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@matteoredaelli
Last active Aug 19, 2021
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library(shiny)
# Define server logic for random distribution application
shinyServer(function(input, output) {
# Reactive function to generate the requested distribution. This is
# called whenever the inputs change. The output functions defined
# below then all use the value computed from this function
data <- reactive(function() {
dist <- switch(input$dist,
norm = rnorm,
unif = runif,
lnorm = rlnorm,
exp = rexp,
rnorm)
dist(input$n)
})
# Generate a plot of the data. Also uses the inputs to build the
# plot label. Note that the dependencies on both the inputs and
# the 'data' reactive function are both tracked, and all functions
# are called in the sequence implied by the dependency graph
output$plot <- reactivePlot(function() {
dist <- input$dist
n <- input$n
hist(data(),
main=paste('r', dist, '(', n, ')', sep=''))
})
# Generate a summary of the data
output$summary <- reactivePrint(function() {
summary(data())
})
# Generate an HTML table view of the data
output$table <- reactiveTable(function() {
data.frame(x=data())
})
})
library(shiny)
# Define UI for random distribution application
shinyUI(pageWithSidebar(
# Application title
headerPanel("Tabsets"),
# Sidebar with controls to select the random distribution type
# and number of observations to generate. Note the use of the br()
# element to introduce extra vertical spacing
sidebarPanel(
radioButtons("dist", "Distribution type:",
list("Normal" = "norm",
"Uniform" = "unif",
"Log-normal" = "lnorm",
"Exponential" = "exp")),
br(),
sliderInput("n",
"Number of observations:",
value = 500,
min = 1,
max = 1000)
),
# Show a tabset that includes a plot, summary, and table view
# of the generated distribution
mainPanel(
tabsetPanel(
tabPanel("Plot", plotOutput("plot")),
tabPanel("Summary", verbatimTextOutput("summary")),
tabPanel("Table", tableOutput("table"))
)
)
))
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