##########################
1. ENVIRONMENT CREATION --------------------------
##########################
install.packages("arules", dependencies = TRUE)
install.packages("TSP")
install.packages("data.table")
install.packages("arulesViz", dependencies = TRUE)
install.packages("sp")
install.packages("dplyr", dependencies = TRUE)
install.packages("purrr", dependencies = TRUE)
install.packages("devtools", dependencies = TRUE)
install.packages("tidyr")
install.packages("devtools")
devtools::install_github("mhahsler/arules")
Sys.which("make")
library(Matrix)
library(viridis)
require(arules)
library(TSP)
library(data.table)
library(tcltk)
library(dplyr)
library(devtools)
library(purrr)
library(tidyr)
library(arulesViz)
library(RColorBrewer)
## Setting the working directory
setwd("C:\\Users\\natal\\OneDrive\\university\\info 5653")
##########################
# 2. DATA IMPORT --------------------------
##########################
basket_data <- read.transactions("Basket Data.csv",
rm.duplicates=FALSE,
format='basket',
header = FALSE,
sep=',',
cols=NULL)
inspect(basket_data[0:1])
##########################
# 3. ASSOCIATION ROLE MINING --------------------------
##########################
apriori_rules <- arules::apriori(basket_data,
parameter = list(support=.05,
confidence=.15,
minlen=2,maxlen=5))
inspect(apriori_rules)
## Plotting the most frequent items
arules::itemFrequencyPlot(basket_data,topN=20,
col=brewer.pal(8, 'Pastel2'),
main = 'Relative Item Frequency Plot',
type='relative',
ylab="Item Frequency (Relative)")
## Sorting the rules
rules_sorted <- sort(apriori_rules,by='lift',
decreasing=TRUE)
inspect(rules_sorted[1:10])
## Saving the rules to a CSV
write(rules_sorted,
file='Full Association Rules.csv',
sep= ',',
quote=TRUE,
row.names = FALSE)
## Visualizing the rules
plot(rules_sorted[1:200],method='graph',engine='interactive',shading='confidence')
##########################
# 3. ASSOCIATION RULE MINING --------------------------
#
# Climate Specific Rules
##########################
## Climate Specific Rules
climate_rules <- arules::apriori(data=basket_data,
parameter = list(support=.001,conf=.05,minlen=2,maxlen=8),
appearance= list(lhs="climate"),
control = list(verbose=TRUE))
inspect(climate_rules)
climate_rules_sorted <- sort(climate_rules,by='lift',
decreasing=TRUE)
inspect(climate_rules_sorted[0:10])
## Saving the rules to a CSV
write(climate_rules_sorted,
file='Climate Association Rules.csv',
sep= ',',
quote=TRUE,
row.names = FALSE)
## Visualizing the rules
plot(climate_rules_sorted,method='graph',shading='confidence')
plot(climate_rules_sorted,method='graph',shading='confidence',engine='interactive')
##########################
# 3. ASSOCIATION RULE MINING --------------------------
#
# Democrat Specific Rules
##########################
## Democrat Specific Rules
democrat_rules <- arules::apriori(data=basket_data,
parameter = list(support=.001,conf=.05,minlen=2,maxlen=8),
appearance= list(lhs="democrat"),
control = list(verbose=TRUE))
inspect(democrat_rules)
democrat_rules_sorted <- sort(democrat_rules,by='lift',
decreasing=TRUE)
inspect(democrat_rules_sorted[0:10])
## Saving the rules to a CSV
write(democrat_rules_sorted,
file='Democrat Association Rules.csv',
sep= ',',
quote=TRUE,
row.names = FALSE)
blue_palette <- colorRampPalette(c("lightblue", "blue", "darkblue"))
## Visualizing the rules
plot(democrat_rules_sorted,method='graph',shading='confidence',control = list(colors = blue_palette(10)))
##########################
# 3. ASSOCIATION RULE MINING --------------------------
#
# Republican Specific Rules
##########################
## Democrat Specific Rules
republican_rules <- arules::apriori(data=basket_data,
parameter = list(support=.001,conf=.05,minlen=2,maxlen=8),
appearance= list(lhs="republican"),
control = list(verbose=TRUE))
inspect(republican_rules)
republican_rules_sorted <- sort(republican_rules,by='lift',
decreasing=TRUE)
inspect(republican_rules_sorted[0:10])
## Saving the rules to a CSV
write(republican_rules_sorted,
file='Republican Association Rules.csv',
sep= ',',
quote=TRUE,
row.names = FALSE)
red_palette <- colorRampPalette(c("pink", "red", "darkred"))
## Visualizing the rules
plot(republican_rules_sorted,method='graph',shading='confidence',control = list(colors = red_palette(10)))
##########################
# 3. ASSOCIATION RULE MINING --------------------------
#
# Republican Specific Rules
##########################
## Democrat Specific Rules
republican_rules <- arules::apriori(data=basket_data,
parameter = list(support=.001,conf=.05,minlen=2,maxlen=8),
appearance= list(lhs="republican"),
control = list(verbose=TRUE))
inspect(republican_rules)
republican_rules_sorted <- sort(republican_rules,by='lift',
decreasing=TRUE)
inspect(republican_rules_sorted[0:10])
## Saving the rules to a CSV
write(republican_rules_sorted,
file='Republican Association Rules.csv',
sep= ',',
quote=TRUE,
row.names = FALSE)
red_palette <- colorRampPalette(c("pink", "red", "darkred"))
## Visualizing the rules
plot(republican_rules_sorted,method='graph',shading='confidence',control = list(colors = red_palette(10)))
##########################
# 3. ASSOCIATION RULE MINING --------------------------
#
# Republican Specific Rules
##########################
## Democrat Specific Rules
republican_rules <- arules::apriori(data=basket_data,
parameter = list(support=.001,conf=.05,minlen=2,maxlen=8),
appearance= list(lhs="republican"),
control = list(verbose=TRUE))
inspect(republican_rules)
republican_rules_sorted <- sort(republican_rules,by='lift',
decreasing=TRUE)
inspect(republican_rules_sorted[0:10])
## Saving the rules to a CSV
write(republican_rules_sorted,
file='Republican Association Rules.csv',
sep= ',',
quote=TRUE,
row.names = FALSE)
red_palette <- colorRampPalette(c("pink", "red", "darkred"))
## Visualizing the rules
plot(republican_rules_sorted,method='graph',shading='confidence',control = list(colors = red_palette(10)))
##########################
# 3. ASSOCIATION RULE MINING --------------------------
#
# Government Specific Rules
##########################
## Democrat Specific Rules
government_rules <- arules::apriori(data=basket_data,
parameter = list(support=.001,conf=.05,minlen=2,maxlen=8),
appearance= list(lhs="government"),
control = list(verbose=TRUE))
inspect(government_rules)
government_rules_sorted <- sort(government_rules,by='lift',
decreasing=TRUE)
inspect(government_rules_sorted[0:10])
## Saving the rules to a CSV
write(republican_rules_sorted,
file='Government Association Rules.csv',
sep= ',',
quote=TRUE,
row.names = FALSE)
earth_tone_palette <- colorRampPalette(c("beige","brown", "darkolivegreen"))
## Visualizing the rules
plot(government_rules_sorted,method='graph',shading='confidence',control = list(colors = earth_tone_palette(10)))
##########################
# 3. ASSOCIATION RULE MINING --------------------------
#
# Government Specific Rules
##########################
## Democrat Specific Rules
government_rules <- arules::apriori(data=basket_data,
parameter = list(support=.001,conf=.05,minlen=2,maxlen=8),
appearance= list(lhs="government"),
control = list(verbose=TRUE))
inspect(government_rules)
government_rules_sorted <- sort(government_rules,by='lift',
decreasing=TRUE)
inspect(government_rules_sorted[0:10])
## Saving the rules to a CSV
write(republican_rules_sorted,
file='Government Association Rules.csv',
sep= ',',
quote=TRUE,
row.names = FALSE)
earth_tone_palette <- colorRampPalette(c("beige","brown", "darkolivegreen"))
## Visualizing the rules
plot(government_rules_sorted,method='graph',shading='confidence',control = list(colors = earth_tone_palette(10)))
##########################
# 3. ASSOCIATION RULE MINING --------------------------
#
# Science Specific Rules
##########################
## Democrat Specific Rules
science_rules <- arules::apriori(data=basket_data,
parameter = list(support=.001,conf=.5,minlen=2,maxlen=8),
appearance= list(lhs="scienc"),
control = list(verbose=TRUE))
inspect(science_rules)
science_rules_sorted <- sort(science_rules,by='lift',
decreasing=TRUE)
inspect(science_rules_sorted[0:7])
## Saving the rules to a CSV
write(science_rules_sorted,
file='Science Association Rules.csv',
sep= ',',
quote=TRUE,
row.names = FALSE)
earth_tone_palette <- colorRampPalette(c("beige","brown", "darkolivegreen"))
## Visualizing the rules
plot(science_rules_sorted,method='graph',shading='confidence',control = list(colors = earth_tone_palette(10)))
##########################
# 3. ASSOCIATION RULE MINING --------------------------
#
# Indigenous Specific Rules
##########################
## Democrat Specific Rules
indg_rules <- arules::apriori(data=basket_data,
parameter = list(support=.001,conf=.05,minlen=2,maxlen=8),
appearance= list(lhs="tribal"),
control = list(verbose=TRUE))
inspect(science_rules)
indg_rules_sorted <- sort(indg_rules,by='lift',
decreasing=TRUE)
inspect(indg_rules_sorted[0:10])
## Saving the rules to a CSV
write(indg_rules_sorted,
file='Indigenous Association Rules.csv',
sep= ',',
quote=TRUE,
row.names = FALSE)
earth_tone_palette <- colorRampPalette(c("beige","brown", "darkolivegreen"))
## Visualizing the rules
plot(indg_rules_sorted,method='graph',shading='confidence',control = list(colors = earth_tone_palette(10)))
