控制台中出现闪亮的应用错误

Question

I am trying to build a shiny app for a project and I think I am all done, everything seems to be working properly but I keep getting this error in my console when I run the app:

Error in grep(input$predict, columns) : invalid 'pattern' argument
Error in parse(text = x) : <text>:2:0: unexpected end of input
1: Ozone  ~  
   ^
Error in parse(text = x) : <text>:2:0: unexpected end of input
1: Ozone  ~  
   ^

Here is my code for ui.R:

library(shiny)

# Define UI for airquality data application
shinyUI(pageWithSidebar(

    # Application title
    headerPanel("Air Quality Relationships & Predictions"),

    sidebarPanel(
            selectInput("outcome", "Outcome:", list("O Zone" = "Ozone", 
                                                    "Solar Radiation" = "Solar.R",
                                                    "Wind" = "Wind",
                                                    "Temperature" = "Temp")),
            uiOutput("predictor"),
            uiOutput("slider"),
            h5(textOutput("guess"))
    ),

    mainPanel(
            plotOutput("ioPlot")
    )
))

And here is the code for server.R:

    library(shiny)
    library(datasets)

    #import dataset
    airData <- airquality

    #make list of columns for dropdowns
    columns <- list("O Zone" = "Ozone", "Solar Radiation" = "Solar.R", "Wind" = "Wind", "Temperature" = "Temp")

    # Define server logic
    shinyServer(function(input, output, session) {

            #set dropdown so it only shows 3 remaining columns, won't compare a variable to itself
            output$predictor = renderUI({
                    outc <- input$outcome
                    selectInput("predict", "Predictor:", columns[-grep(outc,columns)])
            })

            #setup the slider for the current selections, default to mean, set max and min to the observed max and min
            output$slider = renderUI({
                    slLabel = paste("Adjust to predict ", names(columns[grep(input$outcome,columns)]), "using this value for ", names(columns[grep(input$predict,columns)]), ":")
                    slVal = round(mean(airData[,input$predict], na.rm=TRUE),0)
                    slMin = round(min(airData[,input$predict], na.rm=TRUE),0)
                    slMax = round(max(airData[,input$predict], na.rm=TRUE),0)
                    sliderInput('slide', slLabel, value = slVal, min = slMin, max = slMax, step=1,)
            })

            #create formula for plot
            formulaText <- reactive({
                    paste(input$outcome, " ~ ", input$predict)
            })

            #create text for prediction
            output$guess <- renderText({
                    fit <- lm(as.formula(formulaText()), data=airData)
                    slp <- fit$coeff[2]
                    yint <- fit$coeff[1]
                    predout <- round(yint + slp * input$slide,2)
                    paste("Predicted outcome for ", names(columns[grep(input$outcome,columns)]), ": ", predout)
            })

            #create plot and fitted line
            output$ioPlot <- renderPlot({
                    fit <- lm(as.formula(formulaText()), data=airData)
                    plot(as.formula(formulaText()), 
                            data = airData, xlab=names(columns[grep(input$predict,columns)]), ylab=names(columns[grep(input$outcome,columns)]))
                    abline(fit, col="red")
                    title(main= paste(names(columns[grep(input$outcome,columns)]), " vs. ", names(columns[grep(input$predict,columns)])))
            })
    })

Answer 1

I think the issue is due to the way you are subsetting columns to make the predict selectInput

Try this instead

output$predictor = renderUI({
            selectInput("predict", "Predictor:", columns[columns!=input$outcome])
})

That should give you everything from the columns list except for whatever was chosen for input$outcome .

Edit I just noticed you are also using a grep many times in order to paste some text for an output. Instead you should use statements like:

 xlab=names(columns[columns==input$predict])

That is, you don't need grep you can just subset columns directly.