In R, mark an ID's rows after an event, but not before it [duplicate]

Question

Background

I've got this R dataframe, df:

df <- data.frame(ID = c("a","a","a","b", "c","c","c"),
                event = c(0,1,0,0,0,1,0),
                event_date = as.Date(c("2011-01-01","2011-08-21","2011-12-23","2014-12-31","2013-03-14","2013-04-07","2014-07-14")),
                stringsAsFactors=FALSE)

It looks like this:

As you can see, it's got three people (ID's), along with a 0/1 indicator of whether an event of interest occurs, and an event_date when that event occurred.

The Problem / Question

I want to edit the event column so that for each ID where any row has event = 1, all rows chronologically (by event_date) after the first event = 1 are also marked event = 1.

In other words, I'd like something that looks like this:

df_want <- data.frame(ID = c("a","a","a","b", "c","c","c"),
                 event = c(0,1,1,0,0,1,1),
                 event_date = as.Date(c("2011-01-01","2011-08-21","2011-12-23","2014-12-31","2013-03-14","2013-04-07","2014-07-14")),
                 stringsAsFactors=FALSE)

Which would look like so:

As you can see, for ID's a and c, the event column now shows event = 1 for that ID's rows after the date of the first event = 1. For ID b, nothing happens, as they do not have any event = 1.

Note that in the "real" dataset I'm doing this procedure on, ID's generally have many more rows after their first event = 1, so a solution would need to apply to all of them. (I add this caveat since ID's a and c only have one row after their event = 1.)

What I've tried

I have some code that will apply event = 1 to everything, but that obviously doesn't get me very far:

df <- df %>% 
  mutate(exposed = 1)

Any help is greatly appreciated.

Answer 1

We could first group and then use cumsum:

library(dplyr)

df %>% 
  group_by(ID) %>% 
  mutate(event = cumsum(event == 1))

  ID    event event_date
  <chr> <int> <date>    
1 a         0 2011-01-01
2 a         1 2011-08-21
3 a         1 2011-12-23
4 b         0 2014-12-31
5 c         0 2013-03-14
6 c         1 2013-04-07
7 c         1 2014-07-14