Aggregate time series data preserving the time series

This function operates on timeseries data not linelists (see time_summarise()) for line lists. If a very granular timeseries is regrouped and this function is applied the resulting dataframe will be

Usage

time_aggregate(
  df = i_timestamped,
  ...,
  .groups = NULL,
  .cols = NULL,
  .fns = NULL
)

Arguments

df: an optionally grouped time series. Grouping should not include the time column. The grouping works differently from dplyr::summarise in that the last level of non-time groups is lost in this operation, so the subgroup you wish to aggregate should be included in the grouping.
...: A set of dplyr::summarise statements, or additional parameters for .fns
.groups: as per dplyr::summarise
.cols: Optional dplyr column specification for the data that will be summarised. This is useful if there are lots of columns you want to summarise using the same function (e.g. sum usually). .cols is passed to a dplyr::across call. if .fns is given and the .cols parameter is not specified then the columns to summarise are automatically identified. In doing this any Date columns are dropped. If this in not what you want then .cols or ... must be given
.fns: Optional a set of function specifications as per dplyr::across

Value

the summarised time series preserving the time column, and with the grouping structure involving one fewer levels than the input

Examples

example_england_covid_by_age() %>%
  time_aggregate(count = sum(count), denom = sum(denom)) %>%
  dplyr::glimpse()
#> Rows: 1,410
#> Columns: 3
#> $ time  <t[day]> 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 4…
#> $ count <dbl> 2, 2, 13, 3, 19, 0, 1, 0, 0, 5, 1, 1, 4, 2, 1, 1, 1, 1, 2, 0, 0,…
#> $ denom <dbl> 38, 38, 247, 57, 361, 0, 19, 0, 0, 95, 19, 19, 76, 38, 19, 19, 1…

example_england_covid_by_age() %>%
  time_aggregate(.fns=mean, .cols=dplyr::where(is.numeric)) %>%
  dplyr::glimpse()
#> Rows: 1,410
#> Columns: 4
#> $ time       <t[day]> 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, …
#> $ count      <dbl> 0.10526316, 0.10526316, 0.68421053, 0.15789474, 1.00000000,…
#> $ denom      <dbl> 2, 2, 13, 3, 19, 0, 1, 0, 0, 5, 1, 1, 4, 2, 1, 1, 1, 1, 2, …
#> $ population <dbl> 2962472, 2962472, 2962472, 2962472, 2962472, 2962472, 29624…