The tsibble package provides a data class of
tbl_ts to represent
tidy temporal data. A tsibble consists of a time index, key and other
measured variables in a data-centric format, which is built on top of
You could install the stable version on CRAN:
You could install the development version from Github using
# install.packages("remotes") remotes::install_github("tidyverts/tsibble")
Coerce to a tsibble with
weather data included in the package
nycflights13 is used as an
example to illustrate. The “index” variable is the
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  #> 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 rows
The key is comprised of one or more variables. See
The tsibble internally computes the interval for given time indices
based on the time representation, ranging from year to nanosecond. The
POSIXct corresponds to sub-daily series,
Date to daily,
yearmon to monthly,
fill_gaps() to turn implicit missing values into explicit missing values
Often there are implicit missing cases in temporal data. If the
observations are made at regular time interval, we could turn these
implicit missings to be explicit simply using
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
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  #> # Groups: 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 2.618e+04 more rows
fill_gaps() also handles filling time gaps by values or functions, and
respects time zones for date-times. Wanna a quick overview of implicit
missing values? Check out
summarise() to aggregate over calendar periods
index_by() is the counterpart of
group_by() in temporal context, but
it groups the index only. In conjunction with
summarise() and its scoped variants aggregate interested variables
over calendar periods.
index_by() goes hand in hand with the index
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
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  #> 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 rows
While collapsing rows (like
index_by() will take care of updating the key and index respectively.
summarise() combo can help with regularising a
tsibble of irregular time space too.
A family of window functions:
Temporal data often involves moving window calculations. Several functions in tsibble allow for different variations of moving windows using purrr-like syntax:
pslide(): sliding window with overlapping observations.
ptile(): tiling window without overlapping observations.
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  #> # Groups: origin  #> 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 rows
Looking for rolling in parallel? Their multiprocessing equivalents are
future_. More examples can be found at
More around tsibble
Tsibble also serves a natural input to 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.