"Warning: Error in eval: could not convert n to scalar integer" - r

Question

I'm trying to make a shiny app that can let you choose different statistical methods to analyze some insurance data and then plot the results in a certain way. I want to be able to select a few values in the interface and the names of methods, and then have one button to trigger the creation of the model, and then another button to trigger the plot creation.
I'm pretty new to Shiny App and I'm getting an error message that I just cannot figure out.
"Warning: Error in eval: could not convert n to scalar integer"
Here is my code:
library(shiny)
library(insuranceData)
library(caret)
library(randomForest)
library(dplyr)
library(ggplot2)
library(pls)
data(AutoBi)
ui <- fluidPage(
# Application title
titlePanel("Ratemaking Tool"),
#fileInput("file", "Upload file"),
actionButton("do", "Make Model"),
actionButton("Go", "Make Graph"),
selectInput("Model", "Model", c("lm", "glm", "pls")),
sidebarLayout(
sidebarPanel(
sliderInput("percentiles",
"Percentile Splits:",
min = 1,
max = 20,
value = 5,round = TRUE),
sliderInput("cv_splits",
"Folds for K-fold validation:",
min = 1,
max = 10,
value = 5,round = TRUE)),mainPanel(plotOutput("distPlot")
)
)
)
server <- function(input, output){
values <- reactiveValues(df_data = NULL)
observeEvent(input$do, {
dataInput <- isolate(reactive({
t <- AutoBi %>% na.omit() %>% mutate(log.age = log(CLMAGE + 1),log.loss = log(LOSS + 1))
train = slice(t,1:700)
test = slice(t,701:nrow(t)) %>% mutate(log.age = log(CLMAGE + 1),log.loss = log(LOSS + 1))
t1 <- select(train,-c(log.loss,LOSS,CLMAGE))
ctrl <- isolate(trainControl(method = "cv",number = input$cv_splits))
model <- train(x = t1[,c(2,3,4,5,6,7)],y = train$LOSS,trControl = ctrl,
method = input$Model)
preds <- predict(model,test[,c(2,3,4,5,6,9)])
test$pred <- preds
ordered <- test %>% arrange(pred) %>% mutate(quantile = ntile(pred,input$percentiles)) %>% group_by(quantile) %>%
summarize(avg_loss = mean(LOSS),avg_prd= mean(pred)) %>% mutate(flag = as.factor(ifelse(avg_loss - avg_prd > 0,0,1)))
values$df_data = ordered
return(ordered)
}))
values$df_data = dataInput()
})
observeEvent(input$Go,{
output$distPlot <- isolate(renderPlot({
ggplot(values$df_data)+ aes(quantile,avg_loss,size = 1) + geom_point() + geom_point(aes(y = avg_prd,color = "avg_pred")) + ggtitle(paste("Statistical Learning Technique:", input$Model)) +
geom_linerange(aes(ymin = avg_prd,ymax = avg_loss,size = .1,color = flag)) + guides(size=FALSE)
}))
})
}
shinyApp(ui = ui, server = server)