March 9, 2017

Welcome to R-Ladies RTP!

Materials

Reactivity 101

Reactions

The input$ list stores the current value of each input object under its name

Reactivity 101

Reactivity automatically occurs when an input value is used to render an output object

# Define server function required to create the scatterplot --------
server <- function(input, output) {
  # Create the scatterplot object the plotOutput function is expecting
  output$scatterplot <- renderPlot(
   ggplot(data = movies, aes_string(x = input$x, y = input$y, 
                                    color = input$z)) +
     geom_point(alpha = input$alpha)
  )
}

Let’s build a simple movie browser app!

movies.Rdata: Data from IMDB and Rotten Tomatoes on random sample of 651 movies released in the US between 1970 and 2014

movies_01.R: Single file app for browsing movies

Exercise

  • Start with movies_01.R
  • Add a new sliderInput defining the size of points (ranging from 0 to 5), see https://shiny.rstudio.com/reference/shiny/latest/sliderInput.html for help with defining a sliderInput
  • Use this variable in the geom of the ggplot function as the size argument
  • Run the app to ensure that point sizes react when you move the slider
  • Compare your code / output with the person sitting next to / nearby you

Solution

See movies_02.R

Demo

Suppose you want the option to plot only certain types of movies as well as report how many such movies are plotted:

  1. Add a UI element for the user to select which type(s) of movies they want to plot
  2. Filter for chosen title type and save as a new (reactive) expression
  3. Use new data frame (which is reactive) for plotting
  4. Use new data frame (which is reactive) also for reporting number of observations

Demo - 1

  1. Add a UI element for the user to select which type(s) of movies they want to plot
# Select which types of movies to plot
checkboxGroupInput(inputId = "selected_type",
                   label = "Select movie type(s):",
                   choice = c("Documentary", "Feature Film", "TV Movie"),
                   selected = "Feature Film")

Demo - 2

  1. Filter for chosen title type and save the new data frame as a reactive expression

before app:

library(dplyr)

server:

# Create a subset of data filtering for chosen title types
movies_subset <- reactive({
  movies %>%
    filter(title_type %in% input$selected_type)
})

Note: reactive({}) creates a cached expression that knows it is out of date when input changes

Demo - 3

Use new data frame (which is reactive) for plotting

# Create the scatterplot object the plotOutput function is expecting
output$scatterplot <- renderPlot({
  ggplot(data = movies_subset(), aes_string(x = input$x, y = input$y,
                                            color = input$z)) +
    geom_point(…) +
    …
})

Note: Cached - only re-run when inputs change

Demo - 4

Use new data frame (which is reactive) also for printing number of observations

ui:

mainPanel(
  ...
  # Print number of obs plotted
  textOutput(outputId = "n"),
  ...
  )

server:

# Print number of movies plotted
output$n <- renderText({
    counts <- movies_subset() %>%
      group_by(title_type) %>%
      summarise(count = n()) %>%
      select(count) %>%
      unlist()
    paste("There are", counts, input$selected_type, "movies in this dataset.")
})

Altogether

See movies_03.R

(also notice the little bit of HTML code, added for visual separation, in the mainPanel)

When to use reactives

  • By using a reactive expression for the subsetted data frame, we were able to get away with subsetting once and then using the result twice

  • In general, reactive conductors let you
    • not repeat yourself (i.e. avoid copy-and-paste code) which is a maintenance boon
    • decompose large, complex (code-wise, not necessarily CPU-wise) calculations into smaller pieces to make them more understandable

  • These benefits are similar to what happens when you decompose a large complex R script into a series of small functions that build on each other