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I've looked all over and can't seem to get a clear idea of how to do this; I have ts data with quarterly frequencies. I simply want to add a dummy variable only for the data corresponding to Q4 but I can't seem to make this work. I thought subset may be correct however I get an error that datasets are of different length when I try and regress data~trend+subset. I ran into a similar problem when trying to use the as.factor approach. I'm a newbie to R so any help would be appreciated.

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    $\begingroup$ The answers here could be of help to you: stackoverflow.com/questions/11952706/generate-a-dummy-variable-in-r. $\endgroup$ – Dr_Be Jan 11 '16 at 13:52
  • $\begingroup$ post the first 10 lines of your data set. Do you have a date column? If yes, then one can extract the month (look for the function .. e.g. the Date objects, timeDate and so forth) $\endgroup$ – Ric Mar 13 '16 at 10:15
  • $\begingroup$ This most probably already has an answer on stats.stackexchange ... or it gets a good one there ... in any case Sergeys answer could be helpful too. $\endgroup$ – Ric Jul 11 '16 at 6:28
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Time series of 4 years of quarterly data with 1 for 4th quarter:

ts(rep(c(0,0,0,1),4), f=4)
  Qtr1 Qtr2 Qtr3 Qtr4
1    0    0    0    1
2    0    0    0    1
3    0    0    0    1
4    0    0    0    1

Is this what you want?

That was a dummy series of ts class. Actually, depending on your needs simple rep(c(0,0,0,1),4) may suffice, e.g. for ARIMA regression as exog dummy (w/o coercion to ts)

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Here's one solution.

For this example I will use the ausres data set.

df <- data.frame(time=time(austres), Value = as.matrix(austres))
df$Quarter <- ifelse(df$time - floor(df$time) == 0, 'Q1',
          ifelse(df$time - floor(df$time) == 0.25, 'Q2',
          ifelse(df$time - floor(df$time) == 0.5, 'Q3',
          ifelse(df$time - floor(df$time) == 0.75, 'Q4',''))))

df$NewData <- ifelse(df$Quarter == 'Q4', df$Value*1.15, df$Value)

First line, convert your time series to a data frame. Then associate each ts to corresponding quarter. Last line you'll want to transform Q4 based on whatever you want to do. In this case I am increasing Q4 by 15%. If you need to transform the data frame back to a time series you can always do that since the first column is preserved.

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