Why does the shiny app works locally but not on server?

I have asked this question before on stackoverflow and is related to shiny. Perhaps there are more people used to shiny on this forum. I created a shiny app, which implements a random forest model (party package) returning results with the caret package and visualizing a conditional interference tree with the ctree function. The app works fine locally. However, when I try the publish it no output gets displayed and I get the error "An error has occurred. Check your logs or contact the app author for clarification" (see, https://snwikaij.shinyapps.io/RF_macrophyte_model/) What am I missing here?

I followed the steps described on from https://support.rstudio.com/hc/en-us/articles/229848967-Why-does-my-app-work-locally-but-not-on-shinyapps-io-, to see if this resoveld the issue, It did not

  • There is no code or data stored in the local environment when running locally.
  • All packages are loaded via library().
  • I load the data via a relative path df <- read.csv("df.csv"). All files are stored in a map called shinyapp as app.R and the df.csv.

The output in the ui gets displayed, such as the names (i.e. selectInput(inputId = "Spec", label = "Select species:", unique(df$Taxon))) and medians. However, the plots and output of the model are not displayed and I get the previous described error.

The dataset is relatively large so I cannot display it here, but I created a dummy dataset which can be used to run the app.


#Create dummy dataset
df  <- data.frame(Sample = 1:500, Taxon = paste0("spec", rbinom(n = 500, 2, 0.5)), SO4 = rnorm(500, 300, 50), pH = rnorm(500, 7, 1), NO3 = rnorm(500, 10, 3))

ui <- fluidPage(
  titlePanel("Random Forest classification"),
    selectInput(inputId = "Spec", label = "Select species:", unique(df$Taxon)),
    selectInput(inputId = "NAN", label = "Select how to use data:", c("All data (Also NAs)", "Complete data (No NAs)")),
    numericInput(inputId = "SO4", label = "Choose value for SO4 (mg/l)", value = median(df$SO4, na.rm = T), min = 0, max = 15000),
    numericInput(inputId = "pH", label = "Choose value for pH", value = median(df$pH, na.rm = T), min = 5, max = 10),
    numericInput(inputId = "NO3", label = "Choose value for NO3 (mg/l)", value = median(df$NO3, na.rm = T), min = 0, max = 50),
    h3("Validation parameters"),
    h3("Voting percentage"),
    h5("Note that every time the output of the model is different from
       the previous. Samples with the absence of species are more
       prevalent. Therefore, Every time the code is run, samples where a species
       was present are the same. However, samples with absences are randomly 
       selected in equall amount and combines this with samples where the. 
       species was present Further, the random forest model randomly creates 
       trees by bootstrapping the dataset a 100 times. Each time a different 
       model is created. This model is a course estimation, since many more 
       important factors are absent. Validation of the model is performed by 
       training the model on 75% of the dataset and validating on the other 25%. 
       The predicting model is based on the total dataset. The Error: 
       replacement has 1 row, data has 0, occurs when the data for a species
       has no measurements if complete data (without NAs) is used.")),

server <- function(input,output){

  #Create model dataset
  modpred           <- reactive({
    present         <- df[df$Taxon == input$Spec,]
    present$Spec    <- 1
    df1             <- df[!duplicated(df$Sample),]
    df1             <- df1[!df1$Sample %in% present$Sample,]

    if(input$NAN == "Complete data (No NAs)"){
      present <- na.omit(present)
      df1     <- na.omit(df1)}

    if((nrow(df)-nrow(present)) > nrow(present)){
      absent        <- df1[sample(1:nrow(df1), nrow(present), replace = F),]}
      absent        <- df1[sample(1:nrow(df1), nrow(present), replace = T),]}
    absent$Spec     <- 0
    model.data      <- rbind(present, absent)
    model.data$Spec <- as.factor(model.data$Spec)

    #Select 75% as training data
    prestrain       <- present[sample(1:nrow(present), floor(nrow(absent)*0.75), replace = F),]
    abstrain        <- absent[sample(1:nrow(absent), floor(nrow(present)*0.75), replace = F),]
    train.data      <- rbind(prestrain, abstrain)
    train.data$Spec <- as.factor(train.data$Spec)

    #Select the other 25% as validation data
    val.data        <- model.data[!rownames(model.data) %in% rownames(train.data),]

    #Create nice conditional interference tree on all data
    ct              <- party::ctree(Spec~SO4+pH+NO3, data = model.data)

    #Train and validate model
    train.model     <- party::cforest(Spec~SO4+pH+NO3, data=train.data, controls = party::cforest_classical(mtry = 1, ntree = 100))
    validation.mod  <- predict(train.model, newdata = val.data)
    conf.mat.val    <- table(val.data$Spec, predict(train.model, newdata = val.data))
    val.results     <- caret::confusionMatrix(conf.mat.val)
    sumval          <- paste0("AUC=", round(val.results$overall[1],2), " (LCI=", round(val.results$overall[3],2),"; ",
                              "HCI=", round(val.results$overall[4],2), "), ",
                              "Cohen's kappa=", round(val.results$overall[2],2), ", ",
                              "n-validation=", nrow(val.data), ", ", "n-training=", nrow(train.data), ", ", "n-total (model)=", nrow(model.data))

    #Extract relative importance parameters
    relimp          <- as.data.frame(party::varimp(train.model))
    relimp          <- cbind.data.frame(rownames(relimp), relimp)
    colnames(relimp)<-c("Parameter", "Relative importance")
    rownames(relimp)<- NULL
    relimp[,2]      <- relimp$`Relative importance`/sum(relimp$`Relative importance`)*100
    relimp          <- relimp[order(-relimp$`Relative importance`),]

    #Apply model on data input user interface
    model           <- party::cforest(Spec~SO4+pH+NO3, data=model.data, controls = party::cforest_classical(mtry = 1, ntree = 100))
    pred.data       <- setNames(data.frame(as.numeric(input$SO4), as.numeric(input$pH), as.numeric(input$NO3)), c("SO4", "pH", "NO3"))
    pred            <- predict(model, newdata = pred.data, type = "prob")
    prob            <- paste0("Voting percentage (Probability of presence) = ", round(pred$`1`[2]*100,0),"%", ",", 
                              " Majority vote indicates = ", ifelse(pred$`1`[2] > 0.5, "Present", "Absent"))

    combo <- list(Probability = prob, Validation = sumval, Tree = ct, Importance = relimp)})

  output$Votingperc <- renderText({
    combo <- modpred()

  output$Validation <- renderText({
    combo <- modpred()

  output$Imp <- renderPlot({
    combo <- modpred()
    bar   <- combo$Imp
    barplot(bar$`Relative importance`,
            names.arg = bar$Parameter, ylab = "Relative importance (%)")})

  output$Tree <- renderPlot({
    combo <- modpred()
    plot(combo$Tree, inner_panel=node_inner(combo$Tree, pval = FALSE))})



Thank you in advance!

Have you checked the logs? What do they say?

The logs are empty. I do not know what this means. I am also struggling with the rsconnect::showLogs() function. Not sure how to use this. The documentation also does not give an example.

you said all packages are loaded via library() but then your code shared here doesn't conform to that standard, was that a simple omission, that you can correct ?

also running your code locally gives me an error that node_inner is not found ...

sorry, my bad, copy pasting is not the best in sharing scripts. I edited the code displayed, adding 3 packages (see below). The inner_node error is possibly caused by the absent of the party package.


I uploaded the app and recreated the error, couldnt use the standard log reading interface, tried using the function in rstudio

2020-04-20T09:21:47.406672+00:00 shinyapps[2149165]: Warning: Error in : package e1071 is required
2020-04-20T09:21:47.410501+00:00 shinyapps[2149165]:   129: stop
2020-04-20T09:21:47.410502+00:00 shinyapps[2149165]:   128: requireNamespaceQuietStop
2020-04-20T09:21:47.410503+00:00 shinyapps[2149165]:   127: confusionMatrix.table
2020-04-20T09:21:47.410503+00:00 shinyapps[2149165]:   125: <reactive:modpred> [/srv/connect/apps/shinypartytest/app.R#73]
2020-04-20T09:21:47.410504+00:00 shinyapps[2149165]:   109: modpred
2020-04-20T09:21:47.410504+00:00 shinyapps[2149165]:   108: renderText [/srv/connect/apps/shinypartytest/app.R#97]
2020-04-20T09:21:47.410514+00:00 shinyapps[2149165]:   107: func
2020-04-20T09:21:47.410515+00:00 shinyapps[2149165]:    94: origRenderFunc
2020-04-20T09:21:47.410515+00:00 shinyapps[2149165]:    93: output$Votingperc
2020-04-20T09:21:47.410515+00:00 shinyapps[2149165]:    13: runApp
2020-04-20T09:21:47.410516+00:00 shinyapps[2149165]:    12: fn
2020-04-20T09:21:47.410516+00:00 shinyapps[2149165]:     7: connect$retry
2020-04-20T09:21:47.410516+00:00 shinyapps[2149165]:     6: eval
2020-04-20T09:21:47.410516+00:00 shinyapps[2149165]:     5: eval
2020-04-20T09:21:47.413043+00:00 shinyapps[2149165]: Warning: Error in : package e1071 is required
2020-04-20T09:21:47.415766+00:00 shinyapps[2149165]:   111: <Anonymous>

I understood from the above that there is a dependency on library(e1071)
Adding this meant I could redeploy the app and it began to work as expected


This worked, thank you!! (https://snwikaij.shinyapps.io/RF_macrophyte_model/). How do I use the rsconnect::showLogs() function in this case? Like the following, rsconnect::showLogs(appName="RF_macrophyte_model",streaming=TRUE) or do I use the folder rsconnect::showLogs(appName="shinyapp",streaming=TRUE)? Why do I not get the error message "could not find package e10171." The help function (?e10171) returns, "These functions are provided for compatibility with older versions of package e1071 only, and may be defunct as soon as of the next release." Which older version of what do I use?

I think this would be correct.

I don't know why you don't show same issues, (unless you fixed it so now theres no issue)
just be careful that its e1071 and not e10171 that we are talking about.

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