Nothing
Association Rule Mining"
In ASSOCShiny: Interactive Document for Working with Association Rule Mining Analysis
knitr::opts_chunk$set(echo = TRUE)
library(shinyMatrix)
library(plotly)
library(arules)
```r
sidebarPanel(
inputPanel(
checkboxInput("ex","Uncheck if you want to use your dataset",value = TRUE),
fileInput("file", "Upload the *.csv file with headers"),
selectInput("format","Choose format of the dataset",choices = c("matrix","database"),selected = "database"),
uiOutput("vx"),
uiOutput("vy")
),
inputPanel(
numericInput("support","Enter minimum support ratio",value = 0.2,step = 0.01),
numericInput("confidence","Enter minimum confidenc ratio", value = 0.5,step =0.01),
numericInput("minlen","Enter minimum length", value = 2,step =1,min = 2),
numericInput("nrules","Enter how many rules", value = 1,step =1)
),
inputPanel(
numericInput("nitems","Enter how many items", value = 1,step =1),
downloadButton("downloadPlot", "Download ItemFrequency Plot")
),
inputPanel(
numericInput("nshelfs","Enter how many shelfs", value = 2,step =1),
downloadButton("downloadPlot2", "Download ItemShelfPlot")
)
)
mainPanel(
h6("Enter the Item Names (on the first row margin) and transactionID(on the first column),Edit the binary values between"),
uiOutput("mat"),
numericInput("nrows","Enter number of transactions",value = 2),
numericInput("ncols","Enter number of items", value = 3),
downloadButton("downloaddata", "Download Dataset in Matrix Format"),
br(),
br(),
tabsetPanel(type = "tab",
tabPanel("Rules",verbatimTextOutput("AssRules")),
tabPanel("Rules-Visuals",plotlyOutput("AssRulesV")),
tabPanel("ItemFrequencyPlot",plotOutput("ItemFP")),
tabPanel("Shelf-Design",plotOutput("AffV"))
)
)
h6("", tags$img(src ="K.JPG", height= 400, width=400))
output$mat <- renderUI({
matrixInput(
"myMatrix",
value = matrix(c(1,0,0,1,0,0),input$nrows,input$ncols),
rows = list(names= TRUE,editableNames = TRUE),
cols = list( names = TRUE,editableNames = TRUE),
copy = TRUE,
paste = TRUE,
class = 'numeric'
)
})
output$AssRules <- renderPrint({
CT = matrix(input$myMatrix,nrow = input$nrows, ncol = input$ncols)
row.names(CT) = row.names(input$myMatrix)
colnames(CT) = colnames(input$myMatrix)
if(input$ex == TRUE)
{ txn = as(CT, "transactions")
cat(sprintf("\n The details of the rules formed are as follows\n"))
rules = arules::apriori(txn,parameter = list(support = input$support, confidence= input$confidence,minlen = input$minlen,target = "rules"))
arules::inspect(head(sort(rules,by = "lift"),n =input$nrules))
}
else
{
if(input$format == "matrix" && input$ex == FALSE)
{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
ds = data.frame(data)
CT = as.matrix(ds)
txn = as(CT, "transactions")
cat(sprintf("\n The details of the rules formed are as follows\n"))
rules = arules::apriori(txn,parameter = list(support= input$support, confidence= input$confidence,minlen = input$minlen,target = "rules"))
arules::inspect(head(sort(rules,by = "lift"),n =input$nrules))
}
else
{
if(input$format == "database" && input$ex == FALSE)
{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
indexproduct= grep(input$variablex, colnames(data))
indextrans= grep(input$variabley, colnames(data))
splitdataset = split(data[,indexproduct],data[,indextrans])
txn = as(splitdataset,"transactions")
cat(sprintf("\n The details of the rules formed are as follows\n"))
rules = arules:: apriori(txn,parameter = list(support = input$support, confidence= input$confidence,minlen = input$minlen,target = "rules"))
arules::inspect(head(sort(rules,by = "lift"),n =input$nrules))
}
}
}
})
output$AssRulesV <- renderPlotly({
CT = matrix(input$myMatrix,nrow = input$nrows, ncol = input$ncols)
row.names(CT) = row.names(input$myMatrix)
colnames(CT) = colnames(input$myMatrix)
if(input$ex == TRUE)
{ txn = as(CT, "transactions")
rules = arules:: apriori(txn,parameter = list(support = input$support, confidence = input$confidence,minlen = input$minlen,target = "rules"))
arulesViz::plotly_arules(head(sort(rules,by = "lift"),n =input$nrules))
}
else
{
if(input$format == "matrix" && input$ex == FALSE)
{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
ds = data.frame(data)
CT = as.matrix(ds)
txn = as(CT, "transactions")
rules = arules:: apriori(txn,parameter = list(support = input$support, confidence = input$confidence,minlen = input$minlen,target = "rules"))
arulesViz::plotly_arules(head(sort(rules,by = "lift"),n =input$nrules))
}
else
{
if(input$format == "database" && input$ex == FALSE)
{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
indexproduct= grep(input$variablex, colnames(data))
indextrans= grep(input$variabley, colnames(data))
splitdataset = split(data[,indexproduct],data[,indextrans])
txn = as(splitdataset,"transactions")
rules = arules::apriori(txn,parameter = list(support = input$support, confidence = input$confidence,minlen = input$minlen,target = "rules"))
arulesViz::plotly_arules(head(sort(rules,by = "lift"),n =input$nrules))
}
}
}
})
output$ItemFP <- renderPlot({
CT = matrix(input$myMatrix,nrow = input$nrows, ncol = input$ncols)
row.names(CT) = row.names(input$myMatrix)
colnames(CT) = colnames(input$myMatrix)
if(input$ex == TRUE)
{ txn = as(CT, "transactions")
arules::itemFrequencyPlot(txn, topN=input$nitems, type="absolute", main="Item Frequency")
}
else
{
if(input$format == "matrix" && input$ex == FALSE)
{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
ds = data.frame(data)
CT = as.matrix(ds)
txn = as(CT, "transactions")
arules::itemFrequencyPlot(txn, topN=input$nitems, type="absolute", main="Item Frequency")
}
else
{
if(input$format == "database" && input$ex == FALSE)
{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
indexproduct= grep(input$variablex, colnames(data))
indextrans= grep(input$variabley, colnames(data))
splitdataset = split(data[,indexproduct],data[,indextrans])
txn = as(splitdataset,"transactions")
arules::itemFrequencyPlot(txn, topN=input$nitems, type="absolute", main="Item Frequency")
}
}
}
})
output$AffV <- renderPlot({
CT = matrix(input$myMatrix,nrow = input$nrows, ncol = input$ncols)
row.names(CT) = row.names(input$myMatrix)
colnames(CT) = colnames(input$myMatrix)
if(input$ex == TRUE)
{ txn = as(CT, "transactions")
distance = arules::dissimilarity(txn,which = "items",method = "dice")
fit = hclust(distance,method = "ward.D")
plot(fit,main = "Dendogram of items")
rect.hclust(fit,k=input$nshelfs,border = "red")
}
else
{
if(input$format == "matrix" && input$ex == FALSE)
{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
ds = data.frame(data)
CT = as.matrix(ds)
txn = as(CT, "transactions")
distance = arules::dissimilarity(txn,which = "items",method = "dice")
fit = hclust(distance,method = "ward.D")
plot(fit,main = "Dendogram of items")
rect.hclust(fit,k=input$nshelfs,border = "red")
}
else
{
if(input$format == "database" && input$ex == FALSE)
{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
indexproduct= grep(input$variablex, colnames(data))
indextrans= grep(input$variabley, colnames(data))
splitdataset = split(data[,indexproduct],data[,indextrans])
txn = as(splitdataset,"transactions")
distance = arules::dissimilarity(txn,which = "items",method = "dice")
fit = hclust(distance,method = "ward.D")
plot(fit,main = "Dendogram of items")
rect.hclust(fit,k=input$nshelfs,border = "red")
}
}
}
})
datasetInput1 <- reactive({
CT = matrix(input$myMatrix,nrow = input$nrows, ncol = input$ncols)
row.names(CT) = row.names(input$myMatrix)
colnames(CT) = colnames(input$myMatrix)
dataset = data.frame(CT)
})
output$downloaddata <- downloadHandler(
filename = function() {
filetitle = paste("dataset")
paste(filetitle, ".csv", sep = "")
},
content = function(file) {
write.csv(datasetInput1(), file, row.names = FALSE)
}
)
output$vx <- renderUI({
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
if(input$format == "database")
{ selectInput("variablex","Select the ItemID column",choices =
colnames(data),selected = colnames(data))}
})
output$vy <- renderUI({
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
ds = dplyr:: select(data,-c(input$variablex))
if(input$format == "database")
{ selectInput("variabley","Select the Transaction ID column",choices =
colnames(ds),selected = colnames(ds))}
})
output$downloadPlot<- downloadHandler(
filename = function() {
paste("ItemFrequencyPlot", ".png", sep = "")
},
content = function(file) {
png(file)
CT = matrix(input$myMatrix,nrow = input$nrows, ncol = input$ncols)
row.names(CT) = row.names(input$myMatrix)
colnames(CT) = colnames(input$myMatrix)
if(input$ex == TRUE)
{ txn = as(CT, "transactions")
arules::itemFrequencyPlot(txn, topN=input$nitems, type="absolute", main="Item Frequency")
}
else
{
if(input$format == "matrix" && input$ex == FALSE)
{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
ds = data.frame(data)
CT = as.matrix(ds)
txn = as(CT, "transactions")
arules::itemFrequencyPlot(txn, topN=input$nitems, type="absolute", main="Item Frequency")
}
else
{
if(input$format == "database" && input$ex == FALSE)
{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
indexproduct= grep(input$variablex, colnames(data))
indextrans= grep(input$variabley, colnames(data))
splitdataset = split(data[,indexproduct],data[,indextrans])
txn = as(splitdataset,"transactions")
arules::itemFrequencyPlot(txn, topN=input$nitems, type="absolute", main="Item Frequency")
}
}
}
dev.off()
}
)
output$downloadPlot2<- downloadHandler(
filename = function() {
paste("ItemShelfPlot", ".png", sep = "")
},
content = function(file) {
png(file)
CT = matrix(input$myMatrix,nrow = input$nrows, ncol = input$ncols)
row.names(CT) = row.names(input$myMatrix)
colnames(CT) = colnames(input$myMatrix)
if(input$ex == TRUE)
{ txn = as(CT, "transactions")
distance = arules::dissimilarity(txn,which = "items",method = "dice")
fit = hclust(distance,method = "ward.D")
plot(fit,main = "Dendogram of items")
rect.hclust(fit,k=input$nshelfs,border = "red")
}
else
{
if(input$format == "matrix" && input$ex == FALSE)
{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
ds = data.frame(data)
CT = as.matrix(ds)
txn = as(CT, "transactions")
distance = arules::dissimilarity(txn,which = "items",method = "dice")
fit = hclust(distance,method = "ward.D")
plot(fit,main = "Dendogram of items")
rect.hclust(fit,k=input$nshelfs,border = "red")
}
else
{
if(input$format == "database" && input$ex == FALSE)
{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
indexproduct= grep(input$variablex, colnames(data))
indextrans= grep(input$variabley, colnames(data))
splitdataset = split(data[,indexproduct],data[,indextrans])
txn = as(splitdataset,"transactions")
distance = arules::dissimilarity(txn,which = "items",method = "dice")
fit = hclust(distance,method = "ward.D")
plot(fit,main = "Dendogram of items")
rect.hclust(fit,k=input$nshelfs,border = "red")
}
}
}
dev.off()
}
)
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ASSOCShiny documentation built on May 14, 2019, 5:05 p.m.
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knitr::opts_chunk$set(echo = TRUE) library(shinyMatrix) library(plotly) library(arules)
```r sidebarPanel( inputPanel(
checkboxInput("ex","Uncheck if you want to use your dataset",value = TRUE), fileInput("file", "Upload the *.csv file with headers"), selectInput("format","Choose format of the dataset",choices = c("matrix","database"),selected = "database"), uiOutput("vx"), uiOutput("vy")
), inputPanel(
numericInput("support","Enter minimum support ratio",value = 0.2,step = 0.01), numericInput("confidence","Enter minimum confidenc ratio", value = 0.5,step =0.01), numericInput("minlen","Enter minimum length", value = 2,step =1,min = 2), numericInput("nrules","Enter how many rules", value = 1,step =1)
), inputPanel( numericInput("nitems","Enter how many items", value = 1,step =1), downloadButton("downloadPlot", "Download ItemFrequency Plot") ), inputPanel( numericInput("nshelfs","Enter how many shelfs", value = 2,step =1), downloadButton("downloadPlot2", "Download ItemShelfPlot") ) ) mainPanel( h6("Enter the Item Names (on the first row margin) and transactionID(on the first column),Edit the binary values between"), uiOutput("mat"), numericInput("nrows","Enter number of transactions",value = 2), numericInput("ncols","Enter number of items", value = 3), downloadButton("downloaddata", "Download Dataset in Matrix Format"), br(), br(), tabsetPanel(type = "tab", tabPanel("Rules",verbatimTextOutput("AssRules")), tabPanel("Rules-Visuals",plotlyOutput("AssRulesV")), tabPanel("ItemFrequencyPlot",plotOutput("ItemFP")), tabPanel("Shelf-Design",plotOutput("AffV")) )
) h6("", tags$img(src ="K.JPG", height= 400, width=400))
output$mat <- renderUI({ matrixInput( "myMatrix", value = matrix(c(1,0,0,1,0,0),input$nrows,input$ncols),
rows = list(names= TRUE,editableNames = TRUE), cols = list( names = TRUE,editableNames = TRUE), copy = TRUE, paste = TRUE, class = 'numeric' )
}) output$AssRules <- renderPrint({
CT = matrix(input$myMatrix,nrow = input$nrows, ncol = input$ncols)
row.names(CT) = row.names(input$myMatrix)
colnames(CT) = colnames(input$myMatrix)
if(input$ex == TRUE)
{ txn = as(CT, "transactions")
cat(sprintf("\n The details of the rules formed are as follows\n")) rules = arules::apriori(txn,parameter = list(support = input$support, confidence= input$confidence,minlen = input$minlen,target = "rules")) arules::inspect(head(sort(rules,by = "lift"),n =input$nrules))
}
else
{
if(input$format == "matrix" && input$ex == FALSE)
{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
ds = data.frame(data)
CT = as.matrix(ds)
txn = as(CT, "transactions")
cat(sprintf("\n The details of the rules formed are as follows\n")) rules = arules::apriori(txn,parameter = list(support= input$support, confidence= input$confidence,minlen = input$minlen,target = "rules")) arules::inspect(head(sort(rules,by = "lift"),n =input$nrules))
}
else
{
if(input$format == "database" && input$ex == FALSE)
{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
indexproduct= grep(input$variablex, colnames(data))
indextrans= grep(input$variabley, colnames(data))
splitdataset = split(data[,indexproduct],data[,indextrans])
txn = as(splitdataset,"transactions")
cat(sprintf("\n The details of the rules formed are as follows\n")) rules = arules:: apriori(txn,parameter = list(support = input$support, confidence= input$confidence,minlen = input$minlen,target = "rules")) arules::inspect(head(sort(rules,by = "lift"),n =input$nrules))
} } }
})
output$AssRulesV <- renderPlotly({ CT = matrix(input$myMatrix,nrow = input$nrows, ncol = input$ncols) row.names(CT) = row.names(input$myMatrix) colnames(CT) = colnames(input$myMatrix)
if(input$ex == TRUE)
{ txn = as(CT, "transactions")
rules = arules:: apriori(txn,parameter = list(support = input$support, confidence = input$confidence,minlen = input$minlen,target = "rules"))
arulesViz::plotly_arules(head(sort(rules,by = "lift"),n =input$nrules))
}
else
{
if(input$format == "matrix" && input$ex == FALSE)
{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
ds = data.frame(data)
CT = as.matrix(ds)
txn = as(CT, "transactions")
rules = arules:: apriori(txn,parameter = list(support = input$support, confidence = input$confidence,minlen = input$minlen,target = "rules")) arulesViz::plotly_arules(head(sort(rules,by = "lift"),n =input$nrules)) } else { if(input$format == "database" && input$ex == FALSE) { file1 = input$file if(is.null(file1)){return()} data = read.table(file = file1$datapath,sep =",",header = TRUE) if(is.null(data())){return()} indexproduct= grep(input$variablex, colnames(data)) indextrans= grep(input$variabley, colnames(data)) splitdataset = split(data[,indexproduct],data[,indextrans]) txn = as(splitdataset,"transactions")
rules = arules::apriori(txn,parameter = list(support = input$support, confidence = input$confidence,minlen = input$minlen,target = "rules")) arulesViz::plotly_arules(head(sort(rules,by = "lift"),n =input$nrules)) } } }
})
output$ItemFP <- renderPlot({ CT = matrix(input$myMatrix,nrow = input$nrows, ncol = input$ncols) row.names(CT) = row.names(input$myMatrix) colnames(CT) = colnames(input$myMatrix)
if(input$ex == TRUE)
{ txn = as(CT, "transactions")
arules::itemFrequencyPlot(txn, topN=input$nitems, type="absolute", main="Item Frequency")
}
else
{
if(input$format == "matrix" && input$ex == FALSE)
{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
ds = data.frame(data)
CT = as.matrix(ds)
txn = as(CT, "transactions")
arules::itemFrequencyPlot(txn, topN=input$nitems, type="absolute", main="Item Frequency") } else { if(input$format == "database" && input$ex == FALSE) { file1 = input$file if(is.null(file1)){return()} data = read.table(file = file1$datapath,sep =",",header = TRUE) if(is.null(data())){return()} indexproduct= grep(input$variablex, colnames(data)) indextrans= grep(input$variabley, colnames(data)) splitdataset = split(data[,indexproduct],data[,indextrans]) txn = as(splitdataset,"transactions")
arules::itemFrequencyPlot(txn, topN=input$nitems, type="absolute", main="Item Frequency") } } }
})
output$AffV <- renderPlot({ CT = matrix(input$myMatrix,nrow = input$nrows, ncol = input$ncols) row.names(CT) = row.names(input$myMatrix) colnames(CT) = colnames(input$myMatrix)
if(input$ex == TRUE)
{ txn = as(CT, "transactions")
distance = arules::dissimilarity(txn,which = "items",method = "dice") fit = hclust(distance,method = "ward.D") plot(fit,main = "Dendogram of items") rect.hclust(fit,k=input$nshelfs,border = "red")
}
else
{
if(input$format == "matrix" && input$ex == FALSE)
{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
ds = data.frame(data)
CT = as.matrix(ds)
txn = as(CT, "transactions")
distance = arules::dissimilarity(txn,which = "items",method = "dice") fit = hclust(distance,method = "ward.D") plot(fit,main = "Dendogram of items") rect.hclust(fit,k=input$nshelfs,border = "red")
}
else
{
if(input$format == "database" && input$ex == FALSE)
{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
indexproduct= grep(input$variablex, colnames(data))
indextrans= grep(input$variabley, colnames(data))
splitdataset = split(data[,indexproduct],data[,indextrans])
txn = as(splitdataset,"transactions")
distance = arules::dissimilarity(txn,which = "items",method = "dice") fit = hclust(distance,method = "ward.D") plot(fit,main = "Dendogram of items") rect.hclust(fit,k=input$nshelfs,border = "red") } } }
})
datasetInput1 <- reactive({ CT = matrix(input$myMatrix,nrow = input$nrows, ncol = input$ncols) row.names(CT) = row.names(input$myMatrix) colnames(CT) = colnames(input$myMatrix) dataset = data.frame(CT)
}) output$downloaddata <- downloadHandler( filename = function() { filetitle = paste("dataset") paste(filetitle, ".csv", sep = "") }, content = function(file) {
write.csv(datasetInput1(), file, row.names = FALSE) }
)
output$vx <- renderUI({
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
if(input$format == "database")
{ selectInput("variablex","Select the ItemID column",choices =
colnames(data),selected = colnames(data))}
}) output$vy <- renderUI({
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
ds = dplyr:: select(data,-c(input$variablex))
if(input$format == "database")
{ selectInput("variabley","Select the Transaction ID column",choices =
colnames(ds),selected = colnames(ds))}
})
output$downloadPlot<- downloadHandler( filename = function() { paste("ItemFrequencyPlot", ".png", sep = "") }, content = function(file) { png(file) CT = matrix(input$myMatrix,nrow = input$nrows, ncol = input$ncols) row.names(CT) = row.names(input$myMatrix) colnames(CT) = colnames(input$myMatrix)
if(input$ex == TRUE)
{ txn = as(CT, "transactions")
arules::itemFrequencyPlot(txn, topN=input$nitems, type="absolute", main="Item Frequency")
}
else
{
if(input$format == "matrix" && input$ex == FALSE)
{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
ds = data.frame(data)
CT = as.matrix(ds)
txn = as(CT, "transactions")
arules::itemFrequencyPlot(txn, topN=input$nitems, type="absolute", main="Item Frequency") } else { if(input$format == "database" && input$ex == FALSE) { file1 = input$file if(is.null(file1)){return()} data = read.table(file = file1$datapath,sep =",",header = TRUE) if(is.null(data())){return()} indexproduct= grep(input$variablex, colnames(data)) indextrans= grep(input$variabley, colnames(data)) splitdataset = split(data[,indexproduct],data[,indextrans]) txn = as(splitdataset,"transactions")
arules::itemFrequencyPlot(txn, topN=input$nitems, type="absolute", main="Item Frequency") } } }
dev.off() } )
output$downloadPlot2<- downloadHandler( filename = function() { paste("ItemShelfPlot", ".png", sep = "") }, content = function(file) { png(file) CT = matrix(input$myMatrix,nrow = input$nrows, ncol = input$ncols) row.names(CT) = row.names(input$myMatrix) colnames(CT) = colnames(input$myMatrix)
if(input$ex == TRUE)
{ txn = as(CT, "transactions")
distance = arules::dissimilarity(txn,which = "items",method = "dice") fit = hclust(distance,method = "ward.D") plot(fit,main = "Dendogram of items") rect.hclust(fit,k=input$nshelfs,border = "red")
}
else
{
if(input$format == "matrix" && input$ex == FALSE)
{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
ds = data.frame(data)
CT = as.matrix(ds)
txn = as(CT, "transactions")
distance = arules::dissimilarity(txn,which = "items",method = "dice") fit = hclust(distance,method = "ward.D") plot(fit,main = "Dendogram of items") rect.hclust(fit,k=input$nshelfs,border = "red") } else { if(input$format == "database" && input$ex == FALSE) { file1 = input$file if(is.null(file1)){return()} data = read.table(file = file1$datapath,sep =",",header = TRUE) if(is.null(data())){return()} indexproduct= grep(input$variablex, colnames(data)) indextrans= grep(input$variabley, colnames(data)) splitdataset = split(data[,indexproduct],data[,indextrans]) txn = as(splitdataset,"transactions")
distance = arules::dissimilarity(txn,which = "items",method = "dice") fit = hclust(distance,method = "ward.D") plot(fit,main = "Dendogram of items") rect.hclust(fit,k=input$nshelfs,border = "red") } } }
dev.off() } )
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