Has anyone experienced the "Not Found" error usin the shiny package in R?
I keep seeing 'Not Found' when trying to load my Shiny application, it seems to be that R cannot find the Shiny server, I have tried reinstalling all packages and still no luck.
The strange thing is that it works the first time I open R Studio, then it doesn't hereafter..
I have attatched a working example and a screenshot for insight.
---
title: "Benchmarking Algorithms for Credit Card Fraud Detection"
output:
flexdashboard::flex_dashboard:
orientation: columns
vertical_layout: fill
social: ["menu"]
runtime: shiny
#options: (shiny.maxRequestSize = 1500*1024^2)
---
```{r setup, include=FALSE}
suppressPackageStartupMessages({
library(shiny)
library(flexdashboard)
library(datasets)
library(caTools)
library(hydroGOF)
})
```
Column {.tabset}
-------------------------------------
### Linear Regression Model
```{r}
ui <- fluidPage(
titlePanel("Upload Transaction Data Set"),
sidebarLayout(
sidebarPanel(
fileInput("file1", "Choose CSV File",
multiple = FALSE,
accept = c("text/csv",
"text/comma-separated-values,text/plain",
".csv")),
p("The transaction data file must contain a 'Class' column that specifies the number 1 for fraudulent transactions, and 0 otherwise"),
p("The 'Actual' graph exhibits the real calculations of the uploaded data, and the 'predicted' graph exhibits the predicted 'Class' column based on the other features of data included in your upload.")
),
mainPanel(
h1("Models"),
plotOutput("actual"),
plotOutput("prediction"),
h1("Root Mean Square Error (RMSE) Accuracy %"),
verbatimTextOutput("accuracy"),
h1("Summary"),
verbatimTextOutput("summary")
)
)
)
server = function(input,output){
options(shiny.maxRequestSize=500*1024^2)
thedata = reactive({
req(input$file1)
read.csv(file = input$file1$datapath)
})
output$prediction = renderPlot({
req(thedata())
set.seed(2)
#Split data
split <- sample.split(thedata(), SplitRatio=0.7)
thedata <- subset(thedata(), split=TRUE)
actual <- subset(thedata(), split=FALSE)
#Create the model
model <- lm(Class ~.,data = thedata())
#Prediction
prediction <- predict(model, actual)
distPred <- predict(model, actual)
#Comparing predicted vs actual model
plot(prediction,type = "l",lty= 1.8,col = "blue")
lines(prediction, type = "l", col = "blue")
})
output$actual = renderPlot({
req(thedata())
set.seed(2)
split <- sample.split(thedata()$Class,0.7)
thedata <- subset(thedata(),split)
actual <- subset(thedata(),!split)
model <- lm(Class ~.,data = thedata())
prediction <- predict(model, actual)
plot(actual$Class,type = "l",lty= 1.8,col = "blue")
output$accuracy <- renderPrint({
rmse <- sqrt(mean(prediction-thedata$Class)^2)/diff(range(thedata$Class))
rmse
})
})
output$summary <- renderPrint({
model <- lm(Class ~.,data = thedata())
summary (model)
})
}
shinyApp(ui, server)
```
-------------------------------------
### Logistic Regression Model
```{r}
ui <- fluidPage(
titlePanel("Upload Transaction Data Set"),
sidebarLayout(
sidebarPanel(
fileInput("file1", "Choose CSV File",
multiple = FALSE,
accept = c("text/csv",
"text/comma-separated-values,text/plain",
".csv")),
p("The transaction data file must contain a 'Class' column that specifies the number 1 for fraudulent transactions, and 0 otherwise"),
p("The 'Actual' graph exhibits the real calculations of the uploaded data, and the 'predicted' graph exhibits the predicted 'Class' column based on the other features of data included in your upload.")
),
mainPanel(
h1("Models"),
plotOutput("actual"),
plotOutput("prediction"),
h1("Confusion Matrix"),
verbatimTextOutput("confMatrix"),
h1("Accuracy (%)"),
verbatimTextOutput("accuracy"),
h1("Summary"),
verbatimTextOutput("summary")
)
)
)
server = function(input,output){
options(shiny.maxRequestSize=500*1024^2) #Max File Size
thedata = reactive({
req(input$file1)
read.csv(file = input$file1$datapath)
})
output$prediction = renderPlot({
req(thedata())
#Split Data
split <- sample.split(thedata(), SplitRatio=0.7)
train <- subset(thedata(), split=TRUE)
Actual <- subset(thedata(), split=FALSE)
#Create Model
mymodel <- glm(Class ~., data = thedata(), family = binomial, control = list(maxit = 50))
#Prediction
Prediction <- predict(mymodel, Actual, type="response")
#Predicted Model
plot(Prediction,type = "l",lty= 1.8,col = "blue")
})
output$actual = renderPlot({
req(thedata())
# Split Data
split <- sample.split(thedata(), SplitRatio=0.7)
train <- subset(thedata(), split=TRUE)
Actual <- subset(thedata(), split=FALSE)
#Create Model
mymodel <- glm(Class ~., data = thedata(), family = binomial, control = list(maxit = 50))
#Prediction
Prediction <- predict(mymodel, Actual, type="response")
#Actual Model
plot(Actual$Class,type = "l",lty= 1.8,col = "blue")
output$confMatrix <- renderPrint({
confusionMatrix <- table(Actual_Value=train$Class, Predicted_Values=Prediction > 0.5)
confusionMatrix
})
output$accuracy <- renderPrint({
confusionMatrix <- table(Actual_Value=train$Class, Predicted_Values=Prediction > 0.5)
(confusionMatrix[[1,1]] + confusionMatrix[[2,2]]) / sum(confusionMatrix) *100
})
})
output$summary <- renderPrint({
mymodel <- glm(Class ~., data = thedata(), family = binomial, control = list(maxit = 50))
summary (mymodel)
})
}
shinyApp(ui, server)
```
-------------------------------------
### Bayesian Model
```{r}
```
