CODEWITHSUNDEEP
rsum
Christoph P.
Christoph P.

Reputation: 99

Summing over constant calendar week interval

I am currently trying to aggregate a weekly data to monthly data, which looks like this:

UPS         WEEK    AP
1111112016  1       385.22
1111112016  2       221.63
1111112016  3       317.47

There are 132 different UPCs and weeks are indicated by 1 - 52. However, they vary across the different UPCs. In total I have 4,027 rows. I would like to aggregate over a 4 week interval until the next UPC category is reached. I have tried this code:

z = aggregate(x$AP, by=list(x$UPC, cut(x$WEEK, breaks=13, lables = T)), FUN = sum)
colnames(z) = c("UPC", "Month", "AP")
z = z[order(z$UPC),]

I get the following output:

    UPC        Month         AP
1   1111112016 (0.951,4.77]  1098.03
88  1111112016 (4.77,8.54]   1180.03
187 1111112016 (8.54,12.3]   491.18
303 1111112016 (12.3,16.1]   896.31

There are several problems here: 1) The month value is wrong. I would like to have a numerical value. (1 - 12) 2) The first two aggregates are correct, however after that the sums seem sometimes to be correct and sometimes not.

Here is a brief example of how my data looks like:

dput(head(x))
structure(list(UPC = c(1111112016, 1111112016, 1111112016, 1111112016, 
1111112016, 1111112016), WEEK = c(1, 2, 3, 4, 5, 6), AP = c(385.22, 
221.63, 317.47, 173.71, 269.55, 311.48)), .Names = c("UPC", "WEEK", 
"AP"), row.names = c(NA, 6L), class = "data.frame")

Upvotes: 3

Views: 58

Answers (1)

User2321
User2321

Reputation: 3062

Would something like this work (where data is your dataframe): 

require(data.table)                                                                                                                                                   "AP"), row.names = c(NA, 6L), class = "data.frame")
setDT(data)
result <- data[, .(AP=sum(AP, na.rm = T)), by = .(UPC, MONTH = (floor(WEEK/ 4.34) + 1))]
result <- result[order(UPC)]

And the result will be:

        UPC   MONTH    AP
1: 1111112016     1 1098.03
2: 1111112016     2  581.03

Upvotes: 2

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