I'm trying to create an autoplot that will show a plot based on what variable the user selects, but it just shows up as a straight line even though the name on the y axis does change depening on what the user chooses. Here is a basic version of the code:
library(shiny)
library(fpp3)
ui <- fluidPage(
selectInput("select", "Choose variable", choices = names(aus_production)),
plotOutput("plot")
)
server <- function(input,output){
output$plot <- renderPlot({
aus_production %>% autoplot(input$select)
})
}
shinyApp(ui = ui,server = server)
You are calling ?autoplot.tbl_ts and that method requires an expressio for the variable, not a string which is what input$select returns. Instead you can use the .data pronoun
server <- function(input,output){
output$plot <- renderPlot({
aus_production %>% autoplot(.data[[input$select]])
})
}
Related
I am having two columns in my data frame, one is "all_pass" which contains numeric values and other is "st_name" which contains string values name of states
The requirement of the plot is , when user give input of the state it will show the plot of that particular state which contains all_pass numbers
Following is the code in which I am trying to plot, where the user will input the name of the state and as per the input of the state name, the graph will plot as per the all_pass as per the related pass scores to that particular state. Kindly help in the following code, will be of great help.
Code is as mentioned below :
library(ggplot2)
library(plotly)
library(dplyr)
library(shiny)
ui <- basicPage(
h1("Total bills passed by state delegation , 110th Congress"),
selectizeInput(inputId = "bins",label = "Choose State",
choices = list("AK","AL","AR","AS","AZ","CA","CO","CT","DC","DE","FL","GA","GU","HI","IA","ID","IL","IN","KS","KY","LA","MA","MD","ME","MI","MN","MO","MS","MT","NC","NE","ND","NH","NJ","NM","NV","NY","OH","OK","OR","PA","PR","RI","SC","SD","TN","TX","UT","VA") ,multiple = TRUE ,plotOutput("plot"))
)
server <- function(input, output) {
data <- reactive({
require(input$bins)
df <- df7 %>% filter(st_name %in% input$bins)
})
output$plot <- renderPlot({
ggplot(df(), aes(y= all_pass,x=st_name ))+geom_bar(stat = "sum")
})
}
shinyApp(ui = ui, server = server)
in the UI definition you have plotOutput("plot") as an argument to selectizeInput() instead of basicPage(). Reformatting your code (Ctrl+Shift+A) would have made that more visible.
You can simplify the server code, as the renderPlot() already creates a reactive dependence on input$bins.
You can use the object datasets::state.abb to get a vector of US state abbreviations. This is loaded automatically in every R session.
Please see some working code below. I am using some mock data for df as you did not provide any data in your question.
library(ggplot2)
library(plotly)
library(dplyr)
library(shiny)
ui <- basicPage(
h1("Total bills passed by state delegation, 110th Congress"),
selectizeInput(inputId = "bins",
label = "Choose State",
choices = state.abb,
multiple = TRUE),
plotOutput("plot")
)
server <- function(input, output) {
df <-
tibble(all_pass = sample(1:500, 350),
st_name = rep(state.abb, 7))
output$plot <- renderPlot({
req(input$bins)
df |>
filter(st_name %in% input$bins) |>
ggplot(aes(y = all_pass,x=st_name )) +
geom_bar(stat = "sum")
})
}
shinyApp(ui = ui, server = server)
This is my first Shiny app, and I just got the basics working to where it allows the user to select from a dropdown menu of clients, then a dropdown menu of test codes to receive a plot of the results for the selected test.
I'd like the second dropdown menu to be updated with the available test codes for that client (all are not present for each client). Also, I would like to be able to hover over the point in the plot and receive more information from the row in the original dataframe.
I've looked into tooltips and the nearPoints() function, but I'm not sure if these can be used on this data since it is manipulated. I'm not sure if at this point it would be easier to import the data in a different way (it will ultimately need to accept either excel files or .csv). Thanks for any help that you would be able to provide, please let me know if there is any other supporting info I can give.
Here is my code:
library(shiny)
library(scales)
library(ggplot2)
labData <-
read.table("MockNLData.csv",
header=TRUE, sep=",")
#convert '<10' and '<20' results
labData$ModResult <- labData$Result
levels(labData$ModResult)[levels(labData$ModResult)=="<10"]
<- "0"
levels(labData$ModResult)[levels(labData$ModResult)=="<20"]
<- "0"
#convert results to scientific notation
SciNotResult <-
formatC(as.numeric(as.character(labData$ModResult)),
format="e", digits=2)
ui <- fluidPage(
headerPanel("Dilution History"),
sidebarLayout(
sidebarPanel(
selectInput(inputId="client", label="Select Client
Name", choices=levels(labData$Client.Name)
),
selectInput(inputId="test", label="Select Test Code",
choices=levels(labData$Analysis))
),
mainPanel(
plotOutput("line", hover="plot_hov"),
verbatimTextOutput("info"))
)
)
server <- function(input, output) {
#selected client into data frame
selDF <- reactive({labData[labData[,1]==input$client,]
})
#selected test code into data frame
subsetDF <- reactive({selDF()[selDF()[,5]==input$test,]
})
#points to be plotted
points <-
reactive({as.numeric(levels(subsetDF()$ModResult))
[subsetDF()$ModResult]
})
#plot
output$line <- renderPlot({
qplot(seq_along(points()), points(), xlab ="Index",
ylab ="Result")
})
#hover information
output$info <- renderText({
paste0("x=", input$plot_hov$x, "\ny=",
input$plot_hov$y)
})
}
shinyApp(ui = ui, server = server)
Here is what the data looks like:
MockNLData.csv
EDIT: I figured out updating the menu with updateSelectInput()
In the future, make sure you share a reproducible example :)
Since your code is not reproducible please find below something you can understand and adapt to your case.
On your first question, if I understand correctly, you want to programatically generate a dropdown (selectInput) which is perfectly do-able. *Inputs are, in essence, just HTML content which you can dynamically generate, just like your plots. You do so with uiOutput (in your ui) and renderUI in your server.
library(shiny)
ui <- fluidPage(
selectInput("dataset", "Select a dataset", choices = c("cars", "mtcars")),
uiOutput("column"), # dynamic column selector
verbatimTextOutput("selected_column")
)
server <- function(input, output, session){
data <- reactive({
if(input$dataset == "cars")
return(cars)
else
return(mtcars)
})
output$column <- renderUI({
# build your selectInput as you normally would
selectInput("column_selector", "Select a column", choices = colnames(data()))
})
output$selected_column <- renderPrint({
# use input$column_selector!
print(input$column_selector)
})
}
shinyApp(ui, server)
On your second question, what you want is an interactive plot. There are numerous packages that will let you do that in R and Shiny. Below are some examples, by no means a comprehensive list:
plotly which will also let you make your ggplot2 charts interactive
highcharter another great, well tested library
echarts4r ECharts for R.
billboarder billboard.js for R and Shiny
Below is an example using highcharter. They all follow the same principle within Shiny, an *Output function coupled with a render* function.
library(shiny)
library(highcharter)
ui <- fluidPage(
highchartOutput("chart")
)
server <- function(input, output, session){
output$chart <- renderHighchart({
hchart(mpg, "scatter", hcaes(x = displ, y = hwy, group = class))
})
}
shinyApp(ui, server)
EDIT
Following your question on the flashing error. You need to require (req) the required input. When launching the app below the error will flash, uncomment the req(input$y) line and it'll go away.
library(shiny)
ui <- fluidPage(
uiOutput("sel"),
plotOutput("plot")
)
server <- function(input, output){
output$sel <- renderUI({
numericInput("y", "N:", value = 200, min = 5, max = 1000, step = 100)
})
output$plot <- renderPlot({
# req(input$y)
hist(runif(input$y, 1, 10))
})
}
shinyApp(ui, server)
In essence, since your plot relies on a dynamically generating input for a fraction of second that input is not available as it is being rendered, using req prevents that.
What I understand from your problem above are:
You want to make next dropdown menu based on what the user have chosen from previous dropdown menu.
When the mouse over the point on the plot, it will show row value.
So, here i will give you reproducible example and i hope it is useful for you.
In this example I use Rabbit dataset from library MASS.
To filter data for next dropdown menu, I use filter from library
dplyr (See line 30).
I use reactive expression to manage next dropdown menu (See line
29).
I use nearPoints() to manage hover point (See line 55).
library(shiny)
library(MASS)
library(dplyr)
library(ggplot2)
ui <- fluidPage(
titlePanel("Rabbit dataset from MASS library"),
fluidRow(
column(4, selectInput("var",
"Animal:",
unique(sort(Rabbit$Animal)))),
column(4, uiOutput("selected_var")),
column(4, uiOutput("selected_var1")),
column(12, plotOutput("selected_var2", hover = "plot_hover")),
column(12, verbatimTextOutput("info"))
)
)
server <- function(input, output) {
###FILTER NEXT DROPDOWN MENU BASED ON PREVIOUS SELECTED BY USER
dataset3 <- reactive({
unique(Rabbit %>% filter(Animal == input$var) %>% select(Treatment))
})
output$selected_var <- renderUI({
selectInput("var1", "Treatment:", c(dataset3()))
})
dataset4 <- reactive({
Rabbit %>% filter(Animal == input$var) %>% filter(Treatment == input$var1) %>% select(Run)
})
output$selected_var1 <- renderUI({
selectInput("var2", "Run:", c(dataset4()))
})
####
output$selected_var2 <- renderPlot({
ggplot(Rabbit %>% filter(Animal == input$var) %>% filter(Treatment == input$var1) %>% filter(Run == input$var2), aes(x = BPchange, y = Dose)) + geom_point()
})
###HOVER POINT USING nearPoints()
output$info <- renderPrint({
nearPoints(Rabbit %>% filter(Animal == input$var) %>% filter(Treatment == input$var1) %>% filter(Run == input$var2), input$plot_hover)
})
}
shinyApp(ui = ui, server = server)
I know renderPlot produces plot that can be shown on Shiny plotOutput function. I also know autoinvalidate() helps to calculate data reactively.
I am displaying a radar chart (in fact can be any chart) using the below codes:
output$plot2 <- renderPlot({
autoInvalidate()
p2<<-ggradar(mtcars_radar[i,])
})
What I dont know is how to change the value of i from 1 to 300 during every event of autoinvalidate().
Or is there anyway I can change the row of data in plot so that the plot is dynamically animating every sec with a new row of data.
Can anyone help me plz?
The full code is here:
library(shiny)
library(ggplot2)
mtcars %>%
rownames_to_column( var = "group" ) %>%
mutate_at(vars(-group),funs(rescale)) %>%
tail(4) %>% select(1:10) -> mtcars_radar
ui <- fluidPage(
sidebarPanel(
actionButton("button", "Go!")
),
# Show the plot
mainPanel(
plotOutput("plot2")
)
)
server <- function(input, output) {
library(ggplot2)
library(ggradar)
suppressPackageStartupMessages(library(dplyr))
library(scales)
autoInvalidate <- reactiveTimer(2000)
plot2 <- NULL
output$plot2 <- renderPlot({
ggradar(mtcars_radar[1,])
})
observeEvent(input$button,{
output$plot2 <- renderPlot({
autoInvalidate()
p2<<-ggradar(mtcars_radar[i,])
p2
})
})
}
# Run the application
shinyApp(ui = ui, server = server)
Any help please?
This is where you need a reactive value that stores the row index and changes every second. I do not have the library ggradar, so I will just print out the current row index value instead. I also used invalidateLater instead of reactiveTimer as suggested by Shiny documentation.
library(shiny)
ui <- fluidPage(
verbatimTextOutput("debug")
)
server <- function(input, output) {
row_idx_max <- 15
row_idx <- reactiveVal(0)
observe({
isolate(row_idx(row_idx() + 1))
cur_row_idx <- isolate(row_idx())
if (cur_row_idx < row_idx_max) {
invalidateLater(1000)
}
})
output$debug <- renderPrint({
row_idx()
})
}
shinyApp(ui, server)
I am new to R&shiny. I'd like to make a shiny app that the plot can be interactive with subset I choose, but ggplot cannot work with warning
Error in ouptut$Trendplot <- renderPlot({ : object 'ouptut' not found
It will be really appreciated if you can help to figure it works.
The following is my code:
library(shiny)
library(ggplot2)
# Define UI for application that draws a histogram
ui <- pageWithSidebar(
# Application title
headerPanel("Pre-report situation"),
# Sidebar with a slider input for number of bins
sidebarPanel(selectizeInput("DMS", "DMS:", choices = unique(datass$DMS)
)),
# Show a plot of the generated distribution
mainPanel(
h3(textOutput("caption")),
plotOutput("Trendplot"))
)
datass <- read.csv("C:/Users/yyu6/Documents/PR.csv", sep=",", stringsAsFactors = FALSE)
# Define server logic required to draw a histogram
server <- function(input, output) {
formulaText <- reactive({
input$DMS })
datasetInput <- reactive({
selection <- Input$DMS
subset(datass, DMS == selection)
})
output$caption <- renderText({formulaText()
})
ouptut$Trendplot <- renderPlot({
ggplot(datasetInput(), mapping = aes(x=DMS))+geom_histogram(stat = "count")
})
}
# Run the application
shinyApp(ui = ui, server = server)
ui <- fluidPage(
checkboxGroupInput("data", "Select data:",
c("Iris" = "iris",
"Cars" = "mtcars")),
plotOutput("myPlot")
)
server <- function(input, output) {
output$myPlot <- renderPlot({
plot(Sepal.Width ~ Sepal.Length, data = input$data)
})
}
shinyApp(ui, server)
I have a shinyApp where I want the user to select a data set. From there, I want to use that data set to make a simple plot. However, it seems that the user input into the checkbox didn't pass in successfully to the server. How can I get around this?
The typical way to do this in shiny is with switch(), which means you don't need to specify the dataset in your input, you can do it all in the server. In your context:
library(shiny)
ui <- fluidPage(
checkboxGroupInput("data", "Select data:",
c("Iris" = "iris",
"Cars" = "mtcars")),
plotOutput("myPlot")
)
server <- function(input, output) {
dat <- reactive({
switch()
})
output$myPlot <- renderPlot({
dat <- switch(input$data,
"iris" = iris,
"mtcars" = mtcars)
plot(Sepal.Width ~ Sepal.Length, data = get(input$data))
})
}
shinyApp(ui, server)
Note that you could use any strings in the checkboxGroupInput, which makes this a more flexible way to work.