时间戳

首页 > 解决方案 > Highcharter 中的自定义颜色未正确映射到图例

r - Highcharter 中的自定义颜色未正确映射到图例

问题描述

我很难将特定颜色映射到堆叠条形图的值，以便它们在从同一数据集构建的大量图表中保持一致。例如，highcharter 会将默认颜色（或我给它的颜色列表）分配给我的分组分类值，但是当图表被修改（比如在 Shiny 中）时，会生成一个新图表，但颜色可能已经改变。这非常令人困惑，可能会误导我的听众。

这是一个示例数据集：

responses <- c('Pro','Against','Neutral','Resigned/Accepting','Not Specified')
constituents <- c('dual degree','law only','undergrad only','friend','parent only')
indiv <- rep(1:50)
Name.Change <- sample(responses,50,replace = TRUE)
constituent.type <- sample(constituents,50,replace = TRUE)

demo <- as.data.frame(cbind(indiv,Name.Change,constituent.type))

我分解了我的堆叠变量，因为这似乎始终如一地确定了堆栈的顺序。然后我将颜色分配给“Name.Change”的值，以便它们在各种图表中保持一致。

demo$Name.Change.fac <- factor(demo$Name.Change, levels = c("Pro","Resigned/Accepting","Neutral","Against","Not Specified"), ordered = TRUE)

demo <- demo %>%
  mutate(
    name.change.color = plyr::mapvalues(
      Name.Change.fac,
      from = c(
        "Pro",
        "Against",
        "Resigned/Accepting",
        "Neutral",
        "Not Specified"
      ),
      to = c("#1395BA","#F16C20","#0D3C55","#EBC844","#A2B86C")
    )
  )

这是我对 HighCharter 的最佳尝试：

demo %>% ## these colors don't match
  group_by(constituent.type,Name.Change.fac,name.change.color) %>%
  summarise(count = n()) %>%
  hchart(type = "bar",
         hcaes(y = count,
               x = constituent.type,
               group = Name.Change.fac,
               color = name.change.color),
         color = unique(demo$name.change.color)) %>%
  hc_plotOptions(bar = list(stacking = "percent")) %>%
  hc_tooltip(shared = TRUE)

如您所见，标签中的颜色与堆叠图表中的（正确分配的）颜色不对应。如果我删除color=name.change.colorwithin hcaes()，则条形和标签之间的颜色匹配，但颜色与我分配的颜色不同，并且可能会因图表而异。我探索了手动重新创建图例的方法，但是我无法使用反应式单击功能来切换图表中的可见条。

这是我的图书馆：

library('plyr')
library('dplyr')
library('tidyr')
library('highcharter')

标签： rchartshighchartsdata-visualizationr-highcharter

解决方案

试试看

color = levels(demo$name.change.color)

代替

color = unique(demo$name.change.color)

给你正确的顺序。

如果您在代码中的某处使用唯一或丢弃因子级别，事情可能会变得异常。

或者，您可以像这样简化一些事情：

invisible(suppressPackageStartupMessages(lapply(c("dplyr","tidyr","highcharter"), 
    require, character.only=TRUE)))
responses <- c('Pro','Against','Neutral','Resigned/Accepting','Not Specified')
constituents <- c('dual degree','law only','undergrad only','friend','parent only')
indiv <- rep(1:50)
set.seed(123)
Name.Change <- factor(sample(responses, 50, replace = TRUE), 
    levels = responses, ordered = TRUE)
constituent.type <- sample(constituents, 50, replace = TRUE)
demo <- tibble(indiv, Name.Change, constituent.type)

name.change.color <- setNames(
    c("#1395BA","#F16C20","#0D3C55","#EBC844","#A2B86C"),
    levels(demo$Name.Change))

demo %>% 
    group_by(constituent.type, Name.Change) %>%
    summarise(count = n()) %>%
    hchart(type = "bar",
           hcaes(y = count,
                 x = constituent.type,
                 group = Name.Change),
           color = name.change.color
    ) %>%
    hc_plotOptions(bar = list(stacking = "percent")) %>%
    hc_tooltip(shared = TRUE)
#> `summarise()` regrouping output by 'constituent.type' (override with `.groups` argument)

由reprex 包（v0.3.0）于 2020-07-31 创建

推荐阅读