The app is intended to display summarized_mod when the action button is clicked. But I keep getting a summarized_mod missing error.
summarized <- data.frame(id = 1:20, group = letters[1:4], TY_COMP = runif(20), LY_COMP = runif(20))
library(shiny)
ui <- fluidPage(
verbatimTextOutput("text"),
actionButton("btn", "Show the summarized")
)
server <- function(input, output){
summarized <- reactive({summarized})
observeEvent(input$btn,{
summarized_mod <-summarized()$TY_COMP / summarized()$LY_COMP-1 }
})
output$text <- renderPrint(summarized_mod())
}
shinyApp(ui, server)
dat <- data.frame(id = 1:20,
group = letters[1:4],
TY_COMP = runif(20),
LY_COMP = runif(20))
library(shiny)
ui <- fluidPage(
verbatimTextOutput("text"),
actionButton("btn", "Show the summarized")
)
server <- function(input, output){
# summarized <- reactive({summarized}) useless !
summarized_mod <- eventReactive(input$btn, {
dat$TY_COMP / dat$LY_COMP-1
})
output$text <- renderPrint(summarized_mod())
}
shinyApp(ui, server)
Related
I am trying to create a dashboard in rShiny which follow the following steps
Select a parameter
Filter data from a source table for this parameter
Create a list of this filtered data for one of the column
Iterate over this list to display graphs etc...
I have tried various options for making this work but the communication between ui and server is not happening as expected
I have created a setup as below fot testing
library(shiny)
df_mtcars <- mtcars
df_mtcars <- cbind(CarName = rownames(df_mtcars), df_mtcars)
df_mtcars$CarName <- sub(" ", "_", df_mtcars$CarName)
select the number of gears
Find the cars with that number of gears
Create a list of these cars
Display the data for each of the car by using loop. Loop is needed as other output types like graphs can be latter added
simpUI <- function(id) {
tagList(tableOutput(NS(id, "dat_output"))
numericInput(NS(id, "GearNumber"), "Gear Numbers", 3),
lapply(seq(1, length(v_lst_CarName), by = 1), function(i) {
v_CarName = v_lst_CarName[i]
v_obj_CarName = paste0('sp_cars_', v_CarName)
tableOutput(NS(id, v_obj_CarName))
}))
}
simpServer <- function(id) {
moduleServer(id, function(input, output, session) {
output$dat_output <- renderTable(df_mtcars)
v_lst_CarName <-
reactive(df_mtcars[GearNumber == input$GearNumber]$CarName)
for (v_CarName in v_lst_CarName)
v_obj_CarName = paste0('sp_cars_', v_CarName)
output$v_obj_CarName <- renderTable(v_obj_CarName)
})
}
ui <- fluidPage(fluidRow(simpUI("cars")))
server <- function(input, output, session) {
simpServer("cars")
}
shinyApp(ui = ui, server = server)
It is better to do server side processing. Try this
library(shiny)
library(ggplot2)
df_mtcars <- mtcars
df_mtcars <- cbind(CarName = rownames(df_mtcars), df_mtcars)
df_mtcars$CarName <- sub(" ", "_", df_mtcars$CarName)
simpUI <- function(id) {
ns <- NS(id)
tagList(tableOutput(ns("dat_output")),
numericInput(ns("GearNumber"), "Gear Numbers", 3),
uiOutput(ns("plotxy")),
tableOutput(ns("v_obj_CarName")),
verbatimTextOutput(ns("mylist")),
plotOutput(ns("myplot"))
)
}
simpServer <- function(id) {
moduleServer(id, function(input, output, session) {
ns <- session$ns
output$dat_output <- renderTable(head(df_mtcars))
mydf <- reactive(df_mtcars[df_mtcars$gear == input$GearNumber,])
v_lst_CarName <- eventReactive(mydf(), {paste0("sp_cars_",mydf()$CarName)})
output$plotxy <- renderUI({
req(mydf())
tagList(
selectInput(ns("xvar"), label = "X-axis variable", choices = names(mydf()), selected=names(mydf())[2] ),
selectInput(ns("yvar"), label = "Y-axis variable", choices = names(mydf()), selected=names(mydf())[5] )
)
})
output$v_obj_CarName <- renderTable({mydf()})
output$mylist <- renderPrint(list(v_lst_CarName() ))
output$myplot <- renderPlot({
req(input$xvar,input$yvar)
ggplot(mydf(),aes(x=.data[[input$xvar]], y=.data[[input$yvar]])) + geom_point()
})
})
}
ui <- fluidPage(fluidRow(simpUI("cars")))
server <- function(input, output, session) {
simpServer("cars")
}
shinyApp(ui = ui, server = server)
I am trying now since days to get my Shiny App working so that when I move my mouse to certain points in the plot they are displayed in a table but unfortunately it is not working.
I am not sure what I am doing wrong, can you help me?
border <- table$A < 0.03
ui <- fluidPage(
mainPanel(
plotOutput("Plot",click="plot_click"),
tableOutput("HitSpots")
)
)
server <- function(input, output){
output$Plot <- renderPlot({
ggplot(table,aes(x=table$A, y=table$B), colour=border)) +
geom_point()
})
hit <- reactive({
nearPoints(table, input$plot_click)
})
output$HitSpots <- renderTable({
hit()
}
}
shinyApp(ui = ui, server = server)
There are some problems with your parentheses. But the main problem is that you do ggplot(table, aes(x=table$A, y=table$B)), and then nearpoints is looking for columns named table$A and table$B. Do ggplot(table, aes(x=A, y=B)) instead.
library(shiny)
library(ggplot2)
table <- data.frame(
A = c(1,2,3),
B = c(3,2,1)
)
ui <- fluidPage(
mainPanel(
plotOutput("Plot", click="plot_click"),
tableOutput("HitSpots")
)
)
server <- function(input, output){
output$Plot <- renderPlot({
ggplot(table, aes(x=A, y=B)) + geom_point()
})
hit <- reactive({ nearPoints(table, input$plot_click) })
output$HitSpots <- renderTable({
hit()
})
}
shinyApp(ui = ui, server = server)
in RStudio the below Shiny code works fine if I run it using Ctrl+Enter, line-by-line. However, if I run the whole code using the "Run App" button it generates this error:
Error in ts(x) : 'ts' object must have one or more observations
I think it is due to "lambda" parameter but I cannot see why. Any help is appreciated.
The link for "data.csv" is https://www.dropbox.com/s/p1bhacdg8j1qx42/data.csv?dl=0
====================================
library(shiny)
library(shinydashboard)
library(plotly)
library(forecast)
df <- read.csv("data.csv")
demand <- ts(df$demand, start = c(1995, 1), frequency = 12)
lbd <- BoxCox.lambda(demand, lower=-5, upper=5)
m <- ar(BoxCox(demand,lambda=lbd))
fit_BC <- forecast(m, h=12, lambda=lbd)
ui <- dashboardPage(
dashboardHeader(title = "Plot"),
dashboardSidebar(disable = TRUE),
dashboardBody(fluidRow(column(width = 12, box(plotlyOutput("forecast_plots"),width = NULL))))
)
server <- function(input, output) {
output$forecast_plots <- renderPlotly({
autoplot(fit_BC)
})
}
shinyApp(ui, server)
==================================
autoplot() returns ggplot object. But your output$forecast_plots requires plotly object(with plotlyOutput() function).
Working code is like the following:
ui <- dashboardPage(
dashboardHeader(title = "Plot"),
dashboardSidebar(disable = TRUE),
dashboardBody(fluidRow(column(width = 12, box(plotOutput("forecast_plots"),width = NULL))))
)
server <- function(input, output) {
output$forecast_plots <- renderPlot({
autoplot(fit_BC)
})
}
ggplot objects can be easily converted with ggplotly function, but unfortunately converted plotly autoplot graph loses the forecasting region. You can verify it like:
ui <- dashboardPage(
dashboardHeader(title = "Plot"),
dashboardSidebar(disable = TRUE),
dashboardBody(fluidRow(column(width = 12, box(plotlyOutput("forecast_plots"),width = NULL))))
)
server <- function(input, output) {
output$forecast_plots <- renderPlotly({
ggplotly(autoplot(fit_BC))
})
}
Add
I found autoplotly library.https://terrytangyuan.github.io/2018/02/12/autoplotly-intro/
autoplotly() function can convert autoplot object to plotly object which is roughly correct.
library(shiny)
library(shinydashboard)
library(plotly)
library(forecast)
library(autoplotly)
df <- read.csv("c:/Users/010170283/Downloads/data.csv")
demand <- ts(df$demand, start = c(1995, 1), frequency = 12)
lbd <- BoxCox.lambda(demand, lower=-5, upper=5)
m <- ar(BoxCox(demand,lambda=lbd))
fit_BC <- forecast(m, h=12, lambda=lbd)
ui <- dashboardPage(
dashboardHeader(title = "Plot"),
dashboardSidebar(disable = TRUE),
dashboardBody(fluidRow(column(width = 12, box(plotlyOutput("forecast_plots"),width = NULL))))
)
server <- function(input, output) {
output$forecast_plots <- renderPlotly({
autoplotly(autoplot(fit_BC))
})
}
shinyApp(ui, server)
The forecast region can be seen with it, and hi/lo 80 % edge values are presented with mouse hover event.
I'm building a shiny app that would display in dygraphs a basic dataset and then offer an option to add new time series upon selecting the checkbox input. However, as I coded it now, I'm 'stuck' at the original dataset and unable to add/remove new content. Any hints how to solve this are very welcome, thanks.
library(shinydashboard)
library(dygraphs)
library(dplyr)
ui <-dashboardPage(
dashboardHeader(),
dashboardSidebar(),
dashboardBody(
useShinyjs(),
checkboxGroupInput(inputId = 'options',
label = 'Choose your plot(s)',
choices = list("mdeaths" = 1,
"ldeaths" = 2)
),
uiOutput("Ui1")
)
)
server <- function(input, output, session) {
output$Ui1 <- renderUI({
output$plot1 <- renderDygraph({
final_ts <- ldeaths
p <- dygraph(final_ts, main = 'Main plot') %>%
dygraphs::dyRangeSelector()
if(1 %in% input$options) {
final_ts <- cbind(final_ts, mdeaths)
p <- p %>%
dySeries('mdeaths', 'Male Deaths')
} else if(2 %in% input$options) {
final_ts <- cbind(final_ts, fdeaths)
p <- p %>%
dySeries('fdeaths', 'Female Deaths')
}
p
})
dygraphOutput('plot1')
})
}
shinyApp(ui, server)
I'd suggest to dynamically filter the data based on the user selection instead of dynamically adding/removing traces from the plot:
library(shinydashboard)
library(shinyjs)
library(dygraphs)
library(dplyr)
lungDeaths <- cbind(ldeaths, mdeaths, fdeaths)
ui <- dashboardPage(
dashboardHeader(),
dashboardSidebar(),
dashboardBody(
useShinyjs(),
selectizeInput(
inputId = "options",
label = "Choose your trace(s)",
choices = colnames(lungDeaths),
selected = colnames(lungDeaths)[1],
multiple = TRUE,
options = list('plugins' = list('remove_button'))
),
uiOutput("Ui1")
)
)
server <- function(input, output, session) {
output$Ui1 <- renderUI({
filteredLungDeaths <- reactive({
lungDeaths[, input$options]
})
output$plot1 <- renderDygraph({
p <- dygraph(filteredLungDeaths(), main = 'Main plot') %>%
dygraphs::dyRangeSelector()
if('mdeaths' %in% colnames(filteredLungDeaths())){
p <- dySeries(p, 'mdeaths', 'Male Deaths')
}
if('fdeaths' %in% colnames(filteredLungDeaths())){
p <- dySeries(p, 'fdeaths', 'Female Deaths')
}
p
})
dygraphOutput('plot1')
})
}
shinyApp(ui, server)
I'm building a shiny app where I want to plot a dataset with one of the variables being a cumulative sum of another variable. The latter needs to be re-calculated every time the start date of dygraphs' dyRangeSelector changes. Below is a basic code without cumsum calculations. Commented out code is what I tried, with no success.
library(shinydashboard)
library(stringr)
library(zoo)
library(dplyr)
library(dygraphs)
ui <-dashboardPage(
dashboardHeader(),
dashboardSidebar(),
dashboardBody(
uiOutput("Ui1")
)
)
server <- function(input, output, session) {
output$Ui1 <- renderUI({
# date range observer
# values <- reactiveValues()
#
# observeEvent(input$plot1_date_window, {
# from <- as.Date(str_sub(input$plot1_date_window[[1]], 1, 10))
# })
## dygraphs plot
output$plot1 <- renderDygraph({
m_df <- data.frame(date=as.Date(zoo::as.yearmon(time(mdeaths))), Y=as.matrix(mdeaths))
# input_data <- m_df %>%
# filter(date >= values$from) %>%
# mutate(cumY = cumsum(Y))
input_xts <- xts(select(m_df, -date),
order.by = m_df$date)
#select(input_data, -date),
#order.by = input_data$date)
p <- dygraph(input_xts) %>%
dyRangeSelector()
p
})
## outputs
dygraphOutput('plot1')
})
}
shinyApp(ui, server)
UPDATE
I modified #Pork Chop's answer to be able to plot the cumulative values with other metrics on one graph, but I'm not even able to display the plot now:
library(xts)
library(shiny)
library(shinydashboard)
library(dygraphs)
ui <- dashboardPage(
dashboardHeader(),
dashboardSidebar(),
dashboardBody(
dygraphOutput('plot1'),
textOutput("cumsum1")
)
)
server <- function(input, output, session) {
m_df <- data.frame(date=as.Date(zoo::as.yearmon(time(mdeaths))), Y=as.matrix(mdeaths))
subdata <- reactive({
cumsum(m_df$Y[m_df$date >= as.Date(input$plot1_date_window[1]) & m_df$date <= as.Date(input$plot1_date_window[2])])
})
output$plot1 <- renderDygraph({
req(input$plot1_date_window)
input_xts <- xts(select(m_df, -date), order.by = m_df$date)
subdata_xts <- xts(select(subdata(), - date), order.by = subdata()$date)
final_xts <- cbind(input_xts, subdata_xts)
dygraph(final_xts) %>%
dyRangeSelector()
})
output$cumsum1 <- renderText({
req(input$plot1_date_window)
subdata <- cumsum(m_df$Y[m_df$date >= as.Date(input$plot1_date_window[1]) & m_df$date <= as.Date(input$plot1_date_window[2])])
subdata
})
}
shinyApp(ui, server)
The problem with your updated code is, that you didn't keep the date information. Also once you start rendering a plot based on a change of the plot itself (recursion) it gets a little tricky. You have to make sure that re-rendering the plot doesn't trigger the rendering again or you'll end up in a loop. That's why I set retainDateWindow = TRUE. Besides that you don't want the plot to re-render right away after the first change of the slider that's why I debounced the subdata.
Nevertheless, using dygraphs you still have the problem, that when you add cumsum as a series your plot for dyRangeSelector is changed (y maximum of all series). Please see the following code:
library(xts)
library(shiny)
library(shinydashboard)
library(dygraphs)
library(dplyr)
ui <- dashboardPage(
dashboardHeader(),
dashboardSidebar(),
dashboardBody(
dygraphOutput('plot1')
)
)
server <- function(input, output, session) {
m_df <- data.frame(date=as.Date(zoo::as.yearmon(time(mdeaths))), Y=as.matrix(mdeaths))
subdata <- reactive({
if(!is.null(input$plot1_date_window)){
subdata <- m_df[m_df$date >= as.Date(input$plot1_date_window[1]) & m_df$date <= as.Date(input$plot1_date_window[2]), ]
subdata$cumsum <- cumsum(subdata$Y)
subdata$Y <- NULL
} else {
subdata <- NULL
}
return(subdata)
})
subdata_d <- subdata %>% debounce(100)
output$plot1 <- renderDygraph({
input_xts <- xts(select(m_df, -date), order.by = m_df$date)
if(is.null(subdata_d())){
final_xts <- input_xts
} else {
subdata_xts <- xts(select(subdata_d(), - date), order.by = subdata_d()$date)
final_xts <- cbind(input_xts, subdata_xts)
}
p <- dygraph(final_xts) %>% dySeries(name="Y") %>%
dyRangeSelector(retainDateWindow = TRUE)
if("cumsum" %in% names(final_xts)){
p <- dySeries(p, name="cumsum", axis = "y2")
}
p
})
}
shinyApp(ui, server)
Just as #PorkChop mentioned I'd recommend multiple outputs for this scenario. Furthermore, I'd suggest to have a look at library(plotly) and it's event_data().
This should do the job, I think it is cleaner to have separate outputs for your dashboard
library(xts)
library(shiny)
library(shinydashboard)
library(dygraphs)
ui <- dashboardPage(
dashboardHeader(),
dashboardSidebar(),
dashboardBody(
dygraphOutput('plot1'),
textOutput("cumsum1")
)
)
server <- function(input, output, session) {
m_df <- data.frame(date=as.Date(zoo::as.yearmon(time(mdeaths))), Y=as.matrix(mdeaths))
output$plot1 <- renderDygraph({
input_xts <- xts(select(m_df, -date), order.by = m_df$date)
dygraph(input_xts) %>%
dyRangeSelector()
})
output$cumsum1 <- renderText({
req(input$plot1_date_window)
subdata <- cumsum(m_df$Y[m_df$date >= as.Date(input$plot1_date_window[1]) & m_df$date <= as.Date(input$plot1_date_window[2])])
subdata
})
}
shinyApp(ui, server)