# Load the library for doing association rules
# install.packages('arules')
library(arules)

# Data preprocessing
data("AdultUCI")
AdultUCI[1:2,]

AdultUCI[["fnlwgt"]] <- NULL
AdultUCI[["education-num"]] <- NULL

AdultUCI[[ "age"]] <- ordered(cut(AdultUCI[[ "age"]], c(15,25,45,65,100)),
                              labels = c("Young", "Middle-aged", "Senior", "Old"))
AdultUCI[[ "hours-per-week"]] <- ordered(cut(AdultUCI[[ "hours-per-week"]],
                                               c(0,25,40,60,168)),
                                           labels = c("Part-time", "Full-time", "Over-time", "Workaholic"))
AdultUCI[[ "capital-gain"]] <- ordered(cut(AdultUCI[[ "capital-gain"]],
                                             c(-Inf,0,median(AdultUCI[[ "capital-gain"]][AdultUCI[[ "capital-gain"]]>0]),Inf)),
                                         labels = c("None", "Low", "High"))
AdultUCI[[ "capital-loss"]] <- ordered(cut(AdultUCI[[ "capital-loss"]],
                                           c(-Inf,0, median(AdultUCI[[ "capital-loss"]][AdultUCI[[ "capital-loss"]]>0]),Inf)),
                                         labels = c("none", "low", "high"))

# Convert the data into a transactions format
Adult <- as(AdultUCI, "transactions")
Adult
# transactions in sparse format with
# 48842 transactions (rows) and
# 115 items (columns)

summary(Adult)

# Plot frequent itemsets
itemFrequencyPlot(Adult, support = 0.1, cex.names=0.8)

# generate rules
min_support = 0.01
confidence = 0.6
rules <- apriori(Adult, parameter = list(support = min_support,
                                         confidence = confidence))
rules

inspect(rules[100:110, ])
# lhs                             rhs                            support    confidence lift
# {occupation=Farming-fishing} => {sex=Male}                     0.02856148 0.9362416  1.4005486
# {occupation=Farming-fishing} => {race=White}                   0.02831579 0.9281879  1.0855456
# {occupation=Farming-fishing} => {native-country=United-States} 0.02671881 0.8758389  0.9759474