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Editorial Team
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Asked: 2026-06-03T14:01:29+00:00

Related to this question here , but I decided to ask another question for

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Related to this question here, but I decided to ask another question for the sake of clarity as the ‘new’ question is not directly related to the original. Briefly, I am using ddply to cumulatively sum a value for each of three years. My code takes data from the first year and repeats in in the second and third-year rows of the column. My guess is that each 1-year chunk is being copied to the whole of the column, but I don’t understand why.

Q. How can I get a cumulatively summed value for each year, in the right rows of the designated column?

[Edit: the for loop – or something similar – is important, as ultimately I want to automagically calculate new columns based on a list of column names, rather than calculating each new column by hand. The loop iterates over the list of column names.]

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I use the ddply and cumsum combination frequently so it is rather vexing to suddenly be having problems with it.

[Edit: this code has been updated to the solution I settled on, which is based on @Chase’s answer below]

require(lubridate)
require(plyr)
require(xts)
require(reshape)
require(reshape2)

set.seed(12345)
# create dummy time series data
monthsback <- 24
startdate <- as.Date(paste(year(now()),month(now()),"1",sep = "-")) - months(monthsback)
mydf <- data.frame(mydate = seq(as.Date(startdate), by = "month", length.out = monthsback),
                   myvalue1 = runif(monthsback, min = 600, max = 800),
                   myvalue2 = runif(monthsback, min = 1900, max = 2400),
                   myvalue3 = runif(monthsback, min = 50, max = 80),
                   myvalue4 = runif(monthsback, min = 200, max = 300))

mydf$year <- as.numeric(format(as.Date(mydf$mydate), format="%Y"))
mydf$month <- as.numeric(format(as.Date(mydf$mydate), format="%m"))

# Select columns to process
newcolnames <- c('myvalue1','myvalue4','myvalue2')

# melt n' cast
mydf.m <- mydf[,c('mydate','year',newcolnames)]
mydf.m <- melt(mydf.m, measure.vars = newcolnames)
mydf.m <- ddply(mydf.m, c("year", "variable"), transform, newcol = cumsum(value))
mydf.m <- dcast(mydate ~ variable, data = mydf.m, value.var = "newcol")
colnames(mydf.m) <- c('mydate',paste(newcolnames, "_cum", sep = ""))
mydf <- merge(mydf, mydf.m, by = 'mydate', all = FALSE)
mydf
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1 Answer

  1. Editorial Team

    I don’t really follow your for loop there, but are you overcomplicating things? Can’t you just directly use transform and ddply?

    #Make sure it's ordered properly
mydf <- mydf[order(mydf$year, mydf$month),]

#Use ddply to calculate the cumsum by year:
ddply(mydf, "year", transform, 
      cumsum1 = cumsum(myvalue1), 
      cumsum2 = cumsum(myvalue2))
#----------
       mydate myvalue1 myvalue2 year month   cumsum1   cumsum2
1  2010-05-01 744.1808 264.4543 2010     5  744.1808  264.4543
2  2010-06-01 775.1546 238.9828 2010     6 1519.3354  503.4371
3  2010-07-01 752.1965 269.8544 2010     7 2271.5319  773.2915
....
9  2011-01-01 745.5411 218.7712 2011     1  745.5411  218.7712
10 2011-02-01 797.9474 268.1834 2011     2 1543.4884  486.9546
11 2011-03-01 606.9071 237.0104 2011     3 2150.3955  723.9650
...
21 2012-01-01 690.7456 225.9681 2012     1  690.7456  225.9681
22 2012-02-01 665.3505 232.1225 2012     2 1356.0961  458.0906
23 2012-03-01 793.0831 206.0195 2012     3 2149.1792  664.1101

    EDIT – this is untested as I don’t have R on this machine, but this is what I had in mind:

    require(reshape2)
mydf.m <- melt(mydf, measure.vars = newcolnames)
mydf.m <- ddply(mydf.m, c("year", "variable"), transform, newcol = cumsum(value))
dcast(mydate + year + month  ~ variable, data = mydf.m, value.var = "newcol")
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