Two Dimensional Density and Hexbin Plots with Simulated Data (ggplot2 & hexbin)

DensityPlots

# Simulating Correlated Data 
# (this is a variant of something I found online but I can't find out who posted it...)
n     <- 2000                  # length
rho   <- 0.6                   # desired correlation = cos(angle)
theta <- acos(rho)             # corresponding angle
pred    <- rnorm(n, 1, 1)      # predictor vector
x2    <- rnorm(n, 2, 0.5)      # new random data for response vector
X     <- cbind(pred, x2)       # matrix of pred and x2
Xctr  <- scale(X, center=TRUE, scale=FALSE)   # centered columns (mean 0)
Id   <- diag(n)                               # an identity matrix
Q    <- qr.Q(qr(Xctr[ , 1, drop=FALSE]))      # QR-decomposition
P    <- tcrossprod(Q)          # = Q Q'       # projection onto space defined by pred
x2o  <- (Id-P) %*% Xctr[ , 2]                 # x2ctr made orthogonal to x1ctr
Xc2  <- cbind(Xctr[ , 1], x2o)                # bind to matrix
Y    <- Xc2 %*% diag(1/sqrt(colSums(Xc2^2)))  # scale columns to length 1
resp <- Y[ , 2] + (1 / tan(theta)) * Y[ , 1]  # final response vector
data <- data.frame(pred,resp)

# 2 Dimensional Plots
library(ggplot2)
# Density Plot
Plot <- ggplot(data,aes(x=pred,y=resp)) + 
  stat_density2d(aes(colour=..level..,fill=..level..,),alpha=.35, geom="polygon")
Plot
library(hexbin)
# Hexbin Plot
Plot2 <- ggplot(data, aes(x=pred, y=resp)) +
  stat_binhex()
Plot2