plotly / plotly/plotly.R

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

Abierto
#2,346 2 comentarios 0 reacciones 0 asignados Ver en GitHub

Nadie ha tomado este issue todavía.

Lenguaje dominante
R
Estrellas
2.7k
Forks
641
Sin PR fusionados en 30 d

Descripción

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)
#>  os       Ubuntu 22.04.4 LTS
#>  system   x86_64, linux-gnu
#>  ui       X11
#>  language (EN)
#>  collate  en_US.UTF-8
#>  ctype    en_US.UTF-8
#>  tz       America/Indiana/Vevay
#>  date     2024-04-06
#>  pandoc   3.1.1 @ /usr/lib/rstudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  package     * version date (UTC) lib source
#>  callr         3.7.5   2024-02-19 [1] CRAN (R 4.3.2)
#>  cli           3.6.2   2023-12-11 [1] CRAN (R 4.3.2)
#>  colorspace    2.1-0   2023-01-23 [1] CRAN (R 4.3.0)
#>  crosstalk     1.2.1   2023-11-23 [1] CRAN (R 4.3.2)
#>  curl          5.2.0   2023-12-08 [1] CRAN (R 4.3.2)
#>  data.table    1.15.0  2024-01-30 [1] CRAN (R 4.3.2)
#>  digest        0.6.34  2024-01-11 [1] CRAN (R 4.3.2)
#>  dplyr         1.1.4   2023-11-17 [1] CRAN (R 4.3.2)
#>  ellipsis      0.3.2   2021-04-29 [1] CRAN (R 4.3.0)
#>  evaluate      0.23    2023-11-01 [1] CRAN (R 4.3.2)
#>  fansi         1.0.6   2023-12-08 [1] CRAN (R 4.3.2)
#>  farver        2.1.1   2022-07-06 [1] CRAN (R 4.3.0)
#>  fastmap       1.1.1   2023-02-24 [1] CRAN (R 4.3.0)
#>  fs            1.6.3   2023-07-20 [1] CRAN (R 4.3.2)
#>  generics      0.1.3   2022-07-05 [1] CRAN (R 4.3.0)
#>  ggplot2     * 3.5.0   2024-02-23 [1] CRAN (R 4.3.2)
#>  glue          1.7.0   2024-01-09 [1] CRAN (R 4.3.2)
#>  gtable        0.3.4   2023-08-21 [1] CRAN (R 4.3.2)
#>  highr         0.10    2022-12-22 [1] CRAN (R 4.3.0)
#>  htmltools     0.5.7   2023-11-03 [1] CRAN (R 4.3.2)
#>  htmlwidgets   1.6.4   2023-12-06 [1] CRAN (R 4.3.2)
#>  httr          1.4.7   2023-08-15 [1] CRAN (R 4.3.2)
#>  jsonlite      1.8.8   2023-12-04 [1] CRAN (R 4.3.2)
#>  knitr         1.45    2023-10-30 [1] CRAN (R 4.3.2)
#>  lazyeval      0.2.2   2019-03-15 [1] CRAN (R 4.3.0)
#>  lifecycle     1.0.4   2023-11-07 [1] CRAN (R 4.3.2)
#>  magrittr      2.0.3   2022-03-30 [1] CRAN (R 4.3.0)
#>  munsell       0.5.0   2018-06-12 [1] CRAN (R 4.3.0)
#>  pillar        1.9.0   2023-03-22 [1] CRAN (R 4.3.0)
#>  pkgconfig     2.0.3   2019-09-22 [1] CRAN (R 4.3.0)
#>  plotly      * 4.10.4  2024-01-13 [1] CRAN (R 4.3.2)
#>  processx      3.8.3   2023-12-10 [1] CRAN (R 4.3.2)
#>  ps            1.7.6   2024-01-18 [1] CRAN (R 4.3.2)
#>  purrr         1.0.2   2023-08-10 [1] CRAN (R 4.3.2)
#>  R.cache       0.16.0  2022-07-21 [1] CRAN (R 4.3.1)
#>  R.methodsS3   1.8.2   2022-06-13 [1] CRAN (R 4.3.1)
#>  R.oo          1.26.0  2024-01-24 [1] CRAN (R 4.3.2)
#>  R.utils       2.12.3  2023-11-18 [1] CRAN (R 4.3.2)
#>  R6            2.5.1   2021-08-19 [1] CRAN (R 4.3.0)
#>  reprex        2.1.0   2024-01-11 [1] CRAN (R 4.3.3)
#>  rlang         1.1.3   2024-01-10 [1] CRAN (R 4.3.2)
#>  rmarkdown     2.25    2023-09-18 [1] CRAN (R 4.3.2)
#>  rstudioapi    0.15.0  2023-07-07 [1] CRAN (R 4.3.2)
#>  scales        1.3.0   2023-11-28 [1] CRAN (R 4.3.2)
#>  sessioninfo   1.2.2   2021-12-06 [1] CRAN (R 4.3.0)
#>  styler        1.10.2  2023-08-29 [1] CRAN (R 4.3.1)
#>  tibble        3.2.1   2023-03-20 [1] CRAN (R 4.3.0)
#>  tidyr         1.3.1   2024-01-24 [1] CRAN (R 4.3.2)
#>  tidyselect    1.2.0   2022-10-10 [1] CRAN (R 4.3.0)
#>  utf8          1.2.4   2023-10-22 [1] CRAN (R 4.3.2)
#>  vctrs         0.6.5   2023-12-01 [1] CRAN (R 4.3.2)
#>  viridisLite   0.4.2   2023-05-02 [1] CRAN (R 4.3.0)
#>  webshot       0.5.5   2023-06-26 [1] CRAN (R 4.3.2)
#>  withr         3.0.0   2024-01-16 [1] CRAN (R 4.3.2)
#>  xfun          0.42    2024-02-08 [1] CRAN (R 4.3.2)
#>  xml2          1.3.6   2023-12-04 [1] CRAN (R 4.3.2)
#>  yaml          2.3.8   2023-12-11 [1] CRAN (R 4.3.2)
#> 
#>  [1] /home/jonathan/R/x86_64-pc-linux-gnu-library/4.3
#>  [2] /usr/local/lib/R/site-library
#>  [3] /usr/lib/R/site-library
#>  [4] /usr/lib/R/library
#> 
#> ──────────────────────────────────────────────────────────────────────────────

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!

Guía de contribución

Abrir la guía de contribución

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

Línea de trabajo

No se nombran archivos fuente ni pruebas. Empieza ejecutando los ejemplos proporcionados de plot_ly con valores de color formula y estáticos, y luego sigue cómo se ensamblan los datos de marker size, color y name. Se considera terminado cuando los tamaños de las burbujas permanecen alineados con las filas originales en los umbrales y casos de name indicados, con cobertura de regresión para los ejemplos.

Escrito por el modelo de indexación a partir del texto del issue.

Evaluación

Stack tecnológico
r
Área
data-visualization
Tipo de issue
Error
Dificultad
4/5
Tiempo estimado
3-5 días
Estado de actividad
Estancado
Claridad
Bastante claro
Aptitud para principiantes
35/100

Recibe los nuevos issues en tu correo

Un resumen breve de issues de GitHub para principiantes.