This function serves as a convenience wrapper of dplyr::summarise()
, which takes the grouped variables and
summarises their gaps in therapy. This function is to be used after propagate_date()
.
summarise_gaps(.data)
.data | Data to be piped into the function |
---|
A summary of gaps in therapy
This function relies an adjusted_date
column to identify gaps in therapy. So, if you don't want to use propagate_date()
beforehand,
you'll need to rename the date variable you wish to use to adjusted_date
.
library(adheRenceRX) library(dplyr) toy_claims %>% filter(ID == "D") %>% propagate_date(.date_var = date, .days_supply_var = days_supply) %>% summarise_gaps()#> # A tibble: 1 x 1 #> Sum_Of_Gaps #> <dbl> #> 1 30