I read data from a csv file, the data has 3 columns, one is transaction id, the other two are product and product catagory. I need to convert this into transactions in order to use the apriori
function in arules. It shows an error when I convert to transactions:
dat <- read.csv("spss.csv",head=TRUE,sep="," , as.is = T)
dat[,2] <- factor(dat[,2])
dat[,3] <- factor(dat[,3])
spssdat <- dat[,c(1,2,3)]
str(spssdat)
'data.frame': 108919 obs. of 3 variables:
$ Transaction_id: int 3000312 3000312 3001972 3003361 3003361 3003361 3003361 3003361 3003361 3004637 ...
$ product_catalog : Factor w/ 9 levels "AIM","BA","IM",..: 1 1 5 7 7 7 7 7 7 1 ...
$ product : Factor w/ 332 levels "ACM","ACTG/AIM",..: 7 7 159 61 61 61 61 61 61 7 ...
trans4 <- as(spssdat, "transactions")
Error in as(spssdat, "transactions") :
no method or default for coercing “data.frame” to “transactions”
If the data only have two columns, it can work by:
trans4 <- as(split(spssdat[,2], spssdat[,1]), "transactions")
But I don't know how to convert when I have 3 columns. Usually there are the additional columns likes category attributes, customer attributes. so the column usually large than 2 columns. need to find rules between multiple columns.
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