它。听起来你想要一个submitButton而不是ActionButtonJDHarrison——我也是这么想的，但是当我使用submitButton时，UI不能正确显示，我想我知道actionButton比submitButton更健壮、更灵活。当然，当

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它。

听起来你想要一个submitButton而不是ActionButtonJDHarrison——我也是这么想的，但是当我使用submitButton时，UI不能正确显示，我想我知道actionButton比submitButton更健壮、更灵活。当然，当我使用actionButton时，UI可以工作。

# filename: ui.r
# Michael Camann, Humboldt State University, 2014

################################################################################
#
# Define the user interface
#
################################################################################

library(shiny)

shinyUI(
    fluidPage(
        titlePanel("BIOL 330 ecological simulations"),
        sidebarLayout(
            sidebarPanel("",
                helpText(HTML("<h3 style='text-align:center;'>Control Panel</h3>"), align='center'),
                tags$hr(style='height:2px; border-width:0; color:gray; background-color:gray'),

                # choose a simulation from a drop down menu
                selectInput("sim", HTML("<b>Select a simulation to explore:</b>"),
                    # list the simulations available
                    c("No simulation selected (Introduction)" = "none",
                    "Dorriefish growth/reproduction trade-offs" = "dorriefish",
                    "Optimal foraging" = "optfor")
                ),
                tags$hr(style='height:2px; border-width:0; color:gray; background-color:gray'),

                conditionalPanel(condition="input.sim=='dorriefish'",
                    helpText(HTML("<b>Simulation model parameters:</b>")),
                    sliderInput("S.df", label=div(HTML("Specify <em>S</em>, the switch point mass (g) for
                        transition from somatic growth to reproduction:")),
                        min = 1, max = 50, value = 10, step=5),
                    sliderInput("p.df", label=div(HTML("Specify <em>p</em>, the probability of mortality
                    by predation in any given time step:")),
                        min = 0, max = 1, value = 0.12, step=0.01),
                    sliderInput("reps.df", label=div(HTML("Specify the number of full simulations to
                        run:")), min = 1, max = 100, value = 1, step=1)
                ),

                # bottom controls common to all panels
                conditionalPanel(condition="input.sim!='none'",
                    tags$hr(style='height:2px; border-width:0; color:gray; background-color:gray'),
                    fluidRow(column(4, actionButton("runSim", "Run simulation")),
                        column(4, actionButton("saveSim", "Save output file")),
                        column(4, actionButton("printSim", "Save/print plot"))),
                    tags$hr(style='height:2px; border-width:0; color:gray; background-color:gray')
                )
            ),

            mainPanel("",

                # no model selected-- show information page
                conditionalPanel(condition="input.sim=='none'",
                    includeHTML("www/simNoneText.html")
                ),

                tabsetPanel(id="outTabs", type="tabs",
                    tabPanel("Plot", plotOutput("modl.plot")
                    ),
                    tabPanel("Summary"
                    ),
                    tabPanel("R Code"

                    )
                )
            )
        )
    )
)

# filename: server.r
# Michael Camann, Humboldt State University, 2014

library(shiny)

################################################################################
#
# Dorrie fish reproduction/growth trade-offs
#
################################################################################

# This function implements the Doriefish simulation exercise distributed in Java
# with Cain et al Ecology, 2nd ed.

# Arguments:
#
# S       The switch point in grams for transitioning from growth to reproduction.
#         Default = 10 g.
#
# p       Probability of predation.  Default = 0.12.
#
# show    Logical.  If TRUE (default), print and plot single simulation output. Set
#         to FALSE when running multiple simulations in order to average the number
#         of offspring across multiple simulations, in which case viewing the
#         results of individual simulations isn't necessary.

df.sim <- function(S=10, p=0.12, show=TRUE)
{
     if (S < 10 | S > 50) stop("S must be between 10 and 50 grams.")
     doriefish <- 10                                                  # cohort size
     imass <- 5                                                       # initial mass
     mass <- rep(imass, doriefish)                                    # init vector of dorrie fish mass
     dead <- rep(FALSE, doriefish)                                    # init vector of mortality status
     mhistory <- matrix(dead, nrow=1, byrow=TRUE)                     # init a matrix of mortality history
     offspring.t <- 0                                                 # init offspring count
     total.offspring <- vector()                                      # storage vector

     while(any(dead==FALSE))                                          # continue until all are dead
     {
          repro.t <- mass >= S & !dead                                # who reproduces during this time step?
          offspring.t <- offspring.t + sum(mass[repro.t==TRUE]/5)     # one offspring per 5 g body mass
          total.offspring <- c(total.offspring, offspring.t)          # keep track of offspring each time step
          mass[!dead & !repro.t] <- mass[!dead & !repro.t] + 5        # incr mass by 5 g if not reproducing
          die.t <- sapply(1:length(dead), function(x) runif(1) <= p)  # stochastic selection of some for predation
          dead <- dead | die.t                                        # update the mortality vector
          mhistory <- rbind(mhistory, dead)                           # update the mortality history matrix
     }
     rownames(mhistory) <- c(1:nrow(mhistory))

     rval <- list(S=S, p=p, mass=mass, mhistory=mhistory, offspring.t=total.offspring,
          offspring=total.offspring[length(total.offspring)], show=show)
     class(rval) <- "df.sim"
     return(rval)
}


print.df.sim <- function(sim)                                         # print function for df.sim()
{
     if(sim$show)
     {
          cat("\n\tDoriefish simulation\n\n")
          cat("Switch point from growth to reproduction:", sim$S, "g.\n\n")
          cat("Probability of predation:", sim$p, "per time step.\n\n")
          cat("Time to cohort extinction:", length(sim$offspring.t), "time steps.\n\n")
          cat("Total adult Doriefish biomass:", sum(sim$mass), "g.\n\n")
          cat("Total offspring produced:", sim$offspring, "\n\n")
          plot(sim)
     }
}

plot.df.sim <- function(sim, sstep=FALSE, s=0.75, ...)                 # plot function for df.sim()
{
    opar <- par(no.readonly=TRUE)                                     # save graphics parameters
    txt <- paste("Total offspring:", sim$offspring,
        "     Time to cohort extinction:", length(sim$offspring.t), "time steps.")

    step.through <- function(sim, s=0.5, txt=txt)
    {
        len <- length(sim$offspring.t)
        par(fig=c(0,1,0.1,0.9), xpd=NA)
        plot(sim$offspring.t, type="n", xlab="Time steps", ylab="Cumulative Doriefish offspring",
            xlim=c(1, max(len, length(sim$mass))))
        mtext("Dorriefish living per time step (green = alive, red = dead):", side=3, at=(max(len)/2)+0.5,
            line=4.2)
        for(i in 1:nrow(sim$mhistory))
        {
            z <- rep("green", length(sim$mass))
            z[sim$mhistory[i,]] <- "red"
            points(seq(1,len, length=length(sim$mass)), rep(max(1.18*sim$offspring.t), length(sim$mass)),
                pch=21, col="black", bg=z, cex=2.5)
            lines(sim$offspring.t[1:i], type="h")
            Sys.sleep(s)
        }
        mtext(txt, side=1, at=0, line=5, adj=0)
    }

    if(sstep) step.through(sim, s, txt)
    else
    {
        par(fig=c(0,1,0.1,1), xpd=NA)
        plot(sim$offspring.t, type="h", xlab="Time steps",
          ylab="Cumulative Doriefish offspring", ...)
        mtext(txt, side=1, at=0, line=5, adj=0)
    }

    par(opar)
}


################################################################################
#
# Define the shiny server
#
################################################################################


shinyServer(function(input, output) {

    observe({
        if(input$runSim == 0) return()
        isolate({
            sim <- reactive({
                switch(input$sim,
                       dorriefish = {
                            df.sim(input$S.df, input$p.df, show=FALSE)
                       }    # end case dorriefish
                )   # end switch(input$sim)
            })  # end reactive()

            output$modl.plot <- renderPlot({
                switch(input$sim,
                    dorriefish = {
                        if (input$reps.df == 1)
                        {
                            dev.new()
                            plot(sim(), sstep=TRUE)
                        }
                    },  # end case dorriefish
                )   # end switch(input$sim)
            })  # end renderPlot()

        })  # end isolate()
    })  # end observe()

})  # end shinyServer()

shinyServer(function(input, output) {

        sim <- reactive({
                    switch(input$sim,
                            dorriefish = {
                                df.sim(input$S.df, input$p.df, show=FALSE)
                            }    # end case dorriefish
                    )   # end switch(input$sim)
                })  # end reactive()

        output$modl.plot <- renderPlot({
                    if(input$runSim == 0) return()
                    isolate({
                        switch(input$sim,
                            dorriefish = {
                                if (input$reps.df == 1)
                                {
                                    plot(sim(), sstep=TRUE)
                                }
                            },  # end case dorriefish
                        ) # end switch(input$sim)
                    })  # end isolate
                })  # end renderPlot()
    })  # end shinyServer()

shinyServer(
    function(input, output) {
        observeEvent(input$run, {
            # simulation code
        })
    })

actionButton("run", label = "Run Simulation")