The tsibble package provides a data class of
tbl_ts to store and manage temporal-context data frames in a tidy manner. A tsibble consists of a time index, keys 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:
You could install the development version from Github using
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 the
id(). The key(s) 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: 26,130 x 5 [1HOUR] #> # Keys: origin  #> origin time_hour temp humid precip #> <chr> <dttm> <dbl> <dbl> <dbl> #> 1 EWR 2013-01-01 11:00:00 37.0 54.0 0 #> 2 EWR 2013-01-01 12:00:00 37.0 54.0 0 #> 3 EWR 2013-01-01 13:00:00 37.9 52.1 0 #> 4 EWR 2013-01-01 14:00:00 37.9 54.5 0 #> 5 EWR 2013-01-01 15:00:00 37.9 57.0 0 #> # ... with 2.612e+04 more rows
The key is not constrained to a single variable, but expressive of nested and crossed data structures. This incorporates univariate, multivariate, hierarchical and grouped time series into the tsibble framework. See
vignette("intro-tsibble") for details.
tsummarise()to summarise over calendar periods
A new verb
tsummarise() is introduced to aggregate interested variables over calendar periods. The
tsummarise goes hand in hand with the index functions including
yearquarter(), as well as other friends from lubridate, such as
ceiling_date(). For example, it would be of interest in computing average temperature and total precipitation per month, by applying the
yearmonth() to the hourly time index.
weather_tsbl %>% group_by(origin) %>% tsummarise( year_month = yearmonth(time_hour), # monthly aggregates avg_temp = mean(temp, na.rm = TRUE), ttl_precip = sum(precip, na.rm = TRUE) ) #> # A tsibble: 36 x 4 [1MONTH] #> # Keys: origin  #> # Groups: origin  #> origin year_month avg_temp ttl_precip #> <chr> <mth> <dbl> <dbl> #> 1 EWR 2013 Jan 35.5 2.70 #> 2 EWR 2013 Feb 34.1 2.76 #> 3 EWR 2013 Mar 40.0 1.92 #> 4 EWR 2013 Apr 52.8 1.07 #> 5 EWR 2013 May 62.8 2.76 #> # ... with 31 more rows
tsummarise() can also help with regularising a tsibble of irregular time space.
Temporal data often involves moving window calculations. Several functions in the tsibble allow for different variations of moving windows using purrr-like syntax:
slide(): sliding window with overlapping observations.
tile(): tiling window without overlapping observations.
stretch(): fixing an initial window and expanding more observations.
For example, a moving average of window size 3 is carried out on hourly temperatures for each group (origin).
weather_tsbl %>% group_by(origin) %>% mutate(temp_ma = slide(temp, ~ mean(., na.rm = TRUE), size = 3)) #> # A tsibble: 26,130 x 6 [1HOUR] #> # Keys: origin  #> # Groups: origin  #> origin time_hour temp humid precip temp_ma #> <chr> <dttm> <dbl> <dbl> <dbl> <dbl> #> 1 EWR 2013-01-01 11:00:00 37.0 54.0 0 NA #> 2 EWR 2013-01-01 12:00:00 37.0 54.0 0 NA #> 3 EWR 2013-01-01 13:00:00 37.9 52.1 0 37.3 #> 4 EWR 2013-01-01 14:00:00 37.9 54.5 0 37.6 #> 5 EWR 2013-01-01 15:00:00 37.9 57.0 0 37.9 #> # ... with 2.612e+04 more rows
fill_na()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. The
fill_na() function not only completes the index and keys to make the
NAs present, but also provides a consistent interface to replace these
NAs using a set of name-value pairs.
full_pedestrian <- pedestrian %>% fill_na( Date = lubridate::as_date(Date_Time), Time = lubridate::hour(Date_Time) ) c("original" = nrow(pedestrian), "full" = nrow(full_pedestrian)) #> original full #> 66071 70176 full_pedestrian #> # A tsibble: 70,176 x 5 [1HOUR] #> # Keys: Sensor  #> Sensor Date_Time Date Time Count #> <chr> <dttm> <date> <int> <int> #> 1 Birrarung Marr 2015-01-01 00:00:00 2015-01-01 0 1630 #> 2 Bourke Street Mall (North) 2015-01-01 00:00:00 2015-01-01 0 NA #> 3 QV Market-Elizabeth St (West) 2015-01-01 00:00:00 2015-01-01 0 490 #> 4 Southern Cross Station 2015-01-01 00:00:00 2015-01-01 0 746 #> 5 Birrarung Marr 2015-01-01 01:00:00 2015-01-01 1 826 #> # ... with 7.017e+04 more rows
In the example of
pedestrian, the missing values of the Date and Time, are supplied by the corresponding component of the
Date_Time. The rest of untouched variables (i.e.
Count) simply leave NA as is.