This presentation is for the week 4 assignment of developing data products from Coursera.

The Shiny App created can simulate the distribution of datasets in a nromalized fashion, which can make it easy to visualize how normalize data is distributed.

The link to the Shiny app is here The link to the code is here, in the document named “week4-assignment-code.R”

library(shiny)
library(ggplot2)
ui <- fluidPage(titlePanel("Statistical Distribution Simulator"),
  sidebarLayout( sidebarPanel(
    selectInput(inputId = "u",
                label = "Mean",
                choices = c("-10", "-5", "0", "5", "10"),
                selected = "0"),
    
    selectInput(inputId = "std",
                label = "Standard Deviation",
                choices = c("1", "3", "5"),
                selected = "1"),
    
    radioButtons(inputId = "NumSamp",
                 label = "Number of Samples",
                 choices = c("100", "500", "1000"),
                 selected = "500"),
    
    sliderInput(inputId = "NumBins",
                label = "Number of Bins in Histogram",
                min = 1,
                max = 50,
                value = 30),
    
    actionButton(inputId = "showStats",
                 label = "Print Stats")),
    
    mainPanel(
      plotOutput("distPlot"),
      textOutput("meanValue"),
      textOutput("stdValue")
    )
  )
)

server <- function(input, output) {
  sample_data <- reactive({
    Sample_size <- as.numeric(input$NumSamp)
    Avg <- as.numeric(input$u)
    Std_dev <- as.numeric(input$std)
    rnorm(n = Sample_size, mean = Avg, sd = Std_dev)
  })
  
  output$distPlot <- renderPlot({
    Num_bins <- input$NumBins
    samples_df <- as.data.frame(sample_data())
    ggplot(samples_df, aes(x = samples_df$sample_data)) + geom_histogram(aes(y = ..density..), 
                                                                         stat = "bin", bins = Num_bins) +  
      geom_density() + coord_cartesian(xlim = c(-20, 20)) + xlab("Data Values")
  })
  
  
  observeEvent(input$showStats, {
    output$meanValue <- renderText(isolate({paste("Mean =", toString(mean(sample_data())), sep = " ")}))
    output$stdValue <- renderText(isolate({paste("Standard Deviation =", toString(sd(sample_data())), sep = " ")}))
  })
}

shinyApp(ui = ui, server = server)