### 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:

Some of these packages are only designed for display, or as extensions to ggplot2:

- corrplot: Visualization of a Correlation Matrix
- GGally: Extension to 'ggplot2'
- ggcorrplot: Visualization of a Correlation Matrix using 'ggplot2'

- PerformanceAnalytics: Econometric tools for performance and risk analysis
- psych: Procedures for Psychological, Psychometric, and Personality Research

```
# 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')
```