15. 相关性图绘制
清除当前环境中的变量
rm(list=ls())
设置工作目录
setwd("C:/Users/Dell/Desktop/R_Plots/15correlation/")
使用corrgram包绘制相关性图
library(corrgram)
# 查看数据集
head(iris)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
corrgram(iris)
image.png
corrgram(iris,
lower.panel=panel.pts, #设置底部panel绘图类型
upper.panel=panel.conf, #设置顶部panel绘图类型
diag.panel=panel.density, #设置对角线panel绘图类型
main = "Iris data pearson correlation" #设置标题
)
image.png
corrgram(iris,
lower.panel=panel.shade,
upper.panel=panel.pie,
diag.panel=panel.density,
cor.method = "spearman", #设置相关性计算方法
gap = 2, #设置图形panel之间的间隔
col.regions=colorRampPalette(c("green", "blue","red"))
)
image.png
使用corrplot包绘制相关性图
library(corrplot)
# 查看数据集
head(mtcars)
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
# 计算相关性
M <- cor(mtcars,method = "pearson")
head(M)
mpg cyl disp hp drat wt
mpg 1.0000000 -0.8521620 -0.8475514 -0.7761684 0.6811719 -0.8676594
cyl -0.8521620 1.0000000 0.9020329 0.8324475 -0.6999381 0.7824958
disp -0.8475514 0.9020329 1.0000000 0.7909486 -0.7102139 0.8879799
hp -0.7761684 0.8324475 0.7909486 1.0000000 -0.4487591 0.6587479
drat 0.6811719 -0.6999381 -0.7102139 -0.4487591 1.0000000 -0.7124406
wt -0.8676594 0.7824958 0.8879799 0.6587479 -0.7124406 1.0000000
qsec vs am gear carb
mpg 0.41868403 0.6640389 0.5998324 0.4802848 -0.5509251
cyl -0.59124207 -0.8108118 -0.5226070 -0.4926866 0.5269883
disp -0.43369788 -0.7104159 -0.5912270 -0.5555692 0.3949769
hp -0.70822339 -0.7230967 -0.2432043 -0.1257043 0.7498125
drat 0.09120476 0.4402785 0.7127111 0.6996101 -0.0907898
wt -0.17471588 -0.5549157 -0.6924953 -0.5832870 0.4276059
corrplot(M)
image.png
corrplot(M,
method = "number", #设置相关性图展示类型
type = "lower", #设置只展示底部panel
bg = "white", #设置背景色
title = "mtcars data correlation", #设置标题
)
image.png
corrplot(M,
method = "pie", #设置相关性图展示类型
type = "upper", #设置只展示底部panel
order = "AOE", #设置排序的方式
cl.ratio = .2, #设置colorlabel的宽度
title = "mtcars data pearson correlation", #设置标题
)
image.png
使用ggcorrplot包绘制相关性图
library(ggcorrplot)
# 查看数据集
head(mtcars)
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
# 计算相关性
M <- cor(mtcars,method = "spearman")
head(M)
mpg cyl disp hp drat wt
mpg 1.0000000 -0.9108013 -0.9088824 -0.8946646 0.6514555 -0.8864220
cyl -0.9108013 1.0000000 0.9276516 0.9017909 -0.6788812 0.8577282
disp -0.9088824 0.9276516 1.0000000 0.8510426 -0.6835921 0.8977064
hp -0.8946646 0.9017909 0.8510426 1.0000000 -0.5201250 0.7746767
drat 0.6514555 -0.6788812 -0.6835921 -0.5201250 1.0000000 -0.7503904
wt -0.8864220 0.8577282 0.8977064 0.7746767 -0.7503904 1.0000000
qsec vs am gear carb
mpg 0.46693575 0.7065968 0.5620057 0.5427816 -0.6574976
cyl -0.57235095 -0.8137890 -0.5220712 -0.5643105 0.5800680
disp -0.45978176 -0.7236643 -0.6240677 -0.5944703 0.5397781
hp -0.66660602 -0.7515934 -0.3623276 -0.3314016 0.7333794
drat 0.09186863 0.4474575 0.6865708 0.7448162 -0.1252229
wt -0.22540120 -0.5870162 -0.7377126 -0.6761284 0.4998120
ggcorrplot(M)
image.png
ggcorrplot(M,
method = "circle", #设置相关性图展示类型
outline.color = "red",#设置相关性图边框的颜色
type = "upper", #设置只展示定部panel
title = "mtcars data spearman correlation" #设置标题
)
image.png
ggcorrplot(M,
method = "square", #设置相关性图展示类型
show.legend = T, #设置是否展示图例
legend.title = "Corr", #设置图例的标题
colors = c("#6D9EC1", "white", "#E46726"), #设置相关性图的颜色
ggtheme = ggplot2::theme_gray, #设置背景
lab = T, #设置是否显示显关系数
hc.order = T #设置排序
)
image.png
使用GGally包绘制相关性图
library(GGally)
# 查看数据集
head(mtcars)
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
# 计算相关性
M <- cor(mtcars,method = "kendall")
head(M)
mpg cyl disp hp drat wt
mpg 1.0000000 -0.7953134 -0.7681311 -0.7428125 0.4645488 -0.7278321
cyl -0.7953134 1.0000000 0.8144263 0.7851865 -0.5513178 0.7282611
disp -0.7681311 0.8144263 1.0000000 0.6659987 -0.4989828 0.7433824
hp -0.7428125 0.7851865 0.6659987 1.0000000 -0.3826269 0.6113081
drat 0.4645488 -0.5513178 -0.4989828 -0.3826269 1.0000000 -0.5471495
wt -0.7278321 0.7282611 0.7433824 0.6113081 -0.5471495 1.0000000
qsec vs am gear carb
mpg 0.31536522 0.5896790 0.4690128 0.4331509 -0.50439455
cyl -0.44896982 -0.7710007 -0.4946212 -0.5125435 0.46542994
disp -0.30081549 -0.6033059 -0.5202739 -0.4759795 0.41373600
hp -0.47290613 -0.6305926 -0.3039956 -0.2794458 0.59598416
drat 0.03272155 0.3751011 0.5755485 0.5839248 -0.09535193
wt -0.14198812 -0.4884787 -0.6138790 -0.5435956 0.37137413
# 使用ggcorr函数绘制相关性图
ggcorr(M)
image.png
ggcorr(M,
label = T, #设置是否显示相关系数
geom = "circle", #设置相关性图展示类型
max_size = 10, #设置circles size的最大值
min_size = 4, #设置circles size的最小值
size = 4, #设置对角线字体大小
angle = 45, #设置对角线字体倾斜角度
low = "green",
mid = "blue",
high = "red"
)
image.png
sessionInfo()
R version 3.6.0 (2019-04-26)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)
Matrix products: default
locale:
[1] LC_COLLATE=Chinese (Simplified)_China.936
[2] LC_CTYPE=Chinese (Simplified)_China.936
[3] LC_MONETARY=Chinese (Simplified)_China.936
[4] LC_NUMERIC=C
[5] LC_TIME=Chinese (Simplified)_China.936
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] GGally_1.4.0 ggcorrplot_0.1.3 ggplot2_3.2.0 corrplot_0.84
[5] corrgram_1.13
loaded via a namespace (and not attached):
[1] gtools_3.8.1 xfun_0.8 tidyselect_0.2.5
[4] purrr_0.3.2 reshape2_1.4.3 colorspace_1.4-1
[7] htmltools_0.3.6 viridisLite_0.3.0 yaml_2.2.0
[10] rlang_0.4.7 pillar_1.4.2 glue_1.3.1
[13] withr_2.1.2 RColorBrewer_1.1-2 registry_0.5-1
[16] foreach_1.4.4 plyr_1.8.4 stringr_1.4.0
[19] munsell_0.5.0 gtable_0.3.0 caTools_1.17.1.2
[22] evaluate_0.14 codetools_0.2-16 knitr_1.23
[25] labeling_0.3 seriation_1.2-7 Rcpp_1.0.5
[28] KernSmooth_2.23-15 scales_1.0.0 gdata_2.18.0
[31] gplots_3.0.1.1 gridExtra_2.3 digest_0.6.20
[34] stringi_1.4.3 gclus_1.3.2 dplyr_0.8.3
[37] grid_3.6.0 tools_3.6.0 bitops_1.0-6
[40] magrittr_1.5 lazyeval_0.2.2 tibble_2.1.3
[43] cluster_2.0.8 crayon_1.3.4 pkgconfig_2.0.2
[46] dendextend_1.12.0 MASS_7.3-51.4 rmarkdown_1.13
[49] assertthat_0.2.1 reshape_0.8.8 rstudioapi_0.10
[52] iterators_1.0.10 viridis_0.5.1 R6_2.4.0
[55] TSP_1.1-7 compiler_3.6.0