Apply a function to a time series by period

Usage

time_apply(
  data,
  target,
  period,
  .fun,
  ...,
  start_date = NULL,
  side = "end",
  clean = FALSE,
  message = TRUE
)

Arguments

data: A tibble with a date or datetime index.
target: A column to apply the function to
period: A time-based definition (e.g. "1 week"). or a numeric number of observations per frequency (e.g. 10). See tibbletime::collapse_by() for period notation.
.fun: A function to apply (e.g. median)
...: Additional parameters passed to the function, .fun
start_date: Optional argument used to specify the start date for the first group. The default is to start at the closest period boundary below the minimum date in the supplied index.
side: Whether to return the date at the beginning or the end of the new period. By default, the "end" of the period. Use "start" to change to the start of the period.
clean: Whether or not to round the collapsed index up / down to the next period boundary. The decision to round up / down is controlled by the side argument.
message: A boolean. If message = TRUE, the frequency used is output along with the units in the scale of the data.

Value

Returns a tibbletime object of class tbl_time.

Details

Uses a time-based period to apply functions to. This is useful in circumstances where you want to compare the observation values to aggregated values such as mean() or median() during a set time-based period. The returned output extends the length of the data frame so the differences can easily be computed.

Examples


library(dplyr)

# Basic Usage
tidyverse_cran_downloads %>%
    time_apply(count, period = "1 week", .fun = mean, na.rm = TRUE)
#> # A time tibble: 6,375 × 4
#> # Index:         date
#> # Groups:        package [15]
#>    package date       count time_apply
#>    <chr>   <date>     <dbl>      <dbl>
#>  1 broom   2017-01-01  1053      1678.
#>  2 broom   2017-01-02  1481      1678.
#>  3 broom   2017-01-03  1851      1678.
#>  4 broom   2017-01-04  1947      1678.
#>  5 broom   2017-01-05  1927      1678.
#>  6 broom   2017-01-06  1948      1678.
#>  7 broom   2017-01-07  1542      1678.
#>  8 broom   2017-01-08  1479      1716 
#>  9 broom   2017-01-09  2057      1716 
#> 10 broom   2017-01-10  2278      1716 
#> # ℹ 6,365 more rows