相关性矩阵图绘制方法大汇总！！

DataCharm

发布于 2021-12-01 16:49:27

1.5K0

发布于 2021-12-01 16:49:27

在一些学术论文中，经常会看到用「相关性矩阵(correlation matrix)」 去表示数据集中每队数据变量间的关系，可以实现对数据集大致情况的一个快速预览，常常用于探索性分析。本期推文，小编就汇总一下相关性矩阵的R和Python的绘制方法。

R绘制相关性矩阵

在R中有很多可视化包可以绘制相关性矩阵图，如R-ggcorrplot、R-ggstatsplot和R-corrplot。接下来，小编就一一为大家介绍：

R-ggcorrplot

R-ggcorrplot包作为ggplot2的拓展包，我们首先进行介绍，具体内容如下：

官网

R-ggcorrplot包的官网如下：https://rpkgs.datanovia.com/ggcorrplot/

样例介绍 R-ggcorrplot包主要提供ggcorrplot()和cor_pmat()两个绘图函数，具体例子如下(这里都做了主题等细节设置)：

「样例一」：默认情况

library(tidyverse)
library(ggtext)
library(hrbrthemes)
library(wesanderson)
library(LaCroixColoR)
library(ggsci)
library(ggcorrplot)

data(mtcars)
corr <- round(cor(mtcars), 1)
p.mat <- cor_pmat(mtcars)
colors = c("#B2182B", "white", "#4D4D4D")

plot01 <- ggcorrplot(corr,colors = colors,
                     ggtheme=hrbrthemes::theme_ipsum(base_family = "Roboto Condensed"))

plot01_cus <- plot01 +
  labs(x="",y="",
     title = "Example of <span style='color:#D20F26'>ggcorrplot charts makes</span>",
     subtitle = "processed charts with <span style='color:#1A73E8'>ggcorrplot()</span>",
     caption = "Visualization by <span style='color:#0057FF'>DataCharm</span>") +
  theme(  
    plot.title = element_markdown(hjust = 0.5,vjust = .5,color = "black",
                                  size = 25, margin = margin(t = 1, b = 12)),
    plot.subtitle = element_markdown(hjust = 0,vjust = .5,size=20),
    plot.caption = element_markdown(face = 'bold',size = 15))

Example01 of ggcorrplot

「样例二」：圆形下半面

plot02 <- ggcorrplot(corr,colors = colors,
                     method = "circle",
                     outline.color = "black",
                     lab = TRUE,
                     type = "lower",
                     lab_size = 4,
                     ggtheme=hrbrthemes::theme_ipsum(base_family = "Roboto Condensed"))

plot02_cus <- plot02 +
  labs(x="",y="",
       title = "Example of <span style='color:#D20F26'>ggcorrplot charts makes</span>",
       subtitle = "processed charts with <span style='color:#1A73E8'>ggcorrplot()</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 = 25, margin = margin(t = 1, b = 12)),
    plot.subtitle = element_markdown(hjust = 0,vjust = .5,size=20),
    plot.caption = element_markdown(face = 'bold',size = 15))

Example02 of ggcorrplot

「样例三」：上半面

plot03 <- ggcorrplot(cor(mtcars),colors = colors,
                     outline.color = "black",
                     lab = TRUE,
                     type = "upper",
                     p.mat = p.mat,
                     digits = 2,
                     ggtheme=hrbrthemes::theme_ipsum(base_family = "Roboto Condensed"))

plot03_cus <- plot03 +
  labs(x="",y="",
       title = "Example of <span style='color:#D20F26'>ggcorrplot charts makes</span>",
       subtitle = "processed charts with <span style='color:#1A73E8'>ggcorrplot()</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 = 25, margin = margin(t = 1, b = 12)),
    plot.subtitle = element_markdown(hjust = 0,vjust = .5,size=20),
    plot.caption = element_markdown(face = 'bold',size = 15))

Example03 of ggcorrplot

好了，以上就是ggcorrplot包绘制的基本情况(基本上重要的参数设置都介绍完了)。

R-ggstatsplot

这个包小编之前也介绍过(统计绘图 | 既能统计分析又能可视化绘制的技能),这次主要介绍里面的ggcorrmat() 绘图函数，可通过如下例子进行介绍：

「样例一」：基础样例

ggstatsplot01 <- ggcorrmat(
    data = mtcars,
    colors = c("#B2182B", "white", "#4D4D4D"),
    title = "Correlalogram Example of ggstatsplot charts makes",
    subtitle = "processed charts with ggcorrmat()",
    caption = "Visualization by DataCharm",
    ggtheme = hrbrthemes::theme_ipsum(base_family = "Roboto Condensed"),
) +
theme(  
    plot.title = element_text(hjust = 0.5,vjust = .5,color = "black",
                                  size = 18, margin = margin(t = 1, b = 12)),
    plot.subtitle = element_text(hjust = 0,vjust = .5,size=16),
    plot.caption = element_text(face = 'bold',size = 12))

Example01 of ggstatsplot

「样例二」：定制化操作

ggstatsplot02 <- ggcorrmat(
    data = mtcars,
    matrix.type = "upper",
    ggcorrplot.args = list(lab_col = "black",lab_size = 4,tl.srt = 90,pch.col = "red",pch.cex = 10),
    title = "Correlalogram Example of ggstatsplot charts makes",
    subtitle = "Processed charts with ggcorrmat()",
    caption = "Visualization by DataCharm",
    ggtheme = hrbrthemes::theme_ipsum(base_family = "Roboto Condensed"),
) +
theme(  
    plot.title = element_text(hjust = 0.5,vjust = .5,color = "black",
                                  size = 18, margin = margin(t = 1, b = 12)),
    plot.subtitle = element_text(hjust = 0,vjust = .5,size=16),
    plot.caption = element_text(face = 'bold',size = 12))

Example02 of ggstatsplot

此外，ggstatsplot还有针对分组数据的的相关性矩阵绘制方法，感兴趣的小伙伴可自行探索哈~~

R-corrplot

介绍完基于ggplot2绘图体系的相关性矩阵图表绘制方法之后，小编再给大家介绍R-corrplot包的绘制方法。详细内容如下：

官网 R-corrplot包的使用方法可参考如下网址：https://cran.r-project.org/web/packages/corrplot/vignettes/corrplot-intro.html
样例介绍 R-corrplot包由于其自身的绘图语法，这里只列举两个小例子做比较，其他详细内容，大家可参考官网哈~：

「样例一」：

opar <- par(family = "Roboto Condensed")
col1 <- colorRampPalette(c("#B2182B", "white", "#4D4D4D"))
corrplot(M, type = "upper",method = "ellipse",col =col1(100),order = "hclust", addrect = 2,
        tl.col = "black", tl.srt = 45)
mtext(text = "Example Of Corrplot", side = 1, line = -4, 
      col = "black", font = 4, adj = 0.05, cex = 2)
mtext(text = "Visualization by DataCharm", side=1, 
      line = - 1, col = "black", font = 3, adj = 0.05, cex = 1)

Example01 of corrplot

「样例二」：组合样式

corrplot.mixed(M, lower = "ellipse", upper = "circle",
               tl.col = "black", tl.srt = 45)

Example02 of corrplot

Python 绘制相关性矩阵

在介绍完R绘制相关性矩阵图的方法后，小编再简单介绍下如何使用Python进行绘制，这里直接列出例子即可：

from string import ascii_letters
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme(style="white")
rs = np.random.RandomState(33)
d = pd.DataFrame(data=rs.normal(size=(100, 26)),
                 columns=list(ascii_letters[26:]))
corr = d.corr()
mask = np.triu(np.ones_like(corr, dtype=bool))
f, ax = plt.subplots(figsize=(11, 9))
cmap = sns.diverging_palette(230, 20, as_cmap=True)
sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,
            square=True, linewidths=.5, cbar_kws={"shrink": .5})