variables. It works with tidy selector (e.g. dplyr::starts_with()).

index

A variable to specify the time index variable.

regular

Regular time interval (TRUE) or irregular (FALSE). The interval is determined by the greatest common divisor of index column, if TRUE.

validate

TRUE suggests to verify that each key or each combination of key variables leads to unique time indices (i.e. a valid tsibble). If you are sure that it's a valid input, specify FALSE to skip the checks.

.drop

If TRUE, empty key groups are dropped.

...

Other arguments passed on to individual methods.

tz

Time zone. May be useful when a ts object is more frequent than daily.

pivot_longer

TRUE gives a "longer" form of the data, otherwise as is.

Details

A tsibble is sorted by its key first and index.

Value

A tsibble object.

Index

An extensive range of indices are supported by tsibble:

• native time classes in R (such as Date, POSIXct, and difftime)

• tsibble's new additions (such as yearweek, yearmonth, and yearquarter).

• other commonly-used classes: ordered, hms::hms, lubridate::period, and nanotime::nanotime.

For a tbl_ts of regular interval, a choice of index representation has to be made. For example, a monthly data should correspond to time index created by yearmonth, instead of Date or POSIXct. Because months in a year ensures the regularity, 12 months every year. However, if using Date, a month containing days ranges from 28 to 31 days, which results in irregular time space. This is also applicable to year-week and year-quarter.

Tsibble supports arbitrary index classes, as long as they can be ordered from past to future. To support a custom class, you need to define index_valid() for the class and calculate the interval through interval_pull().

Key

Key variable(s) together with the index uniquely identifies each record:

• Empty: an implicit variable. NULL resulting in a univariate time series.

• A single variable: For example, data(pedestrian) uses Sensor as the key.

• Multiple variables: For example, Declare key = c(Region, State, Purpose) for data(tourism). Key can be created in conjunction with tidy selectors like starts_with().

Interval

The interval function returns the interval associated with the tsibble.

• Regular: the value and its time unit including "nanosecond", "microsecond", "millisecond", "second", "minute", "hour", "day", "week", "month", "quarter", "year". An unrecognisable time interval is labelled as "unit".

• Irregular: as_tsibble(regular = FALSE) gives the irregular tsibble. It is marked with !.

• Unknown: Not determined (⁠?⁠), if it's an empty tsibble, or one entry for each key variable.

An interval is obtained based on the corresponding index representation:

• integerish numerics between 1582 and 2499: "year" (Y). Note the year of 1582 saw the beginning of the Gregorian Calendar switch.

• yearquarter: "quarter" (Q)

• yearmonth: "month" (M)

• yearweek: "week" (W)

• Date: "day" (D)

• difftime: "week" (W), "day" (D), "hour" (h), "minute" (m), "second" (s)

• POSIXt/hms: "hour" (h), "minute" (m), "second" (s), "millisecond" (us), "microsecond" (ms)

• period: "year" (Y), "month" (M), "day" (D), "hour" (h), "minute" (m), "second" (s), "millisecond" (us), "microsecond" (ms)

• nanotime: "nanosecond" (ns)

• other numerics &ordered (ordered factor): "unit" When the interval cannot be obtained due to the mismatched index format, an error is issued.

The interval is invariant to subsetting, such as filter(), slice(), and ⁠[.tbl_ts⁠. However, if the result is an empty tsibble, the interval is always unknown. When joining a tsibble with other data sources and aggregating to different time scales, the interval gets re-calculated.

See Also

tsibble

Examples

# coerce tibble to tsibble w/o a key
tbl1 <- tibble(
  date = as.Date("2017-01-01") + 0:9,
  value = rnorm(10)
)
as_tsibble(tbl1)
# supply the index to suppress the message
as_tsibble(tbl1, index = date)

# coerce tibble to tsibble with a single variable for key
# use `yearquarter()` to represent quarterly data
tbl2 <- tibble(
  qtr = rep(yearquarter("2010 Q1") + 0:9, 3),
  group = rep(c("x", "y", "z"), each = 10),
  value = rnorm(30)
)
# "qtr" is automatically considered as the index var
as_tsibble(tbl2, key = group)
as_tsibble(tbl2, key = group, index = qtr)

# create a tsibble with multiple variables for key
# use `yearmonth()` to represent monthly data
tbl3 <- tibble(
  mth = rep(yearmonth("2010 Jan") + 0:8, each = 3),
  xyz = rep(c("x", "y", "z"), each = 9),
  abc = rep(letters[1:3], times = 9),
  value = rnorm(27)
)
as_tsibble(tbl3, key = c(xyz, abc))
# coerce ts to tsibble
as_tsibble(AirPassengers)
as_tsibble(sunspot.year)
as_tsibble(sunspot.month)
as_tsibble(austres)
# coerce mts to tsibble
z <- ts(matrix(rnorm(300), 100, 3), start = c(1961, 1), frequency = 12)
as_tsibble(z)
as_tsibble(z, pivot_longer = FALSE)