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16846563
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2013-05-30T22:01:58.943
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2013-05-30T22:01:58.943
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text
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<p>I'm almost embarrassed to post this. I'm usually pretty good as these, but there's got to be a better way. </p> <p>This first uses <code>zoo</code>'s <code>as.yearmon</code> to get the dates in terms of just month and year, then reshapes it to get one column for each <code>id</code>/<code>class</code> combination, then fills in with zeros before, after, and for missing months, then uses <code>zoo</code> to get the rolling sum, then pulls out just the desired months and merges back with the original data frame.</p> <pre><code>library(reshape2) library(zoo) df$yearmon <- as.yearmon(df$t) dfa <- dcast(id + class ~ yearmon, data=df, value.var="count") ida <- dfa[,1:2] dfa <- t(as.matrix(dfa[,-c(1:2)])) months <- with(df, seq(min(yearmon)-3/12, max(yearmon)+3/12, by=1/12)) dfb <- array(dim=c(length(months), ncol(dfa)), dimnames=list(paste(months), colnames(dfa))) dfb[rownames(dfa),] <- dfa dfb[is.na(dfb)] <- 0 dfb <- rollsumr(dfb,4, fill=0) rownames(dfb) <- paste(months) dfb <- dfb[rownames(dfa),] dfc <- cbind(ida, t(dfb)) dfc <- melt(dfc, id.vars=c("class", "id")) names(dfc)[3:4] <- c("yearmon", "desired2") dfc$yearmon <- as.yearmon(dfc$yearmon) out <- merge(df,dfc) > out id class yearmon t count desired desired2 1 1 A Feb 2010 2010-02-15 2 3 3 2 1 A Jan 2010 2010-01-15 1 1 1 3 1 B Apr 2010 2010-04-15 3 3 3 4 1 B Sep 2010 2010-09-15 4 4 4 5 2 A Jan 2010 2010-01-15 5 5 5 6 2 B Aug 2010 2010-08-15 7 13 13 7 2 B Jun 2010 2010-06-15 6 6 6 8 2 B Sep 2010 2010-09-15 8 21 21 </code></pre>
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1. POCompute rolling sum by id variables, with missing timepoints
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