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# Loading the data

install.packages("groceries.csv")
library(arules)
# Use the groceries object from the arules package

# Frequent Items

frequentItems <- eclat (Groceries, parameter = list(supp = 0.07, maxlen = 15))
inspect(frequentItems)
itemFrequencyPlot(Groceries, topN=10, type="absolute", main="Item Frequency")


# REMOVE REPEATED RULES
subsetRules <- which(colSums(is.subset(rules, rules)) > 1) # get subset rules in vector
length(subsetRules)  #> 3913
rules <- rules[-subsetRules] # remove subset rules. 

#FIND RULES RELATED TO SOME ITEM
rules <- apriori (data=Groceries, parameter=list (supp=0.001,conf = 0.08), appearance = list (default="lhs",rhs="whole milk"), control = list (verbose=F)) # get rules that lead to buying 'whole milk'
rules_conf <- sort (rules, by="confidence", decreasing=TRUE) # 'high-confidence' rules.
inspect(head(rules_conf))