A more specific reproducible example.
set.seed(1023)
library(arules)
# starting from a dataframe whose fields are characters (see stringsAsFactors =
FALSE), as asked
products <- c("P1", "P2", "P3", "P4", "P5", "P6", "P7", "P8", "P9", "P10")
mydf <- data.frame(user = sample(LETTERS[1:20], 100, replace=T),
prod = sample(products, 100, replace=T),
stringsAsFactors=FALSE)
str(mydf)
# convert to factors
mydf$user <- factor(mydf$user)
mydf$prod <- factor(mydf$prod)
# splitting by user
prodlist <- split(x=mydf[,"prod"], f=mydf$user)
prodlist
# remove duplicates
prodlist <- lapply(prodlist, unique)
prod.trans <- as(prodlist, "transactions")
itemFrequency(prod.trans)
# generating rules
rules <- apriori(prod.trans,parameter=list(support=.1, confidence=.5))
inspect(rules)
--
Best,
GG
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