Summarize values for overlapping time periods - r

Question

I'm trying to summarize values for overlapping time periods.
I can use only tidyr, ggplot2 and dplyr libraries. Base R is preferred though.
My data looks like this, but usually it has around 100 records:
df <- structure(list(Start = structure(c(1546531200, 1546531200, 546531200, 1546638252.6316, 1546549800, 1546534800, 1546545600, 1546531200, 1546633120, 1547065942.1053), class = c("POSIXct", "POSIXt"), tzone = "UTC"), Stop = structure(c(1546770243.1579, 1546607400, 1547110800, 1546670652.6316, 1547122863.1579, 1546638252.6316, 1546878293.5579, 1546416000, 1546849694.4, 1547186400), class = c("POSIXct", "POSIXt"), tzone = "UTC"), Value = c(12610, 520, 1500, 90, 331380, 27300, 6072, 4200, 61488, 64372)), .Names = c("Start", "Stop", "Value"), row.names = c(41L, 55L, 25L, 29L, 38L, 28L, 1L, 20L, 14L, 31L), class = c("tbl_df", "tbl", "data.frame"))
head(df) and str(df) gives:
Start Stop Value
2019-01-03 16:00:00 2019-01-06 10:24:03 12610
2019-01-03 16:00:00 2019-01-04 13:10:00 520
2019-01-03 16:00:00 2019-01-10 09:00:00 1500
2019-01-04 21:44:12 2019-01-05 06:44:12 90
2019-01-03 21:10:00 2019-01-10 12:21:03 331380
2019-01-03 17:00:00 2019-01-04 21:44:12 27300
Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 10 obs. of 3 variables:
$ Start: POSIXct, format: "2019-01-03 16:00:00" "2019-01-03 16:00:00" ...
$ Stop : POSIXct, format: "2019-01-06 10:24:03" "2019-01-04 13:10:00" ...
$ Value: num 12610 520 1500 90 331380 ...
So there are overlapping time periods with "Start" and "Stop" dates with assigned value. In any given record when there is a value between df$Start and df$Stop and outside of this scope the value is 0.
I want to create another dataframe based on which I could show how this values summarize and change over time. The Desired output would look like this (the "sum" column is made up):
> head(df2)
timestamp sum
"2019-01-02 09:00:00 CET" 14352
"2019-01-03 17:00:00 CET" 6253
"2019-01-03 18:00:00 CET" 23465
"2019-01-03 21:00:00 CET" 3241
"2019-01-03 22:10:00 CET" 23235
"2019-01-04 14:10:00 CET" 123321
To get unique timestamps:
timestamps <- sort(unique(c(df$`Start`, df$`Stop`)))
With df2 dataframe I could easily draw a graph with ggplot, but how to get this sums?
I think I should iterate over df data frame either some custom function or any built-it summarize function which would work like this:
fnct <- function(date, min, max, value) {
if (date >= min && date <=max) {
a <- value
}
else {
a <- 0
}
return(a)
}
...for every given date from timestamps iterate through df and give me a sum of values for the timestamp.
It looks really simple and I'm missing something very basic.

Answer 1

Here's a tidyverse solution similar to my response to this recent question. I gather to bring the timestamps (Starts and Stops) into one column, with another column specifying which. The Starts add the value and the Stops subtract it, and then we just take the cumulative sum to get values at all the instants when the sum changes.
For 100 records, there won't be any perceivable speed improvement from using data.table; in my experience it starts to make more of a difference around 1M records, especially when grouping is involved.
library(dplyr); library(tidyr)
df2 <- df %>%
gather(type, time, Start:Stop) %>%
mutate(chg = if_else(type == "Start", Value, -Value)) %>%
arrange(time) %>%
mutate(sum = cumsum(chg)) # EDIT: corrected per OP comment
> head(df2)
## A tibble: 6 x 5
# Value type time chg sum
# <dbl> <chr> <dttm> <dbl> <dbl>
#1 1500 Start 1987-04-27 14:13:20 1500 1500
#2 4200 Stop 2019-01-02 08:00:00 -4200 -2700
#3 12610 Start 2019-01-03 16:00:00 12610 9910
#4 520 Start 2019-01-03 16:00:00 520 10430
#5 4200 Start 2019-01-03 16:00:00 4200 14630
#6 27300 Start 2019-01-03 17:00:00 27300 41930

Answer 2

In the past I have tried to solve similar problems using the tidyverse/baseR... But nothing comes even remotely close to the speeds that data.table provides for these kind of operations, so I encourage you to give it a try...
For questions like this, my favourite finction is foverlaps() from the data.table-package. With this function you can (fast!) perform an overlap-join. If you want more flexibility in your joining than foverlaps() provides, a non-equi-join (again using data.table) is probably the best (and fastest!) option. But foverlaps() will do here (I guess).
I used the sample data you provided, but filtered out rows where Stop <= Start (probably a tyop in your sample data). When df$Start is not before df$Stop, foverlaps give a warning and won't execute.
library( data.table )
#create data.table with periods you wish to simmarise on
#NB: UTC is used as timezone, since this is also the case in the sample data provided!!
dt.dates <- data.table( id = paste0( "Day", 1:31 ),
Start = seq( as.POSIXct( "2019-01-01 00:00:00", format = "%Y-%m-%d %H:%M:%S", tz = "UTC" ),
as.POSIXct( "2019-01-31 00:00:00", format = "%Y-%m-%d %H:%M:%S", tz = "UTC" ),
by = "1 days"),
Stop = seq( as.POSIXct( "2019-01-02 00:00:00", format = "%Y-%m-%d %H:%M:%S", tz = "UTC" ) - 1,
as.POSIXct( "2019-02-01 00:00:00", format = "%Y-%m-%d %H:%M:%S", tz = "UTC" ) - 1,
by = "1 days") )
If you do not want to summarise on a daily basis, but by hour, minute, second, of year. Just change the values (and stepsize) in dt.dates data.table so that they match your periods.
#set df as data.table
dt <- as.data.table( df )
#filter out any row where Stop is smaller than Start
dt <- dt[ Start < Stop, ]
#perform overlap join
#first set keys
setkey(dt, Start, Stop)
#then perform join
result <- foverlaps( dt.dates, dt, type = "within" )
#summarise
result[, .( Value = sum( Value , na.rm = TRUE ) ), by = .(Day = i.Start) ]
output
# Day Value
# 1: 2019-01-01 1500
# 2: 2019-01-02 1500
# 3: 2019-01-03 1500
# 4: 2019-01-04 351562
# 5: 2019-01-05 413050
# 6: 2019-01-06 400440
# 7: 2019-01-07 332880
# 8: 2019-01-08 332880
# 9: 2019-01-09 332880
# 10: 2019-01-10 64372
# 11: 2019-01-11 0
# 12: 2019-01-12 0
# 13: 2019-01-13 0
# 14: 2019-01-14 0
# 15: 2019-01-15 0
# 16: 2019-01-16 0
# 17: 2019-01-17 0
# 18: 2019-01-18 0
# 19: 2019-01-19 0
# 20: 2019-01-20 0
# 21: 2019-01-21 0
# 22: 2019-01-22 0
# 23: 2019-01-23 0
# 24: 2019-01-24 0
# 25: 2019-01-25 0
# 26: 2019-01-26 0
# 27: 2019-01-27 0
# 28: 2019-01-28 0
# 29: 2019-01-29 0
# 30: 2019-01-30 0
# 31: 2019-01-31 0
# Day Value
plot
#summarise for plot
result.plot <- result[, .( Value = sum( Value , na.rm = TRUE ) ), by = .(Day = i.Start) ]
library( ggplot2 )
ggplot( data = result.plot, aes( x = Day, y = Value ) ) + geom_col()