#NumericInput

[mdmankgt-blog]

A

 

Create a submit button

submitButton(text = "Apply Changes", icon = NULL, width = NULL)

Arguments

text

Button caption

icon

Optional icon() to appear on the button

width

The width of the button, e.g. '400px', or '100%'; see validateCssUnit().

Description

Create a submit button for an app. Apps that include a submit button do not automatically update their outputs when inputs change, rather they wait until the user explicitly clicks the submit button. The use of submitButton is generally discouraged in favor of the more versatile actionButton() (see details below).

Value

A submit button that can be added to a UI definition.

Details

Submit buttons are unusual Shiny inputs, and we recommend using actionButton() instead of submitButton when you want to delay a reaction. See this article for more information (including a demo of how to "translate" code using a submitButton to code using an actionButton).

In essence, the presence of a submit button stops all inputs from sending their values automatically to the server. This means, for instance, that if there are two submit buttons in the same app, clicking either one will cause all inputs in the app to send their values to the server. This is probably not what you'd want, which is why submit button are unwieldy for all but the simplest apps. There are other problems with submit buttons: for example, dynamically created submit buttons (for example, with renderUI() or insertUI()) will not work.

See also

Other input elements: actionButton,checkboxGroupInput, checkboxInput, dateInput, dateRangeInput, fileInput,numericInput, passwordInput,radioButtons, selectInput,sliderInput, textAreaInput, textInput, varSelectInput

Examples

if (interactive()) { shinyApp( ui = basicPage( numericInput("num", label = "Make changes", value = 1), submitButton("Update View", icon("refresh")), helpText("When you click the button above, you should see", "the output below update to reflect the value you", "entered at the top:"), verbatimTextOutput("value") ), server = function(input, output) { # submit buttons do not have a value of their own, # they control when the app accesses values of other widgets. # input$num is the value of the number widget. output$value <- renderPrint({ input$num }) } ) }

[parthamajumdar]

Maintaining Data on Excel is becoming very difficult as the data is growing. Excel has become very slow as a result. Though my Mac has 16 GB of RAM, it takes between 2-3 minutes to save my Excel Workbook. So, I started making Applications and storing the data in Database. I have taken space in IBM Cloud for DB2 Database.

I spent the 2 days to make my Price Index Application. I programmed on Shiny for the first time and so had to frequently refer for syntax, etc. Shiny was my choice as it is easy to integrate R Language code in Shiny and present the output as a web page. I hosted my application on the R Studio server.

The application can be found at this URL – PriceIndex. It is also available from within this site under the Menu Option “Applications“.

The graphs do not make much sense now as the data set is small. However, as the data grows, I will add predictive analytics. Then, the site will recommend how prices of Stock Market and Gold will behave in future.

Also, I will next make this available as a Mobile App.

While writing this application, I came up some code snippets which can be useful for others trying to write applications in Shiny.

I used the following libraries to create this application. Note the syntax to installing the package if it is already not present in the environment.

if("shiny" %in% rownames(installed.packages()) == FALSE) {install.packages("shiny")} library(shiny) if("ggplot2" %in% rownames(installed.packages()) == FALSE) {install.packages("ggplot2")} library(ggplot2) if("scatterplot3d" %in% rownames(installed.packages()) == FALSE) {install.packages("scatterplot3d")} library(scatterplot3d) if("lubridate" %in% rownames(installed.packages()) == FALSE) {install.packages("lubridate")} library(lubridate)

I used RJDBC to connect to the DB2 database on IBM Cloud. To connect to the database, the following code was used. Note the use of dyn.load. It is absolutely required.

Also, find the path of db2jcc4.jar on your machine and provide the full path.

dyn.load('/Library/Java/JavaVirtualMachines/jdk-9.0.4.jdk/Contents/Home/lib/server/libjvm.dylib') library(rJava) library(RJDBC) #Enter the values for you database connection dsn_driver = "com.ibm.db2.jcc.DB2Driver" dsn_database = "BLUDB" # e.g. "BLUDB" dsn_hostname = "dashdb-entry-yp-lon02-01.services.eu-gb.bluemix.net" dsn_port = "50000" dsn_protocol = "TCPIP" dsn_uid = "<em>user_id</em>" dsn_pwd = "<em>password</em>" jcc = JDBC("com.ibm.db2.jcc.DB2Driver", "<em>path</em>/db2jcc4.jar"); jdbc_path = paste("jdbc:db2://", dsn_hostname, ":", dsn_port, "/", dsn_database, sep=""); conn = dbConnect(jcc, jdbc_path, user=dsn_uid, password=dsn_pwd) query = "SELECT * FROM PRICEINDEX"; rs = dbSendQuery(conn, query); df = fetch(rs, -1);

I use a validation to allow entry of prices as long as they are within 3 standard deviations from the mean. The maximum and minimum allowed values can be calculated as follows.

number_of_sd <- 3 minGold <- max(1, round(mean(df$Gold) - (number_of_sd * sd(df$Gold)))); maxGold <- round(mean(df$Gold) + (number_of_sd * sd(df$Gold)));

The code to create the User Interface is as follows. A number of layouts and panels have been used. You can check the way to use them. Also, notice that controls have been created at runtime using uiOutput() function.

ui <- fluidPage( # Application title fluidRow( column(width = 12, align = "center", h1("Price Index")) ), fluidRow( column(width = 12, align = "center", h3("Maintained by Partha Majumdar")) ), fluidRow( column(width = 12, align = "center", h6(paste("Last Updated On:", "21-March-2018"))) ), navbarPage("Price Index", tabPanel("Graphs", # Sidebar sidebarLayout( sidebarPanel( radioButtons("rbGraphType", h4("Select Graph Type"), choices = list("Bar Plot" = 1, "Histogram" = 2, "Line Chart" = 3, "Box Plot" = 4, "Scatter Plot" = 5, "Scatter Plot 3D" = 6), selected = 4), uiOutput("optionSelect"), uiOutput("subOptionSelect"), uiOutput("scatterSelect2"), uiOutput("scatterSelect3") ), # Show a plot mainPanel( plotOutput("dispPlot") ) ) ), tabPanel("Price Movement", # Sidebar sidebarLayout( sidebarPanel( fluidRow(column(12, span(h5("Prices of Potato, Tomato, Onion coming DOWN is GREEN, going UP is RED, no mevement is WHITE."),align='Center')), span(h5("Price of Gold going UP is GREEN, coming DOWN is RED, no movement is WHITE."),align='Center'), span(h5("Index of Sensex and NIFTY going UP is GREEN, coming DOWN is RED, no movement is WHITE."),align='Center') ), selectInput("ddlYearHTML", h4("Select Year"), factor(df$Year), selected = TRUE, multiple = FALSE), selectInput("ddlMonthHTML", h4("Select Month"), factor(df$MonthName)), fluidRow(column(12, span(h4("Prices of Potato, Tomato, Onion recorded at Shampura, Bangalore - 560032, India."),align='Center') )) ), # Show a plot mainPanel( fluidRow(column(12, htmlOutput("dispHTML") )) ) ) ), tabPanel("Data", # Sidebar sidebarLayout( sidebarPanel( radioButtons("rbDataType", h4("Select the Data to View"), choices = list("Raw Data" = 1, "Normalised Data" = 2), selected = 1), selectInput("ddlYear", h4("Select Year"), factor(df$Year), selected = TRUE, multiple = FALSE), selectInput("ddlMonth", h4("Select Month"), factor(df$MonthName)), downloadButton('downloadData', 'Download'), fluidRow(column(12, span(h4("Prices of Potato, Tomato, Onion recorded at Shampura, Bangalore - 560032, India."),align='Center') )) ), # Show a plot mainPanel( tableOutput("dispData") ) ) ), navbarMenu("Data Maintenance", tabPanel("Add", fluidRow( column(3, h5("Enter the Date for which you want to record the Prices. By default, today's date id entered. Date cannot be more than Today's Date."), h5("Enter the prices of the different commodities. By detault, the previous days prices are displayed."), h5("Click on Save button to save the prices") ), column(9, fluidRow(column(6, dateInput("diDate", "Enter Date of Observation", Sys.Date(), max = Sys.Date()) ), column(3, h5(paste("Should be less than",Sys.Date(),sep = " ")) ) ), fluidRow(column(6, numericInput("niPotato", "Enter Price of Potato per KG", subset(df, df$Date == max(df$Date))$Potato, min = minPotato, max = maxPotato, step = 1) ), column(3, h5(paste("Range:",minPotato,"-",maxPotato)) ) ), fluidRow(column(6, numericInput("niTomato", "Enter Price of Tomato per KG", subset(df, df$Date == max(df$Date))$Tomato, min = minTomato, max = maxTomato, step = 1) ), column(3, h5(paste("Range:",minTomato,"-",maxTomato)) ) ), fluidRow(column(6, numericInput("niOnion", "Enter Price of Onion per KG", subset(df, df$Date == max(df$Date))$Onion, min = minOnion, max = maxOnion, step = 1) ), column(3, h5(paste("Range:",minOnion,"-",maxOnion)) ) ), fluidRow(column(6, numericInput("niGold", "Enter Price of Gold per Gram", subset(df, df$Date == max(df$Date))$Gold, min = minGold, max = maxGold, step = 1) ), column(3, h5(paste("Range:",minGold,"-",maxGold)) ) ), fluidRow(column(6, numericInput("niSensex", "Enter SENSEX Index Value", subset(df, df$Date == max(df$Date))$Sensex, min = minSensex, max = maxSensex, step = 1) ), column(3, h5(paste("Range:",minSensex,"-",maxSensex)) ) ), fluidRow(column(6, numericInput("niNIFTY", "Enter NIFTY Index Value", subset(df, df$Date == max(df$Date))$Nifty, min = minNIFTY, max = maxNIFTY, step = 1) ), column(3, h5(paste("Range:",minNIFTY,"-",maxNIFTY)) ) ), fluidRow(column(6, passwordInput("piPassword", "Enter the Password") ), column(3, h5("Password is required to be able to save data") ) ), actionButton(inputId = "sbSave", label = "Save") ) ), fluidRow( column(12, h4(textOutput("DataEntry")) ) ) ), tabPanel("Update", fluidRow( column(2, "Updatesidebar" ), column(10, "Updatemain" ) )), plotOutput("dataUpdate") ) ) )

First let us look at the data entry window. To save the data, the "Submit" button to be pressed. This event can be captured using the observeEvent() function. A snippet of the validation code is provided. After the validation, the ode for saving the record was written.

observeEvent(input$sbSave, { validRecord <- 0; if(!is.null(input$piPassword)) { if(input$piPassword == password) {validRecord <- 1;} else {validRecord <- 0; msg <<- "Invalid Password"} } if(validRecord == 1) { if(!is.null(input$diDate) & length(input$diDate) != 0) { oldRec <- subset(dfDownload, dfDownload$Date == input$diDate); if(exists("oldRec")) { if(nrow(oldRec) == 0) { validRecord <- 1 } else { validRecord <- 0 msg <<- "Record already exists" } } else { validRecord <- 0 msg <<- "Invalid Date provided" } } else { validRecord <- 0 msg < Sys.Date()) { msg <<- "Date cannot be greater than Today" validRecord <- 0 } else { validRecord <- 1 } } ...

If you want to suppress the output of a part of the code, then put the code inside a sink block.

{ sink("/dev/null"); newRecord <- data.frame( Date = as.Date(input$diDate, "%Y-%m-%d"), Potato = input$niPotato, Tomato = input$niTomato, Onion = input$niOnion, Gold = input$niGold, Sensex = input$niSensex, Nifty = input$niNIFTY ) ; sink(); }

By default, the variables in a code block are local. If you want the values of a variable to be available throughout the code, then use the

<<-

operator instead of

<-

operator.

dfDownload <<- rbind(dfDownload, newRecord)

To download the data into a Comma Separate Values file (CSV File), you can use the following code.

output$downloadData <- downloadHandler(

# This function returns a string which tells the client # browser what name to use when saving the file. filename = function() { paste("PriceIndex", "csv", sep = ".") },

# This function should write data to a file given to it by # the argument 'file'. content = function(file) { # Write to a file specified by the 'file' argument write.table(dfDownload, file, sep = ",", row.names = FALSE) } )

Based on event, if you would like your code to respond, you would need to program the reactive() function.

outputData <- reactive({ if(is.null(input$ddlMonth)) { df1 <- subset(df, df$Year == input$ddlYear) } else { if(input$ddlMonth == "") { df1 <- subset(df, df$Year == input$ddlYear) } else { df1 <- subset(df, df$MonthName == input$ddlMonth & df$Year == input$ddlYear) } } ... } output$dispData <- renderTable({ outputData() })

To build controls based on conditions, following code provides an example. Here, based on the type of Graph, the choices Radio Buttons are built dynamically.

choiceRadioButton <- reactive({ if(as.numeric(input$rbGraphType) == 1 | as.numeric(input$rbGraphType) == 2 | as.numeric(input$rbGraphType) == 3 | as.numeric(input$rbGraphType) == 5) { radioButtons("rbCommodity", h4("Select Commodity"), choices = list("Potato" = 1, "Tomato" = 2, "Onion" = 3, "Gold" = 4, "Sensex" = 5, "Nifty" = 6), selected = 4) } } )

…

output$optionSelect <- renderUI({ choiceRadioButton() })

To create the Bar Chart, the following code can be used. There are more features in the qplot function. I will update them later.

output$dispPlot <- renderPlot({ if(as.numeric(input$rbGraphType) == 1) { #Bar Plot if(is.null(input$rbCommodity)) { x <- data.matrix(df[2]) } else { x <- data.matrix(df[as.numeric(input$rbCommodity) + 1]) } qplot(x, geom = "bar", fill = I("yellow"), colour = I("red")) }

To display the Histogram, the following code was used.

else if(as.numeric(input$rbGraphType) == 2) { #Histogram if(is.null(input$rbCommodity)) { x <- data.matrix(df[2]) histTitle <- "Potato" } else { x <- data.matrix(df[as.numeric(input$rbCommodity) + 1]) histTitle <- switch(as.numeric(input$rbCommodity), "Potato", "Tomato", "Onion", "Gold", "Sensex", "NIFTY") } histTitle <- paste("Histogram of", histTitle, "Prices between", as.character(min(df$Date)), "and", as.character(max(df$Date)), sep = " ")

# generate bins based on input$bins from ui.R if(is.null(input$bins)) { l <- 30 } else { l <- input$bins } bins <- seq(min(x), max(x), length.out = l + 1)

# draw the histogram with the specified number of bins hist(x, breaks = bins, col = 'darkgray', border = 'white', xlab = "Price Bins", ylab = "Frequency", main = histTitle) }

To display the Line Chart, the following code was used.

else if(as.numeric(input$rbGraphType) == 3) { #Line Chart if(is.null(input$rbCommodity)) { all_prices <- ggplot() + geom_line(data = df,aes(x=c(1:nrow(df)), y = Potato), size=1, colour="brown") + geom_line(data = df,aes(x=c(1:nrow(df)), y = Tomato), size =1, colour = "red") + geom_line(data = df,aes(x=c(1:nrow(df)), y = Onion), size =1, colour = "purple") + labs(x = "Date", y = "Price in Rs.") print(all_prices) } …

To display the Box Plot, the following code was used.

else if(as.numeric(input$rbGraphType) == 4) { #Box Plot dfBox <- data.frame(label = factor(rep(c("Potato","Tomato", "Onion", "Gold", "Sensex", "Nifty"), each=nrow(df))), value = c(df$N_Potato, df$N_Tomato, df$N_Onion, df$N_Gold, df$N_Sensex, df$N_Nifty)) ggplot(dfBox, aes(x=label, y=value)) + geom_boxplot() }

To display the Scatter Plot, the following code was used.

ggplot(data = dfnew, aes(dfnew[3], dfnew[4], color = factor(MonthNum))) + geom_point(aes(x = data.matrix(dfnew[3]), y = data.matrix(dfnew[4]))) + scale_size_area() + xlab(XLabel) + ylab(YLabel) + ggtitle(paste("Scatterplot of", XLabel, "vs.", YLabel, "and Month", sep = " "))

The user interfaces are shown below.

  Programming with Shiny Maintaining Data on Excel is becoming very difficult as the data is growing. Excel has become very slow as a result.