identifying rows in data frame that exhibit patterns - r

Question

Below I have code with 3 columns: a group field, a open/close field for the store, and the rolling sum of 3 month opens for the store. I also have the desired solution output.
My dataset can be thought of as an employees availability. You can assume each row to be a different time period (hour, day,month, year, whatever). In the open/closed column I have whether or not the employee was present. The 3month rolling column is a sum of the previous rows.
What I want to identify is the non-zero values in this rolling sum column following a gap of at least 3 zero rows for that particular group. While not present in this dataset, you can assume that there might be more than one 'gap' of zeros present.
structure(list(Group = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L), .Label = c("A", "B"), class = "factor"), X0_closed_1_open = c(0L,
1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L), X3month_roll_open = c(0L,
0L, 1L, 2L, 2L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 2L, 0L, 1L, 1L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L), desired_solution = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L), .Label = c("no", "yes"), class ="factor")), .Names = c("Group", "X0_closed_1_open", "X3month_roll_open", "desired_solution"), class = "data.frame", row.names = c(NA,
-26L))

Answer 1

One option is:
res <- unsplit(
lapply(split(df1, df1$Group), function(x) {
rl <- with(x,rle(X3month_roll_open==0))
indx <- cumsum(c(0,diff(inverse.rle(within.list(rl,
values[values] <- lengths[values]>=3)))<0))
x$Flag <- indx!=0 & x[,3]!=0
x}),
df1$Group)
NOTE: Instead of 'yes/no', it may be better to have 'TRUE/FALSE' for easing subsetting.
identical(c('no', 'yes')[res$Flag+1L], as.character(res$desired_solution))
#[1] TRUE