csv - R: Histogram of missing data -

i have csv of sec-by-sec values, looks this:

"x","timestamp","value" "1",2016-01-01 00:00:00,124 "2",2016-01-01 00:00:01,121 "3",2016-01-01 00:00:02,na "4",2016-01-01 00:00:03,na "5",2016-01-01 00:00:04,na "6",2016-01-01 00:00:05,123 "7",2016-01-01 00:00:06,122 "8",2016-01-01 00:00:07,124 "9",2016-01-01 00:00:08,na "10",2016-01-01 00:00:09,124 

so there data missing , marked na. want make histogram of length of missing data blocks. in given example mean count how many missing data blocks have length of 1 sec (1), of 2 sec (0), of 3 sec (1) , on.

in real life data set bins/intervals bit different, think of these 8 categories:

= 1 sec 2 5 sec 6 10 sec 11 30 sec  31 300 sec  301 3600 sec  3600 86400 sec > 86400 sec 

so idea let r code run through lines of csv , whenever detects na value, count lines until finds real value again. 8 categories integer variable counted +1 everytime fitting block of na-values detected.

as complete r-noob have no idea how that. highly appreciated :)

i sure there must timeseries solution, started (using set.seed generate repeatable random values):

set.seed(42)  # create sample data df <- data.frame(x = 1:100,                   timestamp = seq(from = sys.time() - 99, = sys.time(), = "secs"),                   value = sample(c(na, 1:3), 100, replace = true))  # runs of identical data runs <- rle(is.na(df$value))  # missing missing <- which(runs$values)  # end positions in sequence missing positions <- cumsum(runs$lengths)  # start times start <- df$timestamp[positions[missing] - runs$lengths[missing] + 1] end <- df$timestamp[positions[missing]]  # time difference delta <- difftime(end, start, "seconds")  # combine in usable data.frame output <- data.frame(startrow = positions[missing] - runs$lengths[missing] + 1,                       endrow = positions[missing],                      starttime = start,                       endtime = end,                       duration = delta)