Question

对于上下文：：我正在查看多个不同的相关系数。对于每个相关，我创建了一个自举分布，并使用了引导百分位数方法来创建每个系数的置信区间。当我查看多个相关性时，实际上我使用的是更严格的alpha级别，以后我将需要对具有不同alpha校正的不同数据集重复此分析。所有这些都进展顺利，但我正在努力创建一个图表以将自定义间隔表示为误差线。

问题：：如何在ggplot中创建图形来表示数据的中值以及误差条的自定义百分位数。我的数据在data.frame中，其中一个变量标识组（分析），第二个变量标识组中的所有分数。实际上，“分析”变量的每个级别有10,000个案例，总共40,000行。为了简洁起见，我在下面提供了索引打印输出。

 >BootDistOverall[c(1:2,10000:10002,20000:20002,30000:30002),]
                             Analysis       Dist
  1      Alpha by Consequences (No Outlier) -0.4286326
  2      Alpha by Consequences (No Outlier) -0.4191646
  10000  Alpha by Consequences (No Outlier) -0.5248891
  10001 Alpha by Past-30-Day Binge Drinking -0.2972018
  10002 Alpha by Past-30-Day Binge Drinking -0.3011621
  20000 Alpha by Past-30-Day Binge Drinking -0.4145920
  20001                  Q0 by Consequences  0.3689336
  20002                  Q0 by Consequences  0.4540535
  30000                  Q0 by Consequences  0.5772917
  30001    Q0 by Past-30-Day Binge Drinking  0.6655952
  30002    Q0 by Past-30-Day Binge Drinking  0.4412748

我已经能够使用ggplot创建数据的小提琴图（请参阅下面的链接和代码），但是我真的很想用每个分布的中值以及百分位数作为误差线来表示。我可以获取中位数或箱线图来表示此数据，但是我需要自定义百分位数。

p0 <- <-ggplot(BootDistOverall, aes(Analysis,Dist))+ 
    geom_violin(scale = "area", 
    color = "#002344", 
    size = 1, 
    fill = "#FECB00")+ 
    ylim(-1,1)+
    geom_hline(yintercept = 0, 
        linetype = "dashed", 
        color = "black")+
    xlab("Analysis")+
    ylab("Bootstrapped Pearson's r")+
    coord_flip()+
    theme_bw()

我需要帮助来创建类似的图形，但要有对应于自定义百分位数的中位数点和误差线。我尝试了多种不同的方法（geom_errorbar，geom_pointrange），但似乎无法使它们起作用。我能够做的唯一方法是像我在使用R（）的基础R图形中所做的那样，分别向图中添加线段（请参见下面的代码和链接），但是必须有更好的方法。是ggplot的新手，所以可能有一个简单的修复方法，但我不知所措。

 #Create percentile points
  Uppers = c(
      quantile(BootDist2$Dist, .995,na.rm=T),
      quantile(BootDist4$Dist, .995,na.rm=T),
      quantile(BootDist1$Dist, .995,na.rm=T),
      quantile(BootDist3$Dist, .995,na.rm=T))

   Lowers = c(
      quantile(BootDist2$Dist, .005,na.rm=T),
      quantile(BootDist4$Dist, .005,na.rm=T),
      quantile(BootDist1$Dist, .005,na.rm=T),
      quantile(BootDist3$Dist, .005,na.rm=T))

 #Create a point graph
   ggplot(BootDistOverall, aes(x=Analysis,y=Dist))+   
      stat_summary(fun.y = mean,
           geom = "point",
           shape=22,
           size=5,
           color = "#002344",
           fill = "#FECB00")+
      theme_bw()+
      coord_flip()+
      ylim(-1,1)+
      geom_hline(yintercept = 0, 
                 linetype = "dashed", 
                 color = "black")+
      xlab("Analysis")+
      ylab("Bootstrapped Pearson's r")+
  #Add error bars with geomsemgents
      geom_segment(x=1,xend=1,y=Lowers[1],yend=Uppers[1])+
      geom_segment(x=2,xend=2,y=Lowers[2],yend=Uppers[2])+
      geom_segment(x=3,xend=3,y=Lowers[3],yend=Uppers[3])+
      geom_segment(x=4,xend=4,y=Lowers[4],yend=Uppers[4])+
      geom_segment(x=.9,xend=1.1,y=Lowers[1],yend=Lowers[1])+
      geom_segment(x=.9,xend=1.1,y=Uppers[1],yend=Uppers[1])+
      geom_segment(x=1.9,xend=2.1,y=Lowers[2],yend=Lowers[2])+
      geom_segment(x=1.9,xend=2.1,y=Uppers[2],yend=Uppers[2])+
      geom_segment(x=2.9,xend=3.1,y=Lowers[3],yend=Lowers[3])+
      geom_segment(x=2.9,xend=3.1,y=Uppers[3],yend=Uppers[3])+
      geom_segment(x=3.9,xend=4.1,y=Lowers[4],yend=Lowers[4])+
      geom_segment(x=3.9,xend=4.1,y=Uppers[4],yend=Uppers[4])

Answer 1

在这里有一点点的信念，假设每个分析组的MAX值是您要绘制的，作为误差线的上限，MIN值是误差线的下限，并且剩下的应该是中位数。注意-您仅为Q0 by Past-30-Day Binge Drinking提供了两行，因此这可能是一个错误的假设...您需要根据匹配项进行修改，无论数据实际代表什么...

...关于如何设置数据以绘制在ggplot()中的方法-工作范例是，每种审美观念只有一个变量。为了绘制误差线，您需要x，y，ymin和ymax。重新格式化数据以匹配此格式后，绘图就很简单了。这是一个工作示例：

library(data.table)
library(ggplot2)

d <- structure(list(Analysis = c("Alpha by Consequences (No Outlier)", 
                                 "Alpha by Consequences (No Outlier)", "Alpha by Consequences (No Outlier)", 
                                 "Alpha by Past-30-Day Binge Drinking", "Alpha by Past-30-Day Binge Drinking", 
                                 "Alpha by Past-30-Day Binge Drinking", "Q0 by Consequences", 
                                 "Q0 by Consequences", "Q0 by Consequences", "Q0 by Past-30-Day Binge Drinking", 
                                 "Q0 by Past-30-Day Binge Drinking"), Dist = c(-0.4286326, -0.4191646, 
                                                                               -0.5248891, -0.2972018, -0.3011621, -0.414592, 0.3689336, 0.4540535, 
                                                                               0.5772917, 0.6655952, 0.4412748), var = c("median", "upper", 
                                                                                                                         "lower", "upper", "median", "lower", "lower", "median", "upper", 
                                                                                                                         "upper", "lower")), row.names = c(NA, -11L), class = c("data.table", 
                                                                                                                                                                                "data.frame"))
#impute which row is the min, max, and median - NOTE you only gave two rows for the last Analysis group
d[, var := ifelse(Dist == min(Dist), "lower", ifelse(Dist == max(Dist), "upper", "median")), by = Analysis]
#cast into one row per Analysis
d_wide <- dcast(Analysis ~ var, data = d, value.var = "Dist")
#plot
ggplot(d_wide, aes(Analysis, median, ymin = lower, ymax = upper)) +
  geom_errorbar(width = .4) +
  geom_point(colour = "orange", size = 4) +
  coord_flip() +
  theme_bw()
#> Warning: Removed 1 rows containing missing values (geom_point).

^{由reprex package（v0.2.1）于2019-03-09创建}

R ggplot自定义百分位数

1 个答案: