Nothing
Classification Tree Modelling"
In CTShiny: Interactive Document for Working with Classification Tree Analysis
knitr::opts_chunk$set(echo = TRUE)
library(dplyr)
library(caret)
library(e1071)
library(rhandsontable)
library(datasets)
library(rpart)
library(rpart.plot)
library(party)
```r
sidebarPanel(
checkboxInput("ex","Uncheck for using your own file",value = TRUE),
fileInput("file", "Upload the *.csv file with headers"),
selectInput("mt","Choose Type of Tree model",choices = c("RPART","CTREE"),selected = "RPART"),
conditionalPanel(condition = "input.mt =='RPART'",
checkboxInput("Prune", label = "Check forPrune",value = FALSE),
numericInput("PruneV", label = "Enter Prune value",value ="0",step = 0.001)
),
sliderInput("train_num", label = "Enter the proportion of training dataset:",
min = 0.6, max = 1, value = 0.6, step = 0.01),
uiOutput("vx"),
uiOutput("vy"),
downloadButton("downloadPlot", "Download Plot")
)
mainPanel(
tabsetPanel(type = "tab",
tabPanel("Model Summary", verbatimTextOutput("AD") ),
tabPanel("Model Visualization", plotOutput("MV") ),
tabPanel("Model Evaluation",verbatimTextOutput("ME")),
tabPanel("Model Deployment",verbatimTextOutput("MD"))
),
h6("Edit the test data record"),
rHandsontableOutput("testdata"),
h6("", tags$img(src ="K.JPG", height= 400, width=400))
)
output$AD<-renderPrint({
if(input$ex == TRUE)
{data("iris")
data = iris}
else{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
ds = data
ds = select(ds,input$variablex)
mod = paste(input$variabley,"~.")
options(scipen = 999)
prop = input$train_num
set.seed(1)
dataframe = ds
train.rows = sample(row.names(dataframe),dim(dataframe)[1]*prop)
dataframet = dataframe[train.rows,]
valid.rows = setdiff(row.names(dataframe),train.rows)
dataframev = dataframe[valid.rows,]
if(input$mt == "RPART")
{
model = rpart(formula = as.formula(mod),data = dataframet)
if(input$Prune == "TRUE")
{model = prune(model,cp = input$PruneV)}
print(summary(model))
}
if(input$mt == "CTREE")
{
model = ctree(formula = as.formula(mod),data = dataframet)
print((model))
}
})
output$MV<-renderPlot({
if(input$ex == TRUE)
{data("iris")
data = iris}
else{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
ds = data
ds = select(ds,input$variablex)
mod = paste(input$variabley,"~.")
options(scipen = 999)
prop = input$train_num
set.seed(1)
dataframe = ds
train.rows = sample(row.names(dataframe),dim(dataframe)[1]*prop)
dataframet = dataframe[train.rows,]
valid.rows = setdiff(row.names(dataframe),train.rows)
dataframev = dataframe[valid.rows,]
if(input$mt == "RPART")
{ model = rpart(formula = as.formula(mod),data = dataframet)
if(input$Prune == "TRUE")
{model = prune(model,cp = input$PruneV)}
prp(model,type = 2, extra =104, fallen.leaves = TRUE,main = "Classification Tree using RPART Algorithm")
}
if(input$mt == "CTREE")
{ model = ctree(formula = as.formula(mod),data = dataframet)
plot(model,main = "Classification Tree using CTREE Algorithm")
}
})
output$ME<-renderPrint({
if(input$ex == TRUE)
{data("iris")
data = iris}
else
{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
ds = data
ds = select(ds,input$variablex)
mod = paste(input$variabley,"~.")
options(scipen = 999)
prop = input$train_num
set.seed(1)
dataframe = ds
train.rows = sample(row.names(dataframe),dim(dataframe)[1]*prop)
dataframet = dataframe[train.rows,]
valid.rows = setdiff(row.names(dataframe),train.rows)
dataframev = dataframe[valid.rows,]
if(input$mt == "RPART")
{ cat(sprintf("\nRPART ALGORITHM\n") )
model = rpart(formula = as.formula(mod),data = dataframet)
if(input$Prune == "TRUE")
{model = prune(model,cp = input$PruneV)}
if(prop <1 )
{ cat(sprintf("\nValidation data is used\n"))
prediction = predict(model,newdata = dataframev,type = "class")
attach(dataframev)
}
else
{
cat(sprintf("\nTraining data is used\n"))
prediction = predict(model,newdata = dataframet,type = "class")
attach(dataframet)
}
#print(summary(model))
}
if(input$mt == "CTREE")
{ cat(sprintf("\nCTREE ALGORITHM\n") )
model = ctree(formula = as.formula(mod),data = dataframet)
if(prop <1 )
{ cat(sprintf("\nValidation data is used\n"))
prediction = predict(model,newdata = dataframev,type = "response")
attach(dataframev)
}
else
{
cat(sprintf("\nTraining data is used\n"))
prediction = predict(model,newdata = dataframet,type = "response")
attach(dataframet)
}
}
# print(prediction)
print(confusionMatrix(as.factor(prediction),as.factor(get(input$variabley))))
}
})
output$MD<-renderPrint({
if(input$ex == TRUE)
{data("iris")
data = iris}
else
{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
ds = data
ds = select(ds,input$variablex)
mod = paste(input$variabley,"~.")
options(scipen = 999)
prop = input$train_num
set.seed(1)
dataframe = ds
train.rows = sample(row.names(dataframe),dim(dataframe)[1]*prop)
dataframet = dataframe[train.rows,]
valid.rows = setdiff(row.names(dataframe),train.rows)
dataframev = dataframe[valid.rows,]
if(input$mt == "RPART")
{ cat(sprintf("\nRPART ALGORITHM\n") )
model = rpart(formula = as.formula(mod),data = dataframet)
if(input$Prune == "TRUE")
{model = prune(model,cp = input$PruneV)}
test_data = data.frame(hot_to_r(input$testdata))
if(ncol(test_data)== 1)
{ df = select(dataframet,-c(input$variabley))
colnames(test_data)= colnames(df)
}
prediction = predict(model,newdata = test_data,type = "class")
test_data$predictedvalue = prediction
print(test_data)
}
if(input$mt == "CTREE")
{
cat(sprintf("\nCTREE ALGORITHM\n") )
model = ctree(formula = as.formula(mod),data = dataframet)
test_data = data.frame(hot_to_r(input$testdata))
if(ncol(test_data)== 1)
{ df = select(dataframet,-c(input$variabley))
colnames(test_data)= colnames(df)
}
prediction = predict(model,newdata = test_data,type = "response")
test_data$predictedvalue = prediction
print(test_data)
}
})
output$vx <- renderUI({
if(input$ex == TRUE)
{data("iris")
data = iris}
else
{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
checkboxGroupInput("variablex","Select the variables",choices = colnames(data),selected = colnames(data))
})
output$vy <- renderUI({
if(input$ex == TRUE)
{data("iris")
data = iris}
else
{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
ds = data
ds = select(ds,input$variablex)
ds = select_if(ds,is.factor)
selectInput("variabley","Select the dependent variable",choices = colnames(ds),selected = "" )
})
output$testdata <- renderRHandsontable({
if(input$ex == TRUE)
{data("iris")
data = iris}
else{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
ds = data
ds = select(ds,input$variablex)
ds = select(ds,-c(input$variabley))
rhandsontable(data.frame(ds[1,]))
})
output$downloadPlot<- downloadHandler(
filename = function() {
paste("Treeplot", ".png", sep = "")
},
content = function(file) {
png(file)
if(input$ex == TRUE)
{data("iris")
data = iris}
else{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
ds = data
ds = select(ds,input$variablex)
mod = paste(input$variabley,"~.")
options(scipen = 999)
prop = input$train_num
set.seed(1)
dataframe = ds
train.rows = sample(row.names(dataframe),dim(dataframe)[1]*prop)
dataframet = dataframe[train.rows,]
valid.rows = setdiff(row.names(dataframe),train.rows)
dataframev = dataframe[valid.rows,]
if(input$mt == "RPART")
{ model = rpart(formula = as.formula(mod),data = dataframet)
if(input$Prune == "TRUE")
{model = prune(model,cp = input$PruneV)}
prp(model,type = 2, extra =104, fallen.leaves = TRUE,main = "Classification Tree using RPART Algorithm")
}
if(input$mt == "CTREE")
{ model = ctree(formula = as.formula(mod),data = dataframet)
plot(model,main = "Classification Tree using CTREE Algorithm")
}
dev.off()
})
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CTShiny documentation built on May 6, 2019, 1:07 a.m.
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knitr::opts_chunk$set(echo = TRUE) library(dplyr) library(caret) library(e1071) library(rhandsontable) library(datasets) library(rpart) library(rpart.plot) library(party)
```r sidebarPanel( checkboxInput("ex","Uncheck for using your own file",value = TRUE), fileInput("file", "Upload the *.csv file with headers"), selectInput("mt","Choose Type of Tree model",choices = c("RPART","CTREE"),selected = "RPART"), conditionalPanel(condition = "input.mt =='RPART'", checkboxInput("Prune", label = "Check forPrune",value = FALSE), numericInput("PruneV", label = "Enter Prune value",value ="0",step = 0.001) ),
sliderInput("train_num", label = "Enter the proportion of training dataset:", min = 0.6, max = 1, value = 0.6, step = 0.01),
uiOutput("vx"), uiOutput("vy"), downloadButton("downloadPlot", "Download Plot")
)
mainPanel( tabsetPanel(type = "tab", tabPanel("Model Summary", verbatimTextOutput("AD") ), tabPanel("Model Visualization", plotOutput("MV") ), tabPanel("Model Evaluation",verbatimTextOutput("ME")), tabPanel("Model Deployment",verbatimTextOutput("MD")) ), h6("Edit the test data record"), rHandsontableOutput("testdata"), h6("", tags$img(src ="K.JPG", height= 400, width=400)) ) output$AD<-renderPrint({ if(input$ex == TRUE) {data("iris") data = iris} else{ file1 = input$file if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
ds = data
ds = select(ds,input$variablex)
mod = paste(input$variabley,"~.")
options(scipen = 999)
prop = input$train_num
set.seed(1)
dataframe = ds
train.rows = sample(row.names(dataframe),dim(dataframe)[1]*prop)
dataframet = dataframe[train.rows,]
valid.rows = setdiff(row.names(dataframe),train.rows)
dataframev = dataframe[valid.rows,]
if(input$mt == "RPART") { model = rpart(formula = as.formula(mod),data = dataframet) if(input$Prune == "TRUE") {model = prune(model,cp = input$PruneV)} print(summary(model)) } if(input$mt == "CTREE") { model = ctree(formula = as.formula(mod),data = dataframet) print((model)) }
}) output$MV<-renderPlot({ if(input$ex == TRUE) {data("iris") data = iris} else{ file1 = input$file if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
ds = data
ds = select(ds,input$variablex)
mod = paste(input$variabley,"~.")
options(scipen = 999)
prop = input$train_num
set.seed(1)
dataframe = ds
train.rows = sample(row.names(dataframe),dim(dataframe)[1]*prop)
dataframet = dataframe[train.rows,]
valid.rows = setdiff(row.names(dataframe),train.rows)
dataframev = dataframe[valid.rows,]
if(input$mt == "RPART")
{ model = rpart(formula = as.formula(mod),data = dataframet) if(input$Prune == "TRUE") {model = prune(model,cp = input$PruneV)} prp(model,type = 2, extra =104, fallen.leaves = TRUE,main = "Classification Tree using RPART Algorithm") } if(input$mt == "CTREE") { model = ctree(formula = as.formula(mod),data = dataframet) plot(model,main = "Classification Tree using CTREE Algorithm") }
})
output$ME<-renderPrint({ if(input$ex == TRUE) {data("iris") data = iris} else { file1 = input$file if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
ds = data
ds = select(ds,input$variablex)
mod = paste(input$variabley,"~.")
options(scipen = 999)
prop = input$train_num
set.seed(1)
dataframe = ds
train.rows = sample(row.names(dataframe),dim(dataframe)[1]*prop)
dataframet = dataframe[train.rows,]
valid.rows = setdiff(row.names(dataframe),train.rows)
dataframev = dataframe[valid.rows,]
if(input$mt == "RPART") { cat(sprintf("\nRPART ALGORITHM\n") ) model = rpart(formula = as.formula(mod),data = dataframet) if(input$Prune == "TRUE") {model = prune(model,cp = input$PruneV)} if(prop <1 ) { cat(sprintf("\nValidation data is used\n"))
prediction = predict(model,newdata = dataframev,type = "class")
attach(dataframev)
}
else
{
cat(sprintf("\nTraining data is used\n"))
prediction = predict(model,newdata = dataframet,type = "class")
attach(dataframet)
}
#print(summary(model))
}
if(input$mt == "CTREE") { cat(sprintf("\nCTREE ALGORITHM\n") ) model = ctree(formula = as.formula(mod),data = dataframet) if(prop <1 ) { cat(sprintf("\nValidation data is used\n"))
prediction = predict(model,newdata = dataframev,type = "response")
attach(dataframev)
}
else
{
cat(sprintf("\nTraining data is used\n"))
prediction = predict(model,newdata = dataframet,type = "response")
attach(dataframet)
}
}
# print(prediction)
print(confusionMatrix(as.factor(prediction),as.factor(get(input$variabley))))
}
})
output$MD<-renderPrint({ if(input$ex == TRUE) {data("iris") data = iris} else { file1 = input$file if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
ds = data
ds = select(ds,input$variablex)
mod = paste(input$variabley,"~.")
options(scipen = 999)
prop = input$train_num
set.seed(1)
dataframe = ds
train.rows = sample(row.names(dataframe),dim(dataframe)[1]*prop)
dataframet = dataframe[train.rows,]
valid.rows = setdiff(row.names(dataframe),train.rows)
dataframev = dataframe[valid.rows,]
if(input$mt == "RPART") { cat(sprintf("\nRPART ALGORITHM\n") ) model = rpart(formula = as.formula(mod),data = dataframet) if(input$Prune == "TRUE") {model = prune(model,cp = input$PruneV)} test_data = data.frame(hot_to_r(input$testdata)) if(ncol(test_data)== 1) { df = select(dataframet,-c(input$variabley)) colnames(test_data)= colnames(df) } prediction = predict(model,newdata = test_data,type = "class") test_data$predictedvalue = prediction print(test_data) }
if(input$mt == "CTREE") { cat(sprintf("\nCTREE ALGORITHM\n") ) model = ctree(formula = as.formula(mod),data = dataframet) test_data = data.frame(hot_to_r(input$testdata)) if(ncol(test_data)== 1) { df = select(dataframet,-c(input$variabley)) colnames(test_data)= colnames(df) } prediction = predict(model,newdata = test_data,type = "response") test_data$predictedvalue = prediction print(test_data) }
})
output$vx <- renderUI({
if(input$ex == TRUE) {data("iris") data = iris} else {
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
checkboxGroupInput("variablex","Select the variables",choices = colnames(data),selected = colnames(data))
}) output$vy <- renderUI({
if(input$ex == TRUE) {data("iris") data = iris} else {
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
ds = data
ds = select(ds,input$variablex)
ds = select_if(ds,is.factor)
selectInput("variabley","Select the dependent variable",choices = colnames(ds),selected = "" )
})
output$testdata <- renderRHandsontable({
if(input$ex == TRUE) {data("iris") data = iris} else{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
ds = data
ds = select(ds,input$variablex)
ds = select(ds,-c(input$variabley))
rhandsontable(data.frame(ds[1,]))
})
output$downloadPlot<- downloadHandler( filename = function() { paste("Treeplot", ".png", sep = "") }, content = function(file) { png(file) if(input$ex == TRUE) {data("iris") data = iris} else{ file1 = input$file if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
ds = data
ds = select(ds,input$variablex)
mod = paste(input$variabley,"~.")
options(scipen = 999)
prop = input$train_num
set.seed(1)
dataframe = ds
train.rows = sample(row.names(dataframe),dim(dataframe)[1]*prop)
dataframet = dataframe[train.rows,]
valid.rows = setdiff(row.names(dataframe),train.rows)
dataframev = dataframe[valid.rows,]
if(input$mt == "RPART")
{ model = rpart(formula = as.formula(mod),data = dataframet) if(input$Prune == "TRUE") {model = prune(model,cp = input$PruneV)} prp(model,type = 2, extra =104, fallen.leaves = TRUE,main = "Classification Tree using RPART Algorithm") } if(input$mt == "CTREE") { model = ctree(formula = as.formula(mod),data = dataframet) plot(model,main = "Classification Tree using CTREE Algorithm") } dev.off() })
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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