Error in Shiny App "error in findrow" and "no points selected"

[biohacker]

Hi everyone,

I'm trying to get some inherited code to work with my data and I am unable to determine why I am receiving the following error messages when using output$SURVPLOT

The warning in the console -> Warning: Error in findrow: no points selected for one or more curves, consider using the extend argument

The warning in the shiny app window when trying to generate the graph -> Error: no points selected for one or more curves, consider using the extend argument

Has anyone seen this errors before and know what they mean?

I am fairly sure it is linked to the below chunk of the code as other text outputs are produced. Just not the graph..

output$SURVPLOT <- renderPlot({
surv_curve_all() %>%
  filter(time<surv_months) %>%
  filter(type=="obs" | type=="Patient") %>%
  mutate(surv=surv*100) %>%
  ggplot() +
  geom_step(aes(x=time,y=surv, linetype=type, color=rg)) +
  scale_colour_brewer(type = "seq", palette = "Set1") +
  ylim(0,100) + xlab("Time (months)") + ylab("Survival %") +
  theme_bw() +
  labs(color="Risk Group") + scale_size(guide=FALSE) +
  scale_linetype_discrete(name = "",labels = c("Observed","Patient"))

This is the relevant code before the output$SURVPLOT code.

surv_curve_all <- reactive({
  rbind(surv_curve_alldata_imp_df,
        data.frame(time=survfit_pred_survcurve()$time,         
                   surv=survfit_pred_survcurve()$surv,
                   rg=rep("Patient",nrow(survfit_pred_survcurve())),
                   type=rep("Patient",nrow(survfit_pred_survcurve()))

Would really appreciate any help with the resolving the error messages. Can provide more info if needed, any help would be appreciated.

Thanks

[nirgrahamuk]

It doesn't seem to me you a shiny problem per-se, this seems to be a problem with the use of library(survival)
CRAN - Package survival (r-project.org)

I would attempt to understand and debug the issue, by abstracting our the relevant parts of the shiny app, to make a minimal plain r script that creates the survfit_pred_survcurve object under your particular circumstance and focus on understanding that.

If you would like assistance from the community here, then it is recommended that you make a reprex.

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…