How to create new variable based on time and preexisting variables? - r

Question

I have a dataset with repeated measurements on multiple individuals over time. It looks something like this:
ID Time Event
1 Jan 1 2012, 4pm Abx
1 Jan 2 2012, 2pm Test
1 Jan 26 2012 3 pm Test
1 Jan 29 2012 10 pm Abx
1 Jan 30 2012, 3 pm Test
1 Jan 5 2012 3 pm Test
2 Jan 1 2012, 4pm Abx
2 Jan 2 2012, 2pm Test
2 Jan 26 2012 3 pm Test
The dataset is currently based around events. It will later be filtered down to just tests. What I need to do is make a new variable that is 1 when certain events (Abx, in this case) occur within a certain time range of tests. So if the event 'Abx' occurs within, let's say, 48 hours of a Test event, the new variable should equal 1. Otherwise, it should equal zero.
I'm hoping to produce something like this:
ID Time Event New_variable
1 Jan 1 2012, 4pm Abx 1
1 Jan 2 2012, 2pm Test 1
1 Jan 26 2012 3 pm Test 0
1 Jan 29 2012 10 pm Abx 1
1 Jan 30 2012, 3 pm Test 1
1 Jan 5 2012 3 pm Test 0
2 Jan 1 2012, 4pm Abx 1
2 Jan 2 2012, 2pm Test 1
2 Jan 26 2012 3 pm Test 0
I know that I could probably solve this with a combination of Dplyr mutate functions combined with ifelse statements, and if I just wanted to make a variable that reads "1" when the antibiotic event occurs I could do that like this:
test %>%
mutate(New_variable = ifelse(Event == 'Abx', 1, 0)) -> test2
But I don't know how to factor in time so that Test events = 1 within 48 hours of an Abx event. I also am not sure how to make sure that the condition is applied only within the same ID. How can I do this?
Any help is appreciated!
Update: Thank you so much for the suggestions! I'm going to try these out on the data, but I think they'll work. If they don't, I'll be back soon. Success! I also modified the suggested helper function to include additional options (for more than one type of Abx):
abxRows <- type == "Abx" | type == "Abx2"

Answer 1

To the data provided, I added two "Abx" events which should not be one (i.e. one that was not within 48 hours and one that wasn't in the same group as the test that was within 48 hours).
library(dplyr)
library(lubridate)
library(purrr)
eventData <-
data.frame(stringsAsFactors = FALSE,
ID = c(1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1),
Time = c("Jan 1 2012 4 pm", "Jan 2 2012, 2pm",
"Jan 26 2012 3 pm", "Jan 29 2012 10 pm",
"Jan 30 2012 3 pm", "Jan 5 2012 3 pm",
"Jan 1 2012 4 pm", "Jan 2 2012, 2pm",
"Jan 26 2012 3 pm", "Feb 12 2012 1pm",
"Jan 16 2012 3 pm", "Jan 16 2012 1 pm"),
Event = c("Abx", "Test", "Test", "Abx", "Test", "Test",
"Abx", "Test", "Test", "Abx", "Abx", "Test")
) %>%
mutate(Time = mdy_h(Time),
window = if_else(Event == "Test",
interval(Time - hours(48), Time + hours(48)),
interval(NA, NA))
)
First, you want to make sure the Time column is a time format. Then create a column of the lubridate Interval class that creates a 48 hr window around "Test" events.
Define the helper function that will check if the event occurred within the window.
chkFun <- function(eventTime, intervals, grp, type){
abxRows <- type == "Abx"
testRows <- !abxRows
hits <- map2_lgl(eventTime, grp,
~any(.x %within% intervals[grp %in% .y], na.rm = TRUE)) &
abxRows
testHits <- map_lgl(which(testRows),
~any(eventTime[abxRows & (grp[.x] == grp)] %within%
intervals[.x]))
hits[testRows] <- testHits
as.integer(hits)
}
This function first goes through and test if the "Abx" events occur within the intervals. It then determines which "Test" rows have an interval that contains a "Abx" event. The function returns the combination of these cast as integers.
Last, just use a mutate statement with the helper function, dropping the window column
eventData %>%
mutate(New_variable = chkFun(Time, window, ID, Event)) %>%
select(-window)
Alternatively, the helper function could just take the data.frame as an argument and assume the column names. In the form above, though, if you define it first in your script, it could also be used in the original definition of eventData
Results:
#> ID Time Event New_variable
#> 1 1 2012-01-01 16:00:00 Abx 1
#> 2 1 2012-01-02 14:00:00 Test 1
#> 3 1 2012-01-26 15:00:00 Test 0
#> 4 1 2012-01-29 22:00:00 Abx 1
#> 5 1 2012-01-30 15:00:00 Test 1
#> 6 1 2012-01-05 15:00:00 Test 0
#> 7 2 2012-01-01 16:00:00 Abx 1
#> 8 2 2012-01-02 14:00:00 Test 1
#> 9 2 2012-01-26 15:00:00 Test 0
#> 10 2 2012-02-12 13:00:00 Abx 0
#> 11 2 2012-01-16 15:00:00 Abx 0
#> 12 1 2012-01-16 13:00:00 Test 0

Answer 2

So I dont have a copy of your data, so Im not sure what for kmat your dates are in...
I would recommend converting the date to the right format using as.POSIXct(Time, format="%b %d %Y, %I%p") For more info on the format look up ?strptime, but I think that is right for your column.
If we assume your data frame is like this... I know I have changed parts of it but this is for simplicity
df <- data.frame(ID = c(rep(1,6),rep(2,3)),
Time=c(seq(from=start, by=interval*6840, to=end)[1:6],seq(from=start, by=interval*6840, to=end)[1:3]),
Event = rep(c("Abs","Test","Test"),3))
This would look like this
ID Time Event
1 1 2012-01-01 00:00:00 Abs
2 1 2012-01-05 18:00:00 Test
3 1 2012-01-10 12:00:00 Test
4 1 2012-01-15 06:00:00 Abs
5 1 2012-01-20 00:00:00 Test
6 1 2012-01-24 18:00:00 Test
7 2 2012-01-01 00:00:00 Abs
8 2 2012-01-05 18:00:00 Test
9 2 2012-01-10 12:00:00 Test
So you can use the following code to test whether a Test falls within 48 hours of an Abs
df[which(df$Event=="Test"),]$Time %in% unlist(Map(`:`, df[which(df$Event=="Abs"),]$Time-48*60*60, df[which(df$Event=="Abs"),]$Time+48*60*60))
So this will return FALSE for all, but that is because the synthetic data is at larger time steps.
To unpack this...
df[which(df$Event=="Test"),]$Time Gives the times of tests
%in% Says look for what precedes this, in a set of values that follows it.
So what follows it is: unlist(Map(`:`, df[which(df$Event=="Abs"),]$Time-48*60*60, df[which(df$Event=="Abs"),]$Time+48*60*60))
This creates a list of dates +/- 48 hours from each Abs. to add or subtract 48 hours, POSIXct objects like this done in seconds, hence 48*60*60