R – Shiny: what is the difference between observeEvent and eventReactive

rreactive-programmingshinyshiny-server

I read the Shiny documentation about reactive programming a few times now, but I can't properly understand the difference between observeEvent and eventReactive.

The documentation says:

Use observeEvent whenever you want to perform an action in response to an event. (Note that "recalculate a value" does not generally count as performing an action–see eventReactive for that.)

….

Use eventReactive to create a calculated value that only updates in response to an event. This is just like a normal reactive expression except it ignores all the usual invalidations that come from its reactive dependencies;

In all the situation I tried I saw no difference between using observeEvent and eventReactive (the code works just fine regardless the function I use, with no apparent impact in performances).

Can you help me figure out what is the real difference between the two? Ideally I would like a few examples showing when they are interchangeable, one when observeEvent would work but not eventReactive and vice versa.

Best Answer

As @daatali is saying the two functions are used for different purposes.

ui <- shinyUI(pageWithSidebar(
  headerPanel("eventReactive and observeEvent"),
  sidebarPanel(
    actionButton("evReactiveButton", "eventReactive"),
    br(),
    actionButton("obsEventButton", "observeEvent"),
    br(),
    actionButton("evReactiveButton2", "eventReactive2")
  ),
  mainPanel(
    verbatimTextOutput("eText"),
    verbatimTextOutput("oText")
  )
))

server <- shinyServer(function(input, output) {
  etext <- eventReactive(input$evReactiveButton, {
    runif(1)
  })
  observeEvent(input$obsEventButton,{
    output$oText <- renderText({ runif(1) })
  })
  eventReactive(input$evReactiveButton2,{
    print("Will not print")
    output$oText <- renderText({ runif(1) })
  })
  output$eText <- renderText({
    etext()
  })
})

shinyApp(ui=ui,server=server)

eventReactive creates a reactive value that changes based on the eventExpr while observeEvent simply is triggered based on eventExpr

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R – The difference between bracket [ ] and double bracket [[ ]] for accessing the elements of a list or dataframe

The R Language Definition is handy for answering these types of questions:

http://cran.r-project.org/doc/manuals/R-lang.html#Indexing

R has three basic indexing operators, with syntax displayed by the following examples
    x[i]
    x[i, j]
    x[[i]]
    x[[i, j]]
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    x$"a"
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For lists, one generally uses [[ to select any single element, whereas [ returns a list of the selected elements.
The [[ form allows only a single element to be selected using integer or character indices, whereas [ allows indexing by vectors. Note though that for a list, the index can be a vector and each element of the vector is applied in turn to the list, the selected component, the selected component of that component, and so on. The result is still a single element.

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The difference in assignment operators is clearer when you use them to set an argument value in a function call. For example:

median(x = 1:10)
x   
## Error: object 'x' not found

In this case, x is declared within the scope of the function, so it does not exist in the user workspace.

median(x <- 1:10)
x    
## [1]  1  2  3  4  5  6  7  8  9 10

In this case, x is declared in the user workspace, so you can use it after the function call has been completed.

There is a general preference among the R community for using <- for assignment (other than in function signatures) for compatibility with (very) old versions of S-Plus. Note that the spaces help to clarify situations like

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Most R IDEs have keyboard shortcuts to make <- easier to type. Ctrl + = in Architect, Alt + - in RStudio (Option + - under macOS), Shift + - (underscore) in emacs+ESS.

If you prefer writing = to <- but want to use the more common assignment symbol for publicly released code (on CRAN, for example), then you can use one of the tidy_* functions in the formatR package to automatically replace = with <-.

library(formatR)
tidy_source(text = "x=1:5", arrow = TRUE)
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The answer to the question "Why does x <- y = 5 throw an error but not x <- y <- 5?" is "It's down to the magic contained in the parser". R's syntax contains many ambiguous cases that have to be resolved one way or another. The parser chooses to resolve the bits of the expression in different orders depending on whether = or <- was used.

To understand what is happening, you need to know that assignment silently returns the value that was assigned. You can see that more clearly by explicitly printing, for example print(x <- 2 + 3).

Secondly, it's clearer if we use prefix notation for assignment. So

x <- 5
`<-`(x, 5)  #same thing

y = 5
`=`(y, 5)   #also the same thing

The parser interprets x <- y <- 5 as

`<-`(x, `<-`(y, 5))

We might expect that x <- y = 5 would then be

`<-`(x, `=`(y, 5))

but actually it gets interpreted as

`=`(`<-`(x, y), 5)

This is because = is lower precedence than <-, as shown on the ?Syntax help page.

Best Answer

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