32  🏗️ App data

Published

2025-04-22

WARNING

This chapter is being developed. Thank you for your patience.

This chapter covers using session$userData vs. reactiveValues() in an application to store values and objects. Whether to use session$userData or reactiveValues() will depend on the purpose you want them to serve (and what you want to store/access) in your application.

I’ve created the shinypak R package in an effort to make each section accessible and easy to follow. Install shinypak using pak (or remotes):

install.packages('pak')
pak::pak('mjfrigaard/shinypak')
library(shinypak)

List the apps in this chapter:

list_apps(regex = '^app_data')

Launch apps with launch()

launch(app = 'app_data')

Download apps with get_app()

get_app(app = 'app_data')

32.1 reactiveValues()

Launch app with the shinypak package:

launch('26.1.0_reactive-values')

Calling reactiveValues() creates “an object for storing reactive values.” We’ve been storing the reactive values returned from the var_input module in the selected_vars object, then passing these values into the scatter_display module. 1

movies_server <- function(input, output, session) {

      selected_vars <- mod_var_input_server("vars")

      mod_scatter_display_server("plot", var_inputs = selected_vars)
      
}
1
reactive values returned from var_input module
2
reactive values passed to scatter_display module

In the steps below we’ll walk through an example of using reactiveValues() to capture the selected_vars values returned from the var_input module and passed to the scatter_display module.

You should note a series of changes made to movies_server() in this branch:

  • reactiveValues() is used to create rVals

  • Instead of creating the selected_vars, the output from mod_var_input_server() is assigned to rVals as inputs

  • rVals is then passed as an input argument to mod_scatter_display_server()

  • .dev arguments have been added to both module server functions (and have been set to FALSE)

# assign inputs to rVals
movies_server <- function(input, output, session) {
  
    # create reactive values
    rVals <- reactiveValues()

    # assign inputs to rVals
    rVals$inputs <- mod_var_input_server("vars", .dev = FALSE)
    
    # view output in the UI
    output$vals <- renderPrint({

    })

    # pass reactive values to display
    mod_scatter_display_server("plot", rVals = rVals, .dev = FALSE)
      
}
1
New reactiveValues() object
2
Returned values from mod_var_input_server() assigned to rVals$inputs
3
renderPrint() for displaying reactive values in UI
4
rVals object passed to mod_scatter_display_server()

In the steps below, we’ll view the structure and function of rVals and the reactive values in the application using methods covered in the Debug section.

32.1.1 Step 1

In movies_server(), the .dev in mod_var_input_server() is set to TRUE (the updated movies_server() function is below):

Launch app with the shinypak package:

launch('26.1.1_step_01')
# assign inputs to rVals
movies_server <- function(input, output, session) {
  
    # create reactive values
    rVals <- reactiveValues()

    # assign inputs to rVals
    rVals$inputs <- mod_var_input_server("vars", .dev = TRUE)

    # pass reactive values to display
    mod_scatter_display_server("plot", rVals = rVals, .dev = FALSE)
      
}
1
.dev has been set to TRUE

When we load the package and run the application, we see the following:2

(a) .dev = TRUE in mod_var_input_server()
Figure 32.1: reactive values from mod_var_input_server()

The output in the sidebar are the reactive values from the variable input module (mod_var_input_server()). The server function has been simplified to return the output from reactiveValuesToList(), and the output is being rendered in the sidebar when .dev is set to TRUE:

mod_var_input_server <- function(id, .dev = TRUE) {

  moduleServer(id, function(input, output, session) {
    
    if (.dev) {
      # view output in the UI
      output$vals <- renderPrint({
        x <- reactiveValuesToList(input, all.names = TRUE)
        str(x)
      })
    }
    
    # return reactives
    return(
      reactive({
        reactiveValuesToList(input, all.names = TRUE)
      })
    )

  })
}

32.1.2 Step 2

The renderPrint() in movies_server() displays the structure of rVals in the mainPanel() (the updated movies_server() function is below):

Launch app with the shinypak package:

launch('26.1.2_step_02')
# assign inputs to rVals
movies_server <- function(input, output, session) {
  
    # create reactive values
    rVals <- reactiveValues()

    # assign inputs to rVals
    rVals$inputs <- mod_var_input_server("vars", .dev = TRUE)
    
    # view output in the UI
    output$vals <- renderPrint({
      str(rVals)
    })

    # pass reactive values to display
    mod_scatter_display_server("plot", rVals = rVals, .dev = FALSE)
      
}
1
Set .dev to TRUE
2
Display output from str(rVals)
(a) str(rVals)from movies_server()
Figure 32.2: rVals$inputs() from movies_server()

32.1.2.1 What is reactiveValues()?

“When you read a value from it, the calling reactive expression takes a reactive dependency on that value, and when you write to it, it notifies any reactive functions that depend on that value. Note that values taken from the reactiveValues() object are reactive, but the reactiveValues() object itself is not.” Shiny Documentation

I’ve added emphasis to the quote above because it’s important to remember that any object assign to reactiveValue() should be treated like any reactive object (i.e., and inputId or object returned from reactive() or observe()).3

For example, if we try to access the input values as a list outside movies_server() or the module server function, we see the following error:4

x <- reactiveValues(
  inputs = list(x = "imdb_rating",
                y = "audience_score",
                z = "mpaa_rating",
                alpha = 0.5,
                size = 2,
                plot_title = "Enter Plot Title")
  )
x$inputs()
## Error in `x$inputs`:
## ! Can't access reactive value 'inputs' outside of reactive consumer.
## ℹ Do you need to wrap inside reactive() or observe()?

32.1.3 Step 3

In this branch, the renderPrint() displays the structure of rVals$inputs() (the updated movies_server() function is below):

Launch app with the shinypak package:

launch('26.1.3_step_03')
# assign inputs to rVals
movies_server <- function(input, output, session) {
  
    # create reactive values
    rVals <- reactiveValues()

    # assign inputs to rVals
    rVals$inputs <- mod_var_input_server("vars", .dev = TRUE)
    
    # view output in the UI
    output$vals <- renderPrint({
      str(rVals$inputs())
    })

    # pass reactive values to display
    mod_scatter_display_server("plot", rVals = rVals, .dev = FALSE)
      
}
1
Set .dev to TRUE
2
Display str(rVals$inputs())
(a) str(rVals)from movies_server()
Figure 32.3: rVals$inputs() from movies_server()

The rVals$inputs() being rendered in movies_server() are the returned values from the variable input module (and they’re identical to the values in the sidebar).

When rVals is passed to mod_scatter_display_server(), the reactive inputs() object (passed inside the function) is built as rVals$inputs():

inputs <- reactive({
  plot_title <- tools::toTitleCase(rVals$inputs()[['plot_title']])
    list(
      x = rVals$inputs()[['x']],
      y = rVals$inputs()[['y']],
      z = rVals$inputs()[['z']],
      alpha = rVals$inputs()[['alpha']],
      size = rVals$inputs()[['size']],
      plot_title = plot_title
    )
})

The mod_scatter_display_server() function will display the structure of rVals$inputs() if the .dev argument is set to TRUE:

    if (.dev) {
      # view output in the UI
      output$display_vals <- renderPrint({
        str(
          rVals$inputs()
          )
      })
    }

32.1.4 Step 4

In this final step, the .dev argument has been set to TRUE in the mod_scatter_display_server() (the updates movies_server() function is below):

Launch app with the shinypak package:

launch('26.1.4_step_04')
# assign inputs to rVals
movies_server <- function(input, output, session) {
  
    # create reactive values
    rVals <- reactiveValues()

    # assign inputs to rVals
    rVals$inputs <- mod_var_input_server("vars", .dev = TRUE)
    
    # view output in the UI
    output$vals <- renderPrint({
      rVals$inputs()
    })

    # pass reactive values to display
    mod_scatter_display_server("plot", rVals = rVals, .dev = TRUE)
      
}
1
Set .dev to TRUE
2
Display rVals$inputs()
3
Set .dev to TRUE
(a) .dev set to TRUE and reactive values from movies_server()
Figure 32.4: Both module .dev arguments set to TRUE and rVals$inputs() from movies_server()

The display we’re seeing directly below the graph (i.e., under inputs() from display module) is the structure of rVals$inputs() from mod_scatter_display_server():

if (.dev) {
  # view output in the UI
  output$display_vals <- renderPrint({
    str(
      rVals$inputs()
      )
  })
}

An important thing to note is that we can only reference rVals$inputs() in a reactive consumer (i.e., using reactive(), observe(), etc.). That’s why when we change any of the UI inputs, the values change in rVals$inputs() and in the inputs() object inside the display module.

You can also view these outputs using launch_app(run = 'b', bslib = TRUE).

32.1.5 Testing reactiveValues()

If you decide to use reactiveValues() or session$userData, you’ll need to confirm these objects in your tests. The module tests for test-mod_scatter_display.R have been redesigned to handle the reactiveValues() input.5

I’ll briefly summarize the changes below:

  • args = list() in testServer() now takes the output from helper functions (stored in tests/testthat/helper.R and R/testthat.R). 6

    make_initial_rVals_inputs <- function() {
  rVals <- reactiveValues(
    inputs =
      reactive(list(
        x = "imdb_rating",
        y = "audience_score",
        z = "mpaa_rating",
        alpha = 0.5,
        size = 2,
        plot_title = "Enter Plot Title"
      ))
  )
  return(rVals)
}

  • This creates a reaciveValues() list that can be used in the test:

    rVals <- make_initial_rVals_inputs()
rVals
## <ReactiveValues> 
##   Values:    inputs 
##   Readonly:  FALSE

  • We can view it’s contents by wrapping it in isolate().

    isolate(rVals$inputs())
## $x
## [1] "imdb_rating"
## 
## $y
## [1] "audience_score"
## 
## $z
## [1] "mpaa_rating"
## 
## $alpha
## [1] 0.5
## 
## $size
## [1] 2
## 
## $plot_title
## [1] "Enter Plot Title"

  • Passing make_initial_rVals_inputs() to the args in testServer() requires creating rVals (which can be referenced in the test as rVals$inputs():

        shiny::testServer(app = mod_scatter_display_server,
      args = list(rVals = make_initial_rVals_inputs()), expr = {
        testthat::expect_equal(
          object = rVals$inputs(),
          expected = list(
                x = "imdb_rating",
                y = "audience_score",
                z = "mpaa_rating",
                alpha = 0.5,
                size = 2,
                plot_title = "Enter Plot Title"
            )
        )
      })

32.2 session$userData

Objects stored in session$userData are not inherently reactive, which makes it ideal for storing persistent values or data that don’t require (or trigger) reactivity. Below is a demonstration of using session$userData to store a non-reactive function to be used in the inst/tidy-data/ application.

Launch app with the shinypak package:

launch('26.2.0_user-data')

32.2.0.1 Non-reactive objects

Objects we want to pass inside the server (and modules) but don’t need to update or change are perfect for session$userData. The example we’ll use below is a function (make_dev_ggp2_movies()) that prepares the ggplot2movies::movies for the application:

View make_dev_ggp2_movies() function 
make_dev_ggp2_movies <- function(con) {
  movies_data <- read.csv(file = con)
  # specify genre columns
  genre_cols <- c(
    "Action", "Animation",
    "Comedy", "Drama",
    "Documentary", "Romance",
    "Short"
  )
  # calculate row sum for genres
  movies_data$genre_count <- rowSums(movies_data[, genre_cols])
  # create aggregate 'genres' for multiple categories
  movies_data$genres <- apply(
    X = movies_data[, genre_cols],
    MARGIN = 1,
    FUN = function(row) {
      genres <- names(row[row == 1])
      if (length(genres) > 0) {
        return(paste(genres, collapse = ", "))
      } else {
        return(NA)
      }
    }
  )
  # format variables
  movies_data$genre_count <- as.integer(movies_data$genre_count)
  movies_data$genre <- ifelse(test = movies_data$genre_count > 1,
    yes = "Multiple genres",
    no = movies_data$genres
  )
  movies_data$genre <- as.factor(movies_data$genre)
  movies_data$mpaa <- factor(movies_data$mpaa,
    levels = c("G", "PG", "PG-13", "R", "NC-17"),
    labels = c("G", "PG", "PG-13", "R", "NC-17")
  )

  # reduce columns to only those in graph
  movies_data[, c(
    "title", "year", "length", "budget",
    "rating", "votes", "mpaa", "genre_count",
    "genres", "genre"
  )]
}

make_dev_ggp2_movies() is designed to take a path or URL (i.e., a connection) as an input and returns a dataset that can be used in the inst/tidy-data/ application.

In the inst/tidy-data/app.R file, the following changes have been made to devServer():

  • session$userData stores the contents of make_dev_ggp2_movies()

  • reactiveValues() is used to create rVals 7

  • The values returned from mod_var_input_server() is assigned to rVals as inputs

  • dev_mod_scatter_server() as been updated to include arguments for rVals, userData, con, and .dev

devServer <- function(input, output, session) {
  
  session$userData$make_dev_ggp2_movies <- make_dev_ggp2_movies
  
  rVals <- reactiveValues()
  
  rVals$inputs <- sap::mod_var_input_server("vars",
                                                  .dev = TRUE)

  dev_mod_scatter_server("plot",
    rVals = rVals,
    data_fun = session$userData$make_dev_ggp2_movies, 
    con = "https://bit.ly/3FQYR8j",
    .dev = FALSE
  )

}
1
Create userData$make_dev_ggp2_movies that holds make_dev_ggp2_movies()
2
Create rVals
3
Assign output from mod_var_input_server() to rVals$inputs
4
Updated dev_mod_scatter_server() function

To view what’s happening with session$userData, we’ll run the application using the Run App button at the top of app.R

(a) Initial app in dev/inst/app.R
Figure 32.5: The reactive values from mod_var_input_server() in the sidebar

We’re using the same version of mod_var_input_server() from above that includes a .dev argument, so we know it’s displaying the contents from reactiveValuesToList() in the sidebar.

32.2.1 Step 1

In devServer(), a renderPrint() call has been added to display the structure of session in the UI:

Launch app with the shinypak package:

launch('26.2.1_step_01')
devServer <- function(input, output, session) {
  
  # add function to userData
  session$userData$make_dev_ggp2_movies <- make_dev_ggp2_movies
  
  # create reactive values
  rVals <- reactiveValues()
  
  # assign inputs to rVals
  rVals$inputs <- sap::mod_var_input_server("vars",
                                                  .dev = TRUE)
  
  # view output in the UI
  output$vals <- renderPrint({
    str(session)
  })

  dev_mod_scatter_server("plot",
    rVals = rVals,
    data_fun = session$userData$make_dev_ggp2_movies, 
    con = "https://bit.ly/3FQYR8j",
    .dev = FALSE
  )
  
}
1
Create userData$make_dev_ggp2_movies that holds make_dev_ggp2_movies()
2
Create rVals
3
Assign output from mod_var_input_server() to rVals$inputs
4
Print the structure of session to UI
5
Updated dev_mod_scatter_server() function
(a) str(session) dev/inst/app.R
Figure 32.6: The str(session) from devServer()

32.2.1.1 What is session?

Each time the app launches, the session list is created and tied to that particular ’session.

“An environment for app authors and module/package authors to store whatever session-specific data they want.” Shiny Documentation

session$userData can store objects that should persist across different reactive contexts, but don’t need reactive updating (and won’t trigger reactivity). On the other hand, reactiveValues() creates objects stored in a reactive ‘state’, which will trigger reactive updates in the UI.’8

dev_mod_scatter_server() includes both reactiveValues() and session$userData. The arguments for rVals, data_fun, con, and .dev are described below:

  • rVals is the reactiveValues() object with our input values

  • data_fun is session$userData$make_dev_ggp2_movies

  • con is the path or URL to the data_fun in session$userData 9

dev_mod_scatter_server("plot",
  
  rVals = rVals,
  
  data_fun = session$userData$make_dev_ggp2_movies,
  
  con = "https://bit.ly/3FQYR8j",
  
  .dev = FALSE)
1
pass reactive values from reactiveValues()
2
pass session$userData with make_dev_ggp2_movies()
3
pass connection to non-reactive object
4
view userData value in module

Inside the display module (dev_mod_scatter_server()), the data_fun() function creates all_data with con:

# use data_fun() function on con
all_data <- data_fun(con)

  • The inputs() list passed to the plotting function is very similar to the methods used in mod_scatter_display_server():

    inputs <- reactive({
  plot_title <- tools::toTitleCase(rVals$inputs()[["plot_title"]])
  list(
    x = rVals$inputs()[["x"]],
    y = rVals$inputs()[["y"]],
    z = rVals$inputs()[["z"]],
    alpha = rVals$inputs()[["alpha"]],
    size = rVals$inputs()[["size"]],
    plot_title = plot_title
  )
})

  • The structure of data_fun is be printed to the UI when the .dev argument is set to TRUE

    # view output in the UI
if (.dev) {
  # view output in the UI
  output$data <- renderPrint({
    data_fun
  })
}

32.2.2 Step 2

Change .dev argument in dev_mod_scatter_server() to TRUE:

Launch app with the shinypak package:

launch('26.2.2_step_02')
  dev_mod_scatter_server("plot",
    rVals = rVals,
    data_fun = session$userData$make_dev_ggp2_movies, 
    con = "https://bit.ly/3FQYR8j",
    .dev = TRUE
  )
1
Change to TRUE

Load (with load_all()) and the app by clicking on the Run App icon:

(a) data_fun dev_mod_scatter_server()
Figure 32.7: The data_fun argument from dev_mod_scatter_server() is not reactive

We can see data_fun() is passed to dev_mod_scatter_server() from devServer() and is not a reactive (it’s a standard function we can apply to app_data).

Tests are more difficult for modules using session$userData, because these values are created when a Shiny app object is created (and exist inside the reactive context). This point is covered in more detail in this blog post.

“reusing objects passed through session violates module independence – there is code inside the module that uses external objects without stating them explicitly as server arguments.”

Recap

Recap: reactiveValues() & session$userData

session$userData

  • session$userData is best used with values or objects that persist across actions or navigation inside the app (i.e., maintaining values or data across pages of a multi-page app). session$userData can react to changes, but we’d need to explicitly create these reactive expressions or observers.

reactiveValues()

  • An object created with reactiveValues() is designed to be reactive, so changing values will trigger reactivity in any observers and/or reactives that depend on those values. Remember that ‘values taken from the reactiveValues() object are reactive, but the reactiveValues() object itself is not.’

Please open an issue on GitHub

  1. Mastering Shiny also has a great section on reactiveVal() and reactiveValues()↩︎

  2. The methods used in this chapter can be found in the section on Debug↩︎

  3. Read more in the Shiny documentation.↩︎

  4. We can access the values by wrapping the assigned object in isolate(). Read more in the documentation on reactiveValues().↩︎

  5. You can view the full test-mod_scatter_display.R test file in the 24.1.4_step_04 branch.↩︎

  6. I resorted to both locations because the tests/testthat/helper.R file wasn’t loading with devtools::load_all()↩︎

  7. We’ll cover how reactiveValues() works in Section 32.1 below.↩︎

  8. Notice session has :Classes 'ShinySession', 'R6'↩︎

  9. In this case, con is a URL for a .csv version of ggplot2movies::movies)↩︎