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Asked: 2026-06-01T13:02:18+00:00

I am trying to calculate a rolling mean using plyr. The data is at

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I am trying to calculate a rolling mean using plyr. The data is at the industry-country-year, with repeated observations for each industry-country. The data is unbalanced, but most industry-countries have approximately 15 observations.

For example the data looks like this:

country       ISIC      Year      Value
Algeria        1        1990       400
Algeria        1        1991       450
Algeria        1        1992       460
Algeria        2        1990       450
Algeria        2        1991       500
Algeria        2        1992       450
Argentina      1        1990       400
Argentina      1        1991       450
Argentina      1        1992       460
Argentina      2        1990       450
Argentina      2        1991       500
Argentina      2        1992       450
.              .        .          .
.              .        .          .

If I subset the data to a specific industry and country I am able to calculate the rolling mean like this

rollmean(subdata$Value, 3)

However, I’ve been unable to get it to work with plyr, so as to calculate the rolling mean for each industry-country group.
I’ve tried:

roll <- ddply(data, .(country, ISIC), summarize, rollmean(data$Value, 3))
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1 Answer

  1. Editorial Team

    a rolling mean necessarily shortens the data which part of why you get the error.

    ddply(dat, .(country, ISIC), function(df) data.frame(country=unique(df$country),                  
                                                     ISIC=unique(df$ISIC),
                                                     rolled=rollmean(df$Value, 3)))
    country ISIC   rolled
1   Algeria    1 436.6667
2   Algeria    2 466.6667
3 Argentina    1 436.6667
4 Argentina    2 466.6667

    However, if you’re doing a rolling mean on 3 samples and your data only has 3 samples, you’re just calculating the mean:

    ddply(dat, .(country, ISIC), summarise, mean(Value))

    country ISIC      ..1
1   Algeria    1 436.6667
2   Algeria    2 466.6667
3 Argentina    1 436.6667
4 Argentina    2 466.6667

    UPDATED FOR COMMENTS:

    To return the dates you can use the na.pad argument to rollmean:

    ddply(dat, .(country, ISIC), function(df) {df$rolled <- rollmean(df$Value, 3, na.pad=TRUE); return(df)})
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