I am relatively new to R, and I'm trying to build a reactive ggplot in Shiny where the X-axis (dates) is reactive to a dateRangeInput in the UI. I've been googling everywhere, but every thing I try returns an error.
In the ggplot, the aes() calls from a dataset called datecorrected_totals, where x is the dates, and y=load are the two values that I would like to be reactive to the dateRangeInput so the ggplot will adjust the scale based on the period within the daterangeinput.
library(tidyverse)
library(shiny)
library(tidyr)
library(lubridate)
library(zoo)
data <- read_csv("--")
# Define UI ----
ui <- fluidPage(
titlePanel("--"),
sidebarLayout(
sidebarPanel(
h3("Calculator"),
dateRangeInput("dates", label = "Dates",
start = ("10-18-2018"),
end = max("05-29-2019"),
min = min("10-18-2018"),
max = max("05-29-2019"),
format = "mm-dd-yyyy"),
sliderInput("slider_a", label = "--",
min = 0,
max = 7,
value = 0),
sliderInput("slider_c", label = "--",
min = 7,
max = 42,
value = 7)
),
mainPanel(plotOutput('bar_chart'))
)
)
# Define server logic ----
server <- function(input, output, session) {
RE <- reactive({
})
output$bar_chart <- renderPlot(
ggplot(data = datecorrected_totals, aes(x = x, y = load)) +
geom_bar(stat = "identity")
)
}
# Run the app ----
shinyApp(ui = ui, server = server)
You need to filter the original dataset by the input dates. In this example data would be your original dataset.
RE <- reactive({
data %>%
filter(x>=input$dates[1] & x<=input$dates[2])
})
output$bar_chart <- renderPlot(
ggplot(data = RE(), aes(x = x, y = load)) +
geom_bar(stat = "identity")
There is no need to create a separate reactive() expression (unless required otherwise). The filter can be applied directly in renderPlot(). Thus, output$bar_chart becomes
output$bar_chart <- renderPlot(
datecorrected_totals %>%
filter(between(x, input$dates[1], input$dates[2])) %>%
ggplot(aes(x = x, y = load)) +
geom_bar(stat = "identity")
)
Below is a self-contained minimal reproducible example:
library(tidyverse)
library(lubridate)
library(shiny)
datecorrected_totals <- tibble(x = seq(as.Date("2018-10-18"), as.Date("2019-05-29"), length.out = 10L),
load = day(x))
# Define UI ----
ui <- fluidPage(
titlePanel("--"),
sidebarLayout(
sidebarPanel(
h3("Calculator"),
dateRangeInput("dates", label = "Dates",
start = mdy("10-18-2018"),
end = mdy("05-29-2019"),
min = mdy("10-18-2018"),
max = mdy("05-29-2019"),
format = "mm-dd-yyyy"),
),
mainPanel(plotOutput('bar_chart'))
)
)
# Define server logic ----
server <- function(input, output, session) {
output$bar_chart <- renderPlot(
datecorrected_totals %>%
filter(between(x, input$dates[1], input$dates[2])) %>%
ggplot(aes(x = x, y = load)) +
geom_col()
)
}
# Run the app ----
shinyApp(ui = ui, server = server)
Note that the date strings have been coerced to valid Date objects by calling mdy() to avoid error messages.
In addition, geom_bar(stat = "identity") has been replaced by geom_col().
I would like two plots to appear. First, a scatter plot and then a line graph. The graphs aren't important. This is my first time using Shiny. What is the best way to have both
plotOutput("needles"),
plotOutput("plot")
use the data from the same needles data frame? I think I'm getting confused as to how to pass the "needles" data frame between the plotOutput functions.
library(shiny)
library(tidyverse)
library(scales)
# Create the data frame ________________________________________________
create_data <- function(num_drops) {
needles <- tibble (
x = runif(num_drops, min = 0, max = 10),
y = runif(num_drops, min = 0, max = 10)
)
}
# Show needles ________________________________________________
show_needles <- function(needles) {
ggplot(data = needles, aes(x = x, y = y)) +
geom_point()
}
# Show plot __________________________________________________
show_plot <- function(needles) {
ggplot(data = needles, aes(x = x, y = y)) +
geom_line()
}
# Create UI
ui <- fluidPage(
sliderInput(inputId = "num_drops",
label = "Number of needle drops:",
value = 2, min = 2, max = 10, step = 1),
plotOutput("needles"),
plotOutput("plot")
)
server <- function(input, output) {
output$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$plot <- renderPlot({
show_plot(needles())
})
}
shinyApp(ui = ui, server = server)
-output
I'm teaching myself Shiny and I am stuck on my ggplot2 graph not being able to use the reactive dateRangeInput as my x-axis. I have a few questions:
Is there a way to use my data frame to grab the min, max values for date range input instead of having to hardcode them in so that when I add more tweets to the data frame I don't have to hardcode the values each time?
I am getting the error: Aesthetics must be either length 1 or the same as the data (33108): x, y when I try to use input$date as my aes(x = input$date...
library(shiny)
library(tidyr)
library(ggplot2)
tweets <- read.csv(file.choose())
colnames(tweets)[1] <- "Content"
tweets <- separate(tweets, created_at, c("Date", "Time"), sep = " ")
tweets$Date <-as.Date(tweets$Date, "%m/%d/%Y")
ui <- fluidPage(
dateRangeInput(inputId = "date",
strong("Date Range"),
start = "2009-05-04", end = "2018-02-28",
min = "2009-05-04", max ="2018-02-28" ),
plotOutput("Graph")
)
server <- function(input, output) {
output$Graph <- renderPlot({
ggplot(tweets, aes(x = input$date, y = count)) +
geom_bar(stat = "identity", position = "stack") +
#scale_y_continuous(name = "Retweet Count", limits = c(0,370000), breaks=seq(0,370000,10000)) +
theme(panel.background = element_rect(fill = "white", colour = "grey50"))
})
}
shinyApp(ui = ui, server = server)
#Pete900's answer summarizes the use of updateDateRangeInput well, for further information you can refer to this part of the shiny documentation.
About your second problem: input$date will return a vector of length 2 with the first element beeing the lower and the second being the upper part of the selected range. You will most likely not use this directly as x-aesthetics but rather subset your data with this and then plot the newly subsettet data. You can e.g. write
library(dpylr) # alternatevly library(tidyverse)
newtweets <- reactive({
filter(tweets, between(date ,input$date[1], input$date[2]))
})
then, in your ggplot, use newtweets() as your data.
Update
The functions filter and between() (which is a shortcut for x is greater than ... and lesser then ...) come fromt the package dplyr, which is great for working with dataframes and part of a collection of packages that play very nicely with each other called tidyverse (see here).
When you refer to the newly created reactive object newtweets(), make sure to not forget the paranthesis because it is now a function call, that enables shiny to update the dataframe should the input change.
Update
A full working example in which I create some artificial data:
library(shiny)
library(tidyverse)
library(lubridate)
# tweets <- read.csv(file.choose())
st <- ymd("2009-05-01")
en <- ymd("2018-02-28")
dates <- seq.Date(from = st, to = en, by = 1)
tweets <- tibble(date = dates, count = rnorm(length(dates), mean = 5, sd = 3))
ui <- fluidPage(
dateRangeInput(inputId = "date",
strong("Date Range"),
start = "2009-05-04", end = "2018-02-28",
min = "2009-05-04", max ="2018-02-28" ),
plotOutput("Graph")
)
server <- function(input, output) {
newtweets <- reactive({
filter(tweets, between(date ,input$date[1], input$date[2]))
})
output$Graph <- renderPlot({
ggplot(newtweets(), aes(x = date, y = count)) +
geom_bar(stat = "identity", position = "stack") +
#scale_y_continuous(name = "Retweet Count", limits = c(0,370000), breaks=seq(0,370000,10000)) +
theme(panel.background = element_rect(fill = "white", colour = "grey50"))
})
}
shinyApp(ui = ui, server = server)
For the first question you can use updateDateRangeInput see here. So you would find your min and max dates in tweets outside of the server function then pass them to the input. Make sure to add session to your function:
server <- function(input, output, session) {
observe({
updateDateRangeInput(session, "date", min = myMinDate, max = myMaxDate)
})
}
For the second question you need to use aes_string to pass variables to ggplot, see here or here.
I tried to fetch streaming data from mosquito test server for creating a real time line chart. I checked some examples of real time chart, but I couldn't seem to achieve the same objective. The chart is updated real time but it always refreshes.
Here is the script I edited from one example:
library(shiny)
library(magrittr)
library(mqtt)
library(jsonlite)
ui <- shinyServer(fluidPage(
plotOutput("plot")
))
server <- shinyServer(function(input, output, session){
myData <- data.frame()
# Function to get new observations
get_new_data <- function(){
d <- character()
mqtt::topic_subscribe(host = "test.mosquitto.org", port = 1883L, client_id = "dcR", topic = "IoTDemoData",
message_callback =
function(id, topic, payload, qos, retain) {
if (topic == "IoTDemoData") {
d <<- readBin(payload, "character")
# print(received_payload)
# received_payload <- fromJSON(received_payload)
# print(d)
return("quit")
}
}
)
d <- fromJSON(d)
d <- as.data.frame(d)
return(d)
# data <- rnorm(5) %>% rbind %>% data.frame
# return(data)
}
# Initialize my_data
myData <- get_new_data()
# Function to update my_data
update_data <- function(){
myData <<- rbind(get_new_data(), myData)
}
# Plot the 30 most recent values
output$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()
})
})
}