plotly / plotly/plotly.R

Bubble sizes out of order, when using a formula for the `color`/`name` attributes

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Description

Hi folks, I'm seeing a strange effect on scatter plot marker sizes, when using a formula for the color (and name) attributes.

(edited to add more test cases)

library(plotly)
#> Loading required package: ggplot2
#> 
#> Attaching package: 'plotly'
#> The following object is masked from 'package:ggplot2':
#> 
#>     last_plot
#> The following object is masked from 'package:stats':
#> 
#>     filter
#> The following object is masked from 'package:graphics':
#> 
#>     layout

df <- data.frame(x = c(1, 2, 3, 4, 5),
                 y = c(1, 2, 3, 4, 5),
                 z = c(1, 2, 3, 4, 5))

# Expected output: marker sizes in order
plot_ly(df,
        x = ~x,
        y = ~y,
        type = "scatter",
        mode = "markers",
        marker = list(size = ~z,
                      sizeref = 0.1),
        color = ~z < 2,
        colors = c(I("green"), I("red")),
        text = ~paste0("z: ", z))



# df has correct data
df
#>   x y z
#> 1 1 1 1
#> 2 2 2 2
#> 3 3 3 3
#> 4 4 4 4
#> 5 5 5 5

# Buggy output: changing "color" formula threshold puts marker sizes out of order
plot_ly(df,
        x = ~x,
        y = ~y,
        type = "scatter",
        mode = "markers",
        marker = list(size = ~z,
                      sizeref = 0.1),
        color = ~z < 3,
        colors = c(I("green"), I("red")),
        text = ~paste0("z: ", z))



# df still has correct data
df
#>   x y z
#> 1 1 1 1
#> 2 2 2 2
#> 3 3 3 3
#> 4 4 4 4
#> 5 5 5 5

# Buggy output: static vector also puts marker sizes out of order
plot_ly(df,
        x = ~x,
        y = ~y,
        type = "scatter",
        mode = "markers",
        marker = list(size = ~z,
                      sizeref = 0.1),
        color = c(TRUE, TRUE, FALSE, FALSE, FALSE),
        colors = c(I("green"), I("red")),
        text = ~paste0("z: ", z))



# Buggy output: use ifelse with (TRUE, FALSE)
plot_ly(df,
        x = ~x,
        y = ~y,
        type = "scatter",
        mode = "markers",
        marker = list(size = ~z,
                      sizeref = 0.1),
        color = ~ifelse(z < 3, TRUE, FALSE),
        colors = c(I("green"), I("red")),
        text = ~paste0("z: ", z))



# Possible workaround: use ifelse with (1, 0)
plot_ly(df,
        x = ~x,
        y = ~y,
        type = "scatter",
        mode = "markers",
        marker = list(size = ~z,
                      sizeref = 0.1),
        color = ~ifelse(z < 3, 1, 0),
        colors = c(I("green"), I("red")),
        text = ~paste0("z: ", z))



# Buggy again when adding "name" field with formula
plot_ly(df,
        x = ~x,
        y = ~y,
        type = "scatter",
        mode = "markers",
        marker = list(size = ~z,
                      sizeref = 0.1),
        color = ~ifelse(z < 3, 1, 0),
        colors = c(I("green"), I("red")),
        name = ~ifelse(z < 3, "Red", "Green"),
        text = ~paste0("z: ", z))

Created on 2024-04-06 with reprex v2.1.0

Session info 
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.3.3 (2024-02-29)
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#>  language (EN)
#>  collate  en_US.UTF-8
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#>  tz       America/Indiana/Vevay
#>  date     2024-04-06
#>  pandoc   3.1.1 @ /usr/lib/rstudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
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#> ──────────────────────────────────────────────────────────────────────────────

Bad color formulas are:

  • z < 3
  • z < 4
  • z < 5

The results from z < 2 may also be incorrect ... they're just indiscernible given my example.

This behavior causes significant skewing/confusion for bubble plots of any size.

Thanks for reading!

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Research direction

No source files or tests are named. Start by running the supplied plot_ly examples with formula and static color values, then trace how marker size, color, and name data are assembled. Done means bubble sizes remain aligned with the original rows across the reported thresholds and name cases, with regression coverage for the examples.

Written by the indexing model from the issue text.

Assessment

Tech stack
r
Domain
data-visualization
Issue type
Bug
Difficulty
4/5
Estimated time
3-5 days
Activity status
Stale
Clarity
Mostly clear
Newbie friendliness
35/100

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