operating not allowed without an active reactive context ggvis - r

Question

I am trying to create a simple ggvis plot in a shiny application. The dropdown has two choices: mpv and mpc. Both options are two column data frames with the first column as V1 and the second column as V2. I'd like to be able to select mpc or mpv and have the ggvis plot to the right update. I have the following ui and server r code:
# ui.R
shinyUI(fluidPage(
titlePanel("Barcelona"),
sidebarLayout(
sidebarPanel(
helpText("Display information about the selected variable"),
selectInput("var",
label = "Choose a variable to display",
choices = c("mpc", "mpv"),
selected = "mpc")),
mainPanel(
ggvisOutput("meanpc")))))
# server.R
shinyServer(
function(input, output) {
mpc <- mean.price.country
mpv <- mean.price.vintage
selection <- reactive({
as.numeric(input$var)
})
selection() %>%
ggvis(~V1, ~V2) %>%
layer_bars() %>%
bind_shiny("meanpc")
})
I get the following error:
Error in .getReactiveEnvironment()$currentContext() :
Operation not allowed without an active reactive context. (You tried to do something that can only be done from inside a reactive expression or observer.)
Any idea what the error is? Thank you.

Answer 1

You need to pack it in an observe statement like this:
library(shiny)
library(ggvis)
library(dplyr)
# ui.R
u <- shinyUI(fluidPage(
titlePanel("Barcelona"),
sidebarLayout(
sidebarPanel(
helpText("Display information about the selected variable"),
selectInput("var",
label = "Choose a variable to display",
choices = c("mpc", "mpv"),
selected = "mpc")),
mainPanel(
ggvisOutput("meanpc")))))
# server.R
s <- shinyServer(
function(input, output) {
n <- 200
set.seed(1234)
wine <- data.frame( vintage=sample(c(2000:2015),n,replace=T),
price=runif(n,10,150),
stock=runif(n,100,1500),
country=sample(c("Country-1","Country-2","Country-3"),n,replace=T)
)
mpc <- wine %>% group_by(country) %>% summarize( V1=mean(stock), V2=mean(price) )
mpv <- wine %>% group_by(country) %>% summarize( V1=mean(stock), V2=mean(vintage) )
selection <- reactive({ifelse (input$var=="mpc",return(mpc),return(mpv))})
observe({
selection() %>%
ggvis(~V1, ~V2) %>%
layer_bars() %>%
bind_shiny("meanpc")
})
})
shinyApp(u,s)
Yielding: