I have create one app that contains a textInput and a selectizeInput. Depending on the user's input and if the input can be found in one dataset, you will see all the possibilities according to that textInput in the selectizeInput.
In this way, if the user introduces a word that it is not in the dataset, the selectizeInput can't display any choice.
Everything works fine, but I found one problem. If the user starts writing a correct word, the user gets a dropdown list... and then, if the input is removed... the dropdown list is still there (the choices from selectizeInput are still there).
Here the code:
library(shiny)
library(dplyr)
library(stringr)
ui <- fluidPage(
textInput("my_input", "Introduce a word"),
selectizeInput(inputId = "dropdown_list", label = "Choose the variable:", choices=character(0)),
)
server <- function(input, output, session) {
my_list <- reactive({
req(input$my_input)
data <- as.data.frame(storms)
res <- subset(data, (grepl(pattern = str_to_sentence(input$my_input), data$name))) %>%
dplyr::select(name)
res <- as.factor(res$name)
return(res)
})
# This is to generate the choices (gene list) depending on the user's input.
observeEvent(input$my_input, {
updateSelectizeInput(
session = session,
inputId = "dropdown_list",
choices = my_list(), options=list(maxOptions = length(my_list())),
server = TRUE
)
})
}
shinyApp(ui, server)
Do you know how can I remove the choices from the selectizeInput if the user deletes the input?
Thanks very much in advance
Regards
The issue is the req(input$myinput). Hence, if the user deletes the input my_list() does not get updated. Instead of req you could use an if to check whether the input is equal to an empty string:
my_list <- reactive({
if (!input$my_input == "") {
data <- as.data.frame(storms)
res <- subset(data, grepl(pattern = str_to_sentence(input$my_input), data$name), name)
res <- as.factor(res$name)
return(res)
}
})
I want to collect user inputs, and output them as a table/data frame within Shiny. Inspired by the post here (Collect All user inputs throughout the Shiny App), I was able to do this but with some issues below. Here I have attached the example code, where I intend to collect the user input using the reactive textInput box. The number of textInput boxes is according to the user input for "Number of box". The output table looks fine when I kept increasing the "Number of box", such as I increased the number of boxes from 1 to 2 (see attached screenshot 1 and 2). However, when I changed the number of boxes from 2 back to 1, the output table still showed the previous results "textBox2 the second input" (see screenshot 3). My question is how to remove this previous input ("textBox2 the second input") and only print out the current user input. Thanks!
Example code
library(shiny)
ui <- basicPage(
fluidRow(column(6,
numericInput("nbox", "Number of box", value = 1,
min = 1))),
fluidRow(column(6,style='padding:0px;',
wellPanel(
uiOutput("textInputBox"))),
column(6, style='padding:0px;',
tableOutput('show_inputs')))
)
server <- shinyServer(function(input, output, session){
output$textInputBox <- renderUI({
num <- as.integer(input$nbox)
lapply(1:num, function(i) {
textInput(paste0("textBox", i),
label = paste0("textBox", i))
})
})
AllInputs <- reactive({
myvalues <- NULL
for(i in 1:length(names(input))){
myvalues <- as.data.frame(rbind(myvalues,
(cbind(names(input)[i],input[[names(input)[i]]]))))
}
names(myvalues) <- c("User input variable","User input value")
myvalues
})
output$show_inputs <- renderTable({
AllInputs()
})
})
shinyApp(ui = ui, server = server)
Screen shot (1,2,3 in order)
Created inputs are not deleted when a new renderUI is called, so there is no solution this way.
A workaround is to use a counter (like the one previously used to create the input) and use it to rebuild the names:
AllInputs <- reactive({
num <- as.integer( input$nbox )
my_names <- paste0("textBox", 1:num)
all_values <- stack( reactiveValuesToList(input) )
myvalues <- all_values[ all_values$ind %in% c("nbox", my_names),
c(2, 1)]
names(myvalues) <- c("User input variable","User input value")
myvalues
})
Note the use of reactiveValuesToList to replace the loop.
I am trying to dynamically populate the values of the selectInput from the data file uploaded by the user. The selectInput must contain only numeric columns.
Here is my code snippet for server.R
...
idx <- sapply(data.file, is.numeric)
numeric_columns <- data.file[, idx]
factor_columns <- data.file[, !idx]
updateSelectInput(session, "bar_x", "Select1", choices = names(numeric_columns))
updateSelectInput(session, "bar_y", "Select2", choices = names(factor_columns))
...
Corresponding ui.r
...
selectInput("bar_x", "Select1", choices = NULL),
selectInput("bar_y", "Select2", choices = NULL)
...
The code works fine as long as there are more than one values in any dropdown. However, it fails as soon as it encounters only one value to be displayed in the selectInput.
How can I handle this specific condition, given that the data is uploaded and it cannot be controlled if there is just one column as numeric?
It appears that in 2019, this issue still exists. The issue that I have seen is that when there is only one option in the dropdown, the name of the column is displayed instead of the one option.
This appears to only be a graphical problem, as querying the value for the selectInput element returns the correct underlying data.
I was unable to figure out why this problem exists, but an easy way around this bug is to simply change the name of the column so that it looks like the first element in the list.
library(shiny)
ui <- fluidPage(
selectInput("siExample",
label = "Example Choices",
choices = list("Loading...")),
)
server <- function(input, output, session) {
# load some choices into a single column data frame
sampleSet <- data.frame(Example = c("test value"))
# rename the set if there is only one value
if (length(sampleSet$Example) == 1) {
# This should only be done on a copy of your original data,
# you don't want to accidentally mutate your original data set
names(sampleSet) <- c(sampleSet$Example[1])
}
# populate the dropdown with the sampleSet
updateSelectInput(session,
"siExample",
choices = sampleSet)
}
shinyApp(ui = ui, server = server)
Info: Code was adapted by OP to make error reproducible.
To solve your issue use val2 <- val[,idx, drop = FALSE]
You dropped the column names by subsetting the data.frame().
To avoid this use drop = FALSE; see Keep column name when select one column from a data frame/matrix in R.
library(shiny)
# Define UI for application that draws a histogram
ui <- fluidPage(
# Sidebar with a slider input for number of bins
sidebarLayout(
sidebarPanel(
# drj's changes START block 1
#selectInput('states', 'Select states', choices = c(1,2,4))
selectInput('states', 'Select states', choices = NULL)
# drj's changes END block 1
),
# Show a plot of the generated distribution
mainPanel(
plotOutput("distPlot")
)
)
)
# Define server logic required to draw a histogram
server <- function(input, output, session) {
observe({
#drj's changes START block 2
#val <- c(1,2,3)
#names(val) <- c("a","b","c")
#updateSelectInput(session, 'states', 'Select states', choices = names(val[1]))
val <- as.data.frame(cbind(c("_1","_2","_3"), c(4, 4, 6)))
names(val) <- c("a","b")
val$b <- as.numeric(val$b)
idx <- sapply(val, is.numeric)
val2 <- val[,idx, drop = FALSE]
updateSelectInput(session, 'states', 'Select states', choices = names(val2))
#drj's changes END block 2
})
}
# Run the application
shinyApp(ui = ui, server = server)
The goal
I am working on a Shiny app that allows the user to upload their own data and focus on the entire data or a subset by providing data filtering widgets described by the below graph
The select input "Variable 1" will display all the column names of the data uploaded by the user and the selectize input "Value" will display all the unique values of the corresponding column selected in "Variable 1". Ideally, the user will be able to add as many such rows ("Variable X" + "Value") as possible by some sort of trigger, one possibility being clicking the "Add more" action button.
A possible solution
After looking up online, I've found one promising solution given by Nick Carchedi pasted below
ui.R
library(shiny)
shinyUI(pageWithSidebar(
# Application title
headerPanel("Dynamically append arbitrary number of inputs"),
# Sidebar with a slider input for number of bins
sidebarPanel(
uiOutput("allInputs"),
actionButton("appendInput", "Append Input")
),
# Show a plot of the generated distribution
mainPanel(
p("The crux of the problem is to dynamically add an arbitrary number of inputs
without resetting the values of existing inputs each time a new input is added.
For example, add a new input, set the new input's value to Option 2, then add
another input. Note that the value of the first input resets to Option 1."),
p("I suppose one hack would be to store the values of all existing inputs prior
to adding a new input. Then,", code("updateSelectInput()"), "could be used to
return inputs to their previously set values, but I'm wondering if there is a
more efficient method of doing this.")
)
))
server.R
library(shiny)
shinyServer(function(input, output) {
# Initialize list of inputs
inputTagList <- tagList()
output$allInputs <- renderUI({
# Get value of button, which represents number of times pressed
# (i.e. number of inputs added)
i <- input$appendInput
# Return if button not pressed yet
if(is.null(i) || i < 1) return()
# Define unique input id and label
newInputId <- paste0("input", i)
newInputLabel <- paste("Input", i)
# Define new input
newInput <- selectInput(newInputId, newInputLabel,
c("Option 1", "Option 2", "Option 3"))
# Append new input to list of existing inputs
inputTagList <<- tagAppendChild(inputTagList, newInput)
# Return updated list of inputs
inputTagList
})
})
The downside
As pointed by Nick Carchedi himself, all the existing input widgets will undesirably get reset every time when a new one is added.
A promising solution for data subsetting/filtering in Shiny
As suggested by warmoverflow, the datatable function in DT package provides a nice way to filter the data in Shiny. See below a minimal example with data filtering enabled.
library(shiny)
shinyApp(
ui = fluidPage(DT::dataTableOutput('tbl')),
server = function(input, output) {
output$tbl = DT::renderDataTable(
iris, filter = 'top', options = list(autoWidth = TRUE)
)
}
)
If you are going to use it in your Shiny app, there are some important aspects that are worth noting.
Filtering box type
For numeric/date/time columns: range sliders are used to filter rows within ranges
For factor columns: selectize inputs are used to display all possible categories
For character columns: ordinary search boxes are used
How to obtain the filtered data
Suppose the table output id is tableId, use input$tableId_rows_all as the indices of rows on all pages (after the table is filtered by the search strings). Please note that input$tableId_rows_all returns the indices of rows on all pages for DT (>= 0.1.26). If you use the DT version by regular install.packages('DT'), only the indices of the current page are returned
To install DT (>= 0.1.26), refer to its GitHub page
Column width
If the data have many columns, column width and filter box width will be narrow, which makes it hard to see the text as report here
Still to be solved
Despite some known issues, datatable in DT package stands as a promising solution for data subsetting in Shiny. The question itself, i.e. how to dynamically append arbitrary number of input widgets in Shiny, nevertheless, is interesting and also challenging. Until people find a good way to solve it, I will leave this question open :)
Thank you!
are you looking for something like this?
library(shiny)
LHSchoices <- c("X1", "X2", "X3", "X4")
#------------------------------------------------------------------------------#
# MODULE UI ----
variablesUI <- function(id, number) {
ns <- NS(id)
tagList(
fluidRow(
column(6,
selectInput(ns("variable"),
paste0("Select Variable ", number),
choices = c("Choose" = "", LHSchoices)
)
),
column(6,
numericInput(ns("value.variable"),
label = paste0("Value ", number),
value = 0, min = 0
)
)
)
)
}
#------------------------------------------------------------------------------#
# MODULE SERVER ----
variables <- function(input, output, session, variable.number){
reactive({
req(input$variable, input$value.variable)
# Create Pair: variable and its value
df <- data.frame(
"variable.number" = variable.number,
"variable" = input$variable,
"value" = input$value.variable,
stringsAsFactors = FALSE
)
return(df)
})
}
#------------------------------------------------------------------------------#
# Shiny UI ----
ui <- fixedPage(
verbatimTextOutput("test1"),
tableOutput("test2"),
variablesUI("var1", 1),
h5(""),
actionButton("insertBtn", "Add another line")
)
# Shiny Server ----
server <- function(input, output) {
add.variable <- reactiveValues()
add.variable$df <- data.frame("variable.number" = numeric(0),
"variable" = character(0),
"value" = numeric(0),
stringsAsFactors = FALSE)
var1 <- callModule(variables, paste0("var", 1), 1)
observe(add.variable$df[1, ] <- var1())
observeEvent(input$insertBtn, {
btn <- sum(input$insertBtn, 1)
insertUI(
selector = "h5",
where = "beforeEnd",
ui = tagList(
variablesUI(paste0("var", btn), btn)
)
)
newline <- callModule(variables, paste0("var", btn), btn)
observeEvent(newline(), {
add.variable$df[btn, ] <- newline()
})
})
output$test1 <- renderPrint({
print(add.variable$df)
})
output$test2 <- renderTable({
add.variable$df
})
}
#------------------------------------------------------------------------------#
shinyApp(ui, server)
Now, I think that I understand better the problem.
Suppose the user selects the datasets::airquality dataset (here, I'm showing only the first 10 rows):
The field 'Select Variable 1' shows all the possible variables based on the column names of said dataset:
Then, the user selects the condition and the value to filter the dataset by:
Then, we want to add a second filter (still maintaining the first one):
Finally, we get the dataset filtered by the two conditions:
If we want to add a third filter:
You can keep adding filters until you run out of data.
You can also change the conditions to accommodate factors or character variables. All you need to do is change the selectInput and numericInput to whatever you want.
If this is what you want, I've solved it using modules and by creating a reactiveValue (tmpFilters) that contains all selections (variable + condition + value). From it, I created a list with all filters (tmpList) and from it I created the proper filter (tmpListFilters) to use with subset.
This works because the final dataset is "constantly" being subset by this reactiveValue (the tmpFilters). At the beginning, tmpFilters is empty, so we get the original dataset. Whenever the user adds the first filter (and other filters after that), this reactiveValue gets updated and so does the dataset.
Here's the code for it:
library(shiny)
# > MODULE #####################################################################
## |__ MODULE UI ===============================================================
variablesUI <- function(id, number, LHSchoices) {
ns <- NS(id)
tagList(
fluidRow(
column(
width = 4,
selectInput(
inputId = ns("variable"),
label = paste0("Select Variable ", number),
choices = c("Choose" = "", LHSchoices)
)
),
column(
width = 4,
selectInput(
inputId = ns("condition"),
label = paste0("Select condition ", number),
choices = c("Choose" = "", c("==", "!=", ">", ">=", "<", "<="))
)
),
column(
width = 4,
numericInput(
inputId = ns("value.variable"),
label = paste0("Value ", number),
value = NA,
min = 0
)
)
)
)
}
## |__ MODULE SERVER ===========================================================
filter <- function(input, output, session){
reactive({
req(input$variable, input$condition, input$value.variable)
fullFilter <- paste0(
input$variable,
input$condition,
input$value.variable
)
return(fullFilter)
})
}
# Shiny ########################################################################
## |__ UI ======================================================================
ui <- fixedPage(
fixedRow(
column(
width = 5,
selectInput(
inputId = "userDataset",
label = paste0("Select dataset"),
choices = c("Choose" = "", ls("package:datasets"))
),
h5(""),
actionButton("insertBtn", "Add another filter")
),
column(
width = 7,
tableOutput("finalTable")
)
)
)
## |__ Server ==================================================================
server <- function(input, output) {
### \__ Get dataset from user selection ------------------------------------
originalDF <- reactive({
req(input$userDataset)
tmpData <- eval(parse(text = paste0("datasets::", input$userDataset)))
if (!class(tmpData) == "data.frame") {
stop("Please select a dataset of class data.frame")
}
tmpData
})
### \__ Get the column names -----------------------------------------------
columnNames <- reactive({
req(input$userDataset)
tmpData <- eval(parse(text = paste0("datasets::", input$userDataset)))
names(tmpData)
})
### \__ Create Reactive Filter ---------------------------------------------
tmpFilters <- reactiveValues()
### \__ First UI Element ---------------------------------------------------
### Add first UI element with column names
observeEvent(input$userDataset, {
insertUI(
selector = "h5",
where = "beforeEnd",
ui = tagList(variablesUI(paste0("var", 1), 1, columnNames()))
)
})
### Update Reactive Filter with first filter
filter01 <- callModule(filter, paste0("var", 1))
observe(tmpFilters[['1']] <- filter01())
### \__ Other UI Elements --------------------------------------------------
### Add other UI elements with column names and update the filter
observeEvent(input$insertBtn, {
btn <- sum(input$insertBtn, 1)
insertUI(
selector = "h5",
where = "beforeEnd",
ui = tagList(variablesUI(paste0("var", btn), btn, columnNames()))
)
newFilter <- callModule(filter, paste0("var", btn))
observeEvent(newFilter(), {
tmpFilters[[paste0("'", btn, "'")]] <- newFilter()
})
})
### \__ Dataset with Filtered Results --------------------------------------
resultsFiltered <- reactive({
req(filter01())
tmpDF <- originalDF()
tmpList <- reactiveValuesToList(tmpFilters)
if (length(tmpList) > 1) {
tmpListFilters <- paste(tmpList, "", collapse = "& ")
} else {
tmpListFilters <- unlist(tmpList)
}
tmpResult <- subset(tmpDF, eval(parse(text = tmpListFilters)))
tmpResult
})
### \__ Print the Dataset with Filtered Results ----------------------------
output$finalTable <- renderTable({
req(input$userDataset)
if (is.null(tmpFilters[['1']])) {
head(originalDF(), 10)
} else {
head(resultsFiltered(), 10)
}
})
}
#------------------------------------------------------------------------------#
shinyApp(ui, server)
# End
If you are looking for a data subsetting/filtering in Shiny Module :
filterData from package shinytools can do the work. It returns an expression as a call but it can also return the data (if your dataset is not too big).
library(shiny)
# remotes::install_github("ardata-fr/shinytools")
library(shinytools)
ui <- fluidPage(
fluidRow(
column(
3,
filterDataUI(id = "ex"),
actionButton("AB", label = "Apply filters")
),
column(
3,
tags$strong("Expression"),
verbatimTextOutput("expression"),
tags$br(),
DT::dataTableOutput("DT")
)
)
)
server <- function(input, output) {
x <- reactive({iris})
res <- callModule(module = filterDataServer, id = "ex", x = x, return_data = FALSE)
output$expression <- renderPrint({
print(res$expr)
})
output$DT <- DT::renderDataTable({
datatable(data_filtered())
})
data_filtered <- eventReactive(input$AB, {
filters <- eval(expr = res$expr, envir = x())
x()[filters,]
})
}
shinyApp(ui, server)
You can also use lazyeval or rlang to evaluate the expression :
filters <- lazyeval::lazy_eval(res$expr, data = x())
filters <- rlang::eval_tidy(res$expr, data = x())
You need to check for existing input values and use them if available:
# Prevent dynamic inputs from resetting
newInputValue <- "Option 1"
if (newInputId %in% names(input)) {
newInputValue <- input[[newInputId]]
}
# Define new input
newInput <- selectInput(newInputId, newInputLabel, c("Option 1", "Option 2", "Option 3"), selected=newInputValue)
A working version of the gist (without the reset problem) can be found here: https://gist.github.com/motin/0d0ed0d98fb423dbcb95c2760cda3a30
Copied below:
ui.R
library(shiny)
shinyUI(pageWithSidebar(
# Application title
headerPanel("Dynamically append arbitrary number of inputs"),
# Sidebar with a slider input for number of bins
sidebarPanel(
uiOutput("allInputs"),
actionButton("appendInput", "Append Input")
),
# Show a plot of the generated distribution
mainPanel(
p("This shows how to add an arbitrary number of inputs
without resetting the values of existing inputs each time a new input is added.
For example, add a new input, set the new input's value to Option 2, then add
another input. Note that the value of the first input does not reset to Option 1.")
)
))
server.R
library(shiny)
shinyServer(function(input, output) {
output$allInputs <- renderUI({
# Get value of button, which represents number of times pressed (i.e. number of inputs added)
inputsToShow <- input$appendInput
# Return if button not pressed yet
if(is.null(inputsToShow) || inputsToShow < 1) return()
# Initialize list of inputs
inputTagList <- tagList()
# Populate the list of inputs
lapply(1:inputsToShow,function(i){
# Define unique input id and label
newInputId <- paste0("input", i)
newInputLabel <- paste("Input", i)
# Prevent dynamic inputs from resetting
newInputValue <- "Option 1"
if (newInputId %in% names(input)) {
newInputValue <- input[[newInputId]]
}
# Define new input
newInput <- selectInput(newInputId, newInputLabel, c("Option 1", "Option 2", "Option 3"), selected=newInputValue)
# Append new input to list of existing inputs
inputTagList <<- tagAppendChild(inputTagList, newInput)
})
# Return updated list of inputs
inputTagList
})
})
(The solution was guided on Nick's hints in the original gist from where you got the code of the promising solution)
I have read this (How do I make the choices in radioButtons reactive in Shiny?) which shows me how to update radioButtons in a reactive way. However, when I try and update two sets of buttons from the same data, only one set renders. Example:
Server:
# Create example data
Wafer <- rep(c(1:3), each=3)
Length <- c(1,1,2,1,1,1,3,5,1)
Width <- c(3,1,6,1,1,1,1,1,6)
dd <- data.frame(Wafer, Length, Width)
shinyServer(function(input, output, session){
# Create reactive dataframe to store data
values <- reactiveValues()
values$df <- data.frame()
# Get Lengths and Widths of wafer from user input
a <- eventReactive(input$do, {
subset(dd, Wafer %in% input$wafer, select = Length:Width)
})
# Update reactive data frame will all Lengths and Widths that have been selected by the user input
observe({
if(!is.null(a())) {
values$df <- rbind(isolate(values$df), a())
}
})
output$wl <- renderDataTable({ a() })
# Update radio buttons with unique Length and Widths stored in values$df
# Which ever "observe" I put first in the code, is the one which updates
# the radio buttons. Cut and paste the other way round and "width"
# updates but not "length" radio buttons
observe({
z <- values$df
updateRadioButtons(session, "length", choices = unique(z$Length), inline=TRUE)
})
observe({
z <- values$df
updateRadioButtons(session, "width", choices = unique(z$Width), inline=TRUE)
})
})
ui:
library(markdown)
shinyUI(fluidPage(
titlePanel("Generic grapher"),
sidebarLayout(
sidebarPanel(
numericInput("wafer", label = h3("Input wafer ID:"), value = NULL),
actionButton("do", "Search wafer"),
radioButtons("length", label="Length", choices=""),
radioButtons("width", label="Width", choices = "")
),
mainPanel(
dataTableOutput(outputId="wl")
)
)
)
)
In the above, radiobuttons do update but only for the first set of buttons in order of code i.e. above "length" updates but "width" doesn't. If I write them in reverse, "width" updates but "length" doesn't. Do I need to define a new session maybe?
It turns out that:
"it's because a JS error occurs if the choices argument isn't a
character vector."
I have posted an issue on Shiny's Github:
https://github.com/rstudio/shiny/issues/1093
This can be resolved by:
To fix your problem, you can either convert your choices to characters
using as.character or set selected to a random string such as "".
See:
Update two sets of radiobuttons - shiny