Poisson Distribution Shiny App

1 Case Study I - Density of the Poisson Distribution

For my shiny app I decided to simulate Poisson Distribution. You can choose any sample size from 10 to 1000, and any lambda from 1 to 10 where the step size on the lambda slider is by 0.5. Through our example we can see that small lambdas are right skewed, but when we increase our lambda size we start to see a more symmetric distribution

library(shiny)

# Define UI for app that draws a histogram ----
ui <- fluidPage(
    # App title ----
    titlePanel("Simulating Poisson Distribution"),
    # Sidebar layout with input and output definitions ----
    sidebarLayout(    # siderbarLayout:       
         # Sidebar panel for inputs ==> the 1st parameter
         sidebarPanel(
               # Input: Slider for the number of bins ==> 2nd parameter
               sliderInput(inputId = "n",
                           label = "Sample size",
                           min = 10,
                           max = 1000,
                           value = 10),
               # slider input: normal population mean
               sliderInput(inputId = "lambda",
                           label = "Population Lambda",
                           min = 1,
                           max = 10,
                           value = 0,step=0.5)),
         # Main panel for displaying outputs ----
         mainPanel(
              # Output: Histogram ----
              plotOutput(outputId = "distPlot")
         )
   ))
# Define server logic required to draw a histogram ----
server <- function(input, output) {
  # Histogram of the simulated normal data ----
  # with requested number of bins
  output$distPlot <- renderPlot({
    # random number generation
    pois.score <- rpois(input$n, input$lambda)
    ##
    hist(pois.score, col = "blue", border = "orange",
         xlab = "Histogram of generated normal data",
         main = "Simulated Normal Distribution")
      })
  }
# Create Shiny app ----
shinyApp(ui = ui, server = server)

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