legendrank scrambled by fillcolor with type='scatter'
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Description
Expected behavior
When making a stacked area chart, the ordering of the legend values follows the ordering of the legendrank argument.
Actual behavior
When making a stacked area chart, the ordering of the legend values depends on the legendrank argument in combination with the alphabetical ordering of the fillcolor argument. Somehow the fillcolors seem to be scrambling the order.
Here's an example:
# The goal is to make a stacked area chart, where each fill color is a severity
# level. Then, the legend should be ordered by severity
library(plotly)
severities = c('Mild', 'Moderate', 'Severe')
severity_colors = c('#FF0000', '#440000', '#AA0000')
# ^ colors chosen specifically to be out of alphabetical order
severity_color_dict = setNames(severity_colors, severities)
timeline = data.frame(Time = rep(1:15, 3),
coverage = rep(1, 45),
Severity = factor(rep(severities, each = 15)))
timeline$fill = severity_color_dict[timeline$Severity]
# default order
plot_ly(timeline, x = ~Time, y = ~coverage, name = ~Severity, type = 'scatter',
mode = 'none', stackgroup = 'one', groupnorm = '')
# add fillcolor-- now the legend is in alphabetical order of fillcolor
plot_ly(timeline, x = ~Time, y = ~coverage, name = ~Severity, type = 'scatter',
mode = 'none', stackgroup = 'one', groupnorm = '', fillcolor = ~fill)
# this should be ordered by Severity, but it's still in alphabetical order of
# fillcolor
plot_ly(timeline, x = ~Time, y = ~coverage, name = ~Severity, type = 'scatter',
mode = 'none', stackgroup = 'one', groupnorm = '', fillcolor = ~fill,
legendrank = ~as.integer(Severity))
# The intended order can only be acheived by using the alphabetical ordering of
# the fill colors to "unscramble" the legendrank
unscramble_dict = order(severity_colors)
plot_ly(timeline, x = ~Time, y = ~coverage, name = ~Severity, type = 'scatter',
mode = 'none', stackgroup = 'one', groupnorm = '', fillcolor = ~fill,
legendrank = ~unscramble_dict[as.integer(Severity)])
Session info
R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.2 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8
[6] LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] plotly_4.10.2 ggplot2_3.4.2
loaded via a namespace (and not attached):
[1] magrittr_2.0.3 tidyselect_1.2.0 munsell_0.5.0 viridisLite_0.4.2 colorspace_2.1-0 R6_2.5.1 rlang_1.1.1
[8] fastmap_1.1.1 fansi_1.0.4 httr_1.4.6 dplyr_1.1.2 tools_4.1.2 parallel_4.1.2 grid_4.1.2
[15] data.table_1.14.8 gtable_0.3.3 utf8_1.2.3 cli_3.6.1 withr_2.5.0 ellipsis_0.3.2 crosstalk_1.2.0
[22] htmltools_0.5.5 yaml_2.3.7 lazyeval_0.2.2 digest_0.6.31 tibble_3.2.1 lifecycle_1.0.3 tidyr_1.3.0
[29] purrr_1.0.1 htmlwidgets_1.6.2 vctrs_0.6.2 glue_1.6.2 compiler_4.1.2 pillar_1.9.0 generics_0.1.3
[36] scales_1.2.1 jsonlite_1.8.5 pkgconfig_2.0.3
Guide de contribution
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Par où commencer
- Lisez l'issue en entier, puis le guide de contribution du projet.
- Signalez en commentaire que vous la prenez — cela évite que deux personnes fassent le même travail.
- Forkez le dépôt et travaillez sur une branche.
- Ouvrez une pull request qui référence le numéro de l'issue.
Piste de recherche
Commencez par exécuter les exemples R reproductibles avec plot_ly en utilisant type='scatter', stackgroup, fillcolor et legendrank. Suivez la manière dont l’ordre de la légende des aires empilées réagit à ces arguments ; le travail est terminé lorsque l’ordre de la légende suit systématiquement legendrank, quel que soit l’ordre de fillcolor, tout en préservant le comportement signalé.
Rédigé par le modèle d'indexation à partir du texte de l'issue.
Évaluation
- Stack technique
- r
- Domaine
- data-visualization
- Type d'issue
- Bug
- Difficulté
- 4/5
- Temps estimé
- 3-5 jours
- Activité
- À l'abandon
- Clarté
- Plutôt claire
- Accessibilité débutants
- 35/100