How to find first non-NA leading or lagging value? - r

Question

I have rows grouped by ID and I want to calculate how much time passes until the next event occurs (if it does occur for that ID).
Here is example code:
year <- c(2015, 2016, 2017, 2018, 2015, 2016, 2017, 2018, 2015, 2016, 2017, 2018)
id <- c(rep("A", times = 4), rep("B", times = 4), rep("C", times = 4))
event_date <- c(NA, 2016, NA, 2018, NA, NA, NA, NA, 2015, NA, NA, 2018)
df<- as.data.frame(cbind(id, year, event_date))
df
id year event_date
1 A 2015 <NA>
2 A 2016 2016
3 A 2017 <NA>
4 A 2018 2018
5 B 2015 <NA>
6 B 2016 <NA>
7 B 2017 <NA>
8 B 2018 <NA>
9 C 2015 2015
10 C 2016 <NA>
11 C 2017 <NA>
12 C 2018 2018
Here is what I want the output to look like:
id year event_date years_till_next_event
1 A 2015 <NA> 1
2 A 2016 2016 0
3 A 2017 <NA> 1
4 A 2018 2018 0
5 B 2015 <NA> <NA>
6 B 2016 <NA> <NA>
7 B 2017 <NA> <NA>
8 B 2018 <NA> <NA>
9 C 2015 2015 0
10 C 2016 <NA> 2
11 C 2017 <NA> 1
12 C 2018 2018 0
Person B does not have the event, so it is not calculated. For the others, I want to calculate the difference between the leading event_date (ignoring NAs, if it exists) and the year.
I want to calculate years_till_next_event such that 1) if there is an event_date for a row, event_date - year. 2) If not, then return the first non-NA leading value - year. I'm having difficulty with the 2nd part of the logic, keeping in mind the event could occur not at all or every year, by ID.

Answer 1

Using zoo with dplyr
library(dplyr)
library(zoo)
df %>%
group_by(id) %>%
mutate(years_till_next_event = na.locf0(event_date, fromLast = TRUE) - year )

Answer 2

Here is a data.table option
setDT(df)[, years_till_next_event := nafill(event_date, type = "nocb") - year, id]
which gives
id year event_date years_till_next_event
1: A 2015 NA 1
2: A 2016 2016 0
3: A 2017 NA 1
4: A 2018 2018 0
5: B 2015 NA NA
6: B 2016 NA NA
7: B 2017 NA NA
8: B 2018 NA NA
9: C 2015 2015 0
10: C 2016 NA 2
11: C 2017 NA 1
12: C 2018 2018 0

Answer 3

You can create a new column to assign a row number within each id if the value is not NA, fill the NA values from the next values and subtract the current row number from it.
library(dplyr)
df %>%
group_by(id) %>%
mutate(years_till_next_event = replace(row_number(),is.na(event_date), NA)) %>%
tidyr::fill(years_till_next_event, .direction = 'up') %>%
mutate(years_till_next_event = years_till_next_event - row_number()) %>%
ungroup
# id year event_date years_till_next_event
# <chr> <dbl> <dbl> <int>
# 1 A 2015 NA 1
# 2 A 2016 2016 0
# 3 A 2017 NA 1
# 4 A 2018 2018 0
# 5 B 2015 NA NA
# 6 B 2016 NA NA
# 7 B 2017 NA NA
# 8 B 2018 NA NA
# 9 C 2015 2015 0
#10 C 2016 NA 2
#11 C 2017 NA 1
#12 C 2018 2018 0
data
df <- data.frame(id, year, event_date)