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MLtRS.R
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MLtRS.R
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library(shiny)
library(shinydashboard)
library(nortest)
library(mvnormtest)
library(MASS)
library(shinyLP)
library(class)
library(gmodels)
library(caret)
library(rattle)
library(ranger)
library(klaR)
library(kernlab)
library(micad)
library(e1071)
library(NeuralNetTools)
library(neuralnet)
library(nnet)
library(mclust)
ui <- fluidPage(
navbarPage(title = "MLtRS",
tabPanel("Home",
jumbotron("Hi Welcome to MLtRS", paste("This is web application for Machine Learning and AI! Right now application supports few techniques related to Supervised Learning."), buttonLabel = "Click Me" )
),
tabPanel("Data Sets",
sidebarLayout(
sidebarPanel(
fileInput("file1", "Choose CSV File", accept=c('text/csv', 'text/comma-separated-values', 'text/plain', '.csv')),
radioButtons("indata", "Choice:", choices = c("Full", "Columns")),
selectInput("cols", "Choose the variable", choices = "", selected = " ", multiple = TRUE),
downloadButton('downloaddatset', "Download"),
hr(),
radioButtons("trans1", "Transformation:", choices = c("Not-Required", "log", "inverselog", "exponential", "lognormal", "standardize")),
hr()
),
mainPanel(tableOutput("tab1"))
)
),
navbarMenu("Statistical Analysis",
tabPanel("Summary Statistics",
sidebarLayout(
sidebarPanel(
selectInput("cols1", "Choose Variable:", choices = "", selected = " ", multiple = TRUE),
radioButtons("ssoption", "Select Option", choices = c("Summary", "Length", "Dim", "Type of", "Class"))
),
mainPanel(
fluidRow(
h3("Summary Statistics"),
div(
verbatimTextOutput("summar")
)
)
)
)
),
tabPanel("Frequency Tables",
sidebarLayout(
sidebarPanel(
selectInput("cols2", "Choose Varibale 1:", choices = "", selected = " ", multiple = TRUE),
selectInput("cols3", "Choose Varibale 2:", choices = "", selected = " ", multiple = TRUE)
),
mainPanel(
fluidRow(
h3("Frequency Tables"),
div(
verbatimTextOutput("freq_tables")
)
)
)
)
),
tabPanel("Plots",
sidebarLayout(
sidebarPanel(
radioButtons("plotoption", "Choose the Option:", choices = c("Histogram", "BarPlot", "Scatter", "Pie" )),
selectInput("cols6", "Choose Varibale 1:", choices = "", selected = " ", multiple = TRUE),
textInput("xaxisname", "Write X Axis Name"),
textInput("yaxisname", "Write Y Axis Name"),
textInput("title", "Write Title For the Graph")
),
mainPanel(
h3("Plots"),
fluidRow(
plotOutput("plot")
)
)
)
),
tabPanel("Statistical Tests",
sidebarLayout(
sidebarPanel(
selectInput("cols7", "Choose Varibale 1:", choices = "", selected = " ", multiple = TRUE),
selectInput("cols8", "Choose Varibale 2:", choices = "", selected = " ", multiple = TRUE),
radioButtons("normaltest", "Select Method:", choices = c("A-D-Test", "Shapiro", "KS-Test", "MV-Shapiro")),
hr(),
helpText("For more details visit:"),
a(href="https://en.wikipedia.org/wiki/Anderson%E2%80%93Darling_test", "Anderson–Darling test"), br(),
a(href="https://en.wikipedia.org/wiki/Shapiro%E2%80%93Wilk_test", "Shapiro–Wilk test"), br(),
a(href="https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test", "Kolmogorov–Smirnov test"), br(),
hr()
),
mainPanel(
h3("Statistical Tests"),
fluidRow(
div(
plotOutput("qqp")
),
div(
verbatimTextOutput("normtest")
)
)
)
)
),
tabPanel("Correlation",
sidebarLayout(
sidebarPanel(
selectInput("cols9", "Choose Variable:", choices = "", selected = " ", multiple = TRUE),
selectInput("cols10", "Choose Variable:", choices = "", selected = " ", multiple = TRUE),
radioButtons("cormethod", "Select Method:", choices = c("Covariance", "KarlPearson", "Spearman", "Kendals")),
hr(),
helpText("For Details Visit:"),
a(href="https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient", "Karl Pearson Correlation Test"),
hr()
),
mainPanel(
h3("Covariance & Correlation"),
verbatimTextOutput("cor_t")
)
)
),
tabPanel("Regression & ANOVA",
sidebarLayout(
sidebarPanel(
selectInput("cols11", "Choose Variable:", choices = "", selected = " ", multiple = TRUE),
selectInput("cols12", "Choose Variable:", choices = "", selected = " ", multiple = TRUE),
radioButtons("regmethod", "Select Method:", choices = c("Fit", "Summary", "ANOVA")),
hr(),
helpText("For more information please visit"),
a(href="https://en.wikipedia.org/wiki/Simple_linear_regression", "Simple Linear Regression"),
hr()
),
mainPanel(
h3("Regression & ANOVA"),
fluidRow(
div(
verbatimTextOutput("regout")
),
div(
plotOutput("regplot")
)
)
)
)
)
),
navbarMenu("Supervised Learning",
tabPanel("Logistic Reg.",
sidebarLayout(
sidebarPanel(
selectInput("logrvar", "Select Variable", choices = "", selected = ""),
textInput("logrprop", "Select Proportion", value = 0.8, placeholder = "Percentage of rows"),
# textInput("logryname", "Class Variable", value = "num", placeholder = "Class Variable"),
radioButtons("logroption", "Select Method", choices = c("Show Prop.", "Fit", "Coef.", "Pred. Accuracy")),
hr(),
helpText("Variable selected must be categorical. Use '1. logr_svm_heart_data' from datasets for testing."),
hr(),
a(href="http://mlwiki.org/index.php/Logistic_Regression", "Logistic Regression")
),
mainPanel(
div(verbatimTextOutput("logroutput"))
)
)
),
tabPanel("kNN",
sidebarLayout(
sidebarPanel(
textInput("knntrain", "Select Proportion", value = 0.8, placeholder = "Percentage of rows"),
radioButtons("knnoption", "Select Option", choices = c("Show Prop.", "Show Train & Test Data", "Show No. of Classes", "Fit", "Accuracy")),
hr(),
helpText("First column of data set must be categorical/use '2. kNN_sepsis_numerical' from datasets for testing."),
a(href="https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm", "kNN")
),
mainPanel(
div(verbatimTextOutput("knnoutput"))
)
)
),
tabPanel("SVM",
sidebarLayout(
sidebarPanel(
selectInput("svmvar", "Select Variable", choices = "", selected = ""),
textInput("svmprop", "Select Proportion", value = 0.8, placeholder = "Percentage of rows"),
textInput("svmyname", "Class Variable", value = "num", placeholder = "Class Variable"),
radioButtons("svmoption", "Select Method", choices = c("Show Prop.", "Fit", "Predicted", "Pred. Accuracy")),
hr(),
helpText("Variable selected must be categorical. Use '1. logr_svm_heart_data' from datasets for testing."),
hr(),
a(href="http://mlwiki.org/index.php/Support_Vector_Machines", "SVM")
),
mainPanel(verbatimTextOutput("svmoutput"))
)
),
tabPanel("Decision Trees",
sidebarLayout(
sidebarPanel(
selectInput("dtvar", "Select Variable", choices = "", selected = ""),
selectInput("dtvar2", "Select Variable", choices = "", selected = "", multiple = TRUE),
textInput("dtprop", "Select Proportion", value = 0.8, placeholder = "Percentage of rows"),
textInput("dtyname", "Class Variable", value = "num", placeholder = "Class Variable"),
radioButtons("dtoption", "Select Method", choices = c("No Option", "Table", "Show Prop.", "Train & Test Data", "Fit", "Predicted", "Pred. Accuracy")),
radioButtons("dtplot", "Select Plot", choices = c("No Plot", "QPlot", "DTree")),
hr(),
helpText("Variable selected must be categorical and numerical. Use '4. DT_breast_cancer.csv' from datasets for testing."),
hr(),
a(href="http://mlwiki.org/index.php/Decision_Tree_Exercises", "Decision Trees")
),
mainPanel(
div(verbatimTextOutput("dtoutput")),
div(plotOutput("dtplot"))
)
)
),
tabPanel("Random Forests",
sidebarLayout(
sidebarPanel(
selectInput("rfvar", "Select Variable", choices = "", selected = ""),
textInput("rfprop", "Select Proportion", value = 0.8, placeholder = "Percentage of rows"),
textInput("rfyname", "Class Variable", value = "old", placeholder = "Class Variable"),
radioButtons("rfoption", "Select Method", choices = c("No Option", "Table", "Show Prop.", "Train & Test Data", "Fit", "Summary", "Predicted", "Pred. Accuracy")),
hr(),
helpText("Variable selected must be categorical and numerical. Use '5. RF_abalone_short.csv' from datasets for testing."),
hr(),
a(href="https://en.wikipedia.org/wiki/Random_forest", "Random Forest")
),
mainPanel(
div(verbatimTextOutput("rfoutput"))
)
)
),
tabPanel("Naive Bayes Classifier",
sidebarLayout(
sidebarPanel(
selectInput("nbvar", "Select Variable", choices = "", selected = ""),
textInput("nbprop", "Select Proportion", value = 0.8, placeholder = "Percentage of rows"),
textInput("nbyname", "Class Variable", value = "sepsis", placeholder = "Class Variable"),
radioButtons("nboption", "Select Method", choices = c("No Option", "Table", "Show Prop.", "Train & Test Data", "Fit", "Summary", "Predicted", "Pred. Accuracy")),
hr(),
helpText("Variable selected must be categorical and numerical. Use '6. Naive_Bayes_sepsis.csv' from datasets for testing."),
hr(),
a(href="https://en.wikipedia.org/wiki/Naive_Bayes_classifier", "Naive Bayes Classifier")
),
mainPanel(
div(verbatimTextOutput("nboutput"))
)
)
),
tabPanel("Neural Networks",
sidebarLayout(
sidebarPanel(
selectInput("nnvar", "Select Variable", choices = "", selected = ""),
textInput("nnprop", "Proportion", value = 0.8, placeholder = "Proportion for training data"),
textInput("nnhl1", "Value for First Hidden Layer", value = 5, placeholder = "Value for first hidden layer"),
textInput("nnhl2", "Value for Second Hidden Layer", value = 3, placeholder = "Value for first hidden layer"),
textInput("nntype", "Method", value = "neuralnet", placeholder = "Write name of the method"),
radioButtons("nnoption", "Select Method", choices = c("No Option", "Table", "Show Prop.", "Train & Test Data", "Fit", "Summary", "Predictions", "Pred. Accuracy", "Plot")),
# radioButtons("nnplottype", "Select Plot", choices = c("No Plot", "Weight Decay", "Network Plot")),
hr(),
helpText("Variable selected must be categorical and numerical. Use '7. NN_prostrate.csv' from datasets for testing."),
hr(),
a(href="http://mlwiki.org/index.php/Neural_Networks", "Neural Networks")
),
mainPanel(
div(verbatimTextOutput("nnoutput")),
div(plotOutput("nnplot"))
)
)
)
),
navbarMenu("Unsupervised Learning",
tabPanel("PCA",
sidebarLayout(
sidebarPanel(
selectInput("pcavar", "Select Variable", choices = "", selected = ""),
textInput("pcaprop", "Proportion", value = 0.8, placeholder = "Proportion for Training"),
textInput("pcanc", "Write Number of Components", value = 2, placeholder = "No. of Components"),
radioButtons("pcaoption", "Select Option", choices = c("No Option", "Table", "Show Prop.", "Components", "Fit", "Predictions", "Mod. Accuracy"))
),
mainPanel(
div(verbatimTextOutput("pcaoutput"))
)
)
),
tabPanel("Gaussian Mixturre Modeling",
sidebarLayout(
sidebarPanel(
selectInput("gmvar", "Select Variable", choices = "", selected = ""),
textInput("gmprop", "Proportion", value = 0.8, placeholder = "Proportion for Training"),
radioButtons("gmoption", "Select Option", choices = c("No Option", "Table", "Data", "BIC", "Fit", "Summary", "Predictions-GMM", "HC")),
radioButtons("gmplottype", "Select Plot", choices = c("No Plot", "CLPairs", "BIC", "Classification", "Uncertainity", "HC"))
),
mainPanel(
div(verbatimTextOutput("gmoutput")),
div(plotOutput("gmplot"))
)
)
),
tabPanel("Novelty & Outlier Detection",
sidebarLayout(
sidebarPanel(
textInput("mldata", "Select Dataset", value ="iris_anomaly", placeholder = "Dataset name"),
radioButtons("mloption", "Select Plot", choices = c("No Opt.", "Fit", "Memberships"))
),
mainPanel(
div(verbatimTextOutput("mloutput"))
)
)
),
tabPanel("Clustering (Fuzy C-Means Clustering)",
sidebarLayout(
sidebarPanel(
selectInput("fmcvar1", "Select Variable", choices = "", selected = ""),
selectInput("fmcvar2", "Select Variable", choices = "", selected = "", multiple = TRUE),
radioButtons("fmcoption", "Select Option", choices = c("No Opt.", "Fit", "Predictions")),
radioButtons("fmcplot", "Select Plot", choices = c("No Plot", "Plot"))
),
mainPanel(
div(verbatimTextOutput("fmcoutput")),
div(plotOutput("fmcplot"))
)
)
),
tabPanel("Manifold Learning (MDS)")
),
tabPanel("Contact",
sidebarLayout(
sidebarPanel(
"Information to contact"
),
mainPanel(htmlOutput("text1"))
)
)
)
)
server <- function(input, output, session) {
# for DATASET TAB
data_input <- reactive({
infile <- input$file1
req(infile)
data.frame(read.csv(infile$datapath))
})
observeEvent(input$file1,{
updateSelectInput(session, inputId = "cols", choices = names(data_input()))
}
)
logno <- reactive({
df <- data_input()
x <- matrix(NA, length(df[, input$cols]), length(df[, input$cols][[1]]))
for(i in 1:length(df[, input$cols])){
for(j in 1:length(df[, input$cols][[1]])){
x[i, j] <- dlnorm(df[, input$cols][[i]][j])
}
}
return(t(x))
})
standout <- reactive({
df <- data_input()
x <- matrix(NA, length(df[, input$cols]), length(df[, input$cols][[1]]))
if(!is.list(df[, input$cols])){
df[, input$cols] <- list(df[, input$cols])
}
for(i in 1:length(df[, input$cols])){
for(j in 1:length(df[, input$cols][[1]])){
x[i, j] <- df[, input$cols][[i]][j]-mean(df[, input$cols][[i]])/sd(df[, input$cols][[i]])
}
}
return(t(x))
})
logdata <- reactive({
df <- data_input()
ld <- log(df[, input$cols])
return(ld)
})
invlogdata <- reactive({
df <- data_input()
ild <- 1/log(df[, input$cols])
return(ild)
})
expdata <- reactive({
df <- data_input()
expd <- log(df[input$cols])
return(expd)
})
output$tab1 <- renderTable(
{
df <- data_input()
if (input$indata == "Full"){
print(df)
} else if(input$trans1 == "Not-Required"){
data <- df[, input$cols]
print(data)
} else if(input$trans1 == "log"){
logdata()
} else if(input$trans1 == "inverselog"){
invlogdata()
} else if(input$trans1 == "exponential"){
expdata()
} else if(input$trans1 == "lognormal"){
logno()
} else if(input$trans1 == "standardize"){
standout()
}
}
)
output$downloaddatset <- downloadHandler(
filename <- function(){
paste("data-", Sys.Date(), ".csv", sep = "")
},
content <- function(file){
df <- data_input()
if(input$trans1 == "log"){
write.csv(logdata(), file, row.names = TRUE)
} else if(input$trans1 == "inverselog"){
write.csv(invlogdata(), file, row.names = TRUE)
} else if(input$trans1 == "exponential"){
write.csv(expdata(), file, row.names = TRUE)
} else if(input$trans1 == "lognormal"){
write.csv(logno(), file, row.names = TRUE)
} else if(input$trans1 == "standardize"){
write.csv(standout(), file, row.names = TRUE)
}
}
)
# summary statistics
observeEvent(input$file1, {
updateSelectInput(session, inputId = "cols1", choices = names(data_input()))
}
)
summ <- reactive({
var1 <- data_input()[,input$cols1]
if (input$ssoption == "Summary"){
su <- summary(var1)
return(su)
} else if (input$ssoption == "Length"){
return(length(var1))
} else if(input$ssoption == "Dim"){
return(dim(var1))
} else if (input$ssoption == "Type of"){
return(typeof(var1))
} else if(input$ssoption == "Class"){
return(class(var1))
}
})
output$summar <- renderPrint({
if (input$ssoption == "Summary"){
summ()
} else if (input$ssoption == "Length"){
summ()
} else if(input$ssoption == "Dim"){
summ()
} else if (input$ssoption == "Type of"){
summ()
} else if(input$ssoption == "Class"){
summ()
}
})
# frequency tab
observeEvent(input$file1, {
updateSelectInput(session, inputId = "cols2", choices = names(data_input()))
updateSelectInput(session, inputId = "cols3", choices = names(data_input()))
}
)
freq <- reactive({
var1 <- data_input()[,input$cols2]
var2 <- data_input()[,input$cols3]
fre <- table(var1, var2)
return(fre)
})
output$freq_tables <- renderPrint({
freq()
})
# Cross tabulation
observeEvent(input$file1, {
updateSelectInput(session, inputId = "cols4", choices = names(data_input()))
updateSelectInput(session, inputId = "cols5", choices = names(data_input()))
}
)
cross <- reactive({
var1 <- data_input()[,input$cols4]
var2 <- data_input()[,input$cols5]
cro <- chisq.test(var1, var2)
return(cro)
})
output$chi_t <- renderPrint({
cross()
})
# Plots
observeEvent(input$file1, {
updateSelectInput(session, inputId = "cols6", choices = names(data_input()))
}
)
output$plot <- renderPlot({
df <- data_input()
if(input$plotoption == "Histogram"){
hist(df[, input$cols6], freq = FALSE, xlab = input$xaxisname, ylab = input$yaxisname, main = input$title); lines(density(df[, input$cols6]), col = "red", lwd = 1.5)
} else if(input$plotoption == "BarPlot"){
barplot(df[, input$cols6], xlab = input$xaxisname, ylab = input$yaxisname, main = input$title)
} else if(input$plotoption == "Scatter"){
scatter.smooth(df[, input$cols6], xlab = input$xaxisname, ylab = input$yaxisname, main = input$title)
} else {
pie(table(df[, input$cols6]))
}
})
# Statistical Tests
observeEvent(input$file1, {
updateSelectInput(session, inputId = "cols7", choices = names(data_input()))
updateSelectInput(session, inputId = "cols8", choices = names(data_input()))
}
)
output$qqp <- renderPlot({
df <- data_input()
qqnorm(df[, input$cols7]);qqline(df[, input$cols7])
})
adt <- reactive({
df <- data_input()
var <- df[, input$cols7]
ad <- ad.test(var)
return(ad)
})
sht <- reactive({
df <- data_input()
var <- df[, input$cols7]
sh <- shapiro.test(var)
return(sh)
})
kst <- reactive({
df <- data_input()
var1 <- df[, input$cols7]
var2 <- df[, input$cols8]
ks <- ks.test(var1, var2)
return(ks)
})
mvst <- reactive({
df <- data_input()
var1 <- df[, input$cols7]
var2 <- df[, input$cols8]
return(mshapiro.test(t(as.data.frame(var1, var2))))
})
output$normtest <- renderPrint({
if(input$normaltest == "A-D-Test"){
print(adt())
} else if(input$normaltest == "Shapiro"){
print(sht())
} else if(input$normaltest == "KS-Test"){
print(kst())
} else if(input$normaltest == "MV-Shapiro"){
print(mvst())
}
}
)
# correlation & regression
observeEvent(input$file1, {
updateSelectInput(session, inputId = "cols9", choices = names(data_input()))
updateSelectInput(session, inputId = "cols10", choices = names(data_input()))
}
)
cortest <- reactive({
var1 <- data_input()[,input$cols9]
var2 <- data_input()[,input$cols10]
if (input$cormethod == "Covariance"){
return(cov(var1, var2))
} else if (input$cormethod == "KarlPearson"){
return(cor.test(var1, var2, method = "pearson"))
} else if(input$cormethod == "Spearman"){
return(cor.test(var1, var2, method="spearman"))
} else {
return(cor.test(var1, var2, method="kendall"))
}
}
)
output$cor_t <- renderPrint({
cortest()
})
# simple linear regression
observeEvent(input$file1, {
updateSelectInput(session, inputId = "cols11", choices = names(data_input()))
updateSelectInput(session, inputId = "cols12", choices = names(data_input()))
}
)
lmout <- reactive({
df <- data_input()
var1 <- df[, input$cols11]
var2 <- df[, input$cols12]
out <- lm(var1 ~ var2, data = df)
return(out)
})
output$regout <- renderPrint({
if(input$regmethod == "Fit"){
lmout()
} else if(input$regmethod == "Summary"){
summary(lmout())
} else if (input$regmethod == "ANOVA"){
anova(lmout())
}
})
output$regplot <- renderPlot({
par(mfrow=c(2,2))
plot(lmout())
})
# efa & sem
faout <- reactive({
df <- data_input()
fit <- fa(df, input$nf1)
return(fit)
}
)
omegaout <- reactive({
df <- data_input()
fit <- omega(df)
return(fit)
})
output$efaoutput <- renderPrint({
if(input$efaplot == "I don't know anything; please fit EFA"){
omegaout()
} else {
faout()
}
})
output$efadiagram <- renderPlot({
if(input$efaplot == "Structure Diagram"){
structure.diagram(faout())
} else if(input$efaomegaplot == "Omega Plot"){
omega.diagram(omegaout())
}
})
# Logistic Regression
observeEvent(input$file1, {
updateSelectInput(session, inputId = "logrvar", choices = names(data_input()))
}
)
logrout <- reactive({
df <- data_input()
var <- input$logrvar
Train <- createDataPartition(df[, var], p=as.numeric(input$logrprop), list=FALSE)
training <- df[Train, ]
testing <- df[-Train, ]
trainprop <- nrow(training)/(nrow(testing)+nrow(training))
# var1 <- input$logryname
mod_fit <- train(as.formula(paste(var, "~", ".")), data=training, method="glm", family="binomial")
expout <- exp(coef(mod_fit$finalModel))
if (input$logroption == "Show Prop."){
return(trainprop)
} else if (input$logroption == "Fit"){
return(mod_fit)
}
if (input$logroption == "Coef."){
return(data.frame(expout))
}
predout <- predict(mod_fit, newdata=testing)
# predoutproba <- predict(mod_fit, newdata=testing, type="prob")
accuracy <- table(predout, testing[, input$logrvar])
out <- sum(diag(accuracy))/sum(accuracy)
confmat <- confusionMatrix(round(predout), testing[, input$logrvar])
if (input$logroption == "Pred. Accuracy"){
return(confmat)
}
return(var1)
})
output$logroutput <- renderPrint({
if(input$logroption == "Coef."){
logrout()
} else if (input$logroption == "Show Prop."){
logrout()
} else if (input$logroption == "Fit"){
logrout()
} else if (input$logroption == "Pred. Accuracy"){
logrout()
}
})
# KNN
knnout <- reactive({
df <- data_input()
rows <- round(as.numeric(input$knntrain)*dim(df)[1])
if(input$knnoption == "Show Prop."){
return(rows)
}
lascol <- dim(df)[2]
train_data <- df[1:rows, 2:lascol]
test_data <- df[-(1:rows), 2:lascol]
if(input$knnoption == "Show Train & Test Data"){
return(list(head(train_data), head(test_data)))
}
train_labels <- df[1:rows, 1]
test_labels <- df[-(1:rows), 1]
k_ = round(sqrt(dim(df)[1]))
if(input$knnoption == "Show No. of Classes"){
return(k_)
}
fit <- knn(train = train_data, test = test_data, cl = train_labels, k = k_)
if(input$knnoption == "Fit"){
return(data.frame(fit))
}
out <- CrossTable(x = test_labels, y = fit, prop.chisq = FALSE)
if(input$knnoption == "Accuracy"){
return(out)
}
})
output$knnoutput <- renderPrint({
if(input$knnoption == "Show Prop."){
knnout()
} else if(input$knnoption == "Show Train & Test Data"){
knnout()
} else if(input$knnoption == "Show No. of Classes"){
knnout()
} else if(input$knnoption == "Fit"){
knnout()
} else if(input$knnoption == "Accuracy"){
knnout()
}
})
# SVM
observeEvent(input$file1, {
updateSelectInput(session, inputId = "svmvar", choices = names(data_input()))
}
)
svmout <- reactive({
df <- data_input()
intrain <- createDataPartition(y = df[, input$svmvar], p= as.numeric(input$svmprop), list = FALSE)
training <- df[intrain,]
testing <- df[-intrain,]
if (input$svmoption == "Show Prop."){
return(list(dim(training), dim(testing)))
}
training[,input$svmvar] = factor(training[,input$svmvar])
var <- input$svmvar
trctrl <- trainControl(method = "repeatedcv", number = 10, repeats = 3)
svm_Linear <- train(as.formula(paste(var, "~", ".")), data = training, method = "svmLinear",
trControl=trctrl,
preProcess = c("center", "scale"),
tuneLength = 10)
if (input$svmoption == "Fit"){
return(svm_Linear)
}
test_pred <- predict(svm_Linear, newdata = testing)
if (input$svmoption == "Predicted"){
return(data.frame(test_pred))
}
confmat <- confusionMatrix(test_pred, testing[, "num"] )
if (input$svmoption == "Pred. Accuracy"){
return(confmat)
}
})
output$svmoutput <- renderPrint({
if(input$svmoption == "Coef."){
svmout()
} else if (input$svmoption == "Show Prop."){
svmout()
} else if (input$svmoption == "Fit"){
svmout()
} else if (input$svmoption == "Pred. Accuracy"){
svmout()
} else if (input$svmoption == "Predicted"){
svmout()
}
})
# DECISION TREE
observeEvent(input$file1, {
updateSelectInput(session, inputId = "dtvar", choices = names(data_input()))
updateSelectInput(session, inputId = "dtvar2", choices = names(data_input()))
}
)
dtout <- reactive({
df <- data_input()
tab = table(df[, input$dtvar])
if (input$dtoption == "Table"){
return(tab)
}
index = createDataPartition(y=df[, input$dtvar], p=0.7, list=FALSE)
train.set = df[index,]
test.set = df[-index,]
if (input$dtoption == "Train & Test Data"){
return(list(head(train.set), head(test.set)))
}
if (input$dtoption == "Show Prop."){
return(dim(train.set))
}
var <- input$dtvar
brest.tree = train(as.formula(paste(var, "~", ".")),
data=train.set,
method="rpart",
trControl = trainControl(method = "cv"))
if (input$dtplot == "QPlot"){
plot(brest.tree$finalModel, uniform=TRUE, main="Classification Tree"); text(brest.tree$finalModel, use.n.=TRUE, all=TRUE, cex=.8)
}
if (input$dtoption == "Fit"){
return(brest.tree)
}
if (input$dtplot == "DTree"){
fancyRpartPlot(brest.tree$finalModel)
}
pred <- predict(brest.tree, test.set)
out <- confusionMatrix(pred, test.set[, "Class_num"])
if (input$dtoption == "Predicted"){
return(data.frame(pred))
}
if (input$dtoption == "Pred. Accuracy"){
return(out)
}