I'm trying create a plot where I'm able to zoom in on a range and the y-axis rescales. Similar to Rescaling of y axis on an interactive plot depending on zoom except that I would like it to happen on double click.
library(shiny)
library(tidyverse)
ui <- fluidPage(
sidebarPanel(
actionButton('getdata', 'Get Data')
),
mainPanel(
plotOutput('plot', brush = brushOpts(id = 'brush'), dblclick = 'dclick')
)
)
server <- function(input, output, session) {
dat <- eventReactive(input$getdata, {
tibble('BusDate' = Sys.Date() - 0:10, 'Val' = rnorm(11))
})
dat2 <- reactive(dat())
observeEvent(input$dclick, {
brush <- input$brush
maxy <- brush$xmax
miny <- brush$xmin
if (is.null(miny)){
dat2(dat())
}else{
dat2(dat() %>% filter(BusDate > miny & BusDate < maxy))
}
}, ignoreInit = TRUE)
output$plot <- renderPlot({
ggplot(dat2(), aes(x = BusDate, y = Val)) + geom_line()
})
}
shinyApp(ui, server)
I keep getting an error that doesn't allow me to update dat2 within observe event.
Error in dat2: unused argument (dat() %>% filter(BusDate > miny & BusDate < maxy))
How do I update dat2 within observeEvent? I understand that its easier to update reactiveValues instead but I would like to know how it works specifically with reactiveVal
Try this:
library(shiny)
library(tidyverse)
ui <- fluidPage(
sidebarPanel(
actionButton('getdata', 'Get Data')
),
mainPanel(
plotOutput('plot', brush = brushOpts(id = 'brush'), dblclick = 'dclick')
)
)
server <- function(input, output, session) {
dat <- eventReactive(input$getdata, {
tibble('BusDate' = Sys.Date() - 0:10, 'Val' = rnorm(11))
})
dat2 <- reactiveVal()
observeEvent(input$dclick, {
brush <- input$brush
maxy <- brush$xmax
miny <- brush$xmin
if (is.null(miny)){
dat2(dat())
}else{
dat2(dat() %>% filter(BusDate > miny & BusDate < maxy))
}
}, ignoreInit = TRUE)
output$plot <- renderPlot({
df <- if(is.null(dat2())) dat() else dat2()
ggplot(df, aes(x = BusDate, y = Val)) + geom_line()
})
}
shinyApp(ui, server)
You are confusing reactive with reactiveVal. You can only update the value of reactive within its definition. Update with obj(xxx) is for reactiveVal.
I would like two plots to appear. First, a scatter plot and then a line graph. The graphs aren't important. This is my first time using Shiny. What is the best way to have both
plotOutput("needles"),
plotOutput("plot")
use the data from the same needles data frame? I think I'm getting confused as to how to pass the "needles" data frame between the plotOutput functions.
library(shiny)
library(tidyverse)
library(scales)
# Create the data frame ________________________________________________
create_data <- function(num_drops) {
needles <- tibble (
x = runif(num_drops, min = 0, max = 10),
y = runif(num_drops, min = 0, max = 10)
)
}
# Show needles ________________________________________________
show_needles <- function(needles) {
ggplot(data = needles, aes(x = x, y = y)) +
geom_point()
}
# Show plot __________________________________________________
show_plot <- function(needles) {
ggplot(data = needles, aes(x = x, y = y)) +
geom_line()
}
# Create UI
ui <- fluidPage(
sliderInput(inputId = "num_drops",
label = "Number of needle drops:",
value = 2, min = 2, max = 10, step = 1),
plotOutput("needles"),
plotOutput("plot")
)
server <- function(input, output) {
output$needles <- renderPlot({
needles <- create_data(input$num_drops)
show_needles(needles)
})
output$plot <- renderPlot({
show_plot(needles)
})
}
shinyApp(ui = ui, server = server)
We could execute the create_data inside a reactive call in the server and then within the renderPlot, pass the value (needles()) as arguments for show_needles and show_plot
server <- function(input, output) {
needles <- reactive({
create_data(input$num_drops)
})
output$needles <- renderPlot({
show_needles(needles())
})
output$plot <- renderPlot({
show_plot(needles())
})
}
shinyApp(ui = ui, server = server)
-output
I tried to fetch streaming data from mosquito test server for creating a real time line chart. I checked some examples of real time chart, but I couldn't seem to achieve the same objective. The chart is updated real time but it always refreshes.
Here is the script I edited from one example:
library(shiny)
library(magrittr)
library(mqtt)
library(jsonlite)
ui <- shinyServer(fluidPage(
plotOutput("plot")
))
server <- shinyServer(function(input, output, session){
myData <- data.frame()
# Function to get new observations
get_new_data <- function(){
d <- character()
mqtt::topic_subscribe(host = "test.mosquitto.org", port = 1883L, client_id = "dcR", topic = "IoTDemoData",
message_callback =
function(id, topic, payload, qos, retain) {
if (topic == "IoTDemoData") {
d <<- readBin(payload, "character")
# print(received_payload)
# received_payload <- fromJSON(received_payload)
# print(d)
return("quit")
}
}
)
d <- fromJSON(d)
d <- as.data.frame(d)
return(d)
# data <- rnorm(5) %>% rbind %>% data.frame
# return(data)
}
# Initialize my_data
myData <- get_new_data()
# Function to update my_data
update_data <- function(){
myData <<- rbind(get_new_data(), myData)
}
# Plot the 30 most recent values
output$plot <- renderPlot({
invalidateLater(1000, session)
update_data()
print(myData)
plot(temperature ~ 1, data=myData[1:30,], ylim=c(-20, -10), las=1, type="l")
})
})
shinyApp(ui=ui,server=server)
I have been struggling with creating real time chart for days. If anyone can point out the problem why the line chart is always refreshed and the solution, it will be highly appreciated!
Below are the revised working script based on Florian's answer:
library(shiny)
library(mqtt)
library(jsonlite)
library(ggplot2)
ui <- shinyServer(fluidPage(
plotOutput("mqttData")
))
server <- shinyServer(function(input, output, session){
myData <- reactiveVal()
get_new_data <- function(){
d <- character()
mqtt::topic_subscribe(host = "localhost", port = 1883L, client_id = "dcR", topic = "IoTDemoData",
message_callback =
function(id, topic, payload, qos, retain) {
if (topic == "IoTDemoData") {
d <<- readBin(payload, "character")
return("quit")
}
}
)
d <- fromJSON(d)
d <- as.data.frame(d)
return(d)
}
observe({
invalidateLater(1000, session)
isolate({
# fetch the new data
new_data <- get_new_data()
# If myData is empty, we initialize it with just the new data.
if(is.null(myData()))
myData(new_data)
else # row bind the new data to the existing data, and set that as the new value.
myData(rbind(myData(),new_data))
})
})
output$mqttData <- renderPlot({
ggplot(mapping = aes(x = c(1:nrow(myData())), y = myData()$temperature)) +
geom_line() +
labs(x = "Second", y = "Celsius")
})
})
shinyApp(ui=ui,server=server)
However, after adding a second plot, the flickering began. When I commented out one of the plots, the plot works great without the need to refresh.
library(shiny)
library(mqtt)
library(jsonlite)
library(ggplot2)
ui <- shinyServer(fluidPage(
plotOutput("mqttData"),
plotOutput("mqttData_RH")
))
server <- shinyServer(function(input, output, session){
myData <- reactiveVal()
get_new_data <- function(){
d <- character()
mqtt::topic_subscribe(host = "test.mosquitto.org", port = 1883L, client_id = "dcR", topic = "IoTDemoData",
# mqtt::topic_subscribe(host = "localhost", port = 1883L, client_id = "dcR", topic = "IoTDemoData",
message_callback =
function(id, topic, payload, qos, retain) {
if (topic == "IoTDemoData") {
d <<- readBin(payload, "character")
return("quit")
}
}
)
d <- fromJSON(d)
d <- as.data.frame(d)
d$RH <- as.numeric(as.character( d$RH))
return(d)
}
observe({
invalidateLater(10000, session)
isolate({
# fetch the new data
new_data <- get_new_data()
# If myData is empty, we initialize it with just the new data.
if(is.null(myData()))
myData(new_data)
else # row bind the new data to the existing data, and set that as the new value.
myData(rbind(myData(),new_data))
})
})
output$mqttData <- renderPlot({
ggplot(mapping = aes(x = c(1:nrow(myData())), y = myData()$temperature)) +
geom_line() +
labs(x = "Second", y = "Celsius")
})
output$mqttData_RH <- renderPlot({
ggplot(mapping = aes(x = c(1:nrow(myData())), y = myData()$RH)) +
geom_line() +
labs(x = "Second", y = "RH %")
})
})
shinyApp(ui=ui,server=server)
One solution I found plot the charts in one renderPlot object. The flickering reduces.
output$mqttData <- renderPlot({
myData() %>%
gather('Var', 'Val', c(temperature, RH)) %>%
ggplot(aes(timestamp,Val, group = 1))+geom_line()+facet_grid(Var ~ ., scales="free_y")
})
However, I wonder if there is way to plot the charts separately without flickering / refreshing.
I found one github example put data to ggplot2 using pipe %>% (https://github.com/mokjpn/R_IoT) and modified it to plot separated charts.
library(shiny)
library(ggplot2)
library(tidyr)
# Dashboard-like layout
ui <- shinyServer(fluidPage(
fluidRow(
column(
6,
plotOutput("streaming_data_1")
),
column(
6,
plotOutput("streaming_data_2")
)
),
fluidRow(
column(
6,
plotOutput("streaming_data_3")
),
column(
6,
plotOutput("streaming_data_4")
)
)
))
server <- shinyServer(function(input, output, session){
myData <- reactiveVal()
# show the first and last timestamp in the streaming charts
realtime_graph_x_labels <- reactiveValues(first = "",last ="")
get_new_data <- function(){
epochTimeStamp <- as.character(as.integer(Sys.time()))
sensor_1 <- -runif(1,min = 10, max = 30)
sensor_2 <- runif(1,min = 0,max = 100)
sensor_3 <- runif(1,min = 0,max = 100000)
sensor_4 <- runif(1,min = 0,max = 10)
newData <- data.frame(ts = epochTimeStamp, val_1 = sensor_1, val_2 = sensor_2, val_3 = sensor_3, val_4 = sensor_4)
return(newData)
}
observe({
invalidateLater(1000, session)
isolate({
# fetch the new data
new_data <- get_new_data()
# If myData is empty, we initialize it with just the new data.
if(is.null(myData()))
{
myData(new_data)
realtime_graph_x_labels$first <- as.character(head(myData()$ts,1))
}
else # row bind the new data to the existing data, and set that as the new value.
myData(rbind(myData(),new_data))
realtime_graph_x_labels$last <- as.character(tail(myData()$ts,1))
})
})
# When displaying two charts, there is no flickering / refreshing, which is desired
output$streaming_data_1 <- renderPlot({
myData() %>%
ggplot(aes(ts,val_1, group = 1))+geom_line() +
scale_x_discrete(breaks = c(realtime_graph_x_labels$first, realtime_graph_x_labels$last), labels = c(realtime_graph_x_labels$first, realtime_graph_x_labels$last)) +
labs(title ="Sensor 1") +
theme(plot.margin = unit(c(1,4,1,1),"lines"))
})
output$streaming_data_2<- renderPlot({
myData() %>%
ggplot(aes(ts,val_2, group = 1))+geom_line() +
scale_x_discrete(breaks = c(realtime_graph_x_labels$first, realtime_graph_x_labels$last), labels = c(realtime_graph_x_labels$first, realtime_graph_x_labels$last)) +
labs(title ="Sensor 2") +
theme(plot.margin = unit(c(1,4,1,1),"lines"))
})
# When adding the 3rd chart, every charts start to flicker / refresh when ploting new value
output$streaming_data_3<- renderPlot({
myData() %>%
ggplot(aes(ts,val_3, group = 1))+geom_line() +
scale_x_discrete(breaks = c(realtime_graph_x_labels$first, realtime_graph_x_labels$last), labels = c(realtime_graph_x_labels$first, realtime_graph_x_labels$last)) +
labs(title ="Sensor 3") +
theme(plot.margin = unit(c(1,4,1,1),"lines"))
})
output$streaming_data_4<- renderPlot({
myData() %>%
ggplot(aes(ts,val_4, group = 1))+geom_line() +
scale_x_discrete(breaks = c(realtime_graph_x_labels$first, realtime_graph_x_labels$last), labels = c(realtime_graph_x_labels$first, realtime_graph_x_labels$last)) +
labs(title ="Sensor 4") +
theme(plot.margin = unit(c(1,4,1,1),"lines"))
})
})
shinyApp(ui=ui,server=server)
The solution works when there are only two charts and starts flickering / refreshing when adding the 3rd.
One possible cause may be that 1000ms is too short for the data to finish processing. Try invalidateLater(10000, session) for example, and see what happens.
I was unable to install mqtt with my R version, so I am unable to reproduce your behavior. However, I looked at your code and I think there is something you could do different to improve your code: Writing data to the global environment with <<- is usually not a good idea. What might be better suited is a reactiveVal, in which you can store data, and on which other functions take a dependency. So in the example below, I have created a reactiveVal and a corresponding observer that updates the reactiveVal every 1000ms.
Below is a working example, where I replaced the contents of your function with a simple one-liner for illustration purposes.
Hope this helps!
set.seed(1)
library(shiny)
ui <- fluidPage(
plotOutput("plotx")
)
server <- function(input, output, session){
# A reactiveVal that holds our data
myData <- reactiveVal()
# Our function to get new data
get_new_data <- function(){
data.frame(a=sample(seq(20),1),b=sample(seq(20),1))
}
# Observer that updates the data every 1000ms.
observe({
# invalidate every 1000ms
invalidateLater(1000, session)
isolate({
# fetch the new data
new_data <- get_new_data()
# If myData is empty, we initialize it with just the new data.
if(is.null(myData()))
myData(new_data)
else # row bind the new data to the existing data, and set that as the new value.
myData(rbind(myData(),new_data))
})
})
# Plot a histrogram
output$plotx <- renderPlot({
hist(myData()$a)
})
}
shinyApp(ui=ui,server=server)
EDIT based on new reproducible example. Seems like it just takes some time to create all the plots. You can add
tags$style(type="text/css", ".recalculating {opacity: 1.0;}")
to your app to prevent them from flickering. Working example:
library(shiny)
library(ggplot2)
library(tidyr)
# Dashboard-like layout
ui <- shinyServer(fluidPage(
tags$style(type="text/css", ".recalculating {opacity: 1.0;}"),
fluidRow(
column(
6,
plotOutput("streaming_data_1")
),
column(
6,
plotOutput("streaming_data_2")
)
),
fluidRow(
column(
6,
plotOutput("streaming_data_3")
),
column(
6,
plotOutput("streaming_data_4")
)
)
))
server <- shinyServer(function(input, output, session){
myData <- reactiveVal()
# show the first and last timestamp in the streaming charts
realtime_graph_x_labels <- reactiveValues(first = "",last ="")
get_new_data <- function(){
epochTimeStamp <- as.character(as.integer(Sys.time()))
sensor_1 <- -runif(1,min = 10, max = 30)
sensor_2 <- runif(1,min = 0,max = 100)
sensor_3 <- runif(1,min = 0,max = 100000)
sensor_4 <- runif(1,min = 0,max = 10)
newData <- data.frame(ts = epochTimeStamp, val_1 = sensor_1, val_2 = sensor_2, val_3 = sensor_3, val_4 = sensor_4)
return(newData)
}
observe({
invalidateLater(1000, session)
isolate({
# fetch the new data
new_data <- get_new_data()
# If myData is empty, we initialize it with just the new data.
if(is.null(myData()))
{
myData(new_data)
realtime_graph_x_labels$first <- as.character(head(myData()$ts,1))
}
else # row bind the new data to the existing data, and set that as the new value.
myData(rbind(myData(),new_data))
realtime_graph_x_labels$last <- as.character(tail(myData()$ts,1))
})
})
# When displaying two charts, there is no flickering / refreshing, which is desired
output$streaming_data_1 <- renderPlot({
myData() %>%
ggplot(aes(ts,val_1, group = 1))+geom_line() +
scale_x_discrete(breaks = c(realtime_graph_x_labels$first, realtime_graph_x_labels$last), labels = c(realtime_graph_x_labels$first, realtime_graph_x_labels$last)) +
labs(title ="Sensor 1") +
theme(plot.margin = unit(c(1,4,1,1),"lines"))
})
output$streaming_data_2<- renderPlot({
myData() %>%
ggplot(aes(ts,val_2, group = 1))+geom_line() +
scale_x_discrete(breaks = c(realtime_graph_x_labels$first, realtime_graph_x_labels$last), labels = c(realtime_graph_x_labels$first, realtime_graph_x_labels$last)) +
labs(title ="Sensor 2") +
theme(plot.margin = unit(c(1,4,1,1),"lines"))
})
# When adding the 3rd chart, every charts start to flicker / refresh when ploting new value
output$streaming_data_3<- renderPlot({
myData() %>%
ggplot(aes(ts,val_3, group = 1))+geom_line() +
scale_x_discrete(breaks = c(realtime_graph_x_labels$first, realtime_graph_x_labels$last), labels = c(realtime_graph_x_labels$first, realtime_graph_x_labels$last)) +
labs(title ="Sensor 3") +
theme(plot.margin = unit(c(1,4,1,1),"lines"))
})
output$streaming_data_4<- renderPlot({
myData() %>%
ggplot(aes(ts,val_4, group = 1))+geom_line() +
scale_x_discrete(breaks = c(realtime_graph_x_labels$first, realtime_graph_x_labels$last), labels = c(realtime_graph_x_labels$first, realtime_graph_x_labels$last)) +
labs(title ="Sensor 4") +
theme(plot.margin = unit(c(1,4,1,1),"lines"))
})
})
shinyApp(ui=ui,server=server)
I am new to R and Shiny package. I have a csv file with 4 col and 600 rows and I am trying to plot some graphs using ggplot2.
My ui and server codes are like:
dt<-read.csv('file.csv')
server <- function(input, output) {
output$aPlot <- renderPlot({
ggplot(data = dt, aes(x = Col1, y = Col2, group = 'Col3', color = 'Col4')) + geom_point()
})
}
ui <- fluidPage(sidebarLayout(
sidebarPanel(
sliderInput("Obs", "Log FC", min = 1, max = 600, value = 100)
),
mainPanel(plotOutput("aPlot")) ))
Here, I can get the ggplot output but I don't know how to use this slider input to control the number of rows to be read i.e., I want this "Obs" input to define the size of Col1 to be used in the graph.
Try something like this, example here is with mtcars dataset:
library(shiny)
library(ggplot2)
dt <- mtcars[,1:4]
ui <- fluidPage(
sidebarPanel(
sliderInput("Obs", "Log FC", min = 1, max = nrow(dt), value = nrow(dt)-10)
),
mainPanel(plotOutput("aPlot"))
)
server <- function(input, output) {
mydata <- reactive({
dt[1:as.numeric(input$Obs),]
})
output$aPlot <- renderPlot({
test <- mydata()
ggplot(data = test, aes(x = test[,1], y = test[,2], group = names(test)[3], color = names(test)[4])) + geom_point()
})
}
shinyApp(ui = ui, server = server)
Change your server to:
server <- function(input, output) {
observe({
dt_plot <- dt[1:input$Obs,]
output$aPlot <- renderPlot({
ggplot(data = dt_plot, aes(x = Col1, y = Col2, group = 'Col3', color = 'Col4')) + geom_point()
})
})
}
When I click one point in the chart, that point is highlighted as red.
But soon it goes back to black.
Is there any way to hold the selection?
library(shiny)
library(ggplot2)
server <- function(input, session, output) {
mtcars$cyl = as.character(mtcars$cyl)
D = reactive({
nearPoints(mtcars, input$click_1,allRows = TRUE)
})
output$plot_1 = renderPlot({
set.seed(123)
ggplot(D(),aes(x=cyl,y=mpg)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(aes(color=selected_),width=0.02,size=4)+
scale_color_manual(values = c("black","red"),guide=FALSE)
})
output$info = renderPrint({
D()
})
}
ui <- fluidPage(
plotOutput("plot_1",click = clickOpts("click_1")),
verbatimTextOutput("info")
)
shinyApp(ui = ui, server = server)
Okay, my approach is slightly different to Valter's: selected points become red, whilst you can deselect them and they turn back to black.
The key to achieve this effect (or even Valter's answer with 1 selected point) is to use reactiveValues to keep track of the selected points.
library(shiny)
library(ggplot2)
server <- function(input, session, output) {
mtcars$cyl = as.character(mtcars$cyl)
vals <- reactiveValues(clicked = numeric())
observeEvent(input$click_1, {
# Selected point/points
slt <- which(nearPoints(mtcars, input$click_1,allRows = TRUE)$selected)
# If there are nearby points selected:
# add point if it wasn't clicked
# remove point if it was clicked earlier
# Else do nothing
if(length(slt) > 0){
remove <- slt %in% vals$clicked
vals$clicked <- vals$clicked[!vals$clicked %in% slt[remove]]
vals$clicked <- c(vals$clicked, slt[!remove])
}
})
D = reactive({
# If row is selected return "Yes", else return "No"
selected <- ifelse(1:nrow(mtcars) %in% vals$clicked, "Yes", "No")
cbind(mtcars, selected)
})
output$plot_1 = renderPlot({
set.seed(123)
ggplot(D(),aes(x=cyl,y=mpg)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(aes(color=selected),width=0.02,size=4)+
scale_color_manual(values = c("black","red"),guide=FALSE)
})
output$info = renderPrint({
D()
})
}
ui <- fluidPage(
plotOutput("plot_1",click = clickOpts("click_1")),
verbatimTextOutput("info")
)
shinyApp(ui = ui, server = server)
I am not sure what is the problem but this is the first workaround I have come up to:
library(shiny)
library(ggplot2)
server <- function(input, session, output) {
mtcars$cyl = as.character(mtcars$cyl)
df <- reactiveValues(dfClikced = mtcars)
observe({
if (!is.null(input$click_1)) {
df$dfClikced <- nearPoints(mtcars, input$click_1, allRows = TRUE)
}})
output$plot_1 = renderPlot({
set.seed(123)
if (names(df$dfClikced)[NCOL(df$dfClikced)]== "selected_") {
ggplot(df$dfClikced,aes(x=cyl,y=mpg)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(aes(color=selected_),width=0.02,size=4)+
scale_color_manual(values = c("black","red"),guide=FALSE)
} else {
ggplot(df$dfClikced,aes(x=cyl,y=mpg)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(width=0.02,size=4)+
scale_color_manual(values = c("black","red"),guide=FALSE)
}
})
output$info = renderPrint({
df$dfClikced
})
}
ui <- fluidPage(
plotOutput("plot_1",click = clickOpts("click_1")),
verbatimTextOutput("info")
)
shinyApp(ui = ui, server = server)
let me know...