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How to smooth / merge overlapping time periods

I have a large dataset of time periods defined by the start and end columns. Some periods overlap.

I would like to combine (smooth / merge / collapse) all overlapping time periods in order to have one “start” value and one “end” value.

Some sample data:

  ID      start        end
1  A 2013-01-01 2013-01-05
2  A 2013-01-01 2013-01-05
3  A 2013-01-02 2013-01-03
4  A 2013-01-04 2013-01-06
5  A 2013-01-07 2013-01-09
6  A 2013-01-08 2013-01-11
7  A 2013-01-12 2013-01-15

Desired Result:

  ID      start        end
1  A 2013-01-01 2013-01-06
2  A 2013-01-07 2013-01-11
3  A 2013-01-12 2013-01-15

What I tried:

  require(dplyr)
  data <- structure(list(ID = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L), class = "factor", .Label = "A"), 
    start = structure(c(1356998400, 1356998400, 1357084800, 1357257600, 
    1357516800, 1357603200, 1357948800), tzone = "UTC", class = c("POSIXct", 
    "POSIXt")), end = structure(c(1357344000, 1357344000, 1357171200, 
    1357430400, 1357689600, 1357862400, 1358208000), tzone = "UTC", class = c("POSIXct", 
    "POSIXt"))), .Names = c("ID", "start", "end"), row.names = c(NA, 
-7L), class = "data.frame")

remove.overlaps <- function(data){
data2 <- data
for ( i in 1:length(unique(data$start))) {
x3 <- filter(data2, start>=data$start[i] & start<=data$end[i])
x4 <- x3[1,]
x4$end <- max(x3$end)
data2 <- filter(data2, start<data$start[i] | start>data$end[i])
data2 <- rbind(data2,x4)  
}
data2 <- na.omit(data2)}

data <- remove.overlaps(data)
+10
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4 answers

. , start " " cummax cummax ,

data %>%
  arrange(ID, start) %>% # as suggested by @Jonno in case the data is unsorted
  group_by(ID) %>%
  mutate(indx = c(0, cumsum(as.numeric(lead(start)) >
                     cummax(as.numeric(end)))[-n()])) %>%
  group_by(ID, indx) %>%
  summarise(start = first(start), end = last(end))

# Source: local data frame [3 x 4]
# Groups: ID
# 
#   ID indx      start        end
# 1  A    0 2013-01-01 2013-01-06
# 2  A    1 2013-01-07 2013-01-11
# 3  A    2 2013-01-12 2013-01-15
+13

@ - - , - last summarise . first(start) last(end) min(start) max(end)

data %>%
  group_by(ID) %>%
  mutate(indx = c(0, cumsum(as.numeric(lead(start)) >
                     cummax(as.numeric(end)))[-n()])) %>%
  group_by(ID, indx) %>%
  summarise(start = min(start), end = max(end))

, @Jonno Bourne, start .

+10

IRanges Bioconductor , . - reduce(), .

, IRanges ( ), IRanges Date POSIXct .

, , dplyr IRanges ( , dplyr), data.table:

library(data.table)
options(datatable.print.class = TRUE)
library(IRanges)
library(lubridate)

setDT(data)[, {
  ir <- reduce(IRanges(as.numeric(start), as.numeric(end)))
  .(start = as_datetime(start(ir)), end = as_datetime(end(ir)))
}, by = ID]

       ID      start        end
   <fctr>     <POSc>     <POSc>
1:      A 2013-01-01 2013-01-06
2:      A 2013-01-07 2013-01-11
3:      A 2013-01-12 2013-01-15


setDT(data)[, as.data.table(reduce(IRanges(as.numeric(start), as.numeric(end))))[
  , lapply(.SD, as_datetime), .SDcols = -"width"], 
  by = ID]

as_datetime() lubridate, POSIXct.

IRanges vs David.

+3

, , @zach , data.table . , , , 20% , tidy . ( IRange R 3.5.1)

In addition, by the way, the accepted answer does not cover the extreme case when one date range is completely in another (for example, 2018-07-07- 2017-07-14- within 2018-05-01- 2018-12-01). @Zach's answer captures this extreme case.

library(data.table)

start_col = c("2018-01-01","2018-03-01","2018-03-10","2018-03-20","2018-04-10","2018-05-01","2018-05-05","2018-05-10","2018-07-07")
end_col = c("2018-01-21","2018-03-21","2018-03-31","2018-04-09","2018-04-30","2018-05-21","2018-05-26","2018-05-30","2018-07-14")

# create fake data, double it, add ID
# change row 17, such that each ID grouping is a little different
# also adds an edge case in which one date range is totally within another
# (this is the edge case not currently captured by the accepted answer)
d <- data.table(start_col = as.Date(start_col), end_col = as.Date(end_col))
d2<- rbind(d,d)
d2[1:(.N/2), ID := 1]
d2[(.N/2 +1):.N, ID := 2]
d2[17,end_col := as.Date('2018-12-01')]

# set keys (also orders)
setkey(d2, ID, start_col, end_col)

# get rid of overlapping transactions and do the date math
squished <- d2[,.(START_DT = start_col, 
                  END_DT = end_col, 
                  indx = c(0, cumsum(as.numeric(lead(start_col)) > cummax(as.numeric(end_col)))[-.N])),
               keyby=ID
               ][,.(start=min(START_DT), 
                    end = max(END_DT)),
                 by=c("ID","indx")
                 ]
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