Tidy Temporal Data Frames and Tools
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README.md

tsibble

/ˈt͡sɪbəl/

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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 the tibble.

Installation

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")

Get started

Coerce to a tsibble with 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 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 rows

The key is comprised of one or more variables. See package?tsibble and vignette("intro-tsibble") for details.

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, yearweek to weekly, yearmonth/yearmon to monthly, yearquarter/yearqtr to quarterly, and etc.

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 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 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 vignette("implicit-na").

index_by() + 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 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 rows

While 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.

A family of window functions: 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 [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 rows

Looking for rolling in parallel? Their multiprocessing equivalents are prefixed with future_. More examples can be found at vignette("window").

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.