I'm trying to create a timed counter within a Shiny app. When it increments each second, it should refresh a plot with some new characteristic that depends on the counter. Here's an example based on the 'Old Faithful' app. It doesn't work, but it gets at the idea. I tried to have reactiveTimer() refresh the plot, and a counter recorded with reactiveValues().
server.R
library(shiny)
shinyServer(function(input, output) {
refreshPlot <- reactiveTimer(intervalMs = 1000)
vals <- reactiveValues(counter = 0)
output$distPlot <- renderPlot({
refreshPlot()
vals$counter <- isolate(vals$counter) + 1
# generate bins based on input$bins from ui.R
x <- faithful[, 2]
n_bins <- input$bins + vals$counter
bins <- seq(min(x), max(x), length.out = n_bins)
# draw the histogram with the specified number of bins
hist(x, breaks = bins, col = 'darkgray', border = 'white')
})
})
ui.R
library(shiny)
shinyUI(fluidPage(
# Application title
titlePanel("Old Faithful Geyser Data"),
# Sidebar with a slider input for number of bins
sidebarLayout(
sidebarPanel(
sliderInput("bins",
"Number of bins:",
min = 1,
max = 50,
value = 30)
),
# Show a plot of the generated distribution
mainPanel(
plotOutput("distPlot")
)
)
invalidateLater is what I'm looking for. Thanks, Dieter. Below is the server.R that works.
library(shiny)
shinyServer(function(input, output, session) {
vals <- reactiveValues(counter = 0)
output$distPlot <- renderPlot({
invalidateLater(millis = 1000, session)
vals$counter <- isolate(vals$counter) + 1
# generate bins based on input$bins from ui.R
x <- faithful[, 2]
n_bins <- input$bins + vals$counter
bins <- seq(min(x), max(x), length.out = n_bins)
# draw the histogram with the specified number of bins
hist(x, breaks = bins, col = 'darkgray', border = 'white')
})
})
Related
I can see how to use actionButton to delay an output, but I haven't seen an example relevant to what I am trying to do, which is delay the start of a defined function that is called within another output.
Simplified for the MRE, let's say I have an output to create the mean of a data set. I have three ways to calculate the mean. One of those ways takes a long time though (simulated here by Method 2). Here is the way it is structured now.
How can I get algo(x) to wait until the button is pressed, then start the calculation and return the value?
library(shiny)
# Define UI
ui <- fluidPage(
# Application title
titlePanel("Old Faithful Geyser Data"),
# Sidebar with a slider input for number of bins
sidebarLayout(
sidebarPanel(
sliderInput("bins",
"Number of bins:",
min = 1,
max = 50,
value = 30),
radioButtons(inputId = "calc_t",label = "Select Calculation",choices = c("Method 1"=1,"Method 2 (long)"=2,"Method 3"=3)),
actionButton(inputId = "go_algo",label = "Start Algo")
),
# Show a plot of the generated distribution
mainPanel(
plotOutput("distPlot"),
textOutput("analyze")
)
)
)
# Define server logic required to draw a histogram
server <- function(input, output) {
output$distPlot <- renderPlot({
# generate bins based on input$bins from ui.R
x <- faithful[, 2]
bins <- seq(min(x), max(x), length.out = input$bins + 1)
# draw the histogram with the specified number of bins
hist(x, breaks = bins, col = 'darkgray', border = 'white',
xlab = 'Waiting time to next eruption (in mins)',
main = 'Histogram of waiting times')
})
output$analyze <- renderText({
calc_type<-input$calc_t
x <- faithful[, 2]
if(calc_type==1){
output<-paste("Mean 1 = ",mean(x))
} else if (calc_type==2){
output<-paste("Mean 2 = ",algo(x))
} else if(calc_type==3){
output<-paste("Mean 3 = ",sum(x)/length(x))
}
})
algo<-function(x){
mean_x<-mean(x)
#stuff that would take a long time
output<-mean_x+100
return(output)
}
}
# Run the application
shinyApp(ui = ui, server = server)
I would suggest using an observeEvent for the action button for the function that needs to wait for the button. For this observeEvent a req is required to limit the button to work only for this choice. Then you can use another observeEvent for the the other choices and again limit what is allowed to run without a button click with req.
Here's the updated server code:
server <- function(input, output) {
output$distPlot <- renderPlot({
# generate bins based on input$bins from ui.R
x <- faithful[, 2]
bins <- seq(min(x), max(x), length.out = input$bins + 1)
# draw the histogram with the specified number of bins
hist(x, breaks = bins, col = 'darkgray', border = 'white',
xlab = 'Waiting time to next eruption (in mins)',
main = 'Histogram of waiting times')
})
observeEvent(input$calc_t, {
req(input$calc_t!=2)
output$analyze <- renderText({
calc_type<-input$calc_t
x <- faithful[, 2]
if(calc_type==1){
output<-paste("Mean 1 = ",mean(x))
} else if(calc_type==3){
output<-paste("Mean 3 = ",sum(x)/length(x))
}
})
})
observeEvent(input$go_algo, {
req(input$calc_t==2)
output$analyze <- renderText({
isolate(calc_type<-input$calc_t)
x <- faithful[, 2]
output<-paste("Mean 2 = ",algo(x))
})
})
algo<-function(x){
mean_x<-mean(x)
#stuff that would take a long time
output<-mean_x+100
return(output)
}
}
given the default example of an Shiny app, I would like adapt the size of the three plots such that they fill the height of the window. My current output looks like this.
library(shiny)
# Define UI for application that draws a histogram
ui <- fillPage(
# Application title
titlePanel("Old Faithful Geyser Data"),
# Sidebar with a slider input for number of bins
sidebarLayout(
sidebarPanel(
sliderInput("bins",
"Number of bins:",
min = 1,
max = 50,
value = 30)
),
# Show a plot of the generated distribution
mainPanel(
plotOutput("distPlot"),
plotOutput("distPlot2"),
plotOutput("distPlot3")
)
)
)
# Define server logic required to draw a histogram
server <- function(input, output) {
output$distPlot <- renderPlot({
# generate bins based on input$bins from ui.R
x <- faithful[, 2]
bins <- seq(min(x), max(x), length.out = input$bins + 1)
# draw the histogram with the specified number of bins
hist(x, breaks = bins, col = 'darkgray', border = 'white')
})
output$distPlot2 <- renderPlot({
# generate bins based on input$bins from ui.R
x <- faithful[, 2]
bins <- seq(min(x), max(x), length.out = input$bins + 1)
# draw the histogram with the specified number of bins
hist(x, breaks = bins, col = 'darkgray', border = 'white')
})
output$distPlot3 <- renderPlot({
# generate bins based on input$bins from ui.R
x <- faithful[, 2]
bins <- seq(min(x), max(x), length.out = input$bins + 1)
# draw the histogram with the specified number of bins
hist(x, breaks = bins, col = 'darkgray', border = 'white')
})
}
# Run the application
shinyApp(ui = ui, server = server)
As you can see from the code, there should be a third plot visible.
Thanks in advance
Add a height argument to your plotOutputs with 30vh (vertical height, essentially % of container). You can mess with the value to get it exactly where you want. More info on vh here.
# Show a plot of the generated distribution
mainPanel(
plotOutput("distPlot", height = '30vh'),
plotOutput("distPlot2", height = '30vh'),
plotOutput("distPlot3", height = '30vh')
)
I am developing a shiny app and want to improve the accuracy of the slider (the slider ranges from 0 to 1000 and it's very difficult to accurately adjust the slider with a step of 1). I can't find an answer to this anywhere.
This is the code for one of my sliders:
sliderInput("mean2", "", min=0, max=1000, value=500, step=1)
Try arranging your code like such:
library(shiny)
ui <- fluidPage(
# Application title
titlePanel("Old Faithful Geyser Data"),
actionButton("minus", "Minus"),
actionButton("plus", "Plus"),
# Sidebar with a slider input for number of bins
sidebarLayout(
sidebarPanel(
sliderInput("mean2",
"Number of bins:",
min = 0,
max = 1000,
value = 500,
step= 1)
),
# Show a plot of the generated distribution
mainPanel(
plotOutput("distPlot")
)
)
)
server <- function(input, output, session) {
v <- reactiveValues(data = 500)
observeEvent(input$minus, {
v$data <- input$mean2 - 1
updateSliderInput(session,"mean2", value = input$mean2 - 1)
})
observeEvent(input$plus, {
v$data <- input$mean2 + 1
updateSliderInput(session,"mean2", value = input$mean2 + 1)
})
observeEvent(input$mean2, {
v$data <- input$mean2
})
output$distPlot <- renderPlot({
# generate bins based on input$bins from ui.R
x <- faithful[, 2]
bins <- seq(min(x), max(x), length.out = v$data + 1)
# draw the histogram with the specified number of bins
hist(x, breaks = bins, col = 'darkgray', border = 'white')
})
}
shinyApp(ui, server)
I am building my first app in shiny. The problem to solve is like this.
I've ui
shinyUI(fluidPage(
# Application title
titlePanel("My"),
# Sidebar with a slider input for number of bins
sidebarLayout(
sidebarPanel("Variables",
sliderInput("bins",
"Number of circles:",
min = 1,
max = 50,
value = 30),
sliderInput("bins",
"Number of triangels:",
min = 1,
max = 50,
value = 1)
),
# Show a plot of the generated distribution
mainPanel(tabsetPanel(
tabPanel("Data", plotOutput("distPlot")),
tabPanel("Data1", textOutput("text1")),
tabPanel("Data2", plotOutput("distPlot1")))
)
)
))
and server
shinyServer(function(input, output) {
output$distPlot <- renderPlot({
# generate bins based on input$bins from ui.R
x <- faithful[, 2]
bins <- seq(min(x), max(x), length.out = input$bins + 1)
# draw the histogram with the specified number of bins
hist(x, breaks = bins, col = 'darkgray', border = 'white')
})
output$distPlot1 <- renderPlot({
x <- faithful[, 2]
plot(density(x))
})
output$text1 <- renderText({
"Hello mother"
})
})
The question concern ui part. I'd like to fave 2 panels in the sidebarPanel - triangles and circles, similar to that what is in the mainPanel i.e. Data, Data1, Data2.
Thanks for the comments!
My Shiny script gets input from a drop down list. Based on this, I set (on the server side) a specific string (in a reactive) that should be displayed in the chart (for example as x axis title). This works only if the string contains no spaces, but no string is shown in the chart if contains spaces.
How can I get it to accept any string?
Here's my code (I modified one of the example from the Shiny tutorial to keep it as simple as possible):
# server.r
# Here the string is set depending on what was chosen by the user
shinyServer(function(input, output, session) {
label1_new <- reactive({
if (input$variable1=="pp_pmw") {label1_new <- "PP pmw"}
if (input$variable1=="perc_pp*100") {label1_new <- "PP percent"}
if (input$variable1=="formality") {label1_new <- "Formality"}
})
label1_new2 <- renderText({label1_new()})
output$distPlot <- renderPlot({
x <- faithful[, 2] # Old Faithful Geyser data
bins <- seq(min(x), max(x), length.out = input$bins + 1)
# draw the histogram with the specified number of bins
# xlabel1_new2() contains the string from above
hist(x, breaks = bins, col = 'darkgray', border = 'white', xlab=label1_new2())
})
})
# ui.r
library(shiny)
shinyUI(fluidPage(
# Application title
titlePanel("Hello Shiny!"),
# Sidebar with a slider input for the number of bins
sidebarLayout(
sidebarPanel(
selectInput("variable1", "Circle size:",
list("PP pmw" = "pp_pmw",
"PP percent" = "perc_pp*100",
"Formality" = "formality")),
sliderInput("bins",
"Number of bins:",
min = 1,
max = 50,
value = 30)
),
# Show a plot of the generated distribution
mainPanel(
plotOutput("distPlot")
)
)
))
renderText is for use with ui.r, not for creating strings to be used in server.r
# server.r
# Here the string is set depending on what was chosen by the user
shinyServer(function(input, output, session) {
label1_new <- reactive({
if (input$variable1=="pp_pmw") return("PP pmw")
if (input$variable1=="perc_pp*100") return("PP percent")
if (input$variable1=="formality") return("Formality")
})
output$distPlot <- renderPlot({
x <- as.numeric(unlist(faithful[, 2])) # Old Faithful Geyser data
bins <- seq(min(x), max(x), length.out = input$bins + 1)
# draw the histogram with the specified number of bins
hist(x, breaks = bins, col = 'darkgray', border = 'white', xlab=label1_new())
})
})
(same ui.r)