这种显著标明怎么绘制？一个技巧解决，超简单~~
我们在绘制可视化图表时经常需要对特定区域、位置等使用文本或箭头等标识性字符进行注释显示，这种注释在可视化制作中尤为重要，它可以突出重要信息，引起人们对图形某个特征的关注。接下来，小编就汇总一下在R和Python可视化绘制中是如何进行注释的。具体内容如下：
- R注释操作
- Python注释操作
R注释操作
在使用R进行可视化绘制中，起注释作用的绘图函数有很多，这里还是介绍基于ggplot2绘图体系中的绘图函数，主要介绍R-ggplot2和R-ggforce 包中关于注释的内容，如下：
R-ggplot2 注释操作
这一部分使用ggplot2中*annotate()*函数进行说明，这里小编直接给出一个具体案例，如下：
library(tidyverse)
library(ggtext)
library(hrbrthemes)
library(ggpubr)
library(ggsci)
library(ggforce)
plot01 <- ggplot(data = iris,aes(Petal.Length, Petal.Width,
)) +
geom_point(shape=21,aes(fill=Species),colour="black",size=3) +
scale_fill_jco()+
# 基础注释方式
annotate(
geom = "curve", x = 2., y = 1, xend = 1.5, yend = .65,
curvature = .3,arrow = arrow(length = unit(2, "mm")))+
annotate(geom = "text", x = 2.1, y = 1, label = "setosa", hjust="left",vjust = .5)+
labs(
title = "Example of <span style='color:#D20F26'>ggplot2::annotate()</span>",
subtitle = "processed charts with <span style='color:#1A73E8'>annotate()</span>",
caption = "Visualization by <span style='color:#0057FF'>DataCharm</span>") +
hrbrthemes::theme_ipsum(base_family = "Roboto Condensed") +
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(face = 'bold',size = 12),
)
Example of ggplot2 annotate()
当然如果想要实现这种“箭头”效果，ggplot2的geom_segment()和geom_curve()都可实现，感兴趣的小伙伴可去ggplot2官网(https://ggplot2.tidyverse.org/reference/index.html) 进行探索。下面小编将介绍一种更为方便直观且简单的方法。
R-ggforce 注释操作
R-ggforce包中有几个绘图函数可以实现较为灵活的注释效果，且语法较为简单。官网为：https://ggforce.data-imaginist.com/reference/index.html。详细如下：
- 「geom_mark_rect()」
ggplot(iris, aes(Petal.Length, Petal.Width)) +
geom_mark_rect(aes(fill = Species, label = Species),
con.cap = 0,label.fill='gray',
label.colour="black") +
geom_point(shape=21,aes(fill=Species),colour="black",size=3) +
scale_fill_nejm() +
labs(
title = "Example of <span style='color:#D20F26'>ggforce::geom_mark_rect()</span>",
subtitle = "processed charts with <span style='color:#1A73E8'>geom_mark_rect()</span>",
caption = "Visualization by <span style='color:#0057FF'>DataCharm</span>") +
hrbrthemes::theme_ipsum(base_family = "Roboto Condensed") +
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(face = 'bold',size = 12),
)
Example of ggforce::geom_mark_rect()
- 「geom_mark_circle()」
ggplot(iris, aes(Petal.Length, Petal.Width)) +
geom_mark_circle(aes(fill = Species, label = Species),
con.cap = 0,label.fill='gray',
label.colour="black") +
geom_point(shape=21,aes(fill=Species),colour="black",size=3) +
scale_fill_nejm() +
labs(
title = "Example of <span style='color:#D20F26'>ggforce::geom_mark_circle()</span>",
subtitle = "processed charts with <span style='color:#1A73E8'>geom_mark_circle()</span>",
caption = "Visualization by <span style='color:#0057FF'>DataCharm</span>") +
hrbrthemes::theme_ipsum(base_family = "Roboto Condensed") +
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(face = 'bold',size = 12),
)
Example of ggforce::geom_mark_circle()
- 「geom_mark_ellipse()」
ggplot(iris, aes(Petal.Length, Petal.Width)) +
geom_mark_ellipse(aes(fill = Species, label = Species),
con.cap = 0,label.fill='gray',
label.colour="black") +
geom_point(shape=21,aes(fill=Species),colour="black",size=3) +
scale_fill_nejm() +
labs(
title = "Example of <span style='color:#D20F26'>ggforce::geom_mark_ellipse()</span>",
subtitle = "processed charts with <span style='color:#1A73E8'>geom_mark_ellipse()</span>",
caption = "Visualization by <span style='color:#0057FF'>DataCharm</span>") +
hrbrthemes::theme_ipsum(base_family = "Roboto Condensed") +
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(face = 'bold',size = 12),
)
Example of ggforce::geom_mark_ellipse()
- 「geom_mark_hull()」
ggplot(iris, aes(Petal.Length, Petal.Width)) +
geom_mark_hull(aes(fill = Species, label = Species),
con.cap = 0,label.fill='gray',
label.colour="black") +
geom_point(shape=21,aes(fill=Species),colour="black",size=3) +
scale_fill_nejm() +
labs(
title = "Example of <span style='color:#D20F26'>ggforce::geom_mark_hull()</span>",
subtitle = "processed charts with <span style='color:#1A73E8'>geom_mark_hull()</span>",
caption = "Visualization by <span style='color:#0057FF'>DataCharm</span>") +
hrbrthemes::theme_ipsum(base_family = "Roboto Condensed") +
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(face = 'bold',size = 12),
)
Example of ggforce::geom_mark_hull()()
以上就是小编对在R中使用注释列举的几个几个小例子，当然，可能还不只这些，也希望小伙伴们可以公号后台看留言告知哈~~
Python 注释操作
介绍完R绘制注释(annotate)的方法，小编这里再简单介绍下Python的注释(annotate)方法，这里主要介绍Matplotlib的注释方法，如下：
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(figsize=(7,5),dpi=100)
plt.rcParams['font.family'] = ['Times New Roman']
t = np.arange(0.0, 5.0, 0.01)
s = np.cos(2*np.pi*t)
line, = ax.plot(t, s, lw=3,color="#BC3C28")
# 各种annotate样式
ax.annotate(
'straight',
xy=(0, 1), xycoords='data',
xytext=(-50, 30), textcoords='offset points',
arrowprops=dict(arrowstyle="->"))
ax.annotate(
'arc3,\nrad 0.2',
xy=(0.5, -1), xycoords='data',
xytext=(-80, -60), textcoords='offset points',
arrowprops=dict(arrowstyle="->",
connectionstyle="arc3,rad=.2"))
ax.annotate(
'arc,\nangle 50',
xy=(1., 1), xycoords='data',
xytext=(-90, 50), textcoords='offset points',
arrowprops=dict(arrowstyle="->",
connectionstyle="arc,angleA=0,armA=50,rad=10"))
ax.annotate(
'arc,\narms',
xy=(1.5, -1), xycoords='data',
xytext=(-80, -60), textcoords='offset points',
arrowprops=dict(
arrowstyle="->",
connectionstyle="arc,angleA=0,armA=40,angleB=-90,armB=30,rad=7"))
ax.annotate(
'angle,\nangle 90',
xy=(2., 1), xycoords='data',
xytext=(-70, 30), textcoords='offset points',
arrowprops=dict(arrowstyle="->",
connectionstyle="angle,angleA=0,angleB=90,rad=10"))
ax.annotate(
'angle3,\nangle -90',
xy=(2.5, -1), xycoords='data',
xytext=(-80, -60), textcoords='offset points',
arrowprops=dict(arrowstyle="->",
connectionstyle="angle3,angleA=0,angleB=-90"))
ax.annotate(
'angle,\nround',
xy=(3., 1), xycoords='data',
xytext=(-60, 30), textcoords='offset points',
bbox=dict(boxstyle="round", fc="0.8"),
arrowprops=dict(arrowstyle="->",
connectionstyle="angle,angleA=0,angleB=90,rad=10"))
ax.annotate(
'angle,\nround4',
xy=(3.5, -1), xycoords='data',
xytext=(-70, -80), textcoords='offset points',
size=20,
bbox=dict(boxstyle="round4,pad=.5", fc="0.8"),
arrowprops=dict(arrowstyle="->",
connectionstyle="angle,angleA=0,angleB=-90,rad=10"))
ax.annotate(
'angle,\nshrink',
xy=(4., 1), xycoords='data',
xytext=(-60, 30), textcoords='offset points',
bbox=dict(boxstyle="round", fc="0.8"),
arrowprops=dict(arrowstyle="->",
shrinkA=0, shrinkB=10,
connectionstyle="angle,angleA=0,angleB=90,rad=10"))
ax.annotate('', xy=(4., 1.), xycoords='data',
xytext=(4.5, -1), textcoords='data',
arrowprops=dict(arrowstyle="<->",
connectionstyle="bar",
ec="k",
shrinkA=5, shrinkB=5))
# 定制化操作
ax.set(xlim=(-1, 5), ylim=(-4, 3))
for spine in ['top','bottom','left','right']:
ax.spines[spine].set_visible(False)
ax.tick_params(left=False,labelleft=False,bottom=False,labelbottom=False)
ax.set_title("Example Of Matplotlib.annotate()",size=15,fontweight="bold")
Example Of Matplotlib.annotate()
更多内容，大家可参看matplotlib官网样例即可。
总结
本期这篇推文，小编给大家简单汇总了绘图过程中一些注释(annotate) 的方法。从中可以看出，R还是比较方便的，且更加美观，还是那句话，适合自己的才是最好的，希望这篇文章能够帮助到大家。
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原始发表：2022-08-01，如有侵权请联系 cloudcommunity@tencent.com 删除
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