介绍
r corrplot包 提供了一个在相关矩阵上的可视化探索工具,该工具支持自动变量重新排序,以帮助检测变量之间的隐藏模式。
corrplot 非常易于使用,并在可视化方法、图形布局、颜色、图例、文本标签等方面提供了丰富的绘图选项。它还提供 p 值和置信区间,以帮助用户确定相关性的统计显著性。
corrplot()有大约50个参数,但最常见的参数只有几个。在大多数场景中,我们可以得到一个只有一行代码的相关矩阵图。
1.加载包
library(corrplot)
2.加载数据
mtcars
3.绘图
corrplot(m, method = 'number')
#order排序方法original(默认),特征向量角度排序aoe,第一个主成分顺序fpc,分层聚类排序hclust,按照字母排序alphabet corrplot(m, method = 'color', order = 'hclust')
#形状默认circle,除此之外还有square,ellipse,number,pie,shade,color corrplot(m,method="circle")
corrplot(m,method="square")
corrplot(m,method="ellipse")
corrplot(m,method="pie")
#diag = false,不显示中间为1的格子 corrplot(m,method="square",diag = false)
#type仅仅显示下部分相关性,除此之外还有参数full,upper corrplot(m, method = 'square', order = 'fpc', type = 'lower', diag = false)
corrplot(m, method = 'ellipse', order = 'fpc', type = 'upper', diag = false)
#数字和图混合 corrplot.mixed(m, order = 'aoe')
#混合上部饼图,下部阴影 corrplot.mixed(m, lower = 'shade', upper = 'pie', order = 'hclust')
#分层聚类,标出2个cluster corrplot(m, order = 'hclust', addrect = 2)
#定义圈出的cluster,以及圈出线的颜色和线条 corrplot(m, method = 'square', diag = false, order = 'hclust', addrect = 3, rect.col = 'blue', rect.lwd = 3, tl.pos = 'd')
4.个性化设置聚类方法
install.packages("seriation") library(seriation) list_seriation_methods('matrix') list_seriation_methods('dist') data(zoo) z = cor(zoo[, -c(15, 17)]) dist2order = function(corr, method, ...) { d_corr = as.dist(1 - corr) s = seriate(d_corr, method = method, ...) i = get_order(s) return(i) } # fast optimal leaf ordering for hierarchical clustering i = dist2order(z, 'olo') corrplot(z[i, i], cl.pos = 'n')
# quadratic assignment problem i = dist2order(z, 'qap_2sum') corrplot(z[i, i], cl.pos = 'n')
# multidimensional scaling i = dist2order(z, 'mds_nonmetric') corrplot(z[i, i], cl.pos = 'n')
5.个性化添加矩阵
library(magrittr) #方法1 i = dist2order(z, 'r2e') corrplot(z[i, i], cl.pos = 'n') %>% corrrect(c(1, 9, 15))
#方法2 corrplot(z, order = 'aoe') %>% corrrect(name = c('tail', 'airborne', 'venomous', 'predator'))
#方法3直接指定 r = rbind(c('eggs', 'catsize', 'airborne', 'milk'), c('catsize', 'eggs', 'milk', 'airborne')) corrplot(z, order = 'hclust') %>% corrrect(namesmat = r)
6.颜色设置
col1(sequential = c("oranges", "purples", "reds", "blues", "greens", "greys", "orrd", "ylorrd", "ylorbr", "ylgn"), n = 200) col2(diverging = c("rdbu", "brbg", "piyg", "prgn", "puor", "rdylbu"), n = 200) #cl.*参数常用于颜色图例:cl.pos颜色标签的位置('r'type='upper''full''b'type='lower''n'),cl.ratio颜色图例的宽度建议0.1~0.2 #tl.*参数常用于文本图例:tl.pos用于文本标签的位置,tl.cex文本大小,tl.srt文本的旋转
corrplot(m, order = 'aoe', col = col2('rdbu', 10))
corrplot(m, order = 'aoe', addcoef.col = 'black', tl.pos = 'd', cl.pos = 'r', col = col2('piyg'))
corrplot(m, method = 'square', order = 'aoe', addcoef.col = 'black', tl.pos = 'd', cl.pos = 'r', col = col2('brbg'))
corrplot(m, order = 'aoe', cl.pos = 'b', tl.pos = 'd',col = col2('prgn'), diag = false)
corrplot(m, type = 'lower', order = 'hclust', tl.col = 'black', cl.ratio = 0.2, tl.srt = 45, col = col2('puor', 10))
corrplot(m, order = 'aoe', cl.pos = 'n', tl.pos = 'n', col = c('white', 'black'), bg = 'gold2')
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