Charting Correlation Matrices in R

I noticed this very simple, very powerful article by James Marquez, Seven Easy Graphs to Visualize Correlation Matrices in R, in the Google+ community, R Programming for Data Analysis, so thought to give it a try, since I started some of my current analyses a decade ago by generating correlation matrices in Excel, which I've sometimes redone and improved in R.

Some of these packages are only designed for display, or as extensions to ggplot2:
These two are focused on more complex analysis:
As for data, I used Hofstede's culture dimensions, limited to developed countries. Using a broader and larger set of of countries would significantly reduce the correlations, in that only individuality and power distance would show a strong relationship, negative. The source data is here.

 
 # Charting correlations in R
 # Source: http://jamesmarquezportfolio.com/correlation_matrices_in_r.html  
   
 # Clear workspace  
 rm(list = ls())  
   
 # Set working directory  
 setwd("../Data")  
 getwd()  

 # load data
 oecdData <- read.table("OECD - Quality of Life.csv", header = TRUE, sep = ",")  
 hofsted.vectors <- oecdData[,c('HofstederPowerDx', 'HofstederMasculinity', 'HofstederIndividuality', 'HofstederUncertaintyAvoidance', 'HofstederLongtermOrientation', 'HofstederIndulgence')]  

 #rename columns
 names(hofsted.vectors)[1:6] = c('PowerDx', 'Masculinity', 'Individuality', 'UAE', 'LTO', 'Indulgence')
  
 # PerformanceAnalytics  
 install.packages("PerformanceAnalytics", dependencies = TRUE)  
 library("PerformanceAnalytics")  
 chart.Correlation(hofsted.vectors, histogram = TRUE, pch = 19)  

# psych install.packages("psych", dependencies = TRUE) library(psych) pairs.panels(hofsted.vectors, scale = TRUE)
# corrplot install.packages("corrplot", dependencies = TRUE) library(corrplot) corrplot.mixed(cor(hofsted.vectors), order = "hclust", tl.col = "black") # GGally install.packages("GGally", dependencies = TRUE) library(GGally) ggpairs(hofsted.vectors)
# ggcorrplot install.packages("ggcorrplot", dependencies = TRUE) library(ggcorrplot) ggcorrplot(cor(hofsted.vectors), p.mat = cor_pmat(hofsted.vectors), hc.order = TRUE, type = 'lower')

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