1.8
cl.pos图例位置
1corrplot(mat_cor, method = "ellipse", order = "AOE", col = palette_2, 2 addCoef.col = "gray20", addCoefasPercent = TRUE, cl.pos = "r", 3 title = "图例在右边", diag = TRUE, mar = c(1,1,1,1)) 4corrplot(mat_cor, method = "ellipse", order = "AOE", col = palette_2, 5 addCoef.col = "gray20", addCoefasPercent = TRUE, cl.pos = "b", 6 title = "图例在底部", diag = TRUE, mar = c(1,1,1,1)) 7corrplot(mat_cor, method = "ellipse", order = "AOE", col = palette_2, 8 addCoef.col = "gray20", addCoefasPercent = TRUE, cl.pos = "n", 9 title = "无图例", diag = TRUE, mar = c(1,1,1,1))
1.9
变量文本tl.pos、tl.cex及tl.col
tl.pos只有在混合布局的时候才有意义。
1corrplot(mat_cor, method = "ellipse", order = "AOE", col = palette_2, 2 addCoef.col = "gray20", addCoefasPercent = TRUE, cl.pos = "r", 3 tl.pos = "lt",tl.cex = 2, tl.col = "blue", 4 title = "蓝色变量文本", diag = TRUE, mar = c(1,1,1,1)) 5 6corrplot(mat_cor, method = "ellipse", order = "AOE", col = palette_2, 7 addCoef.col = "gray20", addCoefasPercent = TRUE, cl.pos = "r", 8 tl.pos = "n", 9 title = "无变量文本", diag = TRUE, mar = c(1,1,1,1))
1.10
阴影设置
只有当method="shade"时，该参数才有用。 addshade添加阴影范围，分为正阴影，负阴影，全阴影。 shade.lwd设置阴影线宽。shade.col设置阴影线颜色。
1corrplot(mat_cor, method = "shade", order = "AOE", col = palette_2, 2 addshade = "negative", shade.lwd = 1, shade.col = "blue", 3 title = "蓝色负阴影", mar = c(1,1,1,1)) 4corrplot(mat_cor, method = "shade", order = "AOE", col = palette_2, 5 addshade = "positive", shade.lwd = 1, shade.col = "blue", 6 title = "蓝色正阴影", mar = c(1,1,1,1)) 7corrplot(mat_cor, method = "shade", order = "AOE", col = palette_2, 8 shade.lwd = 1, shade.col = "blue", 9 title = "默认全阴影", mar = c(1,1,1,1))
1.11
显著性标记sig.level及p.mat
只有指定矩阵的P值，sig.level，pch等参数才有效。 只有当insig = "pch"时，pch.col和pch.cex参数才有效。 对于p值不清楚的同学，可以参考 知乎的答案
（https://www.zhihu.com/question/23149768） 概况起来，就一句话：小于p值的不可能信，没有意义。
1library(corrplot) 2 3res1 <- cor.mtest(mtcars, conf.level = .95) 4 5corrplot(mat_cor, method = "circle", col = palette_2, 6 p.mat = res1$p, sig.level = 0.01, 7 title = "增加显著性标记", mar = c(1,1,1,1)) 8 9corrplot(mat_cor, method = "circle", col = palette_2, 10 p.mat = res1$p, sig.level = 0.01, insig = "pch", pch.col = "blue", pch.cex = 3, 11 title = "蓝色显著性标记", mar = c(1,1,1,1)) 12
1.12
add混合布局
add参数表示是否添加到已经存在的plot中。默认FALSE生成新plot。
1# 第一个图， 2corrplot(mat_cor, method = "ellipse", type = "upper", order = "AOE", 3 col = palette_2, tl.pos = "d", 4 title = "上椭圆下百分比混合布局", mar = c(1,1,1,1)) 5corrplot(mat_cor, method = "number", type = "lower", order = "AOE", col = palette_2, 6 add = TRUE, diag = FALSE, tl.pos = "n", addCoefasPercent = TRUE, cl.pos = "n", 7 mar = c(1,1,1,1)) 8# 第2个图， 9corrplot(mat_cor, method = "pie", type = "lower", order = "AOE", 10 col = palette_2, tl.pos = "tp", tl.col = "blue", cl.pos = "r", 11 title = "上数字下饼图混合布局", mar = c(1,1,1,1)) 12corrplot(mat_cor, method = "number", type = "upper", order = "AOE", col = palette_2, 13 add = TRUE, diag = FALSE, tl.pos = "n", cl.pos = "n", 14 mar = c(1,1,1,1))
2.ggcorrplot包 ggcorrplot包内就2个函数，一个cor_pmat()用于计算p值, 一个ggcorrplot()用于绘图。 ggcorrplot相当于精简版的corrplot包。只有主题更加丰富多样。
2.1
语法及参数
语法：
1ggcorrplot(corr, method = c("square", "circle"), type = c("full", "lower", 2 "upper"), ggtheme = ggplot2::theme_minimal, title = "", 3 show.legend = TRUE, legend.title = "Corr", show.diag = FALSE, 4 colors = c("blue", "white", "red"), outline.color = "gray", 5 hc.order = FALSE, hc.method = "complete", lab = FALSE, 6 lab_col = "black", lab_size = 4, p.mat = NULL, sig.level = 0.05, 7 insig = c("pch", "blank"), pch = 4, pch.col = "black", pch.cex = 5, 8 tl.cex = 12, tl.col = "black", tl.srt = 45, digits = 2)
关键参数：
method,相比corrplot，少了很多种，只有方形和圆形，默认方形。
colors，需要长度为3的颜色向量，同时指定low,mid和high处的颜色。
outline.color,指定方形或圆形的边线颜色。
hc.order,是否按hclust(层次聚类顺序)排列。
hc.method,相当于corrplot中的hclust.method, 指定方法一样，详情见?hclust。
lab, 是否添加相关系数。
lab_col,指定相关系数的颜色，只有当lab=TRUE时有效。
lab_size,指定相关系数大小，只有当lab=TRUE时有效。
show.legend, 是否显示图例。
legend.title,指定图例标题。
sig.level,insig,pch,pch.col,pch.cex，与corrplot中完全一样。
tl.cex, 指定变量文本的大小，
tl.col, 指定变量文本的颜色，
tl.srt, 指定变量文本的旋转角度。
digits, 指定相关系数的显示小数位数(默认2)。
2.2
实例
1library(ggplot2) 2library(ggcorrplot) 3library(showtext) 4 5# 更改字体 6windowsFonts(YaHei_rontine = windowsFont("微软雅黑"), 7 Time_NewR = windowsFont("Times New Romans 常规")) 8font_add("YaHei_rontine", regular = "msyh.ttc", bold = "msyhbd.ttc") 9font_add("Time_NewR", "times.ttf", 10 bold = "timesbd.ttf", 11 italic = "timesi.ttf", 12 bolditalic = "timesbd.ttf") 13 14showtext_auto() 15 16# 自定义主题 17mytheme <- theme_bw() + 18 theme( 19 plot.title = element_text(colour = "blue", hjust = 0.5, size = 20), 20 legend.text = element_text(colour = "blue"), 21 legend.title = element_text(family = "YaHei_rontine", colour = "blue"), 22 legend.position = "bottom", legend.direction = "horizontal" 23 ) 24 25 26# 绘图 27ggcorrplot(mat_cor, 28 method="circle", 29 hc.order = TRUE, 30 type = "lower", 31 lab = TRUE, # 显示相关系数 32 lab_col = "blue", lab_size = 3, 33 colors = c("cyan", "white", "magenta"), 34 tl.cex = 10, tl.col = "blue", digits = 1, 35 title="下三角圆形，hclust排列", 36 legend.title = "相关系数", 37 ggtheme = theme_bw()) 38 39# 自定义主题 40ggcorrplot(mat_cor, 41 method="circle", 42 hc.order = TRUE, 43 type = "lower", 44 lab = TRUE, # 显示相关系数 45 lab_col = "blue", lab_size = 3, 46 colors = c("cyan", "white", "magenta"), 47 tl.cex = 10, tl.col = "blue", digits = 1, 48 title="自定义主题", 49 legend.title = "相关系数", 50 ggtheme = mytheme)
2.3
显著性标记
1library(ggplot2) 2library(ggcorrplot) 3 4p_mat <- cor_pmat(mtcars) 5 6ggcorrplot(mat_cor, 7 method="circle", hc.order = TRUE, type = "lower", 8 lab = TRUE, lab_col = "blue", lab_size = 3, # 显示相关系数 9 colors = c("cyan", "white", "magenta"), 10 tl.cex = 10, tl.col = "blue", digits = 1, 11 title="显著性标记", 12 legend.title = "相关系数", 13 p.mat = p_mat, 14 ggtheme = theme_bw()) 15 16ggcorrplot(mat_cor, 17 hc.order = TRUE, type = "full", 18 colors = c("cyan", "white", "magenta"), 19 tl.cex = 10, tl.col = "blue", digits = 1, 20 title="低于p值为空", 21 legend.title = "相关系数", 22 p.mat = p_mat, insig = "blank", 23 ggtheme = theme_bw()) 24 25ggcorrplot(mat_cor, 26 method="circle", hc.order = TRUE, type = "upper", 27 colors = c("cyan", "white", "magenta"), 28 tl.cex = 10, tl.col = "blue", digits = 1, 29 title="红色显著性标记", 30 legend.title = "相关系数", 31 p.mat = p_mat, insig = "pch", pch.col = "red", pch.cex = 4, 32 ggtheme = theme_bw()) 33
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