corrplot包与ggcorrplot相关图

简介：
相关图是基于相关系数矩阵绘制的图。
通常是将1个变量映射到多个视觉元素，所以看起来很花哨。
如果是椭圆：

• 则椭圆的色相对应相关性的正负，

• 颜色深浅对应相关性绝对值大小，越深则绝对值越大。

• 椭圆的形状对应相关性绝对值大小，默认越扁，则相关性绝对值越大。

如果是圆，则圆的面积对应相关性大小，
如果是扇形，则扇形的弧度对应相关性大小。

相关系数：
自变量X和因变量Y的协方差/标准差的乘积。也可以反映两个变量变化时是同向还是反向，
如果同向变化就为正，反向变化就为负。
它消除了两个变量变化幅度的影响，而只是单纯反应两个变量每单位变化时的相似程度。

表达式： cor(x, y = NULL, use = "everything", method = c("pearson", "kendall", "spearman"))

参数解释：

• x 为数字型向量，矩阵或数据框，表示自变量

• y 表示应变量，默认y=x

2个向量计算得到一个值，n个变量组成的数据框计算得到长度为n*n维度的矩阵。
绘制相关图主要涉及2个包：corrplot, ggcorrplot，后一个是ggplot2的扩展包。

计算相关系数矩阵：

 1height <- c(6, 5.92, 5.58, 5.83) 2wei <- c(20, 15, 7, 12) 3cor(height, exp(height)) 4cor(height, wei) 5ncol(mtcars) 6dim(cor(mtcars)) #  7class(cor(mtcars)) 8colnames(cor(mtcars)) 9row.names(cor(mtcars))1011# 展示系数矩阵,保留3位小数，12DT::datatable(round(cor(mtcars), 3), 13              options = list(pageLength = 11)) # 显示11行

1## [1] 0.99830742## [1] 0.96288113## [1] 114## [1] 11 115## [1] "matrix"6##  [1] "mpg"  "cyl"  "disp" "hp"   "drat" "wt"   "qsec" "vs"   "am"   "gear"7## [11] "carb"8##  [1] "mpg"  "cyl"  "disp" "hp"   "drat" "wt"   "qsec" "vs"   "am"   "gear"9## [11] "carb"

图片

（原图可交互）

corrplot包绘图：

结果按行和按列排是一样的，说明，只要cor(x,y)中，只要x=y，按行排和按列排没有区别。

1library(corrplot)2corrplot(cor(mtcars))

图片

1library(ggplot2)2library(ggcorrplot)34ggcorrplot(cor(mtcars), method="circle")

图片
1.corrplot包

1.1

语法与参数

语法：

 1corrplot(corr, 2    method = c("circle", "square", "ellipse", "number", "shade", "color", "pie"), 3    type = c("full", "lower", "upper"), add = FALSE, 4    col = NULL, bg = "white", title = "",  is.corr = TRUE,       5    diag = TRUE, outline = FALSE, mar = c(0,0,0,0), 6    addgrid.col = NULL, addCoef.col = NULL, addCoefasPercent = FALSE,  7    order = c("original", "AOE", "FPC", "hclust", "alphabet"), 8    hclust.method = c("complete", "ward", "single", "average", 9                      "mcquitty", "median", "centroid"),10    addrect = NULL, rect.col = "black", rect.lwd = 2,11    tl.pos = NULL, tl.cex = 1,12    tl.col = "red", tl.offset = 0.4, tl.srt = 90,13    cl.pos = NULL, cl.lim = NULL,14    cl.length = NULL, cl.cex = 0.8, cl.ratio = 0.15, 15    cl.align.text = "c",cl.offset = 0.5,16    addshade = c("negative", "positive", "all"),17    shade.lwd = 1, shade.col = "white",18    p.mat = NULL, sig.level = 0.05,19    insig = c("pch","p-value","blank", "n"),20    pch = 4, pch.col = "black", pch.cex = 3,21    plotCI = c("n","square", "circle", "rect"),22    lowCI.mat = NULL, uppCI.mat = NULL, ...)

关键参数：

• corr, 需要可视化的相关系数矩阵，

• method, 指定可视化的形状，可以是circle圆形(默认)，square方形， ellipse, 椭圆形，number数值，shade阴影，color颜色，pie饼图。

• type，指定显示范围，可以是full完全(默认)，lower下三角，upper上三角。

• col, 指定图形展示的颜色，默认以均匀的颜色展示。
  支持grDevices包中的调色板，也支持RColorBrewer包中调色板。

• bg, 指定背景颜色。

• add, 表示是否添加到已经存在的plot中。默认FALSE生成新plot。

• title, 指定标题，

• is.corr，是否为相关系数绘图，默认为TRUE,FALSE则可将其它数字矩阵进行可视化。

• diag, 是否展示对角线上的结果，默认为TRUE，

• outline, 是否添加圆形、方形或椭圆形的外边框，默认为FALSE。

• mar， 设置图形的四边间距。数字分别对应(bottom, left, top, right)。

• addgrid.col, 设置网格线颜色，当指定method参数为color或shade时， 默认的网格线颜色为白色，其它method则默认为灰色，也可以自定义颜色。

• addCoef.col， 设置相关系数值的颜色，只有当method不是number时才有效。

• addCoefasPercent, 是否将相关系数转化为百分比形式，以节省空间，默认为FALSE。

• order, 指定相关系数排序的方法, 可以是original原始顺序，AOE特征向量角序， FPC第一主成分顺序，hclust层次聚类顺序，alphabet字母顺序。

• hclust.method, 指定hclust中细分的方法，只有当指定order参数为hclust时有效，
  有7种可选：complete, ward, single, average, mcquitty, median, centroid。

• addrect， 是否添加矩形框，只有当指定order参数为hclust时有效， 默认不添加， 用整数指定即可添加。

• rect.col, 指定矩形框的颜色。

• rect.lwd, 指定矩形框的线宽。

• tl.pos, 指定文本标签(变量名称)相对绘图区域的位置，为"lt"(左侧和顶部), "ld"(左侧和对角线), "td"(顶部和对角线),"d"(对角线),"n"(无)之一。

• 当type="full"时,默认"lt"。

• 当type="lower"时，默认"ld"。

• 当type="upper"时，默认"td"。

• tl.cex, 设置文本标签的大小。

• tl.col, 设置文本标签的颜色。

• cl.pos, 设置图例位置，为"r"(右边), "b"(底部),"n"(无)之一。
  当type="full"/"upper"时，默认"r"; 当type="lower"时，默认"b"。

• addshade, 表示给增加阴影，只有当method="shade"时有效。
  为"negative"(对负相关系数增加阴影),负相关系数的阴影是135度； "positive"(对正相关系数增加阴影), 正相关系数的阴影是45度； "all"(对所有相关系数增加阴影)，之一。

• shade.lwd, 指定阴影线宽。

• shade.col, 指定阴影线的颜色。

1.2

method与type

 1library(corrplot) 2library(showtext) 3mat_cor <- cor(mtcars) 4 5par(mfrow = c(2,2)) # 多图排版，2x2矩阵排列 6 7corrplot(mat_cor, title = "默认圆形全显示", # 默认method为圆形，默认type为full 8         mar = c(1,1,1,1)) # 指定边距，否则标题显示不完全 9corrplot(mat_cor, method = "ellipse", type = "upper", title = "椭圆上三角",10         mar = c(1,1,1,1))11corrplot(mat_cor, method = "number", type = "lower", title = "数字下三角",12         mar = c(1,1,1,1))13corrplot(mat_cor, method = "circle", type = "upper", title = "圆形上三角",14         mar = c(1,1,1,1))15corrplot(mat_cor, method = "square", type = "lower", title = "方形下三角",16         mar = c(1,1,1,1))17corrplot(mat_cor, method = "shade", type = "full", title = "阴影全显示",18         mar = c(1,1,1,1))19corrplot(mat_cor, method = "color", type = "upper", title = "颜色上三角",20         mar = c(1,1,1,1))21corrplot(mat_cor, method = "pie", type = "lower", title = "饼图下三角",22         mar = c(1,1,1,1))

图片 图片

1.3

col颜色

颜色可以自定义，支持grDevices包中的调色板。也支持RColorBrewer中的调色板。

 1# 自定义色板 2color_1 <- colorRampPalette(c("cyan", "magenta")) 3color_2 <- colorRampPalette(c("magenta", "cyan")) # 色板反向 4palette_1 <- RColorBrewer::brewer.pal(n=11, name = "RdYlGn") 5palette_2 <- rev(palette_1) # 色板反向 6 7par(mfrow = c(2, 2)) 8 9corrplot(mat_cor, method = "number", col = "black", cl.pos = "n", 10         title = "黑色数字", mar = c(1,1,1,1))1112corrplot(mat_cor, method = "ellipse", col = color_1(10), 13         title = "自定义颜色", mar = c(1,1,1,1))1415corrplot(mat_cor, method = "ellipse", col = color_1(200), # 矩阵维度不够大，所以颜色没区别16         title = "自定义颜色", mar = c(1,1,1,1))1718corrplot(mat_cor, method = "ellipse", col = color_2(10), 19         title = "色板反向", mar = c(1,1,1,1))2021par(mfrow = c(1,1))22corrplot(mat_cor, method = "ellipse", col = palette_1,23         title = "brewer.pal调色板", mar = c(1,1,1,1))24corrplot(mat_cor, method = "ellipse", col = palette_2,25         title = "色板反向", mar = c(1,1,1,1))  26

图片 图片 图片

1.4

diag和bg

1corrplot(mat_cor, method = "ellipse", type = "lower", col = palette_2,2         title = "默认显示对角线",diag = TRUE, mar = c(1,1,1,1))3corrplot(mat_cor, method = "ellipse", type = "lower", col = palette_2,4         title = "不显示对角线", diag = FALSE, mar = c(1,1,1,1))5corrplot(mat_cor, method = "ellipse", type = "lower", col = palette_2,6         title = "灰色背景", bg = "gray60", mar = c(1,1,1,1))7corrplot(mat_cor, method = "ellipse", type = "lower", col = palette_2,8         title = "浅绿背景", bg = "lightblue", mar = c(1,1,1,1))

图片 图片 图片 图片

1.5

order顺序

 1corrplot(mat_cor, method = "ellipse", col = palette_2, 2         title = "默认original顺序", diag = TRUE, mar = c(1,1,1,1)) 3corrplot(mat_cor, method = "ellipse", order = "AOE",  col = palette_2, 4         title = "AOE特征向量角序", diag = TRUE, mar = c(1,1,1,1)) 5corrplot(mat_cor, method = "ellipse", order = "FPC",  col = palette_2, 6         title = "FPC第一主成分顺序", diag = TRUE, mar = c(1,1,1,1)) 7corrplot(mat_cor, method = "ellipse", order = "hclust",  col = palette_2, 8         title = "hclust层次聚类顺序", diag = TRUE, mar = c(1,1,1,1)) 9corrplot(mat_cor, method = "ellipse", order = "alphabet",  col = palette_2,10         title = "alphabet字母顺序", diag = TRUE, mar = c(1,1,1,1))

图片 图片 图片 图片 图片

1.6

hclust.method和addrect

只有当order="hclust"才有效。

 1corrplot(mat_cor, method = "ellipse", order = "hclust",  col = palette_2, 2         hclust.method = "complete", addrect = 1, rect.col = "blue", rect.lwd = 2, 3         title = "hclust.method = \"complete\"", diag = TRUE, mar = c(1,1,1,1)) 4corrplot(mat_cor, method = "ellipse", order = "hclust",  col = palette_2, 5         hclust.method = "ward", addrect = 2, rect.col = "blue", rect.lwd = 2, 6         title = "hclust.method = \"ward\"", diag = TRUE, mar = c(1,1,1,1)) 7corrplot(mat_cor, method = "ellipse", order = "hclust",  col = palette_2, 8         hclust.method = "single", addrect = 3, rect.col = "blue", rect.lwd = 2, 9         title = "hclust.method = \"single\"", diag = TRUE, mar = c(1,1,1,1))10corrplot(mat_cor, method = "ellipse", order = "hclust",  col = palette_2,11         hclust.method = "average", addrect = 4, rect.col = "blue", rect.lwd = 2,12         title = "hclust.method = \"average\"", diag = TRUE, mar = c(1,1,1,1))13corrplot(mat_cor, method = "ellipse", order = "hclust",  col = palette_2,14         hclust.method = "mcquitty", addrect = 2, rect.col = "blue", rect.lwd = 2,15         title = "hclust.method = \"mcquitty\"", diag = TRUE, mar = c(1,1,1,1))16corrplot(mat_cor, method = "ellipse", order = "hclust",  col = palette_2,17         hclust.method = "median", addrect = 2, rect.col = "blue", rect.lwd = 2,18         title = "hclust.method = \"median\"", diag = TRUE, mar = c(1,1,1,1))19corrplot(mat_cor, method = "ellipse", order = "hclust",  col = palette_2,20         hclust.method = "centroid", addrect = 2, rect.col = "blue", rect.lwd = 2,21         title = "hclust.method = \"centroid\"", diag = TRUE, mar = c(1,1,1,1))

图片 图片 图片 图片 图片 图片 图片

1.7

addCoef.col与addCoefasPercent

 1corrplot(mat_cor, method = "ellipse", order = "AOE",  col = palette_2,  2         addCoef.col = "blue", 3         title = "添加蓝色系数值", diag = TRUE, mar = c(1,1,1,1)) 4corrplot(mat_cor, method = "ellipse", order = "AOE",  col = palette_2,  5         addCoef.col = "gray20", 6         title = "添加灰色系数值", diag = TRUE, mar = c(1,1,1,1)) 7 8corrplot(mat_cor, method = "ellipse", order = "AOE",  col = palette_2,  9         addCoef.col = "blue", addCoefasPercent = TRUE,10         title = "添加蓝色百分比系数", diag = TRUE, mar = c(1,1,1,1))11corrplot(mat_cor, method = "ellipse", order = "AOE",  col = palette_2, 12         addCoef.col = "gray20", addCoefasPercent = TRUE,13         title = "添加灰色百分比系数", diag = TRUE, mar = c(1,1,1,1))

图片 图片 图片 图片

——————————————

1.8

cl.pos图例位置

1corrplot(mat_cor, method = "ellipse", order= "AOE", col = palette_2,

2addCoef.col = "gray20", addCoefasPercent = TRUE, cl.pos = "r",

3title = "图例在右边", diag = TRUE, mar = c(1,1,1,1))

4corrplot(mat_cor, method = "ellipse", order= "AOE", col = palette_2,

5addCoef.col = "gray20", addCoefasPercent = TRUE, cl.pos = "b",

6title = "图例在底部", diag = TRUE, mar = c(1,1,1,1))

7corrplot(mat_cor, method = "ellipse", order= "AOE", col = palette_2,

8addCoef.col = "gray20", addCoefasPercent = TRUE, cl.pos = "n",

9title = "无图例", diag = TRUE, mar = c(1,1,1,1))

image image image

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,

2addCoef.col = "gray20", addCoefasPercent = TRUE, cl.pos = "r",

3tl.pos = "lt",tl.cex = 2, tl.col = "blue",

4title = "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,

7addCoef.col = "gray20", addCoefasPercent = TRUE, cl.pos = "r",

8tl.pos = "n",

9title = "无变量文本", diag = TRUE, mar = c(1,1,1,1))

image image

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,

2addshade = "negative", shade.lwd = 1, shade.col = "blue",

3title = "蓝色负阴影", mar = c(1,1,1,1))

4corrplot(mat_cor, method = "shade", order= "AOE", col = palette_2,

5addshade = "positive", shade.lwd = 1, shade.col = "blue",

6title = "蓝色正阴影", mar = c(1,1,1,1))

7corrplot(mat_cor, method = "shade", order= "AOE", col = palette_2,

8shade.lwd = 1, shade.col = "blue",

9title = "默认全阴影", mar = c(1,1,1,1))

image image image

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,

6p.mat= res1$p,sig.level= 0.01,

7title= "增加显著性标记", mar= c(1,1,1,1))

8

9corrplot(mat_cor, method= "circle", col= palette_2,

10p.mat= res1$p,sig.level= 0.01,insig= "pch", pch.col= "blue", pch.cex= 3,

11title= "蓝色显著性标记", mar= c(1,1,1,1))

12

image image

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",

3col = palette_2, tl.pos = "d",

4title = "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,

6add = TRUE, diag = FALSE, tl.pos = "n", addCoefasPercent = TRUE, cl.pos = "n",

7mar = c(1,1,1,1))

8# 第2个图，

9corrplot(mat_cor, method = "pie", type = "lower", order= "AOE",

10col = palette_2, tl.pos = "tp", tl.col = "blue", cl.pos = "r",

11title = "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,

13add = TRUE, diag = FALSE, tl.pos = "n", cl.pos = "n",

14mar = c(1,1,1,1))

image image

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 = "",

3show.legend = TRUE, legend.title = "Corr", show.diag = FALSE,

4colors = c("blue", "white", "red"), outline.color = "gray",

5hc.order = FALSE, hc.method = "complete", lab = FALSE,

6lab_col = "black", lab_size = 4, p.mat = NULL, sig.level = 0.05,

7insig = c("pch", "blank"), pch = 4, pch.col = "black", pch.cex = 5,

8tl.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"),

7Time_NewR = windowsFont("Times New Romans 常规"))

8font_add("YaHei_rontine", regular = "msyh.ttc", bold = "msyhbd.ttc")

9font_add("Time_NewR", "times.ttf",

10bold = "timesbd.ttf",

11italic = "timesi.ttf",

12bolditalic = "timesbd.ttf")

13

14showtext_auto()

15

16# 自定义主题

17mytheme <- theme_bw() +

18theme(

19plot.title = element_text(colour = "blue", hjust = 0.5, size = 20),

20legend.text = element_text(colour = "blue"),

21legend.title = element_text(family = "YaHei_rontine", colour = "blue"),

22legend.position = "bottom", legend.direction = "horizontal"

24

25

26# 绘图

27ggcorrplot(mat_cor,

28method="circle",

29hc.order = TRUE,

30type = "lower",

31lab = TRUE, # 显示相关系数

32lab_col = "blue", lab_size = 3,

33colors = c("cyan", "white", "magenta"),

34tl.cex = 10, tl.col = "blue", digits = 1,

35title="下三角圆形，hclust排列",

36legend.title = "相关系数",

37ggtheme = theme_bw())

38

39# 自定义主题

40ggcorrplot(mat_cor,

41method="circle",

42hc.order = TRUE,

43type = "lower",

44lab = TRUE, # 显示相关系数

45lab_col = "blue", lab_size = 3,

46colors = c("cyan", "white", "magenta"),

47tl.cex = 10, tl.col = "blue", digits = 1,

48title="自定义主题",

49legend.title = "相关系数",

50ggtheme = mytheme)

image image

2.3

显著性标记

1library(ggplot2)

2library(ggcorrplot)

3

4p_mat <-cor_pmat(mtcars)

5

6ggcorrplot(mat_cor,

7method="circle", hc.order= TRUE,type= "lower",

8lab= TRUE,lab_col= "blue", lab_size= 3,# 显示相关系数

9colors= c("cyan", "white", "magenta"),

10tl.cex= 10,tl.col= "blue", digits= 1,

11title="显著性标记",

12legend.title= "相关系数",

13p.mat= p_mat,

14ggtheme= theme_bw())

15

16ggcorrplot(mat_cor,

17hc.order= TRUE,type= "full",

18colors= c("cyan", "white", "magenta"),

19tl.cex= 10,tl.col= "blue", digits= 1,

20title="低于p值为空",

21legend.title= "相关系数",

22p.mat= p_mat,insig= "blank",

23ggtheme= theme_bw())

24

25ggcorrplot(mat_cor,

26method="circle", hc.order= TRUE,type= "upper",

27colors= c("cyan", "white", "magenta"),

28tl.cex= 10,tl.col= "blue", digits= 1,

29title="红色显著性标记",

30legend.title= "相关系数",

31p.mat= p_mat,insig= "pch", pch.col= "red", pch.cex= 4,

32ggtheme= theme_bw())

33

image image image

参

考资料返回搜狐，查看更多

• 使用corrplot包绘制相关性图 https://www.jianshu.com/p/00000f6f32df
• R画月亮阴晴圆缺：corrplot绘图相关系数矩阵 https://blog.csdn.net/woodcorpse/article/details/79417978
• ggcorrplot: Visualization of a correlation matrix using ggplot2 http://www.sthda.com/english/wiki/ggcorrplot-visualization-of-a-correlation-matrix-using-ggplot2
• R语言相关系数可视化之corrplot包 https://zhuanlan.zhihu.com/p/28076189
• R语言相关关系可视化函数梳理 https://zhuanlan.zhihu.com/p/36925332
• 相关性分析了解一下 https://mp.weixin.qq.com/s/Nm9NEGG9gy-lEX34kyxFgQ?token=867568924&lang=zh_CN
• ggplot2最顶级的50种可视化图 http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html
• DT包用法 https://rstudio.github.io/DT/