目录
- 0.问题导入
- 1.ggplot2精仿版瀑布图预览
- 2.示例数据生成
- 3.绘制主图1
- 4.绘制主图2
- 5.主图1-2 图例提取
- 6.绘制瀑布图
- 7.本文总结
- 8.本篇所用软件包（没有的，还需要install.packages('包名')进行安装哦～）
- 9.致谢
0. 问题导入
前些天，我在简书平台看到一篇文章（R数据可视化13:瀑布图/突变图谱），深深被其中所介绍的瀑布图所吸引，觉得兼职太漂亮了吧！！！于是乎，我认真阅读了本文，试图根据文章介绍的步骤复现瀑布图（图1，源自R数据可视化13:瀑布图/突变图谱）。但是，在安装包的过程中就遇到了问题：不支持install.packages('')安装。嗯。。对于一个深度的ggplot2 使用者，个人觉得太麻烦啦，索性有了今天这篇利用ggplot2 来绘制瀑布图。
图1 SNP瀑布图
1. ggplot2 精仿版瀑布图预览
由于今天操作可能稍微比较多步骤，为减枯燥，先给大家展示下昨日精仿一天的结果图。但为了突出说明ggplot2绘制瀑布图的方法，本文采用较为简单的数据结构。
图2 ggplot2 精仿版瀑布图
2. 示例数据生成
2.1 主图1所需数据
samples = letters[1:26]
index_generate <- function(x){
temp = round(runif(x, 1,26))
temp = temp[order(temp)]
temp = samples[temp]
value = round(runif(x,-5,5),2)
temp_df = data.frame(Sample = temp, value = value)
}
len_seq = round(runif(10,1,12))
df = lapply(len_seq,index_generate)
pl_df = 1
Type = LETTERS[1:10]
for(i in 1:length(df)){
temp = df[[i]]
temp$Type = Type[i]
pl_df = rbind(pl_df, temp)
}
pl_df = pl_df[-1,]
pl_df$cuts = cut(pl_df$value,breaks = seq(-5,5,1))
pl_df$Type = factor(pl_df$Type,levels = rev(Type))
pl_df$Sample = factor(pl_df$Sample,levels = samples)
2.3 主图1数据结构预览
head(pl_df)
Sample value Type cuts
2 g -4.25 A (-5,-4]
3 h 4.53 A (4,5]
4 j 0.52 A (0,1]
5 j -3.31 A (-4,-3]
6 q -0.94 A (-1,0]
7 r 4.61 A (4,5]
2.3 主图2所需数据
df2 = 1
for(i in 1:length(samples)){
temp_index = which(pl_df$Sample == samples[i])
temp_value = pl_df$value[temp_index]
temp_neg = length(which(temp_value<0))
temp_pos = length(which(temp_value>=0))
temp_df2 = data.frame(Sample = samples[i],Number_Neg = temp_neg,Number_Pos = temp_pos)
df2 = rbind(df2, temp_df2)
}
df2 = df2[-1,]
re_h = which(df2$Number_Neg==0 & df2$Number_Pos == 0)
df2 = df2[-re_h,]
df2_m = melt(df2,'Sample')
colnames(df2_m) = c('Sample',"Group",'Number_of_Types')
2.4 主图2数据结构预览
head(df2_m)
Sample Group Number_of_Types
1 b Number_Neg 1
2 c Number_Neg 1
3 d Number_Neg 2
4 e Number_Neg 2
5 f Number_Neg 1
6 g Number_Neg 1
3. 绘制主图1
mycolors = colorRampPalette(brewer.pal(11,'Spectral'))(10)
p1 = ggplot()+
geom_tile(data = pl_df, aes(x = Sample, y = Type, fill = cuts))+
scale_fill_manual(values = mycolors)+
theme(
panel.background = element_rect(fill = 'transparent'),
axis.text = element_text(color = 'black',size = 12, face = 'bold',hjust = 0.5),
axis.title = element_text(color = 'black',size = 14,face = 'bold',hjust = 0.5),
legend.position = 'none',
legend.direction = 'horizontal'
)+scale_y_discrete(position = 'right')
png('plot1.png',
height = 25,
width = 25,
units = 'cm',
res = 800)
print(p1)
dev.off()
图3 主图1
4. 绘制主图2
mycolor2 = colorRampPalette(brewer.pal(11,'Spectral'))(2)
p2 = ggplot()+
geom_bar(data = df2_m,aes(x = Sample, y = Number_of_Types, fill = Group),
stat = 'identity',position = 'stack')+
scale_fill_manual(values = mycolor2)+
theme(
panel.background = element_rect(fill = 'transparent'),
axis.text = element_text(color = 'black',size = 12, face = 'bold',hjust = 0.5),
axis.text.x = element_blank(),
axis.line.y = element_line(colour = 'black',size = 0.5,linetype = 'solid'),
axis.title = element_text(color = 'black',size = 14,face = 'bold',hjust = 0.5),
axis.title.x = element_blank(),
legend.position = 'none',
legend.direction = 'horizontal'
)
png('plot2.png',
height = 10,
width = 25,
units = 'cm',
res = 800)
print(p2)
dev.off()
图4 主图2
5. 主图1-2 图例提取
p1_legend = get_legend(p1+theme(legend.position = 'bottom'))
p2_legend = get_legend(p2+theme(legend.position = 'bottom'))
p1_legend = as_ggplot(p1_legend)
p2_legend = as_ggplot(p2_legend)
6. 绘制瀑布图
p3 = plot_grid(p2,p1, align = 'v',
axis = c('lr'),
rel_widths = c(4,4),
rel_heights = c(1,4),
ncol = 1)
p4 = plot_grid(p3,p1_legend,p2_legend,align = 'v',
axis = c('lr'),
rel_heights = c(5,0.4,0.4),
ncol = 1)
png('plot3.png',
height = 26,
width = 25,
units = 'cm',
res = 800)
print(p4)
dev.off()
图5 ggplot2 版瀑布图
7. 总结
瀑布图实则由统计图与热度图组成，而这两种图基于ggplot2 都可以实现，故昨日耗费一日完成基于ggplot2的瀑布图的绘制，即本篇核心问题为如何利用ggplot2绘制瀑布图？
8. 本篇所用软件包（没有的，还需要install.packages('包名')进行安装哦～）
library(ggplot2)
library(cowplot)
library(grid)
library(gridExtra)
library(RColorBrewer)
library(ggpubr)
library(reshape2)
9. 致谢
感谢R数据可视化13:瀑布图/突变图谱这篇文章的作者让我了解到了新的数据可视化方法～同时感谢大家的持续关注，小编会继续努力，持续更新下去的！
大家如果觉得有用，还麻烦大家关注点赞，也可以扩散到朋友圈，多谢大家啦～
大家如果在使用本文代码的过程有遇到问题的，可以留言评论，也可以私信我哈~~
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