将一个向量与另一个向量的每个元素进行比较

Question

I'm trying to track accumulation of events over time, eg the graphs of total number of COVID Cases & deaths over the past year. My starting data is a list of individuals (rows) with the date for each event in the column. A simplified example would be:

library(data.table)
#   Set up 20 subjects and # of days at which each of 3 events happen
(events<-data.table(Subject=1:20, Event1=100*runif(20), Event2=200*runif(20), Event3=500*runif(20)))
(accrual<-data.table(days=10*1:10))  
# Col. 1 has timepoints at which I want to count events occurring by that date

My quick way to count is to compare the whole list of dates for an event (a column)to a single date, eg for day 50:

> events[Event1 < 70, length(Subject)]
[1] 12

I've been trying to compare each of 3 columns iteratively against each of single dates in my list to build a table I can use to graph accruals (see end of question for example). Any time I try to do this as a vector operation (data.table, apply functions), the result is only one count, not a vector of counts for each date

> events[Event1 < accrual$days, length(Subject)]
[1] 11
> events[Event1 < accrual[,days], length(Subject)]
[1] 11
> sum(events$Event1 < accrual$days[1:10])
[1] 11

This seems to compare the vectors of events and dates pairwise, which is the advertised behavior. What I really want is for the whole column to be evaluated against the first element of dates, then the 2nd element of dates, etc. Having used data.table and dpylr for years, I think there should be a more elegant way to do this than looping and counting as I go. The following code works, but I feel I'm missing a simpler, more elegant solution.

> # Ugly, manual way to count events for each date.
> t2<-NULL
> for(i in accrual$days) {
+   t1<-sum( events[, Event1] < i )
+   t2<-c(t2, t1)
+ }
> accrual[,Events1:=t2]
> t2<-NULL
> for(i in accrual$days) {
+   t1<-sum( events[, Event2] < i )
+   t2<-c(t2, t1)
+ }
> accrual[,Events2:=t2]
> t2<-NULL
> for(i in accrual$days) {
+   t1<-sum( events[, Event3] < i )
+   t2<-c(t2, t1)
+ }
> accrual[,Events3:=t2]
> accrual
    days Events1 Events2 Events3
 1:   10       2       1       0
 2:   20       7       2       0
 3:   30       9       2       0
 4:   40      10       4       0
 5:   50      11       5       1
 6:   60      11       6       1
 7:   70      12       6       1
 8:   80      16       6       1
 9:   90      18       8       3
10:  100      20       8       3

Thank you for your suggestions.

Answer 1

Here is one data.table option that may help

> accrual[, as.list(colSums(events[, -c("Subject")] <= days)), days]
    days Event1 Event2 Event3
 1:   10      4      2      0
 2:   20      6      3      0
 3:   30     10      5      1
 4:   40     12      7      3
 5:   50     13      7      3
 6:   60     15      8      4
 7:   70     16      8      4
 8:   80     19      9      4
 9:   90     20     11      4
10:  100     20     13      4

Answer 2

Here is an option using non-equi join:

cols <- paste0("Event", 1:3)
for (x in cols) {
    accrual[, (x) := events[.SD, on=paste0(x,"<days"), by=.EACHI, .N]$N]
}
accrual[]

output:

          days Event1 Event2 Event3
 1: 1970-01-11      1      1      0
 2: 1970-01-21      2      1      1
 3: 1970-01-31      5      3      1
 4: 1970-02-10      8      4      2
 5: 1970-02-20      9      5      3
 6: 1970-03-02     10      6      3
 7: 1970-03-12     13      7      3
 8: 1970-03-22     15      9      3
 9: 1970-04-01     17      9      3
10: 1970-04-11     20     11      3

data:

library(data.table)
set.seed(0L)
events <- data.table(Subject=1:20, Event1=100*runif(20), Event2=200*runif(20), Event3=500*runif(20))
cols <- paste0("Event", 1:3)
events[, (cols) := lapply(.SD, as.IDate), .SDcols=cols]
accrual <- data.table(days=as.IDate(10*1:10))