/ˈt͡sɪbəl/
The tsibble package provides a data class of tbl_ts to represent
tidy time series data. A tsibble consists of a time index, key and
other measured variables in a data-centric format, which is built on top
of the tibble.
You could install the stable version on CRAN:
install.packages("tsibble")You could install the development version from Github using
# install.packages("remotes")
remotes::install_github("tidyverts/tsibble")The weather data included in the package nycflights13 is used as an
example to illustrate. The “index” variable is the time_hour
containing the date-times, and the “key” is the origin as weather
stations created via id(). The key together with the index uniquely
identifies each observation, which gives a valid tsibble. Other
columns can be considered as measured variables.
library(tsibble)
weather <- nycflights13::weather %>%
select(origin, time_hour, temp, humid, precip)
weather_tsbl <- as_tsibble(weather, key = id(origin), index = time_hour)
weather_tsbl
#> # A tsibble: 26,115 x 5 [1h] <America/New_York>
#> # Key: origin [3]
#> 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 2.611e+04 more rowsThe key is comprised of one or more variables. See package?tsibble
and
vignette("intro-tsibble")
for details.
Tsibble internally computes the interval for given time indices based
on the time representation, ranging from year to nanosecond, from
numerics to ordered factors. The POSIXct corresponds to sub-daily
series, Date to daily, yearweek to weekly, yearmonth to monthly,
yearquarter to quarterly, and
etc.
Often there are implicit missing cases in time series. If the
observations are made at regular time interval, we could turn these
implicit missingness to be explicit simply using fill_gaps(), filling
gaps in precipitation (precip) with 0 in the meanwhile. It is quite
common to replaces NAs with its previous observation for each origin
in time series analysis, which is easily done using fill() from
tidyr.
full_weather <- weather_tsbl %>%
fill_gaps(precip = 0) %>%
group_by(origin) %>%
fill(temp, humid, .direction = "down")
full_weather
#> # A tsibble: 26,190 x 5 [1h] <America/New_York>
#> # Key: origin [3]
#> # Groups: origin [3]
#> 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 2.618e+04 more rowsfill_gaps() also handles filling in time gaps by values or functions,
and respects time zones for date-times. Wanna a quick overview of
implicit missing values? Check out
vignette("implicit-na").
index_by() is the counterpart of group_by() in temporal context, but
it groups the index only. In conjunction with index_by(),
summarise() and its scoped variants aggregate interested variables
over calendar periods. index_by() goes hand in hand with the index
functions including as.Date(), yearweek(), yearmonth(), and
yearquarter(), as well as other friends from lubridate. For example,
it would be of interest in computing average temperature and total
precipitation per month, by applying yearmonth() to the hourly time
index.
full_weather %>%
group_by(origin) %>%
index_by(year_month = yearmonth(time_hour)) %>% # monthly aggregates
summarise(
avg_temp = mean(temp, na.rm = TRUE),
ttl_precip = sum(precip, na.rm = TRUE)
)
#> # A tsibble: 36 x 4 [1M]
#> # Key: origin [3]
#> origin year_month avg_temp ttl_precip
#> <chr> <mth> <dbl> <dbl>
#> 1 EWR 2013 Jan 35.6 3.53
#> 2 EWR 2013 Feb 34.2 3.83
#> 3 EWR 2013 Mar 40.1 3
#> 4 EWR 2013 Apr 53.0 1.47
#> 5 EWR 2013 May 63.3 5.44
#> # … with 31 more rowsWhile collapsing rows (like summarise()), group_by() and
index_by() will take care of updating the key and index respectively.
This index_by() + summarise() combo can help with regularising a
tsibble of irregular time space too.
Time series often involves moving window calculations. Several functions in tsibble allow for different variations of moving windows using purrr-like syntax:
slide()/slide2()/pslide(): sliding window with overlapping observations.tile()/tile2()/ptile(): tiling window without overlapping observations.stretch()/stretch2()/pstretch(): fixing an initial window and expanding to include more observations.
For example, a moving average of window size 3 is carried out on hourly temperatures for each group (origin).
full_weather %>%
group_by(origin) %>%
mutate(temp_ma = slide_dbl(temp, ~ mean(., na.rm = TRUE), .size = 3))
#> # A tsibble: 26,190 x 6 [1h] <America/New_York>
#> # Key: origin [3]
#> # Groups: origin [3]
#> origin time_hour temp humid precip temp_ma
#> <chr> <dttm> <dbl> <dbl> <dbl> <dbl>
#> 1 EWR 2013-01-01 01:00:00 39.0 59.4 0 NA
#> 2 EWR 2013-01-01 02:00:00 39.0 61.6 0 NA
#> 3 EWR 2013-01-01 03:00:00 39.0 64.4 0 39.0
#> 4 EWR 2013-01-01 04:00:00 39.9 62.2 0 39.3
#> 5 EWR 2013-01-01 05:00:00 39.0 64.4 0 39.3
#> # … with 2.618e+04 more rowsLooking for rolling in parallel? Their multiprocessing equivalents are
prefixed with future_. More examples can be found at
vignette("window").
Tsibble also serves as a natural input for forecasting and many other downstream analytical tasks. Stay tuned for tidyverts.org.
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.