使用 facet_wrap 和 ggplotly 的第一个和最后一个方面比中间方面大

Question

使用示例数据：

library(tidyverse)
library(plotly)

myplot <- diamonds %>% ggplot(aes(clarity, price)) +
  geom_boxplot() +
  facet_wrap(~ clarity, ncol = 8, scales = "free", strip.position = "bottom") +
  theme(axis.ticks.x = element_blank(),
        axis.text.x = element_blank(),
        axis.title.x = element_blank())

ggplotly(myplot)

Returns 类似于：

与第一个和最后一个相比，内部刻面的缩放比例非常可怕，并且有很多额外的填充。我试图从这些问题中找到解决方案：

ggplotly not working properly when number are facets are more

经过反复试验，我在 theme() 中使用了 panel.spacing.x = unit(-0.5, "line")，它看起来好多了，很多额外的填充都消失了，但内部面仍然明显更小。

另外一个问题，但不是那么重要，当我将条带标签设置在底部时，条带标签在 ggplotly() 调用中位于顶部。似乎是一个持续存在的问题 here，有人有解决方法吗？

编辑：在我的真实数据集中，我需要每个方面的 y 轴标签，因为它们的比例完全不同，所以我将它们保留在示例中，这就是我需要 facet_wrap 的原因。我的真实数据集的截图用于解释：

Answer 1

更新后的答案 (2)：只需使用 `fixfacets()`

我整理了一个函数 fixfacets(fig, facets, domain_offset) 可以将此转换为：

...通过使用这个：

f <- fixfacets(figure = fig, facets <- unique(df$clarity), domain_offset <- 0.06)

...进入这个：

此函数现在在面数方面应该非常灵活。

完整代码：

library(tidyverse)
library(plotly)

# YOUR SETUP:

df <- data.frame(diamonds)

df['price'][df$clarity == 'VS1', ] <- filter(df['price'], df['clarity']=='VS1')*2

myplot <- df %>% ggplot(aes(clarity, price)) +
  geom_boxplot() +
  facet_wrap(~ clarity, scales = 'free', shrink = FALSE, ncol = 8, strip.position = "bottom", dir='h') +
  theme(axis.ticks.x = element_blank(),
        axis.text.x = element_blank(),
        axis.title.x = element_blank())
fig <- ggplotly(myplot)

# Custom function that takes a ggplotly figure and its facets as arguments.
# The upper x-values for each domain is set programmatically, but you can adjust
# the look of the figure by adjusting the width of the facet domain and the 
# corresponding annotations labels through the domain_offset variable
fixfacets <- function(figure, facets, domain_offset){

  # split x ranges from 0 to 1 into
  # intervals corresponding to number of facets
  # xHi = highest x for shape
  xHi <- seq(0, 1, len = n_facets+1)
  xHi <- xHi[2:length(xHi)]

  xOs <- domain_offset

  # Shape manipulations, identified by dark grey backround: "rgba(217,217,217,1)"
  # structure: p$x$layout$shapes[[2]]$
  shp <- fig$x$layout$shapes
  j <- 1
  for (i in seq_along(shp)){
    if (shp[[i]]$fillcolor=="rgba(217,217,217,1)" & (!is.na(shp[[i]]$fillcolor))){
       #$x$layout$shapes[[i]]$fillcolor <- 'rgba(0,0,255,0.5)' # optionally change color for each label shape
       fig$x$layout$shapes[[i]]$x1 <- xHi[j]
       fig$x$layout$shapes[[i]]$x0 <- (xHi[j] - xOs)
       #fig$x$layout$shapes[[i]]$y <- -0.05
       j<-j+1
    }
  }

  # annotation manipulations, identified by label name
  # structure: p$x$layout$annotations[[2]]
  ann <- fig$x$layout$annotations
  annos <- facets
  j <- 1
  for (i in seq_along(ann)){
    if (ann[[i]]$text %in% annos){
       # but each annotation between high and low x,
       # and set adjustment to center
       fig$x$layout$annotations[[i]]$x <- (((xHi[j]-xOs)+xHi[j])/2)
       fig$x$layout$annotations[[i]]$xanchor <- 'center'
       #print(fig$x$layout$annotations[[i]]$y)
       #fig$x$layout$annotations[[i]]$y <- -0.05
       j<-j+1
    }
  }

  # domain manipulations
  # set high and low x for each facet domain
  xax <- names(fig$x$layout)
  j <- 1
  for (i in seq_along(xax)){
    if (!is.na(pmatch('xaxis', lot[i]))){
      #print(p[['x']][['layout']][[lot[i]]][['domain']][2])
      fig[['x']][['layout']][[xax[i]]][['domain']][2] <- xHi[j]
      fig[['x']][['layout']][[xax[i]]][['domain']][1] <- xHi[j] - xOs
      j<-j+1
    }
  }

  return(fig)
}

f <- fixfacets(figure = fig, facets <- unique(df$clarity), domain_offset <- 0.06)
f

更新答案 (1)：如何以编程方式处理每个元素！

图形中需要进行一些编辑以满足您在保持每个面的缩放比例和修复奇怪布局方面的需要的元素是：

x 标签注释通过 fig$x$layout$annotations,

fig$x$layout$shapes

x 个标签形状
通过fig$x$layout$xaxis$domain

唯一真正的挑战是在许多其他形状和注释中引用正确的形状和注释。下面的代码片段将精确地执行此操作以生成以下图：

代码片段可能需要针对每个案例在方面名称和名称数量方面进行一些仔细的调整，但代码本身非常基础，因此您应该不会有任何问题。有时间我自己再打磨一下。

完整代码：

ibrary(tidyverse)
library(plotly)

# YOUR SETUP:

df <- data.frame(diamonds)

df['price'][df$clarity == 'VS1', ] <- filter(df['price'], df['clarity']=='VS1')*2

myplot <- df %>% ggplot(aes(clarity, price)) +
  geom_boxplot() +
  facet_wrap(~ clarity, scales = 'free', shrink = FALSE, ncol = 8, strip.position = "bottom", dir='h') +
  theme(axis.ticks.x = element_blank(),
        axis.text.x = element_blank(),
        axis.title.x = element_blank())
#fig <- ggplotly(myplot)

# MY SUGGESTED SOLUTION:

# get info about facets
# through unique levels of clarity
facets <- unique(df$clarity)
n_facets <- length(facets)

# split x ranges from 0 to 1 into
# intervals corresponding to number of facets
# xHi = highest x for shape
xHi <- seq(0, 1, len = n_facets+1)
xHi <- xHi[2:length(xHi)]

# specify an offset from highest to lowest x for shapes
xOs <- 0.06

# Shape manipulations, identified by dark grey backround: "rgba(217,217,217,1)"
# structure: p$x$layout$shapes[[2]]$
shp <- fig$x$layout$shapes
j <- 1
for (i in seq_along(shp)){
  if (shp[[i]]$fillcolor=="rgba(217,217,217,1)" & (!is.na(shp[[i]]$fillcolor))){
     #fig$x$layout$shapes[[i]]$fillcolor <- 'rgba(0,0,255,0.5)' # optionally change color for each label shape
     fig$x$layout$shapes[[i]]$x1 <- xHi[j]
     fig$x$layout$shapes[[i]]$x0 <- (xHi[j] - xOs)
     j<-j+1
  }
}

# annotation manipulations, identified by label name
# structure: p$x$layout$annotations[[2]]
ann <- fig$x$layout$annotations
annos <- facets
j <- 1
for (i in seq_along(ann)){
  if (ann[[i]]$text %in% annos){
     # but each annotation between high and low x,
     # and set adjustment to center
     fig$x$layout$annotations[[i]]$x <- (((xHi[j]-xOs)+xHi[j])/2)
     fig$x$layout$annotations[[i]]$xanchor <- 'center'

     j<-j+1
  }
}

# domain manipulations
# set high and low x for each facet domain
lot <- names(fig$x$layout)
j <- 1
for (i in seq_along(lot)){
  if (!is.na(pmatch('xaxis', lot[i]))){
    #print(p[['x']][['layout']][[lot[i]]][['domain']][2])
    fig[['x']][['layout']][[lot[i]]][['domain']][2] <- xHi[j]
    fig[['x']][['layout']][[lot[i]]][['domain']][1] <- xHi[j] - xOs
    j<-j+1
  }
}

fig

基于内置功能的初始答案

由于许多变量的值非常不同，看来无论如何您最终都会得到一个具有挑战性的格式，这意味着

面将具有不同的宽度，或者
标签会覆盖面或太小而无法阅读，或者
如果没有滚动条，图形会太宽而无法显示。

所以我的建议是针对每个独特的清晰度重新调整您的 price 列并设置 scale='free_x。我仍然希望有人能提出更好的答案。但我会这样做：

绘图 1： 重新缩放的值和 scale='free_x

代码 1:

#install.packages("scales")
library(tidyverse)
library(plotly)
library(scales)

library(data.table)
setDT(df)

df <- data.frame(diamonds)

df['price'][df$clarity == 'VS1', ] <- filter(df['price'], df['clarity']=='VS1')*2

# rescale price for each clarity
setDT(df)
clarities <- unique(df$clarity)
for (c in clarities){
  df[clarity == c, price := rescale(price)]
}

df$price <- rescale(df$price)

myplot <- df %>% ggplot(aes(clarity, price)) +
  geom_boxplot() +
  facet_wrap(~ clarity, scales = 'free_x', shrink = FALSE, ncol = 8, strip.position = "bottom") +
  theme(axis.ticks.x = element_blank(),
        axis.text.x = element_blank(),
        axis.title.x = element_blank())

p <- ggplotly(myplot)
p

这当然只会深入了解每个类别的内部分布，因为值已经重新调整。如果您想显示原始价格数据并保持可读性，我建议通过将 width 设置得足够大来为滚动条腾出空间。

地块 2： scales='free' 宽度足够大：

代码 2:

library(tidyverse)
library(plotly)

df <- data.frame(diamonds)

df['price'][df$clarity == 'VS1', ] <- filter(df['price'], df['clarity']=='VS1')*2

myplot <- df %>% ggplot(aes(clarity, price)) +
  geom_boxplot() +
  facet_wrap(~ clarity, scales = 'free', shrink = FALSE, ncol = 8, strip.position = "bottom") +
  theme(axis.ticks.x = element_blank(),
        axis.text.x = element_blank(),
        axis.title.x = element_blank())

p <- ggplotly(myplot, width = 1400)
p

当然，如果您的值在各个类别之间变化不大，scales='free_x' 就可以了。

情节 3： scales='free_x

代码 3:

library(tidyverse)
library(plotly)

df <- data.frame(diamonds)

df['price'][df$clarity == 'VS1', ] <- filter(df['price'], df['clarity']=='VS1')*2

myplot <- df %>% ggplot(aes(clarity, price)) +
  geom_boxplot() +
  facet_wrap(~ clarity, scales = 'free_x', shrink = FALSE, ncol = 8, strip.position = "bottom") +
  theme(axis.ticks.x = element_blank(),
        axis.text.x = element_blank(),
        axis.title.x = element_blank())

p <- ggplotly(myplot)
p

Answer 2

有时，如果您对选定的情节感到困惑，那么考虑完全不同的情节会很有帮助。这一切都取决于你想想象的是什么。有时箱形图有效，有时直方图有效，有时密度有效。下面是密度图如何让您快速了解许多参数的数据分布的示例。

library(tidyverse)
library(plotly)
myplot <- diamonds %>% ggplot(aes(price, colour = clarity)) +
  geom_density(aes(fill = clarity), alpha = 0.25) +
  theme(axis.ticks.x = element_blank(),
        axis.text.x = element_blank(),
        axis.title.x = element_blank())

使用 facet_wrap 和 ggplotly 的第一个和最后一个方面比中间方面大

First and last facets using facet_wrap with ggplotly are larger than middle facets

r

ggplot2

plotly

ggplotly

更新后的答案 (2)：只需使用 `fixfacets()`

更新答案 (1)：如何以编程方式处理每个元素！

基于内置功能的初始答案

使用 facet_wrap 和 ggplotly 的第一个和最后一个方面比中间方面大

First and last facets using facet_wrap with ggplotly are larger than middle facets

r

ggplot2

plotly

ggplotly

更新后的答案 (2)：只需使用 fixfacets()

更新答案 (1)：如何以编程方式处理每个元素！

基于内置功能的初始答案

更新后的答案 (2)：只需使用 `fixfacets()`