seedHealth: Scenario Analysis for Seed Health

#' Generate an interactive Shiny interface for one simulation
#'
#' This function simulates one parameter combination across nseasons once
#'
#' Updated 2018-08-23

#' This Shiny application interface is generated by function \code{\link{onesim}}.
#'
#' @inheritParams onesim
#' @import shiny
#' @importFrom  shiny shinyApp
#' @import RColorBrewer
#' @import KernSmooth
#' @importFrom  magrittr %>%
#' @importFrom  dplyr mutate
#' @import ggplot2
#' @importFrom utils write.csv
#' @importFrom grDevices colorRampPalette
#' @importFrom stats median quantile rnorm var
#' @keywords seed health
#' @examples
#' onesim_app()
#' @export
#'
#'


# to do - GT

#library(RColorBrewer)
#library(KernSmooth)

onesim_app <- function(pHSinit=0.8, Kx = 100, betax=0.02, wxtnormm=0.8, wxtnormsd=0.3, hx=1, mxtnormm=1,
                            mxtnormsd=0.1, axtnormm=1, axtnormsd=0.1, rx=0.1, zxtnormm=1, zxtnormsd= 0.1, gx=4,
                            cx=0.9, phix=0, nseasons=10, HPcut=0.5, pHScut=0.5, maY=100, miY=0, thetax=0.2,  Ex=0) {
  #require(shiny)
  shiny::shinyApp(

    # ui -------------------------------------------------
    ui = fluidPage(
      #--------------------------------------------------

      #------------------------------------------------
      headerPanel("Evaluating yield loss due to seed degeneration over time"),


      #------------------------------------------------
      sidebarPanel(width=4,

                   #-------------------------------
                   numericInput("group1Par1","Initial proportion of healthy seed (1=only healthy seed used, 0=only infected seed used)",min=0,max=1,value = 0.8, step = 0.1),
                   numericInput("group1Par2","External inoculum around farm (50=high level of external inoculum, 0=absence of external inoculum)",min=0,max=50,value = 0, step = 1),
                   numericInput("group1Par3","Maximum seasonal transmission rate (Maximum rate of disease transmission during the growing season when there are no limitations for disease to spread)",min=0.001,max=0.2,value = 0.02, step = 0.001),
                   numericInput("group1Par4","Weather conduciveness for disease (1=highly disease conducive weather, 0=weather completely restricts disease spread)",min=0,max=1,value = 0.8, step = 0.01),
                   #-------------------------------
                   numericInput("group1Par5","Host susceptibility (1='completely' susceptible, 0=immune)",min=0,max=1,value = 1, step = 0.01),
                   numericInput("group1Par6","Vector/weed management conducted (1= no management of vectors/weeds, 0=vector/weed eradication)",min=0,max=1,value = 1, step = 0.01),
                   numericInput("group1Par7","Roguing conducted during season (1=no symptomatic plants removed, 0=all symptomatic plants removed)",min=0,max=1,value = 1, step = 0.01),


                   numericInput("group1Par8","Seed production rate in healthy plants (Number of seed produced per healthy plant)",min=0,max=20,value = 4, step = 1),
                   numericInput("group1Par9","Plant (seed) selection (1=random selection, 0=complete selection against diseased plants)",min=0,max=1,value = 1, step = 0.1),
                   numericInput("group1Par10","Differential seed production (1=no difference in seed production between healthy and infected plants, 0=no seed production in diseased plants)",min=0,max=1,value = 0.9, step = 0.1),

                   numericInput("group1Par11","Reversion in infected plants expressed as the proportion of disese-free seed produced by diseased plants (1=only healthy seed produced by an infected plant, 0=only infected seed produced by an infected plant)",min=0,
                                max=1,value =0.1, step = 0.1),
                   numericInput("group1Par12","Certified seed usage (1= only certified seed used, 0=no certified seed used)",min=0,max=1,value = 0, step = 0.1),
                   numericInput("group1Par13","Rate of yield decline (0=constant rate of yield decline (straight line); for 0 to 0.5,yield declines slowly as disease incidence increases (concave); for -1 to 0, yield declines rapidly as disease incidence increases (convex)) ",min=-1,max=0.55,value = 0.2, step = 0.01),
                   downloadButton("downloadParameter",label = "Download values of Parameters")

      ),#sidebarPanel
      #------------------------------------------------
      #------------------------------------------------
      #------------------------------------------------
      #------------------------------------------------
      #------------------------------------------------
      #------------------------------------------------
      mainPanel(column(width=12, height=700, class="well",


                       h2("A risk assessment framework for seed degeneration: Informing an integrated seed
                          health strategy for vegetatively-propagated crops"),
                       h5("S. Thomas-Sharma, J. Andrade-Piedra, M. Carvajal Yepes, J. F. Hernandez Nopsa,
                          M. J. Jeger, R. A. C. Jones, P. Kromann, J. P. Legg, J. Yuen, G. A. Forbes, and K. A. Garrett"),

                       h5(" https://doi.org/10.1094/PHYTO-09-16-0340-R"),

                       #img(src="UFandCo.png",height=80,width=1000),

                       h3("This dashboard provides an estimate of yield loss due to seed degeneration, as a function of multiple environmental,
                          biological, and management parameters that influence the development of seedborne diseases. It is a general model
                          for vegetatively-propagated crops. Changing the values of the parameters on the left will result in
                          a new estimate of yield loss over time")
                       )
                       ),
      #------------------------------------------------
      hr(),
      hr(),
      mainPanel(column(width=12, class="well",
                       plotOutput("figure1",height=550,width = 1000),
                       hr()
      ))

                       ),


    # ---------------------------------------------------


    # server --------------------------------------------

    server = function(input, output) {

      # for generating truncated normal random variables
      altrtruncnorm <- function(n,a=0,b=1,meana=0,sda=1){
        j <- rnorm(n,mean=meana,sd=sda)
        j[j < a] <- a
        j[j > b] <- b
        j
      }
      # Weather (wx), vector management (mx), positive selection (zx) and roguing (zx) are stochastic
      # Each have a mean and associated standard deviation

      set.seed(1234)
      #************************************************************
      #############################################################
      Data<-reactive({
        #registration input variables
        Initial_Proportion_of_Healthy_Seed<-input$group1Par1
        External_Inoculum_around_Farm<-input$group1Par2
        Max_Seasonal_Transmission_Rate<-input$group1Par3
        Weather_Conduciveness_for_Disease<-input$group1Par4
        Host_Susceptibility<-input$group1Par5
        Vector_Weed_Management_Conducted<-input$group1Par6
        Roguing_Conducted_During_Season<-input$group1Par7
        Seed_Production_Rate_in_Healthy_Plants<-input$group1Par8
        Plant_Seed_Selection<-input$group1Par9
        Differential_Seed_Production<-input$group1Par10
        Reversion_in_Infected_Plants<-input$group1Par11
        Certified_Seed_Usage<-input$group1Par12
        Rate_of_Yield_Decline<-input$group1Par13

        df<-as.data.frame(cbind(
          Initial_Proportion_of_Healthy_Seed<-input$group1Par1,
          External_Inoculum_around_Farm<-input$group1Par2,
          Max_Seasonal_Transmission_Rate<-input$group1Par3,
          Weather_Conduciveness_for_Disease<-input$group1Par4,
          Host_Susceptibility<-input$group1Par5,
          Vector_Weed_Management_Conducted<-input$group1Par6,
          Roguing_Conducted_During_Season<-input$group1Par7,
          Seed_Production_Rate_in_Healthy_Plants<-input$group1Par8,
          Plant_Seed_Selection<-input$group1Par9,
          Differential_Seed_Production<-input$group1Par10,
          Reversion_in_Infected_Plants<-input$group1Par11,
          Certified_Seed_Usage<-input$group1Par12,
          Rate_of_Yield_Decline<-input$group1Par13
        ))

        return(list(SeedDegenerationData=df))

      })# reactive data frame end

      #Creates the final data frame

      output$table<-renderTable({
        if(is.null(Data())){return()}
        #print(Data()$df)
      })
      #The download button
      output$downloadParameter <- downloadHandler(filename = "SDData.csv",
                                                  content = function(file) {
                                                    write.csv(Data(), file, row.names=TRUE)

                                                  }
      )



      #############################################################

      output$figure1<-renderPlot({

        out1 <- onesim(pHSinit=input$group1Par1, Kx = 100, Ex=input$group1Par2,betax=input$group1Par3, wxtnormm=input$group1Par4,
                             hx=input$group1Par5,mxtnormm=input$group1Par6,axtnormm=input$group1Par7,gx=input$group1Par8,zxtnormm=input$group1Par9,
                             cx=input$group1Par10,rx=input$group1Par11,phix=input$group1Par12,thetax=input$group1Par13,
                             wxtnormsd= 0.1,mxtnormsd=0.1,axtnormsd=0.1,zxtnormsd= 0.1,nseasons=10,HPcut=0.5, pHScut=0.5, maY=100,miY=0 )


        Yield_Loss <- out1$outm$YL[-1]
        Season <- out1$outm$season[-1]

        for(i in 1:100){ # higher values make a smoother plot
          out1<- onesim(pHSinit=input$group1Par1, Kx = 100, Ex=input$group1Par2,betax=input$group1Par3, wxtnormm=input$group1Par4,
                              hx=input$group1Par5,mxtnormm=input$group1Par6,axtnormm=input$group1Par7,gx=input$group1Par8,zxtnormm=input$group1Par9,
                              cx=input$group1Par10,rx=input$group1Par11,phix=input$group1Par12,thetax=input$group1Par13,
                              wxtnormsd= 0.1,mxtnormsd=0.1,axtnormsd=0.1,zxtnormsd= 0.1,nseasons=10,HPcut=0.5, pHScut=0.5, maY=100,miY=0 )
          Yield_Loss <- c(Yield_Loss,out1$outm$YL[-1])
          Season <- c(Season,out1$outm$season[-1])
        }
#----------
        data <- as.data.frame(cbind(Yield_Loss,Season))
        data=data %>%
          mutate(SimulateCol = rep(1:(nrow(data)/10), each=10))

        ggplot(data, aes(Season, Yield_Loss)) +
          geom_point(alpha=0.1, color="dodgerblue") +
          geom_line(aes(group = data$SimulateCol), color="dodgerblue", alpha=0.1) +
          stat_summary() +
          stat_summary(geom="line") +
          theme_classic() +
          scale_x_continuous(breaks=1:10) +
          xlab('Season') +
          ylab('Yield Loss (%)') +
          theme(axis.title = element_text(face = "bold",
                                          size = 20),
                axis.text = element_text(size = 16),
                legend.background = element_blank(),
                #legend.box.background = element_blank(),
                panel.grid.major = element_blank(),
                panel.grid.minor = element_blank(),
                panel.background = element_rect(fill = "transparent",colour = NA),
                plot.background = element_rect(fill = "transparent",colour = NA)
          )
        #---------------------------------------------
      })
    }
    # ---------------------------------------------------
      )
}