72 lines
2.0 KiB
R
72 lines
2.0 KiB
R
# Installing Packages
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#install.packages('gridExtra')
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#Run install.packages if you haven't installed it before (only once)
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#install.packages("cluster")
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#install.packages('ggplot2')
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# Loading package
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#library(ClusterR)
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library(cluster)
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library(gridExtra)
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library(ggplot2)
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library(grid)
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library(arules)
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library(RColorBrewer)
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#library(cluster)
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coul <- brewer.pal(5, "Set2")
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#Read spreadsheet file
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grocery_entries <- read.csv(file.choose())
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#Compare cash and credit totals -Sewelam
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cash_credit <- cbind(grocery_entries[3], grocery_entries[8])
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sum_cash <-sum(cash_credit[which(cash_credit$paymentType=='Cash'),1])
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sum_credit <-sum(cash_credit[which(cash_credit$paymentType=='Credit'),1])
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CompCashCredit <- c(sum_cash,sum_credit)
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barplot(CompCashCredit,names.arg = c('Cash','Credit'),horiz = FALSE,col = coul)
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#City and Total Spent comparison -Jimmy
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city_total <- cbind(grocery_entries[3], grocery_entries[7])
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sum_cities<-aggregate(total ~city ,city_total,sum)
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pie(sum_cities$total
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,col = coul
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,labels = sum_cities$city
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,main = "Cities and total spent")
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#Compare between ages and their total spent (Youssri)
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age <- cbind(grocery_entries[6] , grocery_entries[3])
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sum_ages <- aggregate(total ~ age,age,sum)
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plot(sum_ages)
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#Distribution of spending - Abdo
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plot(grocery_entries$total, col = coul,type = "l", main = "spending")
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#kmeans --Yousri
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name_total_age<-cbind(grocery_entries[5],grocery_entries[3],grocery_entries[6])
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n<-as.numeric(readline("Enter number of clusters: "))
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keameans<-cbind(grocery_entries[3],grocery_entries[6])
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result<-kmeans(keameans,centers =n)
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final_result<-cbind(name_total_age,result$cluster)
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#Association Rules --Sewelam
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clean_data <- grocery_entries[,-5]
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minsup <- as.numeric(readline("Enter minimum support: "))
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minconf <- as.numeric(readline("Enter minimum confidence: "))
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asoc_rules <- apriori(clean_data,parameter = list(supp = minsup,conf = minconf))
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asociation_rules <- inspect(asoc_rules)
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write.csv(as(asoc_rules,"data.frame"),file = "Output.csv")
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