如何使用 dplyr 或 ggplot 绘制连续参数的中值？答案

【问题标题】：How to plot a median value of a continuous parameter using dplyr or ggplot?如何使用 dplyr 或 ggplot 绘制连续参数的中值？
【发布时间】：2023-04-02 16:13:01
【问题描述】：

我正在尝试绘制每个ee$Ki67 表示的中位时间ee$rfs，这是肿瘤样本中许多细胞增殖的标志物，即。也是一个连续的协变量。

我在下面附上了我的数据ee。我正在dplyr 或ggplot 中寻找解决方案。显然，我已经寻求帮助，例如here，但没有运气。

我目前的剧情：

如下

ggplot(ee, (aes(x=Ki67,y=rfs))) + 

  geom_point(aes(color=as.factor(WHO)),size=6,shape=20,alpha=0.5) +
  facet_wrap(.~EOR)

我尝试了mutate、group_by、filter 和geom_line 的变体。我试过geom_smooth，但我担心这会画出最合适的（？）而不是中位数。

ee <- structure(list(rfs = c(26.4, 84, 42, 13.2, 18, 33.6, 39.6, 9.6, 
16.8, 19.2, 10.8, 7.2, 10.8, 76.8, 58.8, 31.2, 18, 182.4, 20.4, 
13.2, 8.4, 2.4, 123.6, 60, 100.8, 82.8, 12, 60, 18, 29.8, 68.3, 
27.2, 18.7, 64.9, 6.5, 50.3, 46.4, 29.9, 31.4, 42.7, 31.1, 98.1, 
80.9, 24.1, 49.2, 12.2, 20.5, 62.8, 9, 69, 30, 91.79, 8.57, 60.88, 
11.5, 56.87, 49.05, 16.95, 4.5, 8.74, 60.06, 37.85, 90.12, 123.76, 
47.41, 55.92, 3.09, 27.34, 4.99, 28.06, 26.71, 23.03, 6.34, 79.34, 
2.5, 19.32, 9.23, 2.6, 4.34, 45.9, 29.34, 8.58, 29.41, 30.72, 
15.97, 37.06, 17.05, 14.29, 5.95, 3.42, 60.58, 19.81, 72.91, 
16.99, 7.29, 74.32, 3.35, 39.95, 4.4, 15.44, 2.5, 28.32, 40.15, 
57.69, 27.86, 21.59, 10.09, 8.18, 21.59, 3.19, 3.12, 8.25, 14, 
14, 2, 23, 15, 9, 9, 28, 14, 23, 21, 26, 24, 63, 25, 34, 26.83333333, 
32.4, 28.76666667, 32.93333333, 32.16666667, 10.06666667, 46.66666667, 
58.06666667, 29.06666667, 30.33333333, 26.56666667, 24.23333333, 
36.5, 31.73333333, 5.733333333, 44.16666667, 46.93333333, 48.5, 
64.7, 37.16666667, 21.56666667, 14.8, 53.83333333, 59.06666667, 
8.7, 13.43333333, 12.56666667, 65.73333333, 54.83333333, 30.63333333, 
5, 65, 7, 12, 14, 6, 15, 36, 99, 16, 87, 6, 33, 3, 3, 11, 24, 
24, 15, 10, 28, 18, 14, 29, 20, 12, 42, 31, 14, 18, 29, 39, 62, 
62, 46), Ki67 = c(25, 15, 8, 15, 18, 5, 2, 18, 6, 12, 12, 13, 
13, 15, 20, 3, 30, 10, 18, 20, 7, 17, 5, 3, 20, 5, 20, 10, 2, 
5, 4, 7, 8, 12, 40, 17, 3, 5, 20, 5, 22, 6, 6, 18, 15, 12, 15, 
5, 15, 15, 3, 4, 10, 5, 2, 4, 3, 5, 7, 7, 4, 2, 4, 3, 20, 15, 
25, 20, 10, 15, 15, 8, 15, 8, 8, 10, 22, 18, 50, 30, 30, 45, 
50, 30, 8, 25, 25, 10, 25, 20, 15, 10, 8, 55, 10, 10, 10, 20, 
30, 5, 20, 8, 30, 10, 15, 25, 30, 38, 15, 30, 25, 15, 5, 8, 35, 
9, 14, 2, 1, 1, 20, 30, 2, 8, 2, 16, 20, 23, 4.5, 2.2, 9.43, 
8.95, 6.47, 1.81, 7.27, 12.4, 7.97, 21.99, 8.98, 17.3, 8, 15, 
15, 20, 6, 5, 12.5, 3, 20, 20, 11.5, 2.66, 14.7, 9.13, 5, 5, 
12, 11, 2, 8, 20, 50, 10, 15, 30, 8, 10, 20, 10, 10, 30, 10, 
10, 13, 10, 15, 10, 10, 40, 10, 5, 15, 15, 15, 25, 15, 30, 30, 
8, 30, 15, 20, 13), EOR = c(1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 
1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 
0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 
0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 
0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 
0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 
0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 
0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 
0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L), WHO = c(2L, 2L, 2L, 3L, 3L, 
2L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 2L, 3L, 3L, 3L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 3L, 1L, 2L, 
1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 
2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L)), class = "data.frame", row.names = c(NA, 
-193L))

【问题讨论】：

实际上，我不确定您要准确绘制的中位数。您是否试图为Ki76 的每个唯一值获得一个中值rfs 值？结果图应该是什么样子？
您是否只是想在您提供的图中显示 WHO、Ki67 和 EOR 的 rfs 中值？还是您只想绘制中值？还是你完全在追求别的东西？

标签： r ggplot2 dplyr

【解决方案1】：

如果您需要了解 rfs 的中位数如何随 Ki67 变化（而不是线性回归中的均值），则需要使用分位数回归。

幸运的是，这是在 ggplot2 中实现的，我首先制作了一个没有 facet 的图，以显示两个 facet 的回归线非常相似：

library(quantreg)
library(ggplot2)

ggplot(ee,aes(x=Ki67,y=rfs)) + 
geom_point(aes(color=as.factor(WHO)),alpha=0.5) +
geom_quantile(aes(linetype=factor(EOR)),quantiles = 0.5,col="black")

并且具有方面（您不需要指定组）：

ggplot(ee,aes(x=Ki67,y=rfs)) + 
geom_point(aes(color=as.factor(WHO)),alpha=0.5) +
geom_quantile(quantiles = 0.5,lty=8,col="black")+
facet_wrap(.~EOR)

或者您可以得出每个组的预测，然后绘制：

func = function(df,X,id){
  fit = rq(rfs ~ Ki67,data=df)
  data.frame(Ki67=X,rfs=predict(fit,data.frame(Ki67=X)))
}

Xrange= 1:max(ee$Ki67)

pred = ee %>% group_by(EOR) %>% group_map(~func(.x,Xrange)) %>% bind_rows()
pred$EOR = rep(unique(ee$EOR),each=length(Xrange))

ggplot(ee,aes(x=Ki67,y=rfs)) + 
geom_point(aes(color=as.factor(WHO)),alpha=0.5) +
geom_line(data=pred)+
    facet_wrap(.~EOR)

【讨论】：

【解决方案2】：

如果您只想按 WHO、Ki67 和 EOR 绘制 rfs 中值，则可以使用 group_by 和 summarise，并将汇总数据提供给 ggplot。

library(tidyverse)

ee %>%
  group_by(WHO, Ki67, EOR) %>%
  summarise(rfs = median(rfs)) %>%
  ggplot(aes(x = Ki67, y = rfs, color = factor(WHO))) +
  geom_point() +
  facet_wrap( ~ EOR)

【讨论】：