Controlling Animation for Points When There Are Missing Values (ggplotly)

I am building a Hans Rosling type visualisation, however, I am having issues with the point movements. I have attached some code that creates an example of my problem, which can be found below.

The best way I can describe it is that whenever there is a missing value, a point has to disappear, therefore a different becomes assigned to a point even if it's in a different group. This logically doesn't make any sense to me.

In the example below, I've removed the Month Year of Feb 2021 from Clinic A, (i.e. I've removed the whole row), which means that the point disappears as shown in the play animation movement. However, if you notice instead of the Clinic A point simply disappearing, it chooses to keep the Clinic A point, turn into a different Clinic, in this case Clinic B and also convert the surrounding points to different clinics as well. I think it's working off the logic of turn into whatever point is the nearest somehow.

To really understand what is happening observe the hover of the points from Jan 2021 to Feb 2021

My question(s) is/are:

• Why is this happening?
• How do I specify some sort of group or ID to the points so that they aren't randomly turning into other Clinics?

In my real world scenario I have over 90+ different unique IDs/Clinics so ideally someone would be able to press play and then follow a single point moving across the viz, specifically noticing for any abnormalities in movement. It's extremely difficult to do this, or rather almost impossible, if a given point is converting itself into another point on the fly.

Note: I have confirmed this problem is replicated when using both plot_ly() and ggplotly() and only seems to happen when there is missing data

Code to Replicate Problem:

set.seed(24)


test <- data.frame(Month_Year = rep(paste(month.abb, rep(2021:2022, each = 12), sep = "_"), 4),
           x = abs(rnorm(24*4)),
           y = abs(rgamma(24&4,2,1)),
           Clinic = rep(c("Clinic A", "Clinic B", "Clinic C", "Clinic D"), each = 24))

test$Month_Year <- factor(test$Month_Year, levels = paste(month.abb, rep(2021:2022, each = 12), sep = "_"))


test <- test[-c(2,8,12),]


pt <- test %>% 
  ggplot(aes(x = x, y = y, frame = paste0(sprintf("%02d", as.numeric(Month_Year)), Month_Year), text = 
               paste(
                 "RAR:", round(y, 3),
                 "\nMR:", round(x, 3),
                 "\nClinic:", Clinic
               ))) +
  geom_point(shape = 1, color = "firebrick", aes(group = Clinic)) +
  # geom_text(aes(y = NAG + 5)) +
  theme_minimal() +
  labs(x = "Rolled Mortality Rate - 3 Month Average", y = "Rolled Admission Rate - 3 Month Average") +
  scale_x_continuous(limits = c(0, 6))



ggplotly(pt, tooltip = "text") %>% 
  animation_opts(
    3000,
    redraw = T
  ) %>% 
  animation_slider(
    currentvalue = list(prefix = "Month Year: ")
  )

Commencez par exécuter la reproduction R fournie avec plot_ly() et ggplotly(), en incluant animation_opts() et animation_slider(). Comparez l’identité des points et les libellés de survol entre Jan 2021 et Feb 2021 lorsque des lignes sont manquantes. Le travail est considéré comme terminé lorsque le comportement est expliqué et qu’une manière documentée de préserver les identités de Clinic, ou un correctif limité, est identifiée.