Add a column to data frame n days before an observation - r

Question

I need an more efficient way to add a marker that shows that an observation is registered 3 days before a specific date. The problem is that these dates are not necessarily consecutive i.e. they can be missing, yet I need the marker to ignore the missing days. The example below illustrates the problem and what I need more clearly:
library(tidyverse)
library(lubridate)
df <- data.frame("Date" = c(as_date(0:9)), ID = rep(paste0("ID", 1:3), each = 10))
df <- df[-c(5, 13, 24),]
date_before <- "1970-01-07"
df[, "three_days_before"] <- 0
for(i in df$ID){
cond <- df[, "ID"] == i &
df[, "Date"] == date_before
before_n <- (which(cond)-3):(which(cond)-1)
df[before_n, "three_days_before"] <- 1
}
df
The loop gives me what I need (three days are marked each time regardless their inclusion in the data.frame), yet it takes quite a long time to calculate on a larger data set. Can someone recommend a better way?

Answer 1

1) Apply a rolling window separately for each ID. The rolling window function checks whether any of the next 3 elements of Date equal date_before.
(Specifying a width of list(1:3) says to use offsets 1, 2 and 3 which means the next 3 ahead.) Note that there are no next 3 elements for the last value so we use fill to fill it in. We add 0 to convert from logical to numeric. This solution involves only two lines of code and no explicit looping.
library(zoo)
roll <- function(x) rollapply(x, list(1:3), FUN = any, partial = TRUE, fill = FALSE)
transform(df, before = ave(Date == date_before, ID, FUN = roll) + 0)
giving:
Date ID before
1 1970-01-01 ID1 0
2 1970-01-02 ID1 0
3 1970-01-03 ID1 1
4 1970-01-04 ID1 1
6 1970-01-06 ID1 1
7 1970-01-07 ID1 0
8 1970-01-08 ID1 0
9 1970-01-09 ID1 0
10 1970-01-10 ID1 0
11 1970-01-01 ID2 0
12 1970-01-02 ID2 0
14 1970-01-04 ID2 1
15 1970-01-05 ID2 1
16 1970-01-06 ID2 1
17 1970-01-07 ID2 0
18 1970-01-08 ID2 0
19 1970-01-09 ID2 0
20 1970-01-10 ID2 0
21 1970-01-01 ID3 0
22 1970-01-02 ID3 0
23 1970-01-03 ID3 1
25 1970-01-05 ID3 1
26 1970-01-06 ID3 1
27 1970-01-07 ID3 0
28 1970-01-08 ID3 0
29 1970-01-09 ID3 0
30 1970-01-10 ID3 0
2) This could also be expressed as a pipeline where roll is from above:
library(dplyr)
library(zoo)
df %>%
group_by(ID) %>%
mutate(before = roll(Date == date_before)) %>%
ungroup

Answer 2

Here is a tidyverse solution using difftime and cumsum:
library(tidyverse);
df %>%
group_by(ID) %>%
mutate(
is_before = difftime(as_date(date_before), Date) >= 0,
three_days_before = as.numeric((max(cumsum(is_before)) - cumsum(is_before)) %in% 1:3)) %>%
select(-is_before) %>%
as.data.frame()
# Date ID three_days_before
#1 1970-01-01 ID1 0
#2 1970-01-02 ID1 0
#3 1970-01-03 ID1 1
#4 1970-01-04 ID1 1
#5 1970-01-06 ID1 1
#6 1970-01-07 ID1 0
#7 1970-01-08 ID1 0
#8 1970-01-09 ID1 0
#9 1970-01-10 ID1 0
#10 1970-01-01 ID2 0
#11 1970-01-02 ID2 0
#12 1970-01-04 ID2 1
#13 1970-01-05 ID2 1
#14 1970-01-06 ID2 1
#15 1970-01-07 ID2 0
#16 1970-01-08 ID2 0
#17 1970-01-09 ID2 0
#18 1970-01-10 ID2 0
#19 1970-01-01 ID3 0
#20 1970-01-02 ID3 0
#21 1970-01-03 ID3 1
#22 1970-01-05 ID3 1
#23 1970-01-06 ID3 1
#24 1970-01-07 ID3 0
#25 1970-01-08 ID3 0
#26 1970-01-09 ID3 0
#27 1970-01-10 ID3 0
Explanation: We group entries by ID; is_before flags entries at or before date_before; we then flag the first three rows before date_before with (max(cumsum(is_before)) - cumsum(is_before)) %in% 1:3).
Sample data
library(lubridate);
df <- data.frame("Date" = c(as_date(0:9)), ID = rep(paste0("ID", 1:3), each = 10))
df <- df[-c(5, 13, 24),]
date_before <- "1970-01-07"