http://drunks-and-lampposts.com/2012/01/23/marketing-mix-lab-visualising-the-correlation-matrix/

Visualising The Correlation Matrix.r

# *--------------------------------------------------------------------
# | FUNCTION: visCorrel
# | Creates an MDS plot where the distance between variables represents
# | correlation between the variables (closer=more correlated)
# *--------------------------------------------------------------------
# | Version |Date      |Programmer  |Details of Change                
# |     01  |05/01/2012|Simon Raper |first version.      
# *--------------------------------------------------------------------
# | INPUTS:  dataset        A dataframe containing only the explanatory
# |                         variables. It should not contain missing 
# |                         values or variables where there is no
# |                         variation
# |          abr            The number of characters used when
# |                         abbreviating the variables. If set to zero
# |                         there is no abbreviation
# |          method         The options are 'metric' or 'ordinal'. The 
# |                         default is metric
# *--------------------------------------------------------------------
# | OUTPUTS: graph          An MDS plot where the similarity measure is
# |                         correlation between the variables.
# |
# *--------------------------------------------------------------------
# | USAGE:         vis_correl(dataset, 
# |                           abr)
# |
# *--------------------------------------------------------------------
# | DEPENDS: ggplot2, directlabels
# |
# *--------------------------------------------------------------------
# | NOTES:   For more information about MDS please see 
# |          http://en.wikipedia.org/wiki/Multidimensional_scaling
# |
# *--------------------------------------------------------------------

visCorrel<-function(dataset, abr, method="metric"){
  
  #Create correlation matrix
  cor_ts<-cor(dataset)
  n<-dim(cor_ts)[2]
  
  # Create dissimilarities
  ones<-matrix(rep(1,n^2), nrow=n)
  dis_ts<-ones-abs(cor_ts)
  
  # Do MDS
  
  
  if ( method=="ordinal"){
      
    fit <- isoMDS(dis_ts, k=2)$points
    
  } else {
    
    cmd.res <- cmdscale(dis_ts, k=2, eig=TRUE)
    eig<-cmd.res$eig
    fit<-cmd.res$points
    prop<-sum(abs(eig[1:2]))/sum(abs(eig))
    
    print(paste("Proportion of squared distances represented:", round(prop*100)))
    
    if(prop<0.5){print("Less than 50% of squared distance is represented. Consider using ordinal scaling instead")}
    
  }
    
  
  x <- fit[,1]
  y <- fit[,2]
  
  labels<-row.names(cor_ts)
  if (abr>0){labels<-substr(labels,1,abr)}
  
  mds_plot<-data.frame(labels, x, y)
  
  #Plot the results
  g<-ggplot(mds_plot, aes(x=x, y=y, colour=labels, main="MDS Plot of Correlations"))+geom_point() + coord_fixed()+ opts(title ="MDS Plot of Correlations")
  direct.label(g, first.qp)
}

# *--------------------------------------------------------------------
# * Examples
# *--------------------------------------------------------------------
# visCorrel(midwest[,4:27],10, method="classical")
# visCorrel(midwest[,4:27],10, method="ordinal")
# visCorrel(Crime[,-c(1,2,11,12)],10)