Rstudio pubs static s3 amazonaws com

if(!require(arules)) install.packages("arules") if(!require(arulesViz)) install.packages("arulesViz") if(!require(dplyr)) install.packages("dplyr") if(!require(lubridate)) install.packages("lubridate") if(!require(ggplot2)) install.packages("ggplot2") if(!require(knitr)) install.packages("knitr") if(!require(RColorBrewer)) install.packages("RColorBrewer") library(arules) library(arulesViz) library(dplyr) library(plyr) library(lubridate) library(ggplot2) library(knitr) library(RColorBrewer) setwd("C:/Users/ialsmadi/Desktop/University_of_Cumberlands/Lectures/Week4/Scripts") getwd() # First example from : http://www.rpubs.com/thirus83/445115 #Other refs: https://rstudio-pubs-static.s3.amazonaws.com/252505_4931623e7ab14851b1002d1bc61e94d9.html #https://towardsdatascience.com/association-rule-mining-in-r-ddf2d044ae50 #https://www.datacamp.com/community/tutorials/market-basket-analysis-r #https://blog.exploratory.io/introduction-to-association-rules-market-basket-analysis-in-r-7a0dd900a3e0 #https://www.r-bloggers.com/association-rule-learning-and-the-apriori-algorithm/ #http://www.rdatamining.com/examples/association-rules #https://github.com/natarajan1993/Market-Basket-Analysis-with-R # https://github.com/Deepaknatural/Training/blob/master/MarketBasket_Latest.R #https://rstudio-pubs-static.s3.amazonaws.com/267119_9a033b870b9641198b19134b7e61fe56.html # First lets use the AdultUCI dataset that comes bundled with the arules package. data() data("Groceries") summary(Groceries) rules <- apriori(Groceries,parameter=list(support=0.002, confidence = 0.5)) print(summary(rules)) print(rules) inspect(head(sort(rules, by = "lift"))) plot(rules) head(quality(rules)) plot(rules, method = "grouped") plot(rules,method = "scatterplot") plot(rules,method = "graph") # Import test data df <- read.csv("OnlineRetailSmall.csv") head(df) df <- df[complete.cases(df), ] # Drop missing values # Change Description and Country columns to factors # Factors are the data objects which are used to categorize the data and store it as levels. df %>% mutate(Description = as.factor(Description), Country = as.factor(Country)) # Change InvoiceDate to Date datatype df$Date <- as.Date(df$InvoiceDate) df$InvoiceDate <- as.Date(df$InvoiceDate) # Extract time from the InvoiceDate column TransTime<- format(as.POSIXct(df$InvoiceDate),"%H:%M:%S") # Convert InvoiceNo into numeric InvoiceNo <- as.numeric(as.character(df$InvoiceNo)) # Add new columns to original dataframe cbind(df, TransTime, InvoiceNo) glimpse(df) # Group by invoice number and combine order item strings with a comma transactionData <- ddply(df,c("InvoiceNo","Date"), function(df1)paste(df1$Description,collapse = ",")) transactionData$InvoiceNo <- NULL # Don't need these columns transactionData$Date <- NULL colnames(transactionData) <- c("items") head(transactionData) write.csv(transactionData,"market_basket_transactionsSmall1.csv", quote = FALSE, row.names = TRUE) # MBA analysis # From package arules tr <- read.transactions('market_basket_transactionsSmall.csv', format = 'basket', sep=',') summary(tr) # plot the frequency of items itemFrequencyPlot(tr) itemFrequencyPlot(tr,topN=20,type="absolute",col=brewer.pal(8,'Pastel2'), main="Absolute Item Frequency Plot") arules::itemFrequencyPlot(tr, topN=20, col=brewer.pal(8,'Pastel2'), main='Relative Item Frequency Plot', type="relative", ylab="Item Frequency (Relative)") # Generate the a priori rules association.rules <- apriori(tr, parameter = list(supp=0.001, conf=0.8,maxlen=10)) summary(association.rules) inspect(association.rules[1:10]) # Top 10 association rules # Select rules which are subsets of larger rules -> Remove rows where the sums of the subsets are > 1 subset.rules <- which(colSums(is.subset(association.rules, association.rules)) > 1) # get subset rules in vector # What did customers buy before buying "METAL" metal.association.rules <- apriori(tr, parameter = list(supp=0.001, conf=0.8),appearance = list(default="lhs",rhs="METAL")) inspect(head(metal.association.rules)) # What did customers buy after buying "METAL" metal.association.rules2 <- apriori(tr, parameter = list(supp=0.001, conf=0.8),appearance = list(lhs="METAL",default="rhs")) inspect(head(metal.association.rules2)) # Plotting # Filter rules with confidence greater than 0.4 or 40% subRules<-association.rules[quality(association.rules)$confidence>0.4] #Plot SubRules plot(subRules) # Top 10 rules viz top10subRules <- head(subRules, n = 10, by = "confidence") plot(top10subRules, method = "graph", engine = "htmlwidget") # Filter top 20 rules with highest lift # Paralell Coordinates plot - visualize which products along with which items cause what kind of sales. # Closer arrows re bought together subRules2<-head(subRules, n=20, by="lift") plot(subRules2, method="paracoord")