#R包下载与载入：
library(remotes)
remotes::install_github('jorvlan/raincloudplots')
library(raincloudplots)
library(ggplot2)
##
#绘图数据整理：
head(iris) #以鸢尾数据集为例；
# Sepal.Length Sepal.Width Petal.Length Petal.Width Species
# 1 5.1 3.5 1.4 0.2 setosa
# 2 4.9 3.0 1.4 0.2 setosa
# 3 4.7 3.2 1.3 0.2 setosa
# 4 4.6 3.1 1.5 0.2 setosa
# 5 5.0 3.6 1.4 0.2 setosa
# 6 5.4 3.9 1.7 0.4 setosa
#需使用特定函数转化为绘图所需格式：
dt1 <- data_1x1(
array_1 = iris$Sepal.Length[1:50], #分组1
array_2 = iris$Sepal.Length[51:100], #分组2
jit_distance = 0.09, #抖动点间的距离
jit_seed = 123#设置随机数种子
)
head(dt1)
# y_axis x_axis id jit
# 1 5.1 1 1 0.9617640
# 2 4.9 1 2 1.0518949
# 3 4.7 1 3 0.9836158
# 4 4.6 1 4 1.0689431
# 5 5.0 1 5 1.0792841
# 6 5.4 1 6 0.9182002
#1.1 横向云雨图绘制：
p1 <- raincloud_1x1(
data = dt1,
colors = (c('#0da9ce','#e74a32')), #描边颜色
fills = (c('#0da9ce','#e74a32')), #填充颜色
size = 1.2, #散点大小
alpha = 0.5, #不透明度
ort = 'h' #横向,v则纵向云雨图
)
scale_x_continuous(breaks = c(1.4, 2.4),#间距
labels = c("setosa", "versicolor"),#标签
limits = c(0.8, 3)) #通过限制/修改坐标轴来更改云雨图位置[两个分组的x轴坐标在绘图数据中为1和2]
labs(x = "Species", y = "Sepal.Length")
theme_classic()
p1
#1.2 纵向云雨图绘制：在上面的基础上进行了翻转操作
p2 <- raincloud_1x1(
data = dt1,
colors = (c('#0da9ce','#e74a32')),
fills = (c('#0da9ce','#e74a32')),
size = 1.2,
alpha = 0.5,
ort = 'v' #翻转
)
scale_x_continuous(breaks = c(1.4, 2.4),
labels = c("setosa", "versicolor"),#x轴标签
limits = c(0.8, 3))
labs(x = "Species", y = "Sepal.Length")
theme_classic()
p2
#1.3 配对云雨图绘制：
head(dt1) #对两组中的相同id进行配对；
p3 <- raincloud_1x1_repmes(
data = dt1,
colors = (c('#0da9ce','#e74a32')),
fills = (c('#0da9ce','#e74a32')),
size = 1.6,
alpha = 0.5,
line_color = 'grey', #配对连线颜色
line_alpha = 0.8, #配对连线不透明度
align_clouds = FALSE
# align_clouds = T#是否对齐云朵[默认F为展开，若为T则将云朵对齐放在一侧]
)
scale_x_continuous(breaks = c(0.6, 2.4),
labels = c("setosa", "versicolor"),
limits = c(0, 3))
labs(x = "Species", y = "Sepal.Length")
theme_classic()
p3
# #对齐云朵：
# p4 <- raincloud_1x1_repmes(
# data = dt1,
# colors = (c('#0da9ce','#e74a32')),
# fills = (c('#0da9ce','#e74a32')),
# size = 1.6,
# alpha = 0.5,
# line_color = 'grey',
# line_alpha = 0.8,
# align_clouds = TRUE #云朵放在一侧
# )
# scale_x_continuous(breaks = c(1, 2.4),
# labels = c("setosa", "versicolor"),
# limits = c(0.5, 3))
# labs(x = "Species", y = "Sepal.Length")
# theme_classic()
# p4
#2.1 双刻度重复配对云雨图绘制
#绘图数据整理：
dt2 <- data_2x2(
array_1 = iris$Sepal.Length[1:50],
array_2 = iris$Sepal.Length[51:100],
array_3 = iris$Sepal.Length[101:150],
array_4 = iris$Sepal.Length[81:130], #共设定4个数组
labels = (c('control','test')), #两个组标签的连接字符串
jit_distance = 0.09,
jit_seed = 123,
spread_x_ticks = FALSE) # 2个x_axis为 FALSE，如果4个x_axis则为TRUE
head(dt2)
p5 <- raincloud_2x2_repmes(
data = dt2,
colors = (c('#0273c2', '#efc001', '#0273c2', '#efc001')),
fills = (c('#0273c2', '#efc001', '#0273c2', '#efc001')),
size = 1.5,
alpha = 0.6,
spread_x_ticks = FALSE
)
scale_x_continuous(breaks = c(0.5, 2.5),
labels = c("day1", "day10"),
limits = c(0, 3))
labs(x = "Time", y = "Length")
theme_classic()
p5
#2.2 四刻度重复配对云雨图绘制
#绘图数据整理：
dt3 <- data_2x2(
array_1 = iris$Sepal.Length[1:50],
array_2 = iris$Sepal.Length[51:100],
array_3 = iris$Sepal.Length[101:150],
array_4 = iris$Sepal.Length[81:130],
labels = (c('control','test')),
jit_distance = 0.09,
jit_seed = 123,
spread_x_ticks = TRUE) #4刻度(x_axis)，设为T
tail(dt3)
p6 <- raincloud_2x2_repmes(
data = dt3,
colors = (c('#0273c2', '#efc001', '#0273c2', '#efc001')),
fills = (c('#0273c2', '#efc001', '#0273c2', '#efc001')),
size = 1.5,
alpha = 0.6,
spread_x_ticks = TRUE #4刻度，设为T
)
scale_x_continuous(breaks = c(1,2,3,4),
labels = c("day1", "day10", "day1", "day10"),
limits = c(0, 5))
labs(x = "Time", y = "Length")
theme_classic()
p6
#数据整理：
dt4 <- data_2x2(
array_1 = iris$Sepal.Length[1:50],
array_2 = iris$Sepal.Length[51:100],
array_3 = iris$Sepal.Length[101:150],
array_4 = iris$Sepal.Length[81:130],
array_5 = iris$Sepal.Length[21:70],
array_6 = iris$Sepal.Length[41:90],
labels = (c('control','test')),
jit_distance = 0.05,
jit_seed = 123)
tail(dt4)
##横向云雨图
p7 <- raincloud_2x3_repmes(
data = dt4,
colors = (c('#137b6a', '#a3bfe6', '#137b6a', '#a3bfe6', '#137b6a', '#a3bfe6')),
fills = (c('#137b6a', '#a3bfe6', '#137b6a', '#a3bfe6', '#137b6a', '#a3bfe6')),
size = 1.5,
alpha = 0.6,
ort = 'h'
)
scale_x_continuous(breaks=c(1,2,3),
labels=c("D1", "D2", "D3"),
limits=c(0.7, 4))
labs(x = "Time", y = "Score")
theme_classic()
p7
##可以用于绘画单细胞的数据基因对应细胞的表现
# 注意数据格式的转化
