Like thicken, it will find the datetime variable in x and add a variable of a higher periodicity to it. However, the variable to which to map the observation is provided by the user. This enables mapping to time points that are unequally spaced.

thicken_cust(x, spanned, colname, by = NULL, drop = FALSE)

Arguments

x

A data frame containing at least one datetime variable of class Date, POSIXct or POSIXlt.

spanned

A datetime vector to which the the datetime variable in x should be mapped.

colname

Character, the column name of the added variable.

by

Only needs to be specified when x contains multiple variables of class Date, POSIXct or POSIXlt. Indicates which to use for thickening.

drop

Should the original datetime variable be dropped from the returned data frame? Defaults to FALSE.

Value

The data frame x with the variable added to it.

Details

Only rounding down is available for custom thickening.

Examples

library(dplyr) # analysis of traffic accidents in traffic jam hours and other hours. accidents <- emergency %>% filter(title == "Traffic: VEHICLE ACCIDENT -") spanning <- span_time("20151210 16", "20161017 17", tz = "EST") %>% subset_span(list(hour = c(6, 9, 16, 19))) thicken_cust(accidents, spanning, "period") %>% count(period) %>% pad_cust(spanning)
#> # A tibble: 1,248 x 2 #> period n #> <dttm> <int> #> 1 2015-12-10 16:00:00 18 #> 2 2015-12-10 19:00:00 11 #> 3 2015-12-11 06:00:00 15 #> 4 2015-12-11 09:00:00 28 #> 5 2015-12-11 16:00:00 24 #> 6 2015-12-11 19:00:00 32 #> 7 2015-12-12 06:00:00 3 #> 8 2015-12-12 09:00:00 52 #> 9 2015-12-12 16:00:00 17 #> 10 2015-12-12 19:00:00 28 #> # … with 1,238 more rows