原文链接：R语言绘图 | 最全的云雨图绘制教程
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R语言绘制图形。每个图形我们会提供对应的R代码、数据和文本文档。此系列将会是一个长期更新的系列。
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本期教程
2023年教程总汇
https://mp.weixin.qq.com/s/wCTswNP8iHMNvu5GQauHdg
Code
- 加载所需R包
library(ggrain)
library(ggplot2)
- 加载数据
iris
- 绘制基础云雨图
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_rain(rain.side = 'l')
ggplot(iris, aes(x = 1, y = Sepal.Length, fill = Species)) +
geom_rain(alpha = .5)
给散点添加颜色
ggplot(iris, aes(1, Sepal.Width, fill = Species, color = Species)) +
geom_rain(alpha = .6,
boxplot.args = list(color = "black", outlier.shape = NA)) +
theme_classic() +
scale_fill_brewer(palette = 'Dark2') +
scale_color_brewer(palette = 'Dark2')
将图形进行翻转，使用
coord_flip()
ggplot(iris, aes(Species, Sepal.Width, fill = Species)) +
geom_rain(alpha = .5) +
theme_classic() +
scale_fill_brewer(palette = 'Dark2') +
guides(fill = 'none', color = 'none') +
coord_flip()
- 两两进行配对，使用线条连线
事例数据整理
set.seed(42) # the magic number
iris_subset <- iris[iris$Species %in% c('versicolor', 'virginica'),]
iris.long <- cbind(rbind(iris_subset, iris_subset, iris_subset),
data.frame(time = c(rep("t1", dim(iris_subset)[1]), rep("t2", dim(iris_subset)[1]), rep("t3", dim(iris_subset)[1])),
id = c(rep(1:dim(iris_subset)[1]), rep(1:dim(iris_subset)[1]), rep(1:dim(iris_subset)[1]))))
# adding .5 and some noise to the versicolor species in t2
iris.long$Sepal.Width[iris.long$Species == 'versicolor' & iris.long$time == "t2"] <- iris.long$Sepal.Width[iris.long$Species == 'versicolor' & iris.long$time == "t2"] + .5 + rnorm(length(iris.long$Sepal.Width[iris.long$Species == 'versicolor' & iris.long$time == "t2"]), sd = .2)
# adding .8 and some noise to the versicolor species in t3
iris.long$Sepal.Width[iris.long$Species == 'versicolor' & iris.long$time == "t3"] <- iris.long$Sepal.Width[iris.long$Species == 'versicolor' & iris.long$time == "t3"] + .8 + rnorm(length(iris.long$Sepal.Width[iris.long$Species == 'versicolor' & iris.long$time == "t3"]), sd = .2)
# now we subtract -.2 and some noise to the virginica species
iris.long$Sepal.Width[iris.long$Species == 'virginica' & iris.long$time == "t2"] <- iris.long$Sepal.Width[iris.long$Species == 'virginica' & iris.long$time == "t2"] - .2 + rnorm(length(iris.long$Sepal.Width[iris.long$Species == 'virginica' & iris.long$time == "t2"]), sd = .2)
# now we subtract -.4 and some noise to the virginica species
iris.long$Sepal.Width[iris.long$Species == 'virginica' & iris.long$time == "t3"] <- iris.long$Sepal.Width[iris.long$Species == 'virginica' & iris.long$time == "t3"] - .4 + rnorm(length(iris.long$Sepal.Width[iris.long$Species == 'virginica' & iris.long$time == "t3"]), sd = .2)
iris.long$Sepal.Width <- round(iris.long$Sepal.Width, 1) # rounding Sepal.Width so t2 data is on the same resolution
iris.long$time <- factor(iris.long$time, levels = c('t1', 't2', 't3'))
iris.long[iris.long$time %in% c('t1', 't2'),]
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原文链接：R语言绘图 | 最全的云雨图绘制教程
往期部分文章
1. 最全WGCNA教程（替换数据即可出全部结果与图形）
2. 精美图形绘制教程
3. 转录组分析教程
4. 转录组下游分析
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