我正在尝试生成一个点图，其中包含具有三个分类变量（mea、tre 和 sex）的设计中单个测量值 (len) 的值。
I've produced a plot that has all I'm looking for split across six different subplots: 
但如果可能的话，我希望将它们全部放在一个图中，优先使用 ggplot。
这是我当前的 R 代码：
ggplot(mydf, aes(x=factor(mea), y=len), group=sex) +
geom_point() + geom_errorbar(limits, width=0.1) + facet_wrap(~ tre + sex)
以及来自 dput 的样本数据：
structure(list(mea = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L), .Label = c("PO_P", "Melaniz"), class = "factor"),
tre = structure(c(1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L,
2L, 3L, 3L), .Label = c("a", "b", "c"), class = "factor"),
Sex = structure(c(1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L), .Label = c("Male", "Female"), class = "factor"),
N = c(26, 26, 25, 25, 27, 27, 14, 13, 12, 11, 14, 13), len = c(10.6615384615385,
10.5807692307692, 10.292, 10.6, 10.2851851851852, 10.6518518518519,
11.4785714285714, 11.7153846153846, 11.7083333333333, 11.5,
11.6214285714286, 11.8923076923077), sd = c(0.869057845290829,
0.779753412698774, 0.722218803410712, 0.654471797202395,
0.906686148609193, 0.8040141456708, 1.0123685642542, 0.805032249712347,
1.13654846981659, 0.822192191643779, 0.833139171519908, 0.739889111580849
), se = c(0.170436265829955, 0.152922225659293, 0.144443760682142,
0.130894359440479, 0.174491830656674, 0.154732594478434,
0.270566879755675, 0.223275773441538, 0.328093282497832,
0.247900273203854, 0.222665809666299, 0.205208317689404),
ci = c(0.351020060264102, 0.314949219318153, 0.298117269908016,
0.270152680174426, 0.358673094717481, 0.318057403068012,
0.584524206501098, 0.486476119728297, 0.722128445903482,
0.552356230143519, 0.481040236068982, 0.447110515336101)), .Names = c("mea", "tre", "sex", "N", "len", "sd", "se", "ci"), row.names = c(NA,
-12L), class = "data.frame")
要区分所有这些类别变量，您可以使用颜色、形状、大小、点类型等。以下是使用颜色和点类型的示例sex and tre,
library(ggplot2)
limits <- aes(ymax=mydf$len+mydf$se, ymin=mydf$len-mydf$se)
ggplot(mydf, aes(x=factor(mea), y=len, color=sex, pch=tre)) +
geom_point(position=position_dodge(width=0.5)) +
geom_errorbar(limits, position=position_dodge(width=0.5))