moving average on different size data frames in R - r

Question

I have a set of data taken every 5 minutes consisting of the following structure:
>df1
Date X1
01/01/2017 0:00 1
01/01/2017 0:30 32
01/01/2017 1:00 65
01/01/2017 1:30 14
01/01/2017 2:00 25
01/01/2017 2:30 14
01/01/2017 3:00 85
01/01/2017 3:30 74
01/01/2017 4:00 74
01/01/2017 4:30 52
01/01/2017 5:00 25
01/01/2017 5:30 74
01/01/2017 6:00 45
01/01/2017 6:30 52
01/01/2017 7:00 21
01/01/2017 7:30 41
01/01/2017 8:00 74
01/01/2017 8:30 11
01/01/2017 9:00 2
01/01/2017 9:30 52
Another vector is given consisting of only dates, but with a different time frequency:
>V1
Date2
1/1/2017 1:30:00
1/1/2017 3:30:00
1/1/2017 5:30:00
1/1/2017 9:30:00
I would like to calculate the moving average of X1 but at the end the only values I really need are the ones associated with the dates in V1 (but preserving the smoothing generated by the moving average)
Would you recommend to calculate the moving average of X1, then associate the value to the corresponding date in V1 and re-apply a moving average? or do you know a function in R that would help me achieve this?
Thank you, I really appreciate your help!
Sofía

Answer 1

filter is a convenient way to construct moving averages
Assuming you want a simple arithmetic moving average, you'll need to decide how many elements you'd like to average together, and if you'd like a one or two-sided average. Arbitrarily, I'll pick 5 and one-sided.
elements <- 5
df1$x1.smooth <- filter(df1$X1, filter = rep(1/elements, elements), sides=1)
Note that "moving.average" will have elements-1 fewer elements than df1$X1 due to the moving average being undefined until there are elements items to average.
df1 is now
Date X1 x1.smooth
1 01/01/2017 0:00 1 NA
2 01/01/2017 0:30 32 NA
3 01/01/2017 1:00 65 NA
4 01/01/2017 1:30 14 NA
5 01/01/2017 2:00 25 27.4
6 01/01/2017 2:30 14 30.0
7 01/01/2017 3:00 85 40.6
8 01/01/2017 3:30 74 42.4
9 01/01/2017 4:00 74 54.4
10 01/01/2017 4:30 52 59.8
11 01/01/2017 5:00 25 62.0
12 01/01/2017 5:30 74 59.8
13 01/01/2017 6:00 45 54.0
14 01/01/2017 6:30 52 49.6
15 01/01/2017 7:00 21 43.4
16 01/01/2017 7:30 41 46.6
17 01/01/2017 8:00 74 46.6
18 01/01/2017 8:30 11 39.8
19 01/01/2017 9:00 2 29.8
20 01/01/2017 9:30 52 36.0
Now you need only merge the two data frames on Date = Date2 or else subset df1 where Date is %in% V1$Date2

Answer 2

Another option could be to use zoo package. One can use rollapply to calculate and add another column in dataframe that will hold moving average for X1.
A implementation with moving average of width 4 (every 2 hours) can be implemented as:
Library(zoo)
#Add another column with mean value
df$mean <- rollapply(df$X1, 4, mean, align = "right", fill = NA)
df
# Date X1 mean
# 1 2017-01-01 00:00:00 1 NA
# 2 2017-01-01 00:30:00 32 NA
# 3 2017-01-01 01:00:00 65 NA
# 4 2017-01-01 01:30:00 14 28.00
# 5 2017-01-01 02:00:00 25 34.00
# 6 2017-01-01 02:30:00 14 29.50
# 7 2017-01-01 03:00:00 85 34.50
# 8 2017-01-01 03:30:00 74 49.50
# 9 2017-01-01 04:00:00 74 61.75
# 10 2017-01-01 04:30:00 52 71.25
# 11 2017-01-01 05:00:00 25 56.25
# 12 2017-01-01 05:30:00 74 56.25
# 13 2017-01-01 06:00:00 45 49.00
# 14 2017-01-01 06:30:00 52 49.00
# 15 2017-01-01 07:00:00 21 48.00
# 16 2017-01-01 07:30:00 41 39.75
# 17 2017-01-01 08:00:00 74 47.00
# 18 2017-01-01 08:30:00 11 36.75
# 19 2017-01-01 09:00:00 2 32.00
# 20 2017-01-01 09:30:00 52 34.75