The tsibble package extends the tidyverse to temporal data. Built on top of the tibble, a tsibble (or
tbl_ts) is a data- and model-oriented object. Compared to the conventional time series objects in R, for example
xts, the tsibble preserves time indices as the essential data column and makes heterogeneous data structures possible. Beyond the tibble-like representation, key comprised of single or multiple variables is introduced to uniquely identify observational units over time (index). The tsibble package aims at managing temporal data and getting analysis done in a fluent workflow.
tsibble() creates a tsibble object, and
as_tsibble() is an S3 method to coerce other objects to a tsibble. An object that a vector/matrix underlies, such as
mts, can be automated to a tsibble using
as_tsibble() without any specification. If it is a tibble or data frame,
as_tsibble() requires a little more setup in order to declare the index and key variables.
#> # A tibble: 26,115 x 5 #> origin time_hour temp humid precip #> <chr> <dttm> <dbl> <dbl> <dbl> #> 1 EWR 2013-01-01 01:00:00 39.0 59.4 0 #> 2 EWR 2013-01-01 02:00:00 39.0 61.6 0 #> 3 EWR 2013-01-01 03:00:00 39.0 64.4 0 #> 4 EWR 2013-01-01 04:00:00 39.9 62.2 0 #> 5 EWR 2013-01-01 05:00:00 39.0 64.4 0 #> # … with 26,110 more rows
weather data included in the package
nycflights13 contains the hourly meteorological records (such as temperature, humid and precipitation) over the year of 2013 at three stations (i.e. JFK, LGA and EWR) in New York City. Since the
time_hour is the only column involving the timestamps,
as_tsibble() defaults it to the index variable; alternatively, the index can be specified by the argument
index = time_hour to disable the verbose message.
Except for index, a tsibble requires “key”, which defines subjects or individuals measured over time. In this example, the
origin variable is the identifier, which is passed to the argument
as_tsibble(). Each observation should be uniquely identified by index and key in a valid tsibble. Others—
precip—are referred to as measured variables. When creating a tsibble, the key will be sorted first, followed by arranging time from past to recent.
weather_tsbl <- as_tsibble(weather, key = origin)
#> Using `time_hour` as index variable.
#> # A tsibble: 26,115 x 5 [1h] <America/New_York> #> # Key: origin  #> origin time_hour temp humid precip #> <chr> <dttm> <dbl> <dbl> <dbl> #> 1 EWR 2013-01-01 01:00:00 39.0 59.4 0 #> 2 EWR 2013-01-01 02:00:00 39.0 61.6 0 #> 3 EWR 2013-01-01 03:00:00 39.0 64.4 0 #> 4 EWR 2013-01-01 04:00:00 39.9 62.2 0 #> 5 EWR 2013-01-01 05:00:00 39.0 64.4 0 #> # … with 26,110 more rows
An interval is automatically obtained based on the corresponding time representation:
ordered: either “unit” or “year” (
yearqtr: “quarter” (
yearmon: “month” (
yearweek: “week” (
Date: “day” (
difftime: “week” (
W), “day” (D), “hour” (
h), “minute” (
m), “second” (
hms: “hour” (
h), “minute” (
m), “second” (
s), “millisecond” (
us), “microsecond” (
nanotime: “nanosecond” (
That is, a tsibble of monthly intervals expects the
yearmon class in the index column. Neither
POSIXct gives a monthly tsibble.
The print display is data-centric and contextually informative, such as data dimension, time interval, and the number of time-based units. Above displays the
weather_tsbl its one-hour interval (
[1h]) and the
origin  as the key along with three time series in the table.
This tidy data representation most naturally supports thinking of operations on the data as building blocks, forming part of a “data pipeline” in time-based context. Users who are familiar with tidyverse would find it easier to perform common temporal analysis tasks. For example,
index_by() is the counterpart of
group_by() in temporal context, but it only groups the time index.
summarise() is used to summarise daily highs and lows at each station. As a result, the index is updated to the
date with one-day interval from the index
time_hour; two new variables are created and computed for daily maximum and minimum temperatures.
#> # A tsibble: 1,092 x 4 [1D] #> # Key: origin  #> origin date temp_high temp_low #> <chr> <date> <dbl> <dbl> #> 1 EWR 2013-01-01 41 28.0 #> 2 EWR 2013-01-02 34.0 24.1 #> 3 EWR 2013-01-03 34.0 26.1 #> 4 EWR 2013-01-04 39.9 28.9 #> 5 EWR 2013-01-05 44.1 32 #> # … with 1,087 more rows
Note that the tsibble handles regularly-spaced temporal data well, from seconds to years based on its time representation (see
?tsibble). The option
regular, by default, is set to
FALSE to create a tsibble for the data collected at irregular time interval. Below shows the scheduled date time of the flights in New York:
flights <- nycflights13::flights %>% mutate(sched_dep_datetime = make_datetime(year, month, day, hour, minute, tz = "America/New_York"))
The key contains columns
flight to identify observational units over time, from a passenger’s point of view. With
regular = FALSE, it turns to an irregularly-spaced tsibble, where
[!] highlights the irregularity.
flights_tsbl <- flights %>% as_tsibble( key = c(carrier, flight), index = sched_dep_datetime, regular = FALSE ) flights_tsbl
#> # A tsibble: 336,776 x 20 [!] <America/New_York> #> # Key: carrier, flight [5,725] #> year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time #> <int> <int> <int> <int> <int> <dbl> <int> <int> #> 1 2013 11 3 1531 1540 -9 1653 1725 #> 2 2013 11 4 1539 1540 -1 1712 1725 #> 3 2013 11 5 1548 1540 8 1708 1725 #> 4 2013 11 6 1535 1540 -5 1657 1725 #> 5 2013 11 7 1549 1540 9 1733 1725 #> # … with 336,771 more rows, and 12 more variables: arr_delay <dbl>, #> # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>, #> # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>, #> # sched_dep_datetime <dttm>