originally from: http://groups.google.com/group/ggplot2/attach/6bf632a9718dddd6/ggcorplot.R?part=2 linked to from here: http://www.r-bloggers.com/five-ways-to-visualize-your-pairwise-comparisons/ I've updated to remove warnings and errors.

ggcorplot

# originally from: 
# http://groups.google.com/group/ggplot2/attach/6bf632a9718dddd6/ggcorplot.R?part=2
# linked to from here:
# http://www.r-bloggers.com/five-ways-to-visualize-your-pairwise-comparisons/
# I've updated to remove warnings and errors.

library(ggplot2)

#define a helper function (borrowed from the "ez" package)
ezLev=function(x,new_order){
  for(i in rev(new_order)){
    x=relevel(x,ref=i)
  }
  return(x)
}

ggcorplot = function(data,var_text_size,cor_text_limits){
  # normalize data
  for(i in 1:length(data)){
    data[,i]=(data[,i]-mean(data[,i]))/sd(data[,i])
  }
  # obtain new data frame
  z=data.frame()
  i = 1
  j = i
  while(i<=length(data)){
    if(j>length(data)){
      i=i+1
      j=i
    }else{
      x = data[,i]
      y = data[,j]
      temp=as.data.frame(cbind(x,y))
      temp=cbind(temp,names(data)[i],names(data)[j])
      z=rbind(z,temp)
      j=j+1
    }
  }
  names(z)=c('x','y','x_lab','y_lab')
  z$x_lab = ezLev(factor(z$x_lab),names(data))
  z$y_lab = ezLev(factor(z$y_lab),names(data))
  z=z[z$x_lab!=z$y_lab,]
  #obtain correlation values
  z_cor = data.frame()
  i = 1
  j = i
  while(i<=length(data)){
    if(j>length(data)){
      i=i+1
      j=i
    }else{
      x = data[,i]
      y = data[,j]
      x_mid = min(x)+diff(range(x))/2
      y_mid = min(y)+diff(range(y))/2
      this_cor = cor(x,y)
      this_cor.test = cor.test(x,y)
      this_col = ifelse(this_cor.test$p.value<.05,'<.05','>.05')
      this_size = (this_cor)^2
      cor_text = ifelse(
        this_cor>0
        ,substr(format(c(this_cor,.123456789),digits=2)[1],2,4)
        ,paste('-',substr(format(c(this_cor,.123456789),digits=2)[1],3,5),sep='')
      )
      b=as.data.frame(cor_text)
      b=cbind(b,x_mid,y_mid,this_col,this_size,names(data)[j],names(data)[i])
      z_cor=rbind(z_cor,b)
      j=j+1
    }
  }
  names(z_cor)=c('cor','x_mid','y_mid','p','rsq','x_lab','y_lab')
  z_cor$x_lab = ezLev(factor(z_cor$x_lab),names(data))
  z_cor$y_lab = ezLev(factor(z_cor$y_lab),names(data))
  diag = z_cor[z_cor$x_lab==z_cor$y_lab,]
  z_cor=z_cor[z_cor$x_lab!=z_cor$y_lab,]
  #start creating layers
  points_layer = layer(
    geom = 'point'
    , data = z
    , mapping = aes(
      x = x
      , y = y
    )
  )
  lm_line_layer = layer(
    geom = 'line' 
    , geom_params = list(colour = 'red')
    , stat = 'smooth'
    , stat_params = list(method = 'lm')
    , data = z
    , mapping = aes(
      x = x
      , y = y
    )
  ) 
  lm_ribbon_layer = layer(
    geom = 'ribbon'
    , geom_params = list(fill = 'green', alpha = .5)
    , stat = 'smooth'
    , stat_params = list(method = 'lm')
    , data = z
    , mapping = aes(
      x = x
      , y = y
    )
  ) 
  cor_text = layer(
    geom = 'text'
    , data = z_cor
    , mapping = aes(
      x=y_mid
      , y=x_mid
      , label=cor
      , size = rsq
      , colour = p
    )
  )
  var_text = layer(
    geom = 'text'
    , geom_params = list(size=var_text_size)
    , data = diag
    , mapping = aes(
      x=y_mid
      , y=x_mid
      , label=x_lab
    )
  )
  f = facet_grid(y_lab~x_lab,scales='free')
  o = theme(
    panel.grid.minor = element_blank()
    ,panel.grid.major = element_blank()
    ,axis.ticks = element_blank()
    ,axis.text.y = element_blank()
    ,axis.text.x = element_blank()
    ,axis.title.y = element_blank()
    ,axis.title.x = element_blank()
    ,legend.position='none'
  )
  size_scale = scale_size(limits = c(0,1),range=cor_text_limits)
  return(
    ggplot()+
    points_layer+
    lm_ribbon_layer+
    lm_line_layer+
    var_text+
    cor_text+
    f+
    o+
    size_scale
  )
}

#set up some fake data
library(MASS)
N=100

#first pair of variables
variance1=1
variance2=2
mean1=10
mean2=20
rho = .8
Sigma=matrix(c(variance1,sqrt(variance1*variance2)*rho,sqrt(variance1*variance2)*rho,variance2),2,2)
pair1=mvrnorm(N,c(mean1,mean2),Sigma,empirical=T)

#second pair of variables
variance1=10
variance2=20
mean1=100
mean2=200
rho = -.4
Sigma=matrix(c(variance1,sqrt(variance1*variance2)*rho,sqrt(variance1*variance2)*rho,variance2),2,2)
pair2=mvrnorm(N,c(mean1,mean2),Sigma,empirical=T)

my_data=data.frame(cbind(pair1,pair2))

ggcorplot(
  data = my_data
  , var_text_size = 30
  , cor_text_limits = c(2,30)
)