如何用ggproto扩展ggplot2 boxplot？ [英] How to extend ggplot2 boxplot with ggproto?

问题描述

我经常在我的作品中使用箱型图，并且像 ggplot2 美学。但标准 geom_boxplot 缺少两件重要的事情：胡须和中位数标签的结尾。

  gBoxplot<  -  function（formula = NULL，data = NULL， （公式）
响应< - 变量[1] 
因子<  -  vars [2] 
＃中值标记函数
 fun_med<  -  function（x）{
 return（data.frame（y = median（x），label = round（median（x），3）））
} 
p < -  ggplot（data，aes_string（x = factor，y = response））+ 
 stat_boxplot（geom =errorbar ，width = 0.6）+ 
 geom_boxplot（）+ 
 stat_summary（fun.data = fun_med，geom =label，family = font，size = fsize / 3，
 vjust = -0.1 ）+ 
 theme_grey（base_size = fsize，base_family = font）
 return（p）
} 
  

还有字体设置，但这只是因为我懒得制作主题。这是一个例子：

pre $ $ c $ g> gBoxplot（hwy〜class，mpg）

对我来说足够好，但有一些限制（不能使用自动躲避等等），并且最好根据 geom_boxplot 。我已阅读过

注意：我们实际上并不需要使用图层函数，我们可以使用原始的 stat_boxplot ， geom_boxplot 和 stat_summary 调用。但是如果我们希望能够从我们自定义的boxplot中控制它们，我们仍然必须填写所有参数，所以我认为这种方式更清晰 - 至少从结构的角度而不是功能。也许它不是，这是一个有趣的问题...

另外我没有那种看起来好多了的字体。但我不想跟踪并安装它。

I'm often using boxplots in my work and like ggplot2 aesthetics. But standard geom_boxplot lacks two things important for me: ends of whiskers and median labels. Thanks to information from here I've written a function:

gBoxplot <- function(formula = NULL, data = NULL, font = "CMU Serif", fsize = 18){
  require(ggplot2)
  vars <- all.vars(formula)
  response <- vars[1]
  factor <- vars[2]
  # A function for medians labelling
  fun_med <- function(x){
    return(data.frame(y = median(x), label = round(median(x), 3)))
  }
  p <- ggplot(data, aes_string(x = factor, y = response)) +
  stat_boxplot(geom = "errorbar", width = 0.6) +
  geom_boxplot() +
  stat_summary(fun.data = fun_med, geom = "label", family = font, size = fsize/3, 
                                                                         vjust = -0.1) +
  theme_grey(base_size = fsize, base_family = font)
  return(p)
}

There are also font settings, but this is just because I'm too lazy to make a theme. Here is an example:

gBoxplot(hwy ~ class, mpg)

Good enough for me, but there are some restrictictions (cannot use auto-dodging, etc.), and it will be better to make a new geom based on geom_boxplot. I've read the vignette Extending ggplot2, but cannot understand how to implement it. Any help will be appreciated.

解决方案

So been thinking about this one for a while. Basically when you create a new primitive, you normally write a combination of:

1. A layer-function
2. A stat-ggproto,
3. A geom-ggproto

Only the layer-function need be visible to the user. You only need to write a stat-ggproto if you need some new way of transforming your data to make your primitive. And you only need write a geom-ggproto if you have some new grid-based graphics to create.

In this case, where we are basically composting layer-function that already exist, we don’t really need to write new ggprotos. It is enough to write a new layer-function. This layer-function will create the three layers that you already are using and map the parameters the way you intend. In this case:

• Layer1 – uses geom_errorbar and stat_boxplot – to get our errorbars
• Layer2 – uses geom_boxplot and stat_boxplot - to create the boxplots
• Layer3 – users geom_label and stat_summary - to create the text labels with the mean value in the center of the boxes.

Of course you could write a new stat-ggproto and a new geom-ggproto that do all of these things at once. Or maybe you compost stat_summary and stat_boxplot into one, and the three geom-protos as well, and this do this with one layer. But there is little point unless we have efficiency problems.

Anyway, here is the code:

geom_myboxplot <- function(formula = NULL, data = NULL,
                           stat = "boxplot", position = "dodge",coef=1.5,
                           font = "sans", fsize = 18, width=0.6,
                           fun.data = NULL, fun.y = NULL, fun.ymax = NULL,
                           fun.ymin = NULL, fun.args = list(),
                           outlier.colour = NULL, outlier.color = NULL,
                           outlier.shape = 19, outlier.size = 1.5,outlier.stroke = 0.5,
                           notch = FALSE,  notchwidth = 0.5,varwidth = FALSE,
                           na.rm = FALSE, show.legend = NA,
                           inherit.aes = TRUE,...) {
    vars <- all.vars(formula)
    response <- vars[1]
    factor <- vars[2]
    mymap <- aes_string(x=factor,y=response)
    fun_med <- function(x) {
        return(data.frame(y = median(x), label = round(median(x), 3)))
    }
    position <- position_dodge(width)
    l1 <- layer(data = data, mapping = mymap, stat = StatBoxplot,
            geom = "errorbar", position = position, show.legend = show.legend,
            inherit.aes = inherit.aes, params = list(na.rm = na.rm,
                coef = coef, width = width, ...))
    l2 <- layer(data = data, mapping = mymap, stat = stat, geom = GeomBoxplot,
            position = position, show.legend = show.legend, inherit.aes = inherit.aes,
            params = list(outlier.colour = outlier.colour, outlier.shape = outlier.shape,
                outlier.size = outlier.size, outlier.stroke = outlier.stroke,
                notch = notch, notchwidth = notchwidth, varwidth = varwidth,
                na.rm = na.rm, ...))
    l3 <- layer(data = data, mapping = mymap, stat = StatSummary,
            geom = "label", position = position, show.legend = show.legend,
            inherit.aes = inherit.aes, params = list(fun.data = fun_med,
                fun.y = fun.y, fun.ymax = fun.ymax, fun.ymin = fun.ymin,
                fun.args = fun.args, na.rm=na.rm,family=font,size=fsize/3,vjust=-0.1,...))
    return(list(l1,l2,l3))
}

which allows you to create your customized boxplots it now like this:

ggplot(mpg) +
  geom_myboxplot( hwy ~ class, font = "sans",fsize = 18)+
  theme_grey(base_family = "sans",base_size = 18 )

And they look like this:

Note: we did not actually have to use the layer function, we could have used the orginal stat_boxplot, geom_boxplot, and stat_summary calls in their place. But we still would have had to fill in all the parameters if we wanted to be able to control them from our custom boxplot, so I think it was clearer this way - at least from the point-of-view of structure as opposed to functionality. Maybe it isn't though, it is a matter of taste...

Also I don't have that font which does look a lot nicer. But I did not feel like tracking it down and installing it.

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