Module 2.3

Line Chart App Server

Prework

• Read Mastering Shiny, Chapter 3, section 3.4

The Server Function

For our server function, we are going to define two separate reactive() functions. This is how we are going to dynamically update the plot based on two different user inputs. First, we will define an input for the indicator where input$indicator takes the user input from the dropdown menu to perform a fresh API call whenever the selected indicator changes. Then, we take that input and filter it based on the input from the slider, e.g. input$range. Then we render the plot based on these updated data. Notice that whenever we want to use the stored data from the reactive calls we need to add parentheses after the objects, e.g. fred_indicator() in the second reactive function or fred_data() in the ggplot call. This is to ensure that the reactive expression is evaluated and its current value is used as the input data for the plot.

Finally, we are going to use the add_rec_shade() helper function to add the recession shading to the chart. We again use the inputs from the two reactive functions to define the start date and end date of the shading as well as the y-min and y-max values of the shaded rectangles.

server <- function(input, output) {
  
    # Download data from FRED with reactive function. 
    # Only updates when user selects new indicator
    fred_indicator <- reactive({
      fredr(series_id = input$indicator,
        observation_start = start_date,
        observation_end = end_date)
    })
  
    # Filter data according to chosen years 
    # Only updates when user selects new data range
    fred_data <- reactive({
      fred_indicator() |>
      filter(between(date, input$range[1],input$range[2])) 
   })

    # Render line chart
    output$lineChart <- renderPlot({
      
      # Build plot with ggplot2
      ggplot(fred_data(), aes(x = date, y = value)) + 
        geom_line(color = "navyblue") +
        labs(
          x = "", 
          y =  names(vars[which(vars == input$indicator)])
        ) +
        theme_minimal() +
        # add recession shading
        add_rec_shade(st_date = input$range[1], 
                      ed_date = input$range[2], 
                      shade_color = "darkgrey",
                      y_min = min(fred_data()$value),
                      y_max = max(fred_data()$value))
    })
}

Call to Shiny app

Once we have our UI and server functions defined we are ready to go. But don’t forget to include the call to the Shiny app or the app won’t run! Once this is in place, you can click “Run App” in the RStudio IDE to view the app locally. Optionally, right now, you can try setting up an account on shinyapps.io and try publishing your app on their server.

# See above for the definitions of ui and server
ui <- ...

server <- ...

# Run the application 
shinyApp(ui = ui, server = server)