I built an app in R Shiny which uses time series data that excludes many dates. Within the app a user can select a new dataset, so the dates available will change. I'm using updateDateInput to update the dateInput selector. However, updateDateInput does not seem to allow the datesdisabled function?
Here is a reprex:
library(shiny)
# Sample 3 dates and disable the rest
my_dates <- sample(seq(as.Date('2021-01-01'), as.Date('2021-01-31'), by = "day"), 3)
date_choices <- seq.Date(from = min(my_dates), to = max(my_dates), by = 1)
dates_disabled <- date_choices[!(date_choices %in% my_dates)]
ui <- fluidPage(
dateInput("date", "Select Date",
min = min(date_choices),
max = max(date_choices),
value = max(date_choices),
datesdisabled = dates_disabled),
actionButton("click", "Click Me")
)
server <- function(input, output, session) {
observeEvent(input$click, {
my_dates <- sample(seq(as.Date('2021-01-01'), as.Date('2021-01-31'), by = "day"), 3)
date_choices <- seq.Date(from = min(my_dates), to = max(my_dates), by = 1)
dates_disabled <- date_choices[!(date_choices %in% my_dates)]
updateDateInput(
session,
"date",
min = min(date_choices),
max = max(date_choices),
value = max(date_choices),
datesdisabled = dates_disabled)
})
}
shinyApp(ui, server)
When the button is clicked and the updateDateInput runs, I get this error:
Warning: Error in updateDateInput: unused argument (datesdisabled =
dates_disabled)
I guess there is the option of changing the date to a character and using selectInput? But then I don't get the nice calendar!
You are right, the datesdisabled argument is not available in the update function. You can change the disabled dates by moving the UI declaration into the server and feed it to the client with renderUI().
The sample does not declare the date input in the UI but a uiOutput("date"). The server can dynamically create the dateInput using the datesdisabled argument. This way you can change the disabled dates.
The example will pick only 3 enabled dates after every button click.
# Reprex: The actual implementation of this uses data from a file:
# 1. Reads data file before ui and server are established
# 2. Does a bunch of calculations
# 3. Identifies dates that exist in data file
# 4. The data file is getting updated in the background from another application.
# 5. Allows user to click the button to update the data file. Reprex shows code
# that is used to update the date selector based on new data read. Dates are
# random in reprex, but would come from data file in actual code.
# Sample 3 dates and disable the rest - actual code reads data file here
# and parses out dates that exist in records
my_dates <- seq(as.Date('2021-01-01'), as.Date('2021-01-31'), by = "day")
date_choices <- sample(my_dates, 31-3)
ui <- fluidPage(
uiOutput("date"), textOutput("disabled"),
actionButton("click", "Click Me")
)
server <- function(input, output, session) {
dates_disabled <- reactiveVal(NULL)
# Init 'dates_disabled()' once before Shiny flushes the reactive system with callback,
# using date_choices that exist in original data set
onFlush(fun = function () {dates_disabled(date_choices)}, once = TRUE)
# dateInput widget
output$date <- renderUI({
maxDate <- as.Date(max(setdiff(my_dates, dates_disabled())),
origin = "1970-01-01")
dateInput(input = "date",
label = "Select Date",
min = min(my_dates),
max = max(my_dates),
value = maxDate,
datesdisabled = dates_disabled())
})
# This output makes it easier to test if it works by showing the enabled dates
output$disabled <- renderPrint({
req(dates_disabled()) # only run this when 'dates_disabled' is initialized properly
Enabled <- as.Date(setdiff(seq(as.Date('2021-01-01'), as.Date('2021-01-31'), by = "day"),
dates_disabled()),
origin = '1970-01-01')
paste("Enabled:", paste(Enabled[order(Enabled)], collapse = ", "))
})
# Set new datesdisabled on button click
# Actual code would read updated data file and parse new dates
observeEvent(input$click, {
SelectedDates <- sample(my_dates, 31-3)
dates_disabled( SelectedDates )
})
}
shinyApp(ui, server)
Related
I currently have the following shiny app and the intention is to add the text input as a new row into the dataset. Currently, the new text input is overriding the old input instead of appending to the end. I understand that's happening because I'm referring to df in the eventReactive, which is the originally seeded empty data frame, but my various attempts around that have led to errors.
Does somebody know of a way to fix that? Bonus if there is a way to clear the text input box after hitting the submit button.
library(shiny)
colClasses = c("factor", "numeric")
col.names = c("Player", "1")
df <- read.table(text = "",
colClasses = colClasses,
col.names = col.names)
ui <- fluidPage(
# Application title
titlePanel("Random Draft Order"),
# Sidebar with a slider input for number of bins
sidebarLayout(
sidebarPanel(
textInput("AddPlayer",
"Add Player",
""),
actionButton("submit", ("Submit"))
),
# Show a plot of the generated distribution
mainPanel(
tableOutput("racingbars")
)
)
)
server <- function(input, output) {
actionButton("submit", ("Submit"))
FinalData = eventReactive(input$submit,{
df = rbind(df, data.frame("Player" = input$AddPlayer, "X1" = ""))
})
output$racingbars <- renderTable({
FinalData()
})
}
# Run the application
shinyApp(ui = ui, server = server)
You could try this creating a reactiveVal that will store the data.
First, you can initialize your data.frame df as a reactiveVal:
df <- reactiveVal(data.frame(Player = character(),
X1 = character()))
To update the reactiveVal you can use:
df(new_dat)
And to reference the data later on, use:
df()
In your example, an observeEvent can be triggered by your submit button. When that happens, you can add your row of data to the reactiveVal data.frame similar to what you have done. The output can then point to the reactiveVal data.frame to display the data.
I also added updateTextInput to the observeEvent to clear the text input after the submit button is pressed. Note this requires session in the server function declaration.
server <- function(input, output, session) {
df <- reactiveVal(data.frame(Player = character(),
X1 = character()))
observeEvent(input$submit, {
new_dat <- rbind(df(), data.frame(Player = input$AddPlayer, X1 = ""))
df(new_dat)
updateTextInput(session, "AddPlayer", value = "")
})
output$racingbars <- renderTable({
df()
})
}
In my tiny Shiny app I am asking the user: how many time periods do you want to cut your time series into? For example, the user selects 3.
I want to use this input to take a fixed vector of dates and make it possible for the user the select from it the desired last date of Time Period 1 (in select box 1), and Time Period 2 (in select box 2). (The last date for time period 3 will be the very last date, so I don't need to ask).
I am not sure how to do it. I understand that because I don't know the desired number of time periods in advance, I have to create a list. But how do I then collect the input from those select boxes?
Thanks a lot!
library(shiny)
### UI #######################################################################
ui = shinyUI(fluidPage(
titlePanel("Defining time periods"),
# Sidebar:
sidebarLayout(
sidebarPanel(
# Slider input for the number of time periods:
numericInput("num_periodsnr", label = "Desired number of time periods?",
min = 1, max = 10, value = 2),
uiOutput("period_cutpoints")
),
# Show just the number of periods so far.
mainPanel(
textOutput("nr_of_periods")
)
)
))
### SERVER ##################################################################
server = shinyServer(function(input, output, session) {
library(lubridate)
output$nr_of_periods <- renderPrint(input$num_periodsnr)
# Define our dates vector:
dates <- seq(ymd('2016-01-02'), ymd('2017-12-31'), by = '1 week')
# STUCK HERE:
# output$period_cutpoints<-renderUI({
# list.out <- list()
# for (i in 1:input$num_periodsnr) {
# list.out[[i]] <- renderPrint(paste0("Sometext", i), ,
# )
# }
# return(list.out)
# })
})
# Run the application
shinyApp(ui = ui, server = server)
This is similar to a question I asked and subsequently worked out an answer to here. The big changes are (predictably) in the server.
Nothing needs to change in the UI, but as you'll see below I've included another textOutput so that you can see the dates you end up selecting, and I've also added an actionButton, which I'll explain later.
The server function has a couple additions, which I'll describe first and then put together at the end. You're right that you need to create a list of input objects inside the renderUI, which you can do through lapply. At this step, you're creating as many selectInputs as you'll have cutpoints, minus one because you say you don't need the last:
output$period_cutpoints<-renderUI({
req(input$num_periodsnr)
lapply(1:(input$num_periodsnr-1), function(i) {
selectInput(inputId=paste0("cutpoint",i),
label=paste0("Select cutpoint for Time Period ", i, ":"),
choices=dates)
})
})
Next, you'll need to access the values selected in each, which you can do in the same way, using a reactiveValues object you create first, and assign the new values to it. In my version of this problem, I couldn't figure out how to get the list to update without using an actionButton to trigger it. Simple reactive() or observe() doesn't do the trick, but I don't really know why.
seldates <- reactiveValues(x=NULL)
observeEvent(input$submit, {
seldates$x <- list()
lapply(1:(input$num_periodsnr-1), function(i) {
seldates$x[[i]] <- input[[paste0("cutpoint", i)]]
})
})
Full working app code then looks like this:
library(shiny)
ui = shinyUI(fluidPage(
titlePanel("Defining time periods"),
sidebarLayout(
sidebarPanel(
numericInput("num_periodsnr", label = "Desired number of time periods?",
min = 1, max = 10, value = 2),
uiOutput("period_cutpoints"),
actionButton("submit", "Submit")
),
mainPanel(
textOutput("nr_of_periods"),
textOutput("cutpoints")
)
)
))
server = shinyServer(function(input, output, session) {
library(lubridate)
output$nr_of_periods <- renderPrint(input$num_periodsnr)
dates <- seq(ymd('2016-01-02'), ymd('2017-12-31'), by = '1 week')
output$period_cutpoints<-renderUI({
req(input$num_periodsnr)
lapply(1:(input$num_periodsnr-1), function(i) {
selectInput(inputId=paste0("cutpoint",i),
label=paste0("Select cutpoint for Time Period ", i, ":"),
choices=dates)
})
})
seldates <- reactiveValues(x=NULL)
observeEvent(input$submit, {
seldates$x <- list()
lapply(1:(input$num_periodsnr-1), function(i) {
seldates$x[[i]] <- input[[paste0("cutpoint", i)]]
})
})
output$cutpoints <- renderText({as.character(seldates$x)})
})
shinyApp(ui = ui, server = server)
you can make the boxes dynamically inside an lapply and send them as 1 output object to the ui
require("shiny")
require('shinyWidgets')
ui = shinyUI(fluidPage(
titlePanel("Defining time periods"),
# Sidebar:
sidebarLayout(
sidebarPanel(
# Slider input for the number of time periods:
numericInput("num_periodsnr", label = "Desired number of time periods?",
min = 1, max = 10, value = 2),
uiOutput("period_cutpoints")
),
# Show just the number of periods so far.
mainPanel(
textOutput("nr_of_periods")
)
)
))
# Define server logic ----
server <- function(session, input, output) {
output$period_cutpoints<- renderUI({
req(input$num_periodsnr > 0)
lapply(1:input$num_periodsnr, function(el) {
airDatepickerInput(inputId = paste('PeriodEnd', el, sep = ''), label = paste('Period End', el, sep = ' '), clearButton = TRUE, range = F, update_on = 'close')
})
})
}
# Run the app ----
shinyApp(ui = ui, server = server)
Since you did not provide a dataset to apply the inputs on, and I don't know what date ranges your data has, I did not add code to set min/max on the date pickers, and not sure what kind of code to provide for you to use the data. You would need to write something to put them in a list indeed
values <- reactiveValues(datesplits = list(),
previous_max = 0)
observeEvent(input$num_periodsnr, {
if(input$num_periodsnr > values$previous_max) {
lapply(values$previous_max:input$num_periodsnr, function(el) {
observeEvent(input[[paste(paste('PeriodEnd', el, sep = '')]], {
values$datesplits[el] <- input[[paste(paste('PeriodEnd', el, sep = '')]]
})
values$previous_max <- max(values$previous_max, input$num_periodsnr)
})
}
})
and then use the list of dates for whatever you need to do with them I think.
I use the trick with run lapenter code hereply from previous_max to input$num_periodsnr if(input$num_periodsnr > values$previous_max){} to avoid the problem you create when you repeatedly create observers for the same input element. Whereas ui elements are overwritten when created in a loop, observeEvents are made as copies, so every time your loop fires, you make another copy of observers 1:n. This results in all copies firing every time, until you have a million observers all firing, creating possible strange bugs, unwanted effects and loss of speed.
I am trying to get my head around RShiny by building what I thought would be a pretty simple but useful app. What I would like the app to do is allow the user to input some data made up of dates, numeric, and characters. Then when the user presses a save/submit button this data is appended onto a pre-existing data frame made up of previous recordings and over write the .csv of these recordings. I would also like this data to be presented to the users in the form of a table in the UI which is updated as soon as the user presses the save/submit button.
I have managed to get the majority of the UI features working, however, I am having real difficulty 1) saving the data in the correct format and 2) updating the table displayed on the UI. My current method of saving the data involves creating an isolated list of the input values and rbinding this to the original data frame. However, the formats of the input values appear to all revert to factors which is especially problematic with the date as the output is meaningless as far as I am aware. In terms of updating the UI I have attempted to create a reactive object out of the data frame and use this object as the data displayed in renderDataTable but this approach seems to have no affect.
I have created a dummy minimal example below.
Thank you for all your help in advance.
require(shiny)
require(tidyverse)
require(lubridate)
require(plotly)
#Would import the data in reality using read.csv() but to allow for an easily
#recreated example I made a dummy data frame
DateRecorded <- dmy(c("10/07/2018", "11/07/2018", "13/07/2018"))
Value <- c(1, 2, 3)
Person <- c("Bob", "Amy", "Charlotte")
df <- data.frame(DateRecorded, Value, Person)
ui <- fluidPage(
#UI Inputs
dateInput(inputId = "SessionDate", label = "Date Recorded", format = "dd-mm-yyyy"),
numericInput(inputId = "SessionValue", label = "Value Recorded", value = 0),
textInput(inputId = "SessionPerson", label = "Person Recording"),
actionButton(inputId = "Save", label = "Save"),
#UI Outputs
dataTableOutput("TheData"),
textOutput("TotRecorded")
)
# Define server logic required to draw a histogram
server <- function(input, output) {
#When "Save" is pressed should append data to df and export
observeEvent(input$Save, {
newLine <- isolate(c(input$SessionDate, input$SessionValue, input$SessionPerson))
isolate(df <- rbind(as.matrix(df), unlist(newLine)))
write.csv(df, "ExampleDataFrame.csv") #This export works but the date is saved incorrectly as "17729" not sure why
})
#Create a reactive dataset to allow for easy updating
ReactiveDf <- reactive({
df
})
#Create the table of all the data
output$TheData <- renderDataTable({
ReactiveDf()
})
#Create the totals print outs
output$TotRecorded <- renderPrint({
data <- ReactiveDf()
cat(nrow(data))
})
}
# Run the application
shinyApp(ui = ui, server = server)
I made some small tweaks.
You do not need isolate in the body of the observeEvent; it does not take a reactive dependency to values in its body.
I made ReactiveDf a reactiveVal instead of a reactive. This allows you to write its value from inside an observeEvent.
Instead of rowbinding a matrix and unlisting a list - the issue is that all the new values are parsed to the same class, while they are obviously not - it might be easier to rbind two data.frames, so create the newLine with newLine <- data.frame(DateRecorded = input$SessionDate, Value = input$SessionValue, Person = input$SessionPerson)
So a working example would be as shown below. Hope this helps!
require(shiny)
require(tidyverse)
require(lubridate)
require(plotly)
#Would import the data in reality using read.csv() but to allow for an easily
#recreated example I made a dummy data frame
DateRecorded <- dmy(c("10/07/2018", "11/07/2018", "13/07/2018"))
Value <- c(1, 2, 3)
Person <- c("Bob", "Amy", "Charlotte")
df <- data.frame(DateRecorded, Value, Person)
ui <- fluidPage(
#UI Inputs
dateInput(inputId = "SessionDate", label = "Date Recorded", format = "dd-mm-yyyy"),
numericInput(inputId = "SessionValue", label = "Value Recorded", value = 0),
textInput(inputId = "SessionPerson", label = "Person Recording"),
actionButton(inputId = "Save", label = "Save"),
#UI Outputs
dataTableOutput("TheData"),
textOutput("TotRecorded")
)
# Define server logic required to draw a histogram
server <- function(input, output) {
#When "Save" is pressed should append data to df and export
observeEvent(input$Save, {
newLine <- data.frame(DateRecorded = input$SessionDate, Value = input$SessionValue, Person = input$SessionPerson)
df <- rbind(df, newLine)
ReactiveDf(df) # set reactiveVal's value.
write.csv(df, "ExampleDataFrame.csv") #This export works but the date is saved incorrectly as "17729" not sure why
})
#Create a reactive dataset to allow for easy updating
ReactiveDf <- reactiveVal(df)
#Create the table of all the data
output$TheData <- renderDataTable({
ReactiveDf()
})
#Create the totals print outs
output$TotRecorded <- renderPrint({
data <- ReactiveDf()
cat(nrow(data))
})
}
# Run the application
shinyApp(ui = ui, server = server)
For testing, please upload a csv file with 1+ column that can be converted to Date in the app.
My app generates date range inputs (input$daterange) dynamically depending on the date columns selected. I'd like to validate each input$daterange from 1 to n (the length of dt$datecols) to make sure the user won't select start date earlier than the oldest date, and end date later than the latest date in the corresponding column. I use lapply on observeEvent to do that.
For ease of debugging, I pass the value of input$daterange(i) to reactive values dt$daterange(i) and print dt$daterange1 (the first date range's value) to the console rendered to check whether the it is smaller or bigger than the min and max of the corresponding date column, as I did in the lapply function. Supposedly, when the check result is FALSE, lappy function shall display an error message warning the user the start or end date is not valid, which, however doesn't work. Please find my code below, please check the comments for explanation of problem.
library("shiny")
library("DT") # Datatable
library("rsconnect") # deploy to shinyapps.io
library("shinyjs") # use toggle button from shinyJS pacakage
library("stats")
library("zoo") # to use as.Date() on numeric value
ui <- fluidPage(
fluidRow(
column(4,
# file upload div
fileInput("file", "Choose a file",
accept=c(
"text/csv",
"text/comma-separated-values,text/plain",
".csv"
)),
# show ui for upload file control
uiOutput("ui")
),
column(4,
# no choices before a file uploaded
uiOutput("columnscontrol")
)
),
hr(),
fluidRow(
column(4,
uiOutput("datecolscontrol")),
column(6,
uiOutput("daterangescontrol"))
),
hr(),
dataTableOutput("datatbl"),
# print console for debugging (delete after completion)
verbatimTextOutput("print_con")
) #end of fluidPage (ui)
# server
server <- function(input, output, session) {
#########################################################
# upload & datatable output
#########################################################
# create dataset reactive objects
dt <- reactiveValues()
# reset all uis upon new file upload
observeEvent(input$file, {
# reset reactive values
dt$data = NULL
dt$df = NULL
dt$cols = NULL
dt$rows = NULL
dt$summary = NULL
dt$colchoices = NULL
dt$datecols = NULL
# remove columns div and datecols div when a new file uploaded
removeUI(selector = "div#columns_div")
removeUI(selector = "div#datecols_div")
# remove all <div> elements indside <div>#daterangescontrol:
removeUI(selector = "div#daterangescontrol div")
# generate upload file control ui once file uploaded
output$ui <- renderUI({
actionButton("readF", "Update")
})
})
# when read file button pressed:
observeEvent (input$readF, {
# store data to dt$data
file <- input$file
dt$data <- read.csv(file$datapath, header = TRUE)
# render columnscontrol
output$columnscontrol <- renderUI({
# get the col names of the dataset and assign them to a list
dt$colchoices <- mapply(list, names(dt$data))
# render column group checkbox ui after loading the data
# tags#div has the advantage that you can give it an id to make it easier to reference or remove it later on
tags$div(id = "columns_div",
checkboxGroupInput("columns", "", choices = NULL, selected = NULL))
})
# render div containing #datecols under datecolscontrol
output$datecolscontrol <- renderUI({
tags$div(id = "datecols_div",
selectInput("datecols", "Filter data by dates):", choices = NULL, multiple = TRUE, selected = NULL))
})
})
# update columns choices when dt$choices is ready
observeEvent(dt$colchoices, {
updateCheckboxGroupInput(session, "columns", "Select Columns:", choices = dt$colchoices, selected = dt$colchoices)
})
# the other reactivity on dt$cols is input$file (when new file uploaded, dt$data and dt$cols set to NULL)
# so that the following line set apart the reactivity of input$columns on dt$cols
observeEvent(input$columns, {
dt$cols <- input$columns
dt$df <- dt$data[dt$cols]
}, ignoreNULL = FALSE)
# upon any change of dt$df
observeEvent(dt$df, {
f <- dt$df
# render output$datatbl
output$datatbl <- DT::renderDataTable(
f, rownames = FALSE,
filter = 'top',
options = list(autoWidth = TRUE)
)
# update datecols choices with those columns can be converted to Date only:
dt$date_ok = sapply(f, function(x) !all(is.na(as.Date(as.character(x), format = "%Y-%m-%d"))))
dt$datecolchoices = colnames(f[dt$date_ok])
updateSelectInput(session, "datecols", "Filter data by dates:", choices = dt$datecolchoices, selected = NULL)
}, ignoreNULL = FALSE)
# whenver columns convertable to date updated to choices of input$datecols, convert the columns to Date in the dataset
observeEvent(dt$datecolchoices, {
dt$df[dt$date_ok] = lapply(dt$df[dt$date_ok], function(x) as.Date(as.character(x)))
})
# generate daterange uis per selected input$datecols
observeEvent(input$datecols, {
dt$datecols = input$datecols
dt$datecols_len = length(dt$datecols)
# render daterange ui(s) per selected datecols
output$daterangescontrol <- renderUI({
# when input$datecols is NULL, no daterange ui
if ( is.null(input$datecols) ) { return(NULL) }
# otherwise
else {
D = dt$df[dt$rows, dt$cols]
output = tagList()
for (i in 1:dt$datecols_len) {
output[[i]]= tagList()
output[[i]][[1]] = tags$div(id = paste("dateranges_div", i, sep = "_"),
dateRangeInput(paste0("daterange", i),
paste("Date range of", dt$datecols[[i]]),
start = min(D[[dt$datecols[[i]]]]),
end = max(D[[dt$datecols[[i]]]])))
}
# return output tagList() with ui elements
output
}
}) # end of renderUI
}, ignoreNULL = FALSE)
# loop observeEvent to check whether each input$daterange is valid:
#### why I can't just call lapply() without observe() as suggested in this post:
#### https://stackoverflow.com/questions/40038749/r-shiny-how-to-write-loop-for-observeevent
observe({
lapply( X = 1:dt$datecols_len,
FUN = function(i) {
observeEvent(input[[paste0("daterange", i)]], {
# update reactive values to test whether this loop is working
dt[[paste0("range",i)]] = input[[paste0("daterange", i)]]
range = dt[[paste0("range",i)]]
req(range)
#########################################
## CODE BLOCK WITH PROBLEM!!!
#########################################
# Why the following doesn't work, when I pick a date earlier than the oldest date
# no error message shows!
shiny::validate(
need( range[[1]] >= min(dt$df[[dt$datecols[[i]]]]), "The start date cannot be earlier than the oldest date!"),
need( range[[2]] <= max(dt$df[[dt$datecols[[i]]]]), "The end date cannot be later than the latest date!")
)
})
}
) # end of lapply
})
# rows displayed in input$datatbl (the rendered data table)
observeEvent( input$datatbl_rows_all, {
dt$rows <- input$datatbl_rows_all
})
#########################################################
# print console
#########################################################
output$print_con <- renderPrint({
req(input$daterange1)
list(
# to verify whether the observeEvent loop is working for input validation
# I used dt$range1 to check the first (input$daterange1) against the date range of the corresponding column of the dataset.
# It's supposed that when the check result is FALSE (either by selecting a start date earlier than the oldest date or selecting an end date later than the latest date),
# the code block with problem shall prompt an error message to warn the user
min(dt$range1) >= min(dt$df[[dt$datecols[[1]]]]),
max(dt$range1) <= max(dt$df[[dt$datecols[[1]]]])
)
})
} # end of shiny server function
shinyApp(ui = ui, server = server)
This may not be the exact answer you are looking for but I think it may simplify things. I would simply order your date column which would allow you to select the oldest and newest date. Then set your start and end dates to those two values (see ?dateRangeInput). Lubridate is also a great package for working with dates
I think the problem maybe related to the format of your dates.
please look at this post:
R: Shiny dateRangeInput format
you may need to use
format(range[[1]])
I am currently building a shiny app and trying to get a set of dates to render as character strings to the end user, while still keeping their date format when invoked in the server side code.
There might be a simple solution here, but unsure how to get the dates to format in the selectInput dropdown. In my actual use case, using a date slider isn't ideal as the dates do not follow a common interval.
Reproducible example below:
# setup
require(lubridate)
test.dates <- as.Date(c('2014-06-01', '2014-07-01', '2014-08-01',
'2014-09-01', '2014-10-01', '2014-11-01',
'2014-12-01', '2015-01-01','2015-02-01',
'2015-03-01', '2015-04-01'))
test.names <- as.character(paste0(month(test.dates, label = T), ' ',
year(test.dates)))
test.df <- data.frame(date = test.dates)
row.names(test.df) <- test.names
# shiny
server <- function(input, output) {
output$table <- renderTable(test.df)
}
ui <- fluidPage(
sidebarLayout(
sidebarPanel(
selectInput("test", label = "DATE FORMAT TEST:", choices =
test.df, selected = test.df[1,])
),
mainPanel(tableOutput('table'))
)
)
shinyApp(ui = ui, server = server)
I believe you will find it much easier to pass around character objects than date objects in Shiny. I would simply use the direct character values of your dates and whenever you need them to be date objects in your subsequent analysis explicitly convert to a date object. The following provides an example where both the dropdown and table have the character formatted dates.
require(lubridate)
myDates <- c('2014-06-01', '2014-07-01', '2014-08-01',
'2014-09-01', '2014-10-01', '2014-11-01',
'2014-12-01', '2015-01-01','2015-02-01',
'2015-03-01', '2015-04-01')
test.names <- as.character(paste0(lubridate::month(test.dates, label=TRUE), ' ',
year(test.dates)))
test.df <- data.frame(date = myDates)
row.names(test.df) <- test.names
# shiny
server <- function(input, output) {
output$table <- renderTable(test.df)
}
ui <- fluidPage(
sidebarLayout(
sidebarPanel(
selectInput("test", label = "DATE FORMAT TEST:", choices =
myDates, selected = myDates[1])
),
mainPanel(tableOutput('table'))
)
)
shinyApp(ui = ui, server = server)