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基于 ggplot2包以及 corrplot包的相关矩阵可视化包 ggcorrplot， ggcorrplot包提供对相关矩阵重排序以及在相关图中展示显著性水平的方法，同时也能计算相关性p-value
计算相关矩阵(cor()计算结果不提供p-value)
library(ggcorrplot)
data("mtcars")
corr <- round(cor(mtcars), 1)
head(corr[, 1:6])
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用ggcorrplot包提供的函数cor_pmat()
p.mat <- cor_pmat(mtcars)
head(p.mat[, 1:4])
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可视化相关性矩阵
ggcorrplot(corr)#method默认为square
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方法为circle
ggcorrplot(corr, method = "circle")
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重排矩阵，使用分等级聚类
ggcorrplot(corr, hc.order = TRUE, outline.color = "white")
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控制矩阵形状
ggcorrplot(corr, hc.order = TRUE, type = "lower", outline.color = "white")#下三角形
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上三角形
ggcorrplot(corr, hc.order = TRUE, type = "upper", outline.color = "white")
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更改颜色以及主题
ggcorrplot(corr, hc.order = TRUE, type = "lower", outline.color = "white",
ggtheme = ggplot2::theme_gray, colors = c("#6D9EC1", "white", "#E46726"))
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添加相关系数
ggcorrplot(corr, hc.order = TRUE, type = "lower", lab = TRUE)
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增加显著性水平，不显著的话就不添加了
ggcorrplot(corr, hc.order = TRUE, type = "lower", p.mat = p.mat)
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将不显著的色块设置成空白
ggcorrplot(corr, p.mat = p.mat, hc.order=TRUE, type = "lower", insig = "blank")
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转载https://ytlogos.github.io/2017/06/25/R%E8%AF%AD%E8%A8%80%E5%8F%AF%E8%A7%86%E5%8C%96%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0%E4%B9%8B%E7%9B%B8%E5%85%B3%E7%9F%A9%E9%98%B5%E5%8F%AF%E8%A7%86%E5%8C%96%E5%8C%85ggcorrplot/