pedroj

# STACK: Turn table (C) into (D):
# C
#    a  b  c  d
# A  3  2  .  .
# B  .  .  1  1

# D
# A  a  3
# A  b  2
# A  c  .

pedroj

#########################################################
# Getting the pairwise interactions list
# Format: "Pant.species-Animal.species"
# assocs is a matrix (AxP). With rownames and colnames.
# 
# pairwise(t(assocs)) will give the interactions list
# as: "Animal.species-Pant.species"
# Pedro Jordano. Sevilla - 15 Abr 2006 02:13:45.
#--------------------------------------------------------
pairwise<-function(assocs) {

pedroj

##############################################################################
# Panel with correlations for paired variables.
# Pedro Jordano. Zahara 9 Aug 2009.
##############################################################################
data(data) 
panel.cor <- function(x, y, digits=2, prefix="", cex.cor) 
{ 
usr <- par("usr"); on.exit(par(usr))
par(usr = c(0, 1, 0, 1)) 
r <- abs(cor(x, y)) 

pedroj

#-----------------------------------------------------------------------------
# Testing the difference between two CV's
# From Example 8.12 in Zar, p. 144-145
#-----------------------------------------------------------------------------
w<-c(72.5,71.5,60.8,63.2,71.4,73.1,77.9,75.7,72.0,69.0)
h<-c(183.0,172.3,180.1,190.2,191.4,169.6,166.4,177.6,184.7,187.5,179.8)
#-----------------------------------------------------------------------------
# Testing the difference between two CV's
library(MBESS)
n1<-length(w); n2<-length(h)

pedroj

#--Citing R:
citation()
To cite R in publications, use
  R Development Core Team (2004). R: A language and environment for
  statistical computing. R Foundation for Statistical Computing,
  Vienna, Austria. ISBN 3-900051-00-3, URL http://www.R-project.org.
We have invested a lot of effort in creating R, please cite it when
using it for data analysis.
A BibTeX entry for LaTeX users is
  @Manual{,

pedroj

doInstall <- TRUE  # Change to FALSE if you don't want packages installed.
toInstall <- c("ggplot2")
if(doInstall){install.packages(toInstall, repos = "http://cran.r-project.org")}
lapply(toInstall, library, character.only = TRUE)

# Generate some randomly-distributed data
nObs <- 5000
myData <- data.frame(X = rnorm(nObs), Y = rnorm(nObs))
nClusters <- 7  # Cluster it
kMeans <- kmeans(myData, centers = nClusters)

pedroj

library("geneplotter")  ## from BioConductor
require("RColorBrewer") ## from CRAN

  x1  <- matrix(rnorm(1e4), ncol=2)
  x2  <- matrix(rnorm(1e4, mean=3, sd=1.5), ncol=2)
  x   <- rbind(x1,x2)

  layout(matrix(1:4, ncol=2, byrow=TRUE))
  op <- par(mar=rep(2,4))
  smoothScatter(x, nrpoints=0)

pedroj

# ----------------------------------------------------------------------------
# [Title]: 
# [Date]:   [Loc]: 
# Pedro Jordano.
# ----------------------------------------------------------------------------
## First version __DATE__. Revised __DATE__
# ----------------------------------------------------------------------------
# __DESCRIPTION__:
# mat<-read.table("mymatrix.txt")  # Read a tab-separated file with 0-1, 
#                                    and no headers, just the matrix

pedroj

ggplot(small)
	+geom_point(aes(x=carat,y=price,colour=cut))
		+scale_y_log10()
			+facet_wrap(~cut)
				+opts(title="First example")

pedroj

Changing the order of levels of a factor

Problem

You want to change the order in which the levels of a factor appear.

Solution

Factors in R come in two varieties: ordered and unordered, e.g., {small, medium, large} and {pen, brush, pencil}. For many analyses, it will not matter whether a factor is ordered or unordered. If the factor is ordered, then the specific order of the levels matters (small < medium < large). If the factor is unordered, then the levels will still appear in some order, but the specific order of the levels matters only for convenience (pen, pencil, brush) -- it will determine, for example, how output will be printed, or the arrangement of items on a graph.