Write R code to create a clickable dashboard for the analysis of csv based business data, which can be imported and then analysed visually. The dashboard should provide explanations of the analysis for lay persons
To create a clickable dashboard in R for the analysis of CSV based business data, we can use the Shiny package. Here's an example code that imports a CSV file, performs some basic analysis, and presents the results in an interactive dashboard:
```R
library(shiny)
library(dplyr)
library(ggplot2)

# Define UI
ui <- fluidPage(
  
  # Sidebar with input file selection
  sidebarLayout(
    sidebarPanel(
      fileInput("file", "Choose CSV file",
                accept = c(".csv"))
    ),
    
    # Output plot and table
    mainPanel(
      tabsetPanel(
        tabPanel("Summary",
                 h2("Data summary"),
                 tableOutput("summaryTable")),
        tabPanel("Plot",
                 h2("Data plot"),
                 plotOutput("plot"))
      )
    )
  )
)

# Define server
server <- function(input, output) {
  
  # Import data from CSV file
  data <- reactive({
    file <- input$file
    if (is.null(file))
      return(NULL)
    read.csv(file$datapath)
  })
  
  # Data summary table
  output$summaryTable <- renderTable({
    data() %>%
      summarise_all(list(mean = mean, sd = sd, min = min, max = max))
  })
  
  # Data plot
  output$plot <- renderPlot({
    ggplot(data(), aes(x = variable, y = value)) +
      geom_boxplot() +
      ggtitle("Distribution of variables")
  })
  
}

# Run the app
shinyApp(ui, server)
```

This code defines a simple UI that allows the user to upload a CSV file, and presents two tabs with a summary table and a boxplot of the data. The analysis is performed using the dplyr and ggplot2 packages, and the results are presented using the Shiny output functions.

To provide explanations for lay persons, we can add some text or tooltips to the UI elements. For example, we could add a "Help" button that displays a modal dialog with some guidance on how to interpret the results, or we could include a "Learn more" link that points to a separate page with more detailed explanations.