The tsibble package provides a data infrastructure for tidy temporal data with wrangling tools. Adapting the tidy data principles, tsibble is a data- and model-oriented object. In tsibble:
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")
To coerce a data frame to tsibble, we need to declare key and index. For example, in the
weather data from the package
time_hour containing the date-times should be declared as index, and the
origin as key. Other columns can be considered as measured variables.
library(dplyr) library(tsibble) weather <- nycflights13::weather %>% select(origin, time_hour, temp, humid, precip) weather_tsbl <- as_tsibble(weather, key = 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 26,110 more rows
The interval is computed from index based on the representation, ranging from year to nanosecond, from numerics to ordered factors. The table below shows how tsibble interprets some common time formats.
A full list of index classes supported by tsibble can be found in
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_key() %>% tidyr::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 26,185 more rows
index_by() is the counterpart of
group_by() in temporal context, but it groups the index only. In conjunction with
summarise() aggregates interested variables over time periods.
index_by() goes hand in hand with the index functions including
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 index variable (referred to as
full_weather %>% group_by_key() %>% index_by(year_month = ~ yearmonth(.)) %>% # 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. This
summarise() combo can help with regularising a tsibble of irregular time space too.
An ecosystem, the tidyverts, is built around the tsibble object for tidy time series analysis.
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.