How to identify consecutive observations based on time steps? - r

Question

I would like to identify if an activity occurs consecutive times and how often during a week. The starting point is t1 that records the occurrence of an activity at t1_1 , t1_2, t1_3 and so on. For example in the case of id 12 activity occurred at t1_2, t1_3, t2_2, t3_1, t3_3, t4_2, t5_2, t6_1, t6_2, t6_3 and t7_3. As here was reported activity during all 7 days I assume the activity occurred consecutively. I would like to identify all id's in which an activity occured consecutively and the sum of occurrence.
Input
id t1_1 t1_2 t1_3 t2_1 t2_2 t2_3 t3_1 t3_2 t3_3 t4_1 t4_2 t4_3 t5_1 t5_2 t5_3 t6_1 t6_2 t6_3 t7_1 t7_2 t7_3
12 0 1 1 0 1 0 1 0 1 0 1 0 0 1 0 1 1 1 0 0 1
123 0 0 0 1 1 1 0 0 0 1 1 1 1 1 1 0 0 0 1 1 1
10 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Output
Id Sum
12 11
10 21

Answer 1

Here is an option with rle. Loop over the rows of the dataset with apply (MARGIN = 1) without the 'id' column, apply rle and extract the lengths where the 'values' are 1 ('x1'). If the length of 'x1' is either 1 or greater than or equal to 7, get the sum (1 is because if all the values are 1). Then, stack the named list to a 2 column data.frame and set the names of the columns ('out')
out <- stack(setNames(apply(df1[-1], 1, function(x) {
x1 <- with(rle(x), lengths[as.logical(values)])
if(length(x1) >=7|length(x1) == 1) sum(x1) }), df1$id))[2:1]
names(out) <- c('Id', 'Sum')
out
# Id Sum
#1 12 11
#2 10 21
data
df1 <- structure(list(id = c(12L, 123L, 10L), t1_1 = c(0L, 0L, 1L),
t1_2 = c(1L, 0L, 1L), t1_3 = c(1L, 0L, 1L), t2_1 = c(0L,
1L, 1L), t2_2 = c(1L, 1L, 1L), t2_3 = c(0L, 1L, 1L), t3_1 = c(1L,
0L, 1L), t3_2 = c(0L, 0L, 1L), t3_3 = c(1L, 0L, 1L), t4_1 = c(0L,
1L, 1L), t4_2 = c(1L, 1L, 1L), t4_3 = c(0L, 1L, 1L), t5_1 = c(0L,
1L, 1L), t5_2 = c(1L, 1L, 1L), t5_3 = c(0L, 1L, 1L), t6_1 = c(1L,
0L, 1L), t6_2 = c(1L, 0L, 1L), t6_3 = c(1L, 0L, 1L), t7_1 = c(0L,
1L, 1L), t7_2 = c(0L, 1L, 1L), t7_3 = c(1L, 1L, 1L)),
class = "data.frame", row.names = c(NA,
-3L))

Answer 2

An option using data.table:
melt(DT, id.vars="id")[,
c("day", "time") := tstrsplit(variable, "_")][
value==1L, if(all(paste0("t", 1L:7L) %chin% day)) .(Sum=sum(value)) , id]
output:
id Sum
1: 10 21
2: 12 11
data:
library(data.table)
DT <- fread("id t1_1 t1_2 t1_3 t2_1 t2_2 t2_3 t3_1 t3_2 t3_3 t4_1 t4_2 t4_3 t5_1 t5_2 t5_3 t6_1 t6_2 t6_3 t7_1 t7_2 t7_3
12 0 1 1 0 1 0 1 0 1 0 1 0 0 1 0 1 1 1 0 0 1
123 0 0 0 1 1 1 0 0 0 1 1 1 1 1 1 0 0 0 1 1 1
10 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1")
Explanation:
Convert into long format using melt
use tstrsplit to split the columns names into day of the week and time
filter for value==1L and then for each id, check if all 7 days are in subset before summing (i.e. if(all(paste0("t", 1L:7L) %chin% day)) .(Sum=sum(value)))