我有一个示例数据框:
dput(数据)
structure(list(DAF = c(0.00704225, 0.00352113, 0.00352113, 0.028169,
0.00352113, 0.00704225, 0.0105634, 0.00352113, 0.0105634, 0.00352113,
0.00352113, 0.00352113, 0.0176056, 0.0140845, 0.00352113, 0.0140845,
0.00352113, 0.0105634, 0.00352113, 0.00352113, 0.0140845, 0.00352113,
0.084507, 0.00352113, 0.0669014, 0.00704225, 0.00352113, 0.00352113,
0.00704225, 0.00352113, 0.00704225, 0.00352113, 0.00352113, 0.028169,
0.00352113, 0.00704225, 0.0105634, 0.00352113, 0.0105634, 0.00352113,
0.00352113, 0.00352113, 0.0176056, 0.0140845, 0.00352113, 0.0140845,
0.00352113, 0.0105634, 0.00352113, 0.00352113, 0.0140845, 0.00352113,
0.084507, 0.00352113, 0.0669014, 0.00704225, 0.00352113, 0.00352113,
0.00704225, 0.00352113), TYPE = structure(c(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,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("All SVs", "bDEL"), class = "factor"),
Function = structure(c(2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 2L, 2L, 2L, 1L), .Label = c("Genic", "Intergenic"
), class = "factor")), .Names = c("DAF", "TYPE", "Function"
), class = "data.frame", row.names = c(NA, -60L))
我使用“
绘制p1<-ggplot(sv,aes(x=DAF,y=..density..,fill=Function))+geom_histogram(position="dodge",binwidth=0.02)+facet_wrap( ~ TYPE, scales = "free",ncol=2)
数据框中带注释的文本:
> dput(dat)
structure(list(x = c(0.05, 0.05, 0.05, 0.05, 0.05, 0.05), y = c(20L,
17L, 14L, 35L, 30L, 25L), labs = structure(c(3L, 4L, 6L, 1L,
2L, 5L), .Label = c("mean=0.0173", "mean=0.0190", "mean=0.0415",
"mean=0.0440", "p=0.0393", "p=1.47e-08"), class = "factor"),
TYPE = structure(c(1L, 1L, 1L, 2L, 2L, 2L), .Label = c("All SVs",
"bDEL"), class = "factor")), .Names = c("x", "y", "labs",
"TYPE"), class = "data.frame", row.names = c(NA, -6L))
p2<-p1+geom_text(aes(x, y, label=labs),size=1,data=dat,inherit.aes = F)
给出了以下情节:
但是,我需要通过相应的图例颜色为每个方面内的文本“mean = ...”着色。即在方面所有SV中,'mean = 0.0415'应为“Genic”颜色,'mean = 0.0440'为'Intergenic'颜色。
答案 0 :(得分:2)
您需要在“dat”中添加一列来表示每行/标签应该在哪个组中。我使用NA
作为p值。
dat$Function = c("Genic", "Intergenic", NA, "Genic", "Intergenic", NA)
然后,您可以将此变量映射到color
中的geom_text
。您可以使用NA
。
show.legend = FALSE
添加到图例中
p1 + geom_text(aes(x, y, label = labs, color = Function),
size = 1, data = dat, inherit.aes = FALSE, show.legend = FALSE)
NA
颜色默认为grey50
,如果scale_color_discrete
需要,您可以将其更改为其他内容。
p1 + geom_text(aes(x, y, label = labs, color = Function),
size = 1, data = dat, inherit.aes = FALSE, show.legend = FALSE) +
scale_color_discrete(na.value = "black")