I am trying to find average of values that are within a certain time frame within the same data.table and save it to a new column.
Below is a sample data set
Updated the dataset to represent the discontinuous timeline in my original dataset.
> x
ts value avg
1: 2015-01-01 00:00:23 9 0
2: 2015-01-01 00:01:56 11 0
3: 2015-01-01 00:02:03 18 0
4: 2015-01-01 00:03:16 1 0
5: 2015-01-01 00:05:19 6 0
6: 2015-01-01 00:05:54 16 0
7: 2015-01-01 00:06:27 13 0
8: 2015-01-01 00:06:50 7 0
9: 2015-01-01 00:08:41 12 0
10: 2015-01-01 00:09:08 17 0
11: 2015-01-01 00:09:28 8 0
12: 2015-01-01 00:10:56 5 0
13: 2015-01-01 00:11:44 10 0
14: 2015-01-01 00:12:23 20 0
15: 2015-01-01 00:12:28 2 0
16: 2015-01-01 00:12:37 15 0
17: 2015-01-01 00:12:42 4 0
18: 2015-01-01 00:12:48 19 0
19: 2015-01-01 00:13:41 3 0
20: 2015-01-01 00:16:04 14 0
My code assigns value 10.5 to all the rows and I donot get the expected results. Here is my code.
require(lubridate)
x[, avg := x[ts>=ts-minutes(2) & ts<=ts , mean(value)], verbose=TRUE ]
Updated
I want the results to be as below
ts value avg
1 01-01-2015 00:00:23 9 0
2 01-01-2015 00:01:56 11 9
3 01-01-2015 00:02:03 18 10
4 01-01-2015 00:03:16 1 14.5
5 01-01-2015 00:05:19 6 0
6 01-01-2015 00:05:54 16 6
7 01-01-2015 00:06:27 13 11
8 01-01-2015 00:06:50 7 11.66666667
9 01-01-2015 00:08:41 12 7
10 01-01-2015 00:09:08 17 12
11 01-01-2015 00:09:28 8 14.5
12 01-01-2015 00:10:56 5 12.5
13 01-01-2015 00:11:44 10 5
14 01-01-2015 00:12:23 20 7.5
15 01-01-2015 00:12:28 2 11.66666667
16 01-01-2015 00:12:37 15 9.25
17 01-01-2015 00:12:42 4 10.4
18 01-01-2015 00:12:48 19 9.333333333
19 01-01-2015 00:13:41 3 11.666667
20 01-01-2015 00:16:04 14 0
I want to do this to a data with a larger data set, also with min and max values in separate columns separately( here I have shown only the average function). Any help would be great.
Updated
Below is the reproducible code.
#reproducible code
ts<- seq(from=ISOdatetime(2015,1,1,0,0,0,tz="GMT"),to=ISOdatetime(2015,1,1,0,0,19,tz="GMT"), by="sec")
set.seed(2)
ts <-ts + seconds(round(runif(20,0,1000),0))
value <- 1:20
avg <- 0
x <- data.table(ts,value,avg)
setkey(x,ts)
x
Solution
Thanks to @Saksham for poining me towards apply functions. Here is the solution that I have come up with.
find <- function(y){
mean(x[ts>=y-minutes(2) & ts<y,value])
}
x$avg <- mapply(find,x[,ts])
> x
ts value avg
1: 2015-01-01 00:00:23 9 NaN
2: 2015-01-01 00:01:56 11 9.000000
3: 2015-01-01 00:02:03 18 10.000000
4: 2015-01-01 00:03:16 1 14.500000
5: 2015-01-01 00:05:19 6 NaN
6: 2015-01-01 00:05:54 16 6.000000
7: 2015-01-01 00:06:27 13 11.000000
8: 2015-01-01 00:06:50 7 11.666667
9: 2015-01-01 00:08:41 12 7.000000
10: 2015-01-01 00:09:08 17 12.000000
11: 2015-01-01 00:09:28 8 14.500000
12: 2015-01-01 00:10:56 5 12.500000
13: 2015-01-01 00:11:44 10 5.000000
14: 2015-01-01 00:12:23 20 7.500000
15: 2015-01-01 00:12:28 2 11.666667
16: 2015-01-01 00:12:37 15 9.250000
17: 2015-01-01 00:12:42 4 10.400000
18: 2015-01-01 00:12:48 19 9.333333
19: 2015-01-01 00:13:41 3 11.666667
20: 2015-01-01 00:16:04 14 NaN
Will this do
ts[,avg] <- ts[,val] - 0.5
As logically and seeing your expected result, it is doing the same thing. You can edit you expected result to make it more flexible if I interpreted it wrong.
EDIT:
This base R approach should do the trick. As I an not familiar with manipulating time, I am assuming that arithmetic works in the same way as it does in most of the languages
interval <- minutes(2) #Assuming this is how we define 5 minutes
x$avg <- apply( x, 1, function(y){
mean(x$value[x$time > ( y["time"]) - interval ) && x$time < y["time"]])
})
来源：https://stackoverflow.com/questions/32524371/finding-average-of-values-in-the-past-2-minutes-in-a-data-table