Nothing
K-nearest neighbour Classification Modelling"
In KNNShiny: Interactive Document for Working with KNN Analysis
Note : 1. This application works only for 2 or more predictors(independent variables)
2. The general rule of thumb of square root of number of rows in validation data is folllowed in figuring out the ideal value of k
knitr::opts_chunk$set(echo = TRUE)
library(dplyr)
library(caret)
library(e1071)
library(rhandsontable)
library(datasets)
library(psycho)
#library(lattice)
library(FNN)
```r
sidebarPanel(
checkboxInput("ex","Uncheck for using your own file",value = TRUE),
fileInput("file", "Upload the *.csv file with headers"),
selectInput("dt","Choose Train/Valid/Full for Visuals",choices = c("Train","Valid","Full"),selected = "Full"),
sliderInput("train_num", label = "Enter the proportion of training dataset:",
min = 0.6, max = 1, value = 0.6, step = 0.01),
checkboxInput("std","Standardize",value = FALSE),
uiOutput("vx"),
uiOutput("vy"),
downloadButton("downloadPlot", "Download Plot")
)
mainPanel(
tabsetPanel(type = "tab",
tabPanel("Model Summary", verbatimTextOutput("AD") ),
tabPanel("Model Visualization", plotOutput("MV") ),
tabPanel("Model Evaluation",verbatimTextOutput("ME")),
tabPanel("Model Deployment",verbatimTextOutput("MD"))
),
h6("Edit the test data record"),
rHandsontableOutput("testdata"),
h6("", tags$img(src ="K.JPG", height= 400, width=400))
)
output$AD<-renderPrint({
if(input$ex == TRUE)
{data("iris")
data = iris
gp = runif(nrow(data))
data[,]= data[order(gp),]
}
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$variablex)
if(ncol(ds)==2){return()}
if(input$std == TRUE)
{ds = standardize(ds)}
options(scipen = 999)
prop = input$train_num
set.seed(1)
dataframe = ds
train.rows = sample(row.names(dataframe),dim(dataframe)[1]*prop)
dataframet = dataframe[train.rows,]
valid.rows = setdiff(row.names(dataframe),train.rows)
dataframev = dataframe[valid.rows,]
indexdependent= grep(input$variabley, colnames(dataframe))
# print(indexdependent)
if(prop<1)
{ accuracy.df = data.frame(k =seq(1,sqrt(nrow(dataframev)),accuracy = rep(0,sqrt(nrow(dataframev)))))
for(i in 1:sqrt(nrow(dataframev)) )
{
prediction= knn(train =dataframet[,-indexdependent],test = dataframev[,-indexdependent],cl = dataframet[,indexdependent],k= i)
accuracy.df[i,2]=confusionMatrix(prediction,dataframev[,indexdependent])$overall[1]
}
names(accuracy.df)=c("k", "Accuracy")
accuracy.df[,] = accuracy.df[order(1-accuracy.df$Accuracy),]
print(accuracy.df)
cat(sprintf("The value for k to be chosen is %d ",accuracy.df[1,1]))
# print("The ideal value for the ")
}
else
{
print("There needs to be a validation/test data set")
}
})
output$MV<-renderPlot({
if(input$ex == TRUE)
{data("iris")
data = iris
gp = runif(nrow(data))
data[,]= data[order(gp),]
}
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$variablex)
if(ncol(ds)==2){return()}
if(input$std == TRUE)
{ds = standardize(ds)}
dataframe = ds
indexdependent= grep(input$variabley, colnames(dataframe))
prop = input$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,]
# super.sym <- trellis.par.get("superpose.symbol")
# ngp = nlevels(dataframe[,indexdependent])
# splom(~dataframe[,-indexdependent]|dataframe[,indexdependent], data=dataframe)
# parallelplot(~dataframe[,-indexdependent] | dataframe[,indexdependent], dataframe)
if(input$dt == "Full")
{
if(ncol(dataframe)>2)
{pairs(dataframe[,-indexdependent], col = dataframe[,indexdependent], oma=c(3,3,3,15))
par(xpd = TRUE)
legend("bottomright", fill = unique( dataframe[,indexdependent]), legend = c( levels( dataframe[,indexdependent])))
}
}
if(input$dt == "Train")
{
if(ncol(dataframet)>2)
{ pairs(dataframet[,-indexdependent], col = dataframet[,indexdependent], oma=c(3,3,3,15))
par(xpd = TRUE)
legend("bottomright", fill = unique( dataframet[,indexdependent]), legend = c( levels( dataframet[,indexdependent])))
}
}
if(input$dt == "Valid")
{
if(ncol(dataframev)>2 && prop<1)
{ pairs(dataframev[,-indexdependent], col = dataframev[,indexdependent], oma=c(3,3,3,15))
par(xpd = TRUE)
legend("bottomright", fill = unique( dataframev[,indexdependent]), legend = c( levels( dataframev[,indexdependent])))
}
}
})
output$ME<-renderPrint({
if(input$ex == TRUE)
{data("iris")
data = iris
gp = runif(nrow(data))
data[,]= data[order(gp),]
}
else{
file1 = input$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)
if(ncol(ds)==2){return()}
if(input$std == TRUE)
{ds = standardize(ds)}
mod = paste(input$variabley,"~.")
options(scipen = 999)
prop = input$train_num
set.seed(1)
dataframe = ds
train.rows = sample(row.names(dataframe),dim(dataframe)[1]*prop)
dataframet = dataframe[train.rows,]
valid.rows = setdiff(row.names(dataframe),train.rows)
dataframev = dataframe[valid.rows,]
indexdependent= grep(input$variabley, colnames(dataframe))
# print(indexdependent)
if(prop<1)
{ accuracy.df = data.frame(k =seq(1,sqrt(nrow(dataframev)),accuracy = rep(0,sqrt(nrow(dataframev)))))
for(i in 1:sqrt(nrow(dataframev)) )
{
prediction= knn(train =dataframet[,-indexdependent],test = dataframev[,-indexdependent],cl = dataframet[,indexdependent],k= i)
accuracy.df[i,2]=confusionMatrix(prediction,dataframev[,indexdependent])$overall[1]
}
names(accuracy.df)=c("k", "Accuracy")
accuracy.df[,] = accuracy.df[order(1-accuracy.df$Accuracy),]
# print(accuracy.df)
#cat(sprintf("The value for k to be chosen is %d ",accuracy.df[1,1]))
# print("The ideal value for the ")
ki = accuracy.df[1,1]
prediction= knn(train =dataframet[,-indexdependent],test = dataframev[,-indexdependent],cl = dataframet[,indexdependent],k= ki)
print(confusionMatrix(prediction,dataframev[,indexdependent]))
}
else
{
print("There needs to be a validation/test data set")
}
})
output$MD<-renderPrint({
if(input$ex == TRUE)
{data("iris")
data = iris
gp = runif(nrow(data))
data[,]= data[order(gp),]
}
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$variablex)
if(ncol(ds)==2){return()}
if(input$std == TRUE)
{ds = standardize(ds)}
options(scipen = 999)
prop = input$train_num
set.seed(1)
dataframe = ds
train.rows = sample(row.names(dataframe),dim(dataframe)[1]*prop)
dataframet = dataframe[train.rows,]
valid.rows = setdiff(row.names(dataframe),train.rows)
dataframev = dataframe[valid.rows,]
indexdependent= grep(input$variabley, colnames(dataframe))
if(prop<1)
{ accuracy.df = data.frame(k =seq(1,sqrt(nrow(dataframev)),accuracy = rep(0,sqrt(nrow(dataframev)))))
for(i in 1:sqrt(nrow(dataframev)) )
{
prediction= knn(train =dataframet[,-indexdependent],test = dataframev[,-indexdependent],cl = dataframet[,indexdependent],k= i)
accuracy.df[i,2]=confusionMatrix(prediction,dataframev[,indexdependent])$overall[1]
}
names(accuracy.df)=c("k", "Accuracy")
accuracy.df[,] = accuracy.df[order(1-accuracy.df$Accuracy),]
# print(accuracy.df)
# cat(sprintf("The value for k to be chosen is %d ",accuracy.df[1,1]))
# print("The ideal value for the ")
ki = accuracy.df[1,1]
}
else
{
print("There needs to be a validation/test data set")
}
if(input$mt == "lda")
# {
# model = MASS::lda(formula = as.formula(mod),data = dataframet)
test_data = data.frame(hot_to_r(input$testdata))
if(ncol(test_data)== 1)
{ df = select(dataframet,-c(input$variabley))
colnames(test_data)= colnames(df)
}
#print(dim(dataframet))
# print(dim(test_data))
if(ncol(test_data)>1 && prop <1)
{ prediction= knn(train =dataframet[,-indexdependent],test= test_data,cl = dataframet[,indexdependent],k= ki)
test_data$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$variablex)
ds = select_if(ds,is.factor)
selectInput("variabley","Select the dependent variable",choices = colnames(ds),selected = "" )
})
output$k <- renderUI({
if(input$ex == TRUE)
{data("iris")
data = iris}
else
{
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
ds = data
prop = input$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,]
sliderInput("k", label = "Enter the value of k ",
min = 1, max = NROW(dataframet), value = 1, step = 1)
})
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$variablex)
if(input$std == TRUE)
{ds = standardize(ds)}
ds = select(ds,-c(input$variabley))
rhandsontable(data.frame(ds[1,]))
})
output$downloadPlot<- downloadHandler(
filename = function() {
paste("Scatterplot", ".png", sep = "")
},
content = function(file) {
png(file)
if(input$ex == TRUE)
{data("iris")
data = iris
gp = runif(nrow(data))
data[,]= data[order(gp),]
}
else{
file1 = input$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)
if(ncol(ds)==2){return()}
if(input$std == TRUE)
{ds = standardize(ds)}
dataframe = ds
indexdependent= grep(input$variabley, colnames(dataframe))
prop = input$train_num
set.seed(1)
dataframe = ds
train.rows = sample(row.names(dataframe),dim(dataframe)[1]*prop)
dataframet = dataframe[train.rows,]
valid.rows = setdiff(row.names(dataframe),train.rows)
dataframev = dataframe[valid.rows,]
# super.sym <- trellis.par.get("superpose.symbol")
# ngp = nlevels(dataframe[,indexdependent])
# splom(~dataframe[,-indexdependent]|dataframe[,indexdependent], data=dataframe)
# parallelplot(~dataframe[,-indexdependent] | dataframe[,indexdependent], dataframe)
if(input$dt == "Full")
{
if(ncol(dataframe)>2)
{pairs(dataframe[,-indexdependent], col = dataframe[,indexdependent], oma=c(3,3,3,15))
par(xpd = TRUE)
legend("bottomright", fill = unique( dataframe[,indexdependent]), legend = c( levels( dataframe[,indexdependent])))
}
}
if(input$dt == "Train")
{
if(ncol(dataframet)>2)
{ pairs(dataframet[,-indexdependent], col = dataframet[,indexdependent], oma=c(3,3,3,15))
par(xpd = TRUE)
legend("bottomright", fill = unique( dataframet[,indexdependent]), legend = c( levels( dataframet[,indexdependent])))
}
}
if(input$dt == "Valid")
{
if(ncol(dataframev)>2 && prop<1)
{ pairs(dataframev[,-indexdependent], col = dataframev[,indexdependent], oma=c(3,3,3,15))
par(xpd = TRUE)
legend("bottomright", fill = unique( dataframev[,indexdependent]), legend = c( levels( dataframev[,indexdependent])))
}
}
dev.off()
})
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KNNShiny documentation built on May 9, 2019, 5:05 p.m.
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Note : 1. This application works only for 2 or more predictors(independent variables) 2. The general rule of thumb of square root of number of rows in validation data is folllowed in figuring out the ideal value of k
knitr::opts_chunk$set(echo = TRUE) library(dplyr) library(caret) library(e1071) library(rhandsontable) library(datasets) library(psycho) #library(lattice) library(FNN)
```r sidebarPanel( checkboxInput("ex","Uncheck for using your own file",value = TRUE), fileInput("file", "Upload the *.csv file with headers"), selectInput("dt","Choose Train/Valid/Full for Visuals",choices = c("Train","Valid","Full"),selected = "Full"), sliderInput("train_num", label = "Enter the proportion of training dataset:", min = 0.6, max = 1, value = 0.6, step = 0.01), checkboxInput("std","Standardize",value = FALSE),
uiOutput("vx"), uiOutput("vy"), downloadButton("downloadPlot", "Download Plot")
) mainPanel( tabsetPanel(type = "tab", tabPanel("Model Summary", verbatimTextOutput("AD") ), tabPanel("Model Visualization", plotOutput("MV") ), tabPanel("Model Evaluation",verbatimTextOutput("ME")), tabPanel("Model Deployment",verbatimTextOutput("MD")) ), h6("Edit the test data record"), rHandsontableOutput("testdata"), h6("", tags$img(src ="K.JPG", height= 400, width=400)) ) output$AD<-renderPrint({ if(input$ex == TRUE) {data("iris") data = iris gp = runif(nrow(data)) data[,]= data[order(gp),] } 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$variablex)
if(ncol(ds)==2){return()}
if(input$std == TRUE)
{ds = standardize(ds)}
options(scipen = 999)
prop = input$train_num
set.seed(1)
dataframe = ds
train.rows = sample(row.names(dataframe),dim(dataframe)[1]*prop)
dataframet = dataframe[train.rows,]
valid.rows = setdiff(row.names(dataframe),train.rows)
dataframev = dataframe[valid.rows,]
indexdependent= grep(input$variabley, colnames(dataframe))
# print(indexdependent)
if(prop<1)
{ accuracy.df = data.frame(k =seq(1,sqrt(nrow(dataframev)),accuracy = rep(0,sqrt(nrow(dataframev)))))
for(i in 1:sqrt(nrow(dataframev)) )
{
prediction= knn(train =dataframet[,-indexdependent],test = dataframev[,-indexdependent],cl = dataframet[,indexdependent],k= i)
accuracy.df[i,2]=confusionMatrix(prediction,dataframev[,indexdependent])$overall[1]
}
names(accuracy.df)=c("k", "Accuracy")
accuracy.df[,] = accuracy.df[order(1-accuracy.df$Accuracy),]
print(accuracy.df)
cat(sprintf("The value for k to be chosen is %d ",accuracy.df[1,1]))
# print("The ideal value for the ")
}
else
{
print("There needs to be a validation/test data set")
}
}) output$MV<-renderPlot({ if(input$ex == TRUE) {data("iris") data = iris gp = runif(nrow(data)) data[,]= data[order(gp),] } 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$variablex)
if(ncol(ds)==2){return()}
if(input$std == TRUE)
{ds = standardize(ds)}
dataframe = ds
indexdependent= grep(input$variabley, colnames(dataframe))
prop = input$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,]
# super.sym <- trellis.par.get("superpose.symbol")
# ngp = nlevels(dataframe[,indexdependent])
# splom(~dataframe[,-indexdependent]|dataframe[,indexdependent], data=dataframe)
# parallelplot(~dataframe[,-indexdependent] | dataframe[,indexdependent], dataframe)
if(input$dt == "Full")
{
if(ncol(dataframe)>2)
{pairs(dataframe[,-indexdependent], col = dataframe[,indexdependent], oma=c(3,3,3,15))
par(xpd = TRUE)
legend("bottomright", fill = unique( dataframe[,indexdependent]), legend = c( levels( dataframe[,indexdependent])))
}
}
if(input$dt == "Train")
{
if(ncol(dataframet)>2)
{ pairs(dataframet[,-indexdependent], col = dataframet[,indexdependent], oma=c(3,3,3,15))
par(xpd = TRUE)
legend("bottomright", fill = unique( dataframet[,indexdependent]), legend = c( levels( dataframet[,indexdependent])))
}
}
if(input$dt == "Valid")
{ if(ncol(dataframev)>2 && prop<1) { pairs(dataframev[,-indexdependent], col = dataframev[,indexdependent], oma=c(3,3,3,15)) par(xpd = TRUE) legend("bottomright", fill = unique( dataframev[,indexdependent]), legend = c( levels( dataframev[,indexdependent]))) } }
})
output$ME<-renderPrint({ if(input$ex == TRUE) {data("iris") data = iris gp = runif(nrow(data)) data[,]= data[order(gp),] } else{ file1 = input$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)
if(ncol(ds)==2){return()}
if(input$std == TRUE)
{ds = standardize(ds)}
mod = paste(input$variabley,"~.")
options(scipen = 999)
prop = input$train_num
set.seed(1)
dataframe = ds
train.rows = sample(row.names(dataframe),dim(dataframe)[1]*prop)
dataframet = dataframe[train.rows,]
valid.rows = setdiff(row.names(dataframe),train.rows)
dataframev = dataframe[valid.rows,]
indexdependent= grep(input$variabley, colnames(dataframe))
# print(indexdependent)
if(prop<1)
{ accuracy.df = data.frame(k =seq(1,sqrt(nrow(dataframev)),accuracy = rep(0,sqrt(nrow(dataframev)))))
for(i in 1:sqrt(nrow(dataframev)) )
{
prediction= knn(train =dataframet[,-indexdependent],test = dataframev[,-indexdependent],cl = dataframet[,indexdependent],k= i)
accuracy.df[i,2]=confusionMatrix(prediction,dataframev[,indexdependent])$overall[1]
}
names(accuracy.df)=c("k", "Accuracy")
accuracy.df[,] = accuracy.df[order(1-accuracy.df$Accuracy),]
# print(accuracy.df)
#cat(sprintf("The value for k to be chosen is %d ",accuracy.df[1,1]))
# print("The ideal value for the ")
ki = accuracy.df[1,1]
prediction= knn(train =dataframet[,-indexdependent],test = dataframev[,-indexdependent],cl = dataframet[,indexdependent],k= ki)
print(confusionMatrix(prediction,dataframev[,indexdependent]))
}
else
{
print("There needs to be a validation/test data set")
}
})
output$MD<-renderPrint({ if(input$ex == TRUE) {data("iris") data = iris gp = runif(nrow(data)) data[,]= data[order(gp),] } 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$variablex)
if(ncol(ds)==2){return()}
if(input$std == TRUE)
{ds = standardize(ds)}
options(scipen = 999)
prop = input$train_num
set.seed(1)
dataframe = ds
train.rows = sample(row.names(dataframe),dim(dataframe)[1]*prop)
dataframet = dataframe[train.rows,]
valid.rows = setdiff(row.names(dataframe),train.rows)
dataframev = dataframe[valid.rows,]
indexdependent= grep(input$variabley, colnames(dataframe))
if(prop<1)
{ accuracy.df = data.frame(k =seq(1,sqrt(nrow(dataframev)),accuracy = rep(0,sqrt(nrow(dataframev)))))
for(i in 1:sqrt(nrow(dataframev)) )
{
prediction= knn(train =dataframet[,-indexdependent],test = dataframev[,-indexdependent],cl = dataframet[,indexdependent],k= i)
accuracy.df[i,2]=confusionMatrix(prediction,dataframev[,indexdependent])$overall[1]
}
names(accuracy.df)=c("k", "Accuracy")
accuracy.df[,] = accuracy.df[order(1-accuracy.df$Accuracy),]
# print(accuracy.df)
# cat(sprintf("The value for k to be chosen is %d ",accuracy.df[1,1]))
# print("The ideal value for the ")
ki = accuracy.df[1,1]
}
else
{
print("There needs to be a validation/test data set")
}
if(input$mt == "lda")
# {
# model = MASS::lda(formula = as.formula(mod),data = dataframet)
test_data = data.frame(hot_to_r(input$testdata))
if(ncol(test_data)== 1)
{ df = select(dataframet,-c(input$variabley))
colnames(test_data)= colnames(df)
}
#print(dim(dataframet))
# print(dim(test_data))
if(ncol(test_data)>1 && prop <1)
{ prediction= knn(train =dataframet[,-indexdependent],test= test_data,cl = dataframet[,indexdependent],k= ki)
test_data$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$variablex)
ds = select_if(ds,is.factor)
selectInput("variabley","Select the dependent variable",choices = colnames(ds),selected = "" )
}) output$k <- renderUI({
if(input$ex == TRUE) {data("iris") data = iris} else {
file1 = input$file
if(is.null(file1)){return()}
data = read.table(file = file1$datapath,sep =",",header = TRUE)
if(is.null(data())){return()}
}
ds = data
prop = input$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,]
sliderInput("k", label = "Enter the value of k ",
min = 1, max = NROW(dataframet), value = 1, step = 1)
})
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$variablex)
if(input$std == TRUE)
{ds = standardize(ds)}
ds = select(ds,-c(input$variabley))
rhandsontable(data.frame(ds[1,]))
})
output$downloadPlot<- downloadHandler( filename = function() { paste("Scatterplot", ".png", sep = "") }, content = function(file) { png(file) if(input$ex == TRUE) {data("iris") data = iris gp = runif(nrow(data)) data[,]= data[order(gp),] } else{ file1 = input$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)
if(ncol(ds)==2){return()}
if(input$std == TRUE)
{ds = standardize(ds)}
dataframe = ds
indexdependent= grep(input$variabley, colnames(dataframe))
prop = input$train_num
set.seed(1)
dataframe = ds
train.rows = sample(row.names(dataframe),dim(dataframe)[1]*prop)
dataframet = dataframe[train.rows,]
valid.rows = setdiff(row.names(dataframe),train.rows)
dataframev = dataframe[valid.rows,]
# super.sym <- trellis.par.get("superpose.symbol")
# ngp = nlevels(dataframe[,indexdependent])
# splom(~dataframe[,-indexdependent]|dataframe[,indexdependent], data=dataframe)
# parallelplot(~dataframe[,-indexdependent] | dataframe[,indexdependent], dataframe)
if(input$dt == "Full")
{
if(ncol(dataframe)>2)
{pairs(dataframe[,-indexdependent], col = dataframe[,indexdependent], oma=c(3,3,3,15))
par(xpd = TRUE)
legend("bottomright", fill = unique( dataframe[,indexdependent]), legend = c( levels( dataframe[,indexdependent])))
}
}
if(input$dt == "Train")
{
if(ncol(dataframet)>2)
{ pairs(dataframet[,-indexdependent], col = dataframet[,indexdependent], oma=c(3,3,3,15))
par(xpd = TRUE)
legend("bottomright", fill = unique( dataframet[,indexdependent]), legend = c( levels( dataframet[,indexdependent])))
}
}
if(input$dt == "Valid")
{ if(ncol(dataframev)>2 && prop<1) { pairs(dataframev[,-indexdependent], col = dataframev[,indexdependent], oma=c(3,3,3,15)) par(xpd = TRUE) legend("bottomright", fill = unique( dataframev[,indexdependent]), legend = c( levels( dataframev[,indexdependent]))) } }
dev.off() })
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