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library(corrgram)
# Create correlagram of scatter against lines with conficence
  # lines are loess smoothed curve
  # eclipse is  bivariate 68% concentration ellipse (68% of data inside!)
corrgram(Dataframe[,VECTORofCOLUMNSofINTEREST], order=FALSE, lower.panel=panel.pts,
    upper.panel=panel.ellipse, text.panel=panel.txt,
    diag.panel=panel.minmax,
    main="Correlations between variables within the vector VECTORofCOLUMNSofINTEREST") 

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#--The following was used to define the colours, but since it's a non-standard
#   package, I've used the hexadecimal colour codes explicity below to avoid the
#   need to install "RColorBrewer". See http://colorbrewer2.org/ for more info.
#require(RColorBrewer)
#colset <- brewer.pal(3, "Pastel2")
colset <- c("#B3E2CD", "#FDCDAC", "#CBD5E8")

path <- "http://www.sr.bham.ac.uk/~ajrs/R/datasets"

#--Data file as taken from XMM website, but with "#" added to comment lines

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# HOW TO BELOW
# http://www.sr.bham.ac.uk/~ajrs/R/r-tutorials.html#NEDplot

#--Read in data file:
url <- "http://www.sr.bham.ac.uk/~ajrs"
file <- "R/datasets/a85_extended_NEDsearch.txt"
A <- read.table(paste(url, file, sep="/"), sep="|", skip=20, header=TRUE)
close(url(paste(url, file, sep="/"))) # close connection after use 

#--Specfify output graphics device:

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#Example Four Panel Contour Plot with One Legend
#Author: Carey R McGilliard
#September 2010

#http://wiki.cbr.washington.edu/qerm/index.php/R/Contour_Plots

#This code uses a modified version of filled.contour called filled.contour3 (created by Carey McGilliard, Ian Taylor, and Bridget Ferris)
 #to make an example figure of four contour plots sharing a legend (to the right).
 #The example demonstrates how to use various color schemes for the contour plots and legend, but the user will want to
 #pick one color scheme for all four plots such that the legend matches the plots.

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filled.contour3 <-
  function (x = seq(0, 1, length.out = nrow(z)),
            y = seq(0, 1, length.out = ncol(z)), z, xlim = range(x, finite = TRUE), 
            ylim = range(y, finite = TRUE), zlim = range(z, finite = TRUE), 
            levels = pretty(zlim, nlevels), nlevels = 20, color.palette = cm.colors, 
            col = color.palette(length(levels) - 1), plot.title, plot.axes, 
            key.title, key.axes, asp = NA, xaxs = "i", yaxs = "i", las = 1, 
            axes = TRUE, frame.plot = axes,mar, ...) 
{
  # modification by Ian Taylor of the filled.contour function

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filled.legend <-
  function (x = seq(0, 1, length.out = nrow(z)), y = seq(0, 1, 
    length.out = ncol(z)), z, xlim = range(x, finite = TRUE), 
    ylim = range(y, finite = TRUE), zlim = range(z, finite = TRUE), 
    levels = pretty(zlim, nlevels), nlevels = 20, color.palette = cm.colors, 
    col = color.palette(length(levels) - 1), plot.title, plot.axes, 
    key.title, key.axes, asp = NA, xaxs = "i", yaxs = "i", las = 1, 
    axes = TRUE, frame.plot = axes, ...) 
{
  # modification of filled.contour by Carey McGilliard and Bridget Ferris

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library(vcd)
# create a matrix with two categorical variables.
  VECTOR <- xtabs(~ Var1 + Var2, data = Dataframe)
# create mosiac
  mosaic(VECTOR, gp = shading_max, split_vertical = TRUE)

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# data
set.seed(1234)
bimodal <- c(rnorm(250, -2, 0.6), rnorm(250, 2, 0.6))
uniform <- runif(500, -4, 4)
normal <- rnorm(500, 0, 1.5)
dataf <- data.frame (group = rep(c("bimodal","uniform", "normal"), each = 500), xv = c(bimodal, uniform, normal), cg = rep( c("A","B"), 750))

require(beeswarm)

# hexagon 

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 # data 
set.seed(4566)
data <- rnorm(100)

# Added to the  plot:
par(mar=c(3.1, 3.1, 1.1, 2.1))
hist(data,xlim=c(-4,4), col = "pink")
boxplot(data, horizontal=TRUE,  outline=TRUE,  ylim=c(-4,4), frame=F, col = "green1", add = TRUE)

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plot using ggplot2 
require(ggplot2)
#data 
set.seed(1233)
data1 <- data.frame(pop =c(rep("A x B", 200), rep("A x C", 200), rep("B x C", 200) ) ,   var1 =  c(rnorm(1000, 90,10), rnorm(1000, 50, 10), rnorm(1000, 20, 30)))

qplot( var1, data = data1, geom = "histogram" , group = pop, fill = pop, alpha=.3) + theme_bw( )