dat=read.csv("Z:/Download/dat.csv")
dat=dat[,c(2:13)]
str(dat)
## 'data.frame':    167 obs. of  12 variables:
##  $ V1 : num  37 40 35 42 45 34 36 32 33 34 ...
##  $ V2 : num  171 171 179 147 156 ...
##  $ V3 : num  60.8 94.8 70.8 55.9 54.1 63.1 53.6 67.1 53.6 74.5 ...
##  $ V4 : num  45.3 66.3 52.5 39.2 38.4 ...
##  $ V5 : num  13 33.8 15.4 20 17.8 14.4 14.9 9.2 17.1 22.4 ...
##  $ V6 : num  21.3 35.7 21.7 35.7 32.9 22.8 27.8 13.7 31.9 30 ...
##  $ V7 : num  26.4 34.8 31 19.5 19.3 27.1 20.7 32.8 19.5 28.9 ...
##  $ V6 : num  47.8 61 55.4 35.9 36.3 48.7 38.7 57.9 36.5 52.1 ...
##  $ V9 : num  1.7 2.12 1.87 1.51 1.53 ...
##  $ V10: num  20.9 32.6 22.2 25.8 22.1 ...
##  $ V11: num  83 93 83 80 89 77 77 77 93 97 ...
##  $ V12: num  349 351 350 351 349 ...
head(dat)
##   V1    V2   V3      V4   V5   V6   V7   V6       V9      V10 V11   V12
## 1 37 170.6 60.8 45.3247 13.0 21.3 26.4 47.8 1.697423 20.89034  83 349.4
## 2 40 170.6 94.8 66.3183 33.8 35.7 34.8 61.0 2.119544 32.57244  93 351.0
## 3 35 178.6 70.8 52.5307 15.4 21.7 31.0 55.4 1.874158 22.19578  83 349.6
## 4 42 147.3 55.9 39.2252 20.0 35.7 19.5 35.9 1.512363 25.76359  80 351.1
## 5 45 156.3 54.1 38.3500 17.8 32.9 19.3 36.3 1.532593 22.14518  89 349.2
## 6 34 167.4 63.1 46.4747 14.4 22.8 27.1 48.7 1.712936 22.51741  77 343.9

1. Correlation network

1.1 qgraph package

https://www.r-bloggers.com/the-r-qgraph-package-using-r-to-visualize-complex-relationships-among-variables-in-a-large-dataset-part-one/
library(qgraph)
cormat=cor(dat) # or cormat=cor(dat[,1:12],dat[,1:12])
qgraph(cormat,shape="circle", posCol="darkgreen", negCol="darkred", layout="groups", vsize=10)

1.2 Thay layout=“spring”

qgraph(cormat,shape="circle", posCol="darkgreen", negCol="darkred", layout="spring", vsize=10)

2. Correlation matrix

2.1 Corrplot package

(Ref: Applied Predictive Modeling)
library(corrplot)
cormat=cor(dat)
corrplot(cormat,order="hclust")

corrplot(cormat, type = "upper", order = "hclust",tl.col = "blue", tl.srt = 50)

For all variables (not good method for large number of variables)

chart.Correlation(dat, histogram=TRUE, pch=19)

For 5 variables only

chart.Correlation(dat[,c(1:5)], histogram=TRUE, pch=19)

2.3 Psych package

library(psych)
pairs.panels(dat)

2.4 Heatmap

(Ref:http://www.sthda.com/english/wiki/correlation-matrix-a-quick-start-guide-to-analyze-format-and-visualize-a-correlation-matrix-using-r-software)
col<- colorRampPalette(c("blue", "white", "red"))(50)
heatmap(x = cormat, col = col, symm = TRUE)