查询ggprism包使用时候发现官网给出的一示例图比较常用，这里记录学习一下。
image-20221208130405959
加载R包准备数据
## 加载R包
sapply(c('dplyr',"ggplot2","ggprism",
"ggbeeswarm","rstatix"), require, character.only = TRUE)
## 准备数据
data("wings")
head(wings)
## 整理数据
wings$measure <- wings$measure %>%
gsub("\\.", " ", .) %>%
tools::toTitleCase() %>%
factor(., levels = c("Wing Size", "Cell Size", "Cell Number"))
head(wings)
# A tibble: 6 × 4
sex genotype measure percent.change
<fct> <fct> <fct> <dbl>
1 male Tps1MIC/+ Wing Size -2.45
2 male Tps1MIC/+ Cell Size 6.33
3 male Tps1MIC/+ Cell Number -8.41
4 male Tps1MIC/+ Wing Size -1.14
5 male Tps1MIC/+ Cell Size -2.53
6 male Tps1MIC/+ Cell Number 1.26
## 基本绘图
p <- ggplot(wings, aes(x = measure, y = percent.change))+
ggbeeswarm::geom_beeswarm(
aes(fill = genotype),
dodge.width = 0.9,
shape = 21,
cex = 3.5
)
p
## 分面
p <- p+facet_wrap(
~sex,
scales = 'free',
labeller = labeller(sex = c(male = "\u2642", female = "\u2640"))
)+
geom_hline(yintercept = 0, linetype = 2, linewidth = 0.3)
p
image-20221222145913758
注意两个细节：
- tools::toTitleCase可以替换字符中每个单词的首字母大写，str_to_title函数也可类似这种效果
TotitleCase函数源码如下：
ToTitleCase <- function(x) {
paste0(toupper(substr(x, 1, 1)), substr(x, 2, nchar(x)))
}
- facet_wrap中的labeller属性对不同的因子类型设置不同的标记，例子中\u2642就是使用了unicode字符标记，推荐个Unicode Character Table便于查询
image-20221222150440938
添加均值
p <- p + stat_summary(
geom = "crossbar",
aes(fill = genotype),
fun = mean,
position = position_dodge(0.9),
colour = "red",
linewidth = 0.4, width = 0.7,
show.legend = FALSE
)
p
image-20221222150633975
计算显著性P值
wings_pvals <- wings %>%
group_by(sex, measure) %>%
rstatix::t_test(
percent.change ~ genotype,
p.adjust.method = "BH",
var.equal = TRUE,
ref.group = "Tps1MIC/+"
) %>%
rstatix::add_x_position(x = "measure", dodge = 0.9) %>% # dodge must match points
mutate(label = c("***", "*", "P = 0.26", "***", "***", "P = 0.65"))
p <- p + add_pvalue(
wings_pvals, y = 10, xmin = "xmin", xmax = "xmax", tip.length = 0,
fontface = "italic", lineend = "round", bracket.size = 0.5
)
p
image-20221222150718356
- 这里主要利用rstatix包中的t_test函数批量计算P值，使用add_x_position自动计算P值的位置,信息 最后通过add_pvalue函数根据位置信息自动标记于图中。
image-20221222151131661
设置主题及配色
## 添加主题元素
p <- p + theme_prism(
base_fontface = "plain",
base_line_size = 0.7,
base_family = "Arial") +
scale_x_discrete(guide = guide_prism_bracket(width = 0.15),
labels = scales::wrap_format(5))+
scale_y_continuous(
limits = c(-20, 12),
expand = c(0, 0),
breaks = seq(-20, 10, 5),
guide = "prism_offset") +
labs(y = "% change") +
theme(
legend.position = "bottom",
axis.title.x = element_blank(),
strip.text = element_text(size = 14),
legend.spacing.x = unit(0, "pt"),
legend.text = element_text(margin = margin(r = 20))
) +
guides(fill = guide_legend(override.aes = list(size = 3)))
p
## 改变颜色及图例文本格式
p <- p + scale_fill_manual(
values = c("#026FEE", "#87FFFF"),
labels = c(expression("Tps"*1^italic("MIC")~"/ +"),
expression("Tps"*1^italic("MIC")))
)
p
## 添加文本注释
p <- p + geom_text(
data = data.frame(
sex = factor("female", levels = c("male", "female")),
measure = "Cell Number",
percent.change = -18.5,
lab = "(n = 10)"
),
aes(label = lab)
)
p
image-20221222151215683
虽说是一个简单的示例图, 但其中有很多细节调整值得去学习，而且这种类型的图论文中也是比较常用的。
