Myujth

I am creating a Shiny app to predict positive flu cases for patients using a random forest model. The user can enter certain number of input variables through the app and woill be able to click on an actionButton and then see probability for positive flu. GeneXpert (binary no) is the target variable for training RF model and all the other variables are numeric in nature. When I run the app I get an error.

Error in sample.int(length(x), size, replace, prob): invalid 'size' argument

Here is my code:

library(shiny)
library(randomForest, quietly=TRUE)
library(ggplot2)

ui <- fluidPage(
 titlePanel("Random Forest Flu Prediction Model"),
 sidebarLayout(
 sidebarPanel(
 numericInput("month",
 label = "Enter the Month:",
 min = 1, max = 12, value = 5, step = 1),

 numericInput("DOI", label = "Enter the days of illness:",
 min = 1, max = 7, value = 5, step = 1),

 numericInput(inputId = "age", label = "Enter the age:",
 min = 1, max = 120, value = 5, step = 1),

 numericInput(inputId = "rhi", label = "cursympt_rhinorrhea:",
 min = 0, max = 1, value = 1, step = 1),

 numericInput(inputId = "wt", label = "Enter your weight:",
 min = 1, max = 400, value = 5, step = 0.5),

 numericInput(inputId = "ht", 
 label = "Enter your Height:",
 min = 1, max = 400, value = 1, step = 0.5),

 numericInput(inputId = "sore", label = "cursympt_sorethroat.facto:",
 min = 0, max = 1, value = 1, step = 1),

 numericInput(inputId = "shkilchi", label = 
 "cursympt_shakingchills.facto:", 
 min = 0, max = 1, value = 1, step = 1),

 numericInput(inputId = "shtbt", label = "cursympt_shortnessbreath.facto:", 
 min = 0, max = 1, value = 1, step = 1),

 numericInput(inputId = "appt", label = "cursympt_appetite.factor:",
 min = 0, max = 1, value = 1, step = 1),

 numericInput(inputId = "incsput", label = "cursympt_incrsputum.facto:",
 min = 0, max = 1, value = 1, step = 1),

 numericInput(inputId = "wheez", label = "cursympt_wheezing.facto:",
 min = 0, max = 1, value = 1, step = 1),

 numericInput(inputId = "stom", label = "cursympt_stomachpain.facto:",
 min = 0, max = 1, value = 1, step = 1),
 actionButton("Action", "Analyze")
),

mainPanel(
 textOutput('dynamicText')
 )
 )
 )

server <- function(input, output) 
 dataset =read.csv(file="Flu_Shiny_Data.csv",header=TRUE)
 nobs <- nrow(dataset)
 sample <- train <- sample(nrow(dataset), 0.8*nobs)
 validate <- NULL
 test <- setdiff(setdiff(seq_len(nrow(dataset)), train), validate)

 input_features <- c("age","heightin","weightlb",
 "cursympt_days","cursympt_incrsputum.factor", 
 "cursympt_sorethroat.factor","cursympt_rhinorrhea.factor", 
 "cursympt_shortnessbreath.factor","cursympt_wheezing.factor",
 "cursympt_shakingchills.factor","cursympt_appetite.factor",
 "cursympt_stomachpain.factor","Month")

 input_numbers <- c("age","heightin","weightlb","cursympt_days", 
 "cursympt_incrsputum.factor","cursympt_sorethroat.factor", 
 "cursympt_rhinorrhea.factor", "cursympt_shortnessbreath.factor", 
 "cursympt_wheezing.factor","cursympt_shakingchills.factor", 
 "cursympt_appetite.factor","cursympt_stomachpain.factor","Month")
 target_feature <- "GeneXpert"
 result.rf <- randomForest::randomForest(as.factor(GeneXpert) ~ .,
 data=dataset[sample,c(input_features, target_feature)], 
 ntree=500,
 mtry=3,
 importance=TRUE,
 na.action=randomForest::na.roughfix,
 replace=FALSE)

 values <- reactiveValues()
 observe(
 if(input$Action>0)
 newLine<-isolate(c(input$month,input$DOI,input$age,input$rhi,input$wt,
 input$ht,input$sore,input$shkilchi,input$shtbt,
 input$appt,input$incsput,input$wheez,input$stom))
 isolate(values$dataset <- unlist(newLine))
 
 )

 output$dynamicText <- renderText(
 if (is.null(values$dataset))) 
 return()
 pr <- predict(result.rf, newdata=na.omit(values$dataset))
 pr <- as.numeric(pr)
 (pr-1)
 )
 
 # Run the application 
 shinyApp(ui = ui, server = server)