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Asked: 2026-06-06T09:50:24+00:00

I have 100 text files which contains time series starting and ending at different

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I have 100 text files which contains time series starting and ending at different point of times. I want to extract the values for common period of time in the series.
Use the following code to generate the sample data:

set.seed(1)
D1 = data.frame(time = seq(ISOdatetime(2012, 6, 26, 3, 15, 00), 
                       length = 500, by = 900),
            value = rnorm(500))
D2 = data.frame(time = seq(ISOdatetime(2012, 6, 24, 5, 30, 00),
                       length = 541, by = 900),
            value = rnorm(541))
D3 = data.frame(time = seq(ISOdatetime(2012, 6, 23, 5, 45, 00),
                       length = 700, by = 900),
            value = rnorm(700))

This data will give you 3 time series starting and ending and different times. I wish to keep only the values for common time period and remove the rest. i.e.
if,

  • 1st series starts with “2012-6-26 3:45:26” ends with “2012-8-07 4:45:26”
  • 2nd with “2012-6-24 5:55:27” ends with “2012-7-28 7:45:26”
  • 3rd with “2012-6-23 5:04:30” ends with “2012-7-27 4:45:26”

Then I wish to keep the data of intersection of the three time series i.e. data corresponding to:-

  • start: “2012-6-26 3:45:26”
  • end:”2012-7-27 4:45:26″
  • for all the 3 series and remove the rest.

I searched SO and other websites but didn’t find any solution. Need help on that.
How do I achieve that?

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1 Answer

  1. Editorial Team

    It seems like you need to familiarize yourself with the xts package. Convert your data frame to xts time series objects and use merge. merge will merge all values, so if you want values occurring in all, you can also use na.omit.

    require(xts)
D1 = xts(d1$Value, d1$Time)
D2 = xts(d2$Value, d2$Time)
D3 = xts(d3$Value, d3$Time)
temp = merge(D1, D2, D3)

    Here is some example output. For head and tail, note the presence of NA values.

    head(temp)
#                              D1 D2 D3
# 2012-06-26 13:15:19 -0.50219235 NA NA
# 2012-06-26 13:30:19  0.13153117 NA NA
# 2012-06-26 13:45:19 -0.07891709 NA NA
# 2012-06-26 14:00:19  0.88678481 NA NA
# 2012-06-26 14:15:19  0.11697127 NA NA
# 2012-06-26 14:30:19  0.31863009 NA NA
tail(temp)
#                     D1 D2         D3
# 2012-07-04 05:45:19 NA NA  1.4799645
# 2012-07-04 06:00:19 NA NA -0.3942801
# 2012-07-04 06:15:19 NA NA -0.6767234
# 2012-07-04 06:30:19 NA NA -0.2425192
# 2012-07-04 06:45:19 NA NA  0.4547177
# 2012-07-04 07:00:19 NA NA  1.1712661
head(na.omit(temp))
#                             D1          D2          D3
# 2012-06-27 14:15:19 -0.3329234 -1.63230970  0.75619287
# 2012-06-27 14:30:19  1.3631137 -0.06299626 -1.36131851
# 2012-06-27 14:45:19 -0.4691473 -0.70544686 -0.60876462
# 2012-06-27 15:00:19  0.8428756 -0.31417818 -0.21174696
# 2012-06-27 15:15:19 -1.4579937 -0.26694627 -0.67847242
# 2012-06-27 15:30:19 -0.4003059  0.15315947  0.06665787
tail(na.omit(temp))
#                              D1         D2          D3
# 2012-07-01 16:45:19 -0.49419020  1.1911322  2.73143169
# 2012-07-01 17:00:19 -1.71111303  0.7613245  0.57057667
# 2012-07-01 17:15:19  0.04005805 -0.1210687  1.32083870
# 2012-07-01 17:30:19 -0.56114348 -1.2250590  0.09951626
# 2012-07-01 17:45:19 -2.55736206 -0.1637461 -0.39435301
# 2012-07-01 18:00:19 -0.69677881 -1.3138963  0.63649492
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