多子图如何一键绘制标签label?这些技巧推荐收藏~~

DataCharm

发布于 2022-02-17 15:02:21

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文章被收录于专栏：数据学术商业新闻数据学术商业新闻

今天小编给大家介绍一个绘图小娇巧-多图汇总时图标签(如A、B (a)、(b)、(I)和(II)等)，这类技巧尤其是在科学文献中经常用到，虽然可以手动添加此类标签，但在绘图过程中自动标记处理则可方便的多。主要内容如下：

R绘图自动添加技巧
Python绘图自动添加技巧

R绘图自动添加技巧

在R语言绘制图表小编主要使用ggplot2和一些第三方包，这里小编就介绍R-ggpubr和R-patchwork包绘制方法(R-ggplot2包可通过labs(tag="**") 方法进行添加，这里不多做介绍)，详细内容如下：

R-ggpubr包添加

在R-ggpubr包中，你可以通过ggarrange()函数在一个页面中添加多个子图和添加对应的tag，如下：

library(tidyverse)
library(hrbrthemes)
library(ggpubr)
library(patchwork)

data("ToothGrowth")
df <- ToothGrowth
df$dose <- as.factor(df$dose)
wdata = data.frame(
   sex = factor(rep(c("F", "M"), each=200)),
   weight = c(rnorm(200, 55), rnorm(200, 58)))
#可视化绘制
# Box plot
bxp <- ggboxplot(df, x = "dose", y = "len",
                 color = "dose", palette = "jco") +
       hrbrthemes::theme_ipsum(base_family = "Roboto Condensed") 
# Dot plot
dp <- ggdotplot(df, x = "dose", y = "len",
                color = "dose", palette = "jco")+
      hrbrthemes::theme_ipsum(base_family = "Roboto Condensed") 
#Density plot
des01 <- ggdensity(wdata, x = "weight",
   add = "mean", rug = TRUE,
   color = "sex", fill = "sex",
   palette = c("#00AFBB", "#E7B800"))

# Density plot
dens02 <- ggdensity(df, x = "len", fill = "dose", palette = "jco")+
       hrbrthemes::theme_ipsum(base_family = "Roboto Condensed") 
# 添加子图tag
ggarrange(bxp, dp, des01,dens02, ncol = 2, nrow = 2,labels = c("(A)","(B","C)","D"))

Example Of ggpubr::ggarrange()

「注意」：这里主要就是介绍ggpubr::ggarrange()函数，其可以设置行列数和labels,灵活添加子图tag,更多详细内容可参考：ggpubr::ggarrange()标签添加[1]

R-patchwork包添加

R-patchwork包主要用于灵活实现多子图的拼接和位置调整等操作，这里则介绍其添加Tag功能的绘图函数plot_annotation(),如下：

p1 <- ggplot(mtcars) + 
  geom_point(aes(mpg, disp)) + 
  ggtitle('Plot 1') 

p2 <- ggplot(mtcars) + 
  geom_boxplot(aes(gear, disp, group = gear)) + 
  ggtitle('Plot 2') 
  

p3 <- ggplot(mtcars) + 
  geom_point(aes(hp, wt, colour = mpg)) + 
  ggtitle('Plot 3') 

patchwork <- (p1 + p2) / p3
patchwork + plot_annotation(
    title = "Example of <span style='color:#D20F26'>patchwork::plot_annotation function</span>",
    subtitle = "processed charts with <span style='color:#1A73E8'>plot_annotation()</span>",
    caption = "Visualization by <span style='color:#0057FF'>DataCharm</span>",
    tag_levels = 'A',
) &
theme(
    plot.title = element_markdown(hjust = 0.5,vjust = .5,color = "black",
                                  size = 20, margin = margin(t = 1, b = 12)),
    plot.subtitle = element_markdown(hjust = 0,vjust = .5,size=15),
    plot.caption = element_markdown(hjust = 1,face = 'bold',size = 12))

Example Of patchwork::plot_annotation()

「注意」：这里的plot_annotation()函数还可以实现多种其他Tag样式，图添加前缀(tag_prefix参数) 和后缀(tag_suffix参数) 等，更多详细内容可参考：patchwork::plot_annotation()[2]

以上就是小编列举的关于R语言绘制多子图Tag的样例，感兴趣的小伙伴可自行查阅，详细理解~~

Python绘图自动添加技巧

介绍完R绘制图表添加Tag的技巧后，小编再介绍如何使用Python进行绘制，这里小编主要介绍的库为Python-ProPlot库，首先，我们看一下官网关于A-b-c labels的例子，如下：

import proplot as pplt
fig = pplt.figure(space=0, refwidth='10em')
axs = fig.subplots(nrows=3, ncols=3)
axs.format(
    abc='A.', abcloc='ul',
    xticks='null', yticks='null', facecolor='gray5',
    xlabel='x axis', ylabel='y axis',
    suptitle='A-b-c label offsetting, borders, and boxes',
)
axs[:3].format(abcloc='l', titleloc='l', title='Title')
axs[-3:].format(abcbbox=True)  # also disables abcborder
# axs[:-3].format(abcborder=True)  # this is already the default

Example01 Of proplot format set

「注意」：从这里可以看出proplot库对子图Tag的设置提供了多种样式，如：abcloc、abcbbox、abcborder等。

接下来举一个具体的图表例子，如下：

import proplot as pplt
import numpy as np
pplt.rc.cycle = '538'
fig, axs = pplt.subplots(ncols=2, span=False, share='labels', refwidth=2.3)
labels = ['a', 'bb', 'ccc', 'dddd', 'eeeee']
hs1, hs2 = [], []

# On-the-fly legends
state = np.random.RandomState(51423)
for i, label in enumerate(labels):
    data = (state.rand(20) - 0.45).cumsum(axis=0)
    h1 = axs[0].plot(
        data, lw=4, label=label, legend='ul',
        legend_kw={'order': 'F', 'title': 'column major'}
    )
    hs1.extend(h1)
    h2 = axs[1].plot(
        data, lw=4, cycle='Set3', label=label, legend='r',
        legend_kw={'lw': 8, 'ncols': 1, 'frame': False, 'title': 'modified\n handles'}
    )
    hs2.extend(h2)

# Outer legends
ax = axs[0]
ax.legend(
    hs1, loc='b', ncols=3, title='row major', order='C',
    facecolor='gray2'
)
ax = axs[1]
ax.legend(hs2, loc='b', ncols=3, center=True, title='centered rows')
axs.format(abc="(A)",xlabel='xlabel', ylabel='ylabel', suptitle='A-b-c labels formatting demo')