/ˈt͡sɪbəl/
The tsibble package provides a data class of tbl_ts to store and manage temporal-context data frames in a “tidy” form. 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:
You could install the development version from Github using
# install.packages("devtools")
devtools::install_github("tidyverts/tsibble", build_vignettes = TRUE)as_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(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: 26,115 x 5 [1HOUR]
#> # 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 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 package?tsibble and vignette("intro-tsibble") for details.
The tsibble internally computes the interval for a given time index, based on its representation. The POSIXct corresponds to sub-daily series, Date to daily, yearweek to weekly, yearmonth/yearmth to monthly, yearquarter/yearqtr to quarterly, and etc.
fill_na() to turn implicit missing values into explicit missing valuesOften 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 fill_na(). Meanwhile, fill NAs in by 0 for precipitation (precip). 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_na(precip = 0) %>%
group_by(origin) %>%
tidyr::fill(temp, humid, .direction = "down")
full_weather
#> # A tsibble: 26,190 x 5 [1HOUR]
#> # 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_na() also handles filling NA by values or functions, and preserves time zones for date-times. Wanna a quick overview of implicit time gaps? Check out count_gaps().
index_by() + summarise() to aggregate over calendar periodsindex_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 [1MONTH]
#> # 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 rowsThis combo can also help with regularising a tsibble of irregular time space.
slide(), tile(), stretch()Temporal data 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 [1HOUR]
#> # 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 rowsIt can be noticed that the tsibble seamlessly works with tidyverse verbs. Use ?tsibble::reexports for a full list of re-exported functions.
arrange(), filter(), slice()mutate(), transmute(), select(), rename(), summarise()/summarize()left/right/full/inner/anti/semi_join()group_by(), ungroup()gather(), spread(), nest(), unnest()glimpse(), as_tibble()/as.tibble()!!, !!!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.