Summarize Gaps in Binary Data using R - r

Question

I am playing around with binary data.
I have data in columns in the following manner:
A B C D E F G H I J K L M N
-----------------------------------------------------
1 1 1 1 1 1 1 1 1 0 0 0 0 0
0 0 0 0 1 1 1 0 1 1 0 0 1 0
0 0 0 0 0 0 0 1 1 1 1 1 0 0
1 - Indicating that the system was on and 0 indicating that the system was off
I am trying to figure out ways to figure out a way to summarize the gaps between the on/off transition of these systems.
For example,
for the first row, it stops working after 'I'
for the second row, it works from 'E' to 'G' and then works again in 'I' and 'M' but is off during other.
Is there a way to summarize this?
I wish to see my result in the following form
row-number Number of 1's Range
------------ ------------------ ------
1 9 A-I
2 3 E-G
2 2 I-J
2 1 M
3 5 H-L

Answer 1

Here's a tidyverse solution:
library(tidyverse)
df %>%
rowid_to_column() %>%
gather(col, val, -rowid) %>%
group_by(rowid) %>%
# This counts the number of times a new streak starts
mutate(grp_num = cumsum(val != lag(val, default = -99))) %>%
filter(val == 1) %>%
group_by(rowid, grp_num) %>%
summarise(num_1s = n(),
range = paste0(first(col), "-", last(col)))
## A tibble: 5 x 4
## Groups: rowid [3]
# rowid grp_num num_1s range
# <int> <int> <int> <chr>
#1 1 1 9 A-I
#2 2 2 3 E-G
#3 2 4 2 I-J
#4 2 6 1 M-M
#5 3 2 5 H-L

Answer 2

An option with data.table. Convert the 'data.frame' to 'data.table' while creating a row number column (setDT), melt from 'wide' to 'long' format specifying the id.var as row number column 'rn', create a run-lenght-id (rleid) column on the 'value' column grouped by 'rn', subset the rows where 'value' is 1, summarise with number of rows (.N), and pasted range of 'variable' values, grouped by 'grp' and 'rn', assign the columns not needed to NULL and order by 'rn' if necessary.
library(data.table)
melt(setDT(df1, keep.rownames = TRUE), id.var = 'rn')[,
grp := rleid(value), rn][value == 1, .(NumberOfOnes = .N,
Range = paste(range(as.character(variable)), collapse="-")),
.(grp, rn)][, grp := NULL][order(rn)]
# rn NumberOfOnes Range
#1: 1 9 A-I
#2: 2 3 E-G
#3: 2 2 I-J
#4: 2 1 M-M
#5: 3 5 H-L
Or using base R with rle
do.call(rbind, apply(df1, 1, function(x) {
rl <- rle(x)
i1 <- rl$values == 1
l1 <- rl$lengths[i1]
nm1 <- tapply(names(x), rep(seq_along(rl$values), rl$lengths),
FUN = function(y) paste(range(y), collapse="-"))[i1]
data.frame(NumberOfOnes = l1, Range = nm1)}))
data
df1 <- structure(list(A = c(1L, 0L, 0L), B = c(1L, 0L, 0L), C = c(1L,
0L, 0L), D = c(1L, 0L, 0L), E = c(1L, 1L, 0L), F = c(1L, 1L,
0L), G = c(1L, 1L, 0L), H = c(1L, 0L, 1L), I = c(1L, 1L, 1L),
J = c(0L, 1L, 1L), K = c(0L, 0L, 1L), L = c(0L, 0L, 1L),
M = c(0L, 1L, 0L), N = c(0L, 0L, 0L)), class = "data.frame", row.names = c(NA,
-3L))