如何使用自定义值绘制 geom_errorbar(R 或森林中的荟萃分析?)

How to plot geom_errorbar with custom values (meta-analysis in R or maybe forest?)

任何人都可以帮助我在 R 中绘制 geom_errorbar,当我的数据看起来时:

> Country Sex Correlation Number  Lower Upper
1  Brazil  Men      -0.108    301 -0.218 0.005
2 Bulgaria Men      -0.012     63 -0.258 0.236
3   Canada Men        0.07     25 -0.334 0.452
4   Brazil Women    -0.074     47 -0.353 0.217
5 Bulgaria Women    -0.042    300 -0.154 0.071
6  Canada  Women     0.092     51 -0.188 0.358

我想可视化各国在性别(filled/coloured 性别)方面的相关性差异。我有一个平均值(相关性)、该平均值的较低置信区间(较低)和较高(较高)。左边应该有国家和...基本上就是这样。不知何故我无法理解它。

在通过 Whosebug 搜索时,我想知道是否应该使用一些森林函数,因为它可能更接近我的想象。

到目前为止我所做的看起来很糟糕: link

提前致谢!

使用 ggplot2 包中的 facet 和 facet_wrap() 尝试这种方法。这里的代码:

library(ggplot2)
library(dplyr)
#Code
df %>%
  ggplot(aes(x=Country,y=Correlation,color=Sex))+
  geom_point()+
  geom_errorbar(aes(ymin=Lower,ymax=Upper))+
  facet_wrap(.~Sex)+
  theme_bw()+
  theme(legend.position = 'top',
        axis.text = element_text(color='black',face='bold'),
        axis.title = element_text(color='black',face='bold'),
        legend.text = element_text(color='black',face='bold'),
        legend.title = element_text(color='black',face='bold'),
        strip.text = element_text(color='black',face='bold'),
        strip.background = element_blank())

输出:

使用了一些数据:

#Data
df <- structure(list(Country = c("Brazil", "Bulgaria", "Canada", "Brazil", 
"Bulgaria", "Canada"), Sex = c("Men", "Men", "Men", "Women", 
"Women", "Women"), Correlation = c(-0.108, -0.012, 0.07, -0.074, 
-0.042, 0.092), Number = c(301L, 63L, 25L, 47L, 300L, 51L), Lower = c(-0.218, 
-0.258, -0.334, -0.353, -0.154, -0.188), Upper = c(0.005, 0.236, 
0.452, 0.217, 0.071, 0.358)), class = "data.frame", row.names = c("1", 
"2", "3", "4", "5", "6"))

听起来您正在寻找这样的东西:

ggplot(df, aes(x = Correlation, y = Country, color = Sex)) +
  geom_point(position = position_dodge(width = 0.75)) +
  geom_errorbarh(aes(xmin = Lower, xmax = Upper),
                 position = position_dodge(width = 0.75))

数据

df <- structure(list(Country = structure(c(1L, 2L, 3L, 1L, 2L, 3L), 
    .Label = c("Brazil", "Bulgaria", "Canada"), class = "factor"), 
    Sex = structure(c(1L, 1L, 1L, 2L, 2L, 2L), .Label = c("Men", "Women"), 
    class = "factor"), 
    Correlation = c(-0.108, -0.012, 0.07, -0.074, -0.042, 0.092
    ), Number = c(301L, 63L, 25L, 47L, 300L, 51L), Lower = c(-0.218, 
    -0.258, -0.334, -0.353, -0.154, -0.188), Upper = c(0.005, 
    0.236, 0.452, 0.217, 0.071, 0.358)), class = "data.frame", 
    row.names = c("1", "2", "3", "4", "5", "6"))