This is one of that tiny function that help you to save yourself a lot of time! I thank the list for the suggestion!

library(gdata)
?keep
data(women, cars)
keep(cars)
## To remove all objects except cars, run:
## keep(cars, sure=TRUE)


Duplicate a figure with R

The following code attempts to reproduce the Figure 3 (top) in Liao L, Noble WS.
Combining pairwise sequence similarity and support vector machines for detecting remote protein evolutionary and structural relationships. Journal of computational biology. 2003;10(6):857-68 using only the Base graphics system in R.
Data can be downloaded from here (if it doesn't work, use Google's cache).
I imported the file in a spreadsheet and then I copied & pasted it in R.

jcb.scores = read.delim("clipboard")
attach(jcb.scores)
pdf("recomb_scores.pdf")
par(las =1) # To have horizontal labels for axes 2 and 4
plot(y~sort(SVM.pairwise.ROC, decreasing = TRUE), pch = 3, cex = 0.5,
xlab = "AUC", ylab = "Number of families", axes = FALSE,
xlim = c(0,1), ylim = c(0,60))
lines(y~sort(SVM.pairwise.ROC, decreasing = TRUE), lty = 1)
points(y~sort(FPS.ROC, decreasing = TRUE), pch = 4, cex = 0.5)
lines(y~sort(FPS.ROC, decreasing = TRUE), lty = 2)
points(y~sort(SVM.Fisher.ROC, decreasing = TRUE), pch = 8, cex = 0.5)
lines(y~sort(SVM.Fisher.ROC, decreasing = TRUE), lty = 3)
points(y~sort(SAM.ROC, decreasing = TRUE), pch = 0, cex = 0.5)
lines(y~sort(SAM.ROC, decreasing = TRUE), lty = 4)
points(y~sort(PSI.BLAST.ROC, decreasing = TRUE), pch = 15, cex = 0.5)
lines(y~sort(PSI.BLAST.ROC, decreasing = TRUE), lty = 5)
axis(1, at = seq(0,1,0.2), labels = c(0,0.2,0.4,0.6,0.8,1), tcl = 0.25, pos = 0) # tcl = 0.25 small ticks toward the curve
axis(2, at = c(0,10,20,30,40,50,60), labels=c(0,10,20,30,40,50,60), tcl= 0.25 , pos = 0)
axis(2, at = c(0,10,20,30,40,60), tcl= 0.25,labels = F, pos = 0)
axis(3, tick = T, tcl= 0.25, labels = F, pos = 60)
axis(4, at = c(0,10,20,30,40,50), tcl= 0.25, labels = F, pos = 1)
axis(4, at = c(0,10,20,30,40,60), tcl= 0.25, labels = F, pos = 1)
# To locate the legend interactively
xy.legend = locator(1)
# right-justifying a set of labels: thanks to Uwe Ligges
temp <- legend(xy.legend, legend = c("SVM-pairwise", "FPS","SVM-Fisher", "SAM","PSI-BLAST"), text.width = strwidth("SVM-pairwise"), xjust = 1, yjust = 1, lty = c(1,2,3,4,5), pch = c(3,4,8,0,15), bty = "n", cex = 0.8, title = "")
dev.off()
detach(jcb.scores)

The original image:

My version of the image (not exacly identical but...):

Plotting two or more overlapping density plots on the same graph

This post was updated.
See this thread from StackOverflow for other ways to solve this task.

plot.multi.dens <- function(s)
{
junk.x = NULL
junk.y = NULL
for(i in 1:length(s))
{
junk.x = c(junk.x, density(s[[i]])$x)
junk.y = c(junk.y, density(s[[i]])$y)
}
xr <- range(junk.x)
yr <- range(junk.y)
plot(density(s[[1]]), xlim = xr, ylim = yr, main = "")
for(i in 1:length(s))
{
lines(density(s[[i]]), xlim = xr, ylim = yr, col = i)
}
}
#usage:
x = rnorm(1000,0,1)
y = rnorm(1000,0,2)
z = rnorm(1000,2,1.5)
# the input of the following function MUST be a numeric list
plot.multi.dens(list(x,y,z))
library(Hmisc)
le <- largest.empty(x,y,.1,.1)
legend(le,legend=c("x","y","z"), col=(1:3), lwd=2, lty = 1)

R package installation and administration

A short list of basic but useful commands for managing
the packages in R:

# install a package
install.packages("ROCR")
# visualize package version
package_version("pamr")
# update a package
update.packages("Cairo")
# remove a package
remove.packages("RGtk2")

Sorting/ordering a data.frame according specific columns


x = rnorm(20)
y = sample(rep(1:2, each = 10))
z = sample(rep(1:4, 5))

data.df <- data.frame(values = x, labels.1 = y, labels.2 = z)
print(data.df)

# data ordered according to "labels.1" column
# and then "labels.2" column
nams <- c("labels.1", "labels.2")
data.df.sorted = data.df[do.call(order, data.df[nams]), ]
print(data.df.sorted)


Receiver Operating Characteristic (ROC) Curve in ROCR and verification packages

The following VERY basic code shows how to plot a simple ROC Curve both by means of ROCR package and by verification package.

# it allows two different plots in the same frame
par(mfrow = c(1,2))
# plot a ROC curve for a single prediction run
# and color the curve according to cutoff.
library(ROCR)
data(ROCR.simple)
pred <- prediction(ROCR.simple$predictions, ROCR.simple$labels)
perf <- performance(pred,"tpr", "fpr")
plot(perf,colorize = TRUE)
# plot a ROC curve for a single prediction run
# with CI by bootstrapping and fitted curve
library(verification)
roc.plot(ROCR.simple$labels,ROCR.simple$predictions, xlab = "False positive rate",
ylab = "True positive rate", main = NULL, CI = T, n.boot = 100, plot = "both", binormal = TRUE)

screen - an other VERY useful Unix tool

from R News Vol. 7/1 April 2007 (http://cran.r-project.org/doc/Rnews/Rnews_2007-1.pdf):
If you need to run R code that executes for long periods of time upon remote machines, this amazing unix tool would became your best friend!
screen is a so-called terminal multiplexor, which allows us to create, shuffle, share, and suspend command line sessions within one window. It provides protection against disconnections and the flexibility to retrieve command line sessions remotely.

Starting using this utility is easy like ABC:

1. Log in to remote server
2. Run screen
3. Run R and the long calculation
4. Detach screen (Ctrl-a, Ctrl-d)
5. Logout

The R session continues working in the background, contained within the screen session. If we want to revisit the session to check its progress, then we:

1. Log in remotely via secure shell
2. Start screen -r, which recalls the unattached session
3. Examine how your calculation/script is performing
4. Detach the screen session, (Ctrl-a, Ctrl-d)
5. Log out

This procedure can be used, clearly, for invoking whatever unix program/command you need to use; it is sufficient to substitute the R invoking command with your invoking command line program(for example python).

As usual in the shell-space, invoking man (man screen in this case) will provide all sort of information you need to know about the tool.

R upgrading on Windows© revisited

From the list:
When I update R the following has worked for me (Windows XP)
1. Install the new version to a new directory (say C:\Program Files\R\R-2.5.1).
2. Rename the new library subdirectory to library2.
3. Copy the entire contents of the old library subdirectory (say
C:\Program Files\R\R-2.4.0\library\ to the new R root to create
C:\Program Files\R\R-2.5.1\library\ .
4. Copy the contents of library2 to library to update your basic library.
5. Now start your new version of R and update packages from the GUI or
from the R console. (You may need to firs check Rprofile .site to
ensure that no packages have been loaded)
6. On occasion I have got warning messages when I tried to load
packages after this procedure. This has been cleared by running
update.packages(checkBuilt = TRUE)
This checks that your packages have been built with the latest
version. When I do this I agree to install all available updates.
7. You may wish to copy various autoloads etc from your old
Rprofile.site to your new Rprofile.site. I understand that there are
some compatibility problems with 2.5.1 and SciViews so be careful.

String manipulation, insert delim

From the list, as usual:

I want to be able to insert delimiters, say commas, into a string
of characters at uneven intervals such that:

foo<-c("haveaniceday")# my string of character
bar<-c(4,1,4,3) # my vector of uneven intervals
my.fun(foo,bar) # some function that places delimiters appropriately
have,a,nice,day # what the function would ideally return

1)

paste(read.fwf(textConnection(foo), bar, as.is = TRUE), collapse = ",")
[1] "have,a,nice,day"

2)

my.function <- function(foo, bar){
# construct a matrix with start/end character positions
start <- head(cumsum(c(1, bar)), -1) # delete last one
sel <- cbind(start=start,end=start + bar -1)
strings <- apply(sel, 1, function(x) substr(foo, x[1], x[2]))
paste(strings, collapse=',')
}

my.function(foo, bar)
[1] "have,a,nice,day"

Back to back historgram

library(Hmisc)
age <- rnorm(1000,50,10)
sex <- sample(c('female','male'),1000,TRUE)
out <- histbackback(split(age, sex), probability=TRUE, xlim=c(-.06,.06), main = 'Back to Back Histogram')
#! just adding color
barplot(-out$left, col="red" , horiz=TRUE, space=0, add=TRUE, axes=FALSE)
barplot(out$right, col="blue", horiz=TRUE, space=0, add=TRUE, axes=FALSE)

How do you get the most common row from a matrix?

If I have a matrix like this:

array(1:3,dim=c(4,5))

[,1] [,2] [,3] [,4] [,5]
[1,] 1 2 3 1 2
[2,] 2 3 1 2 3
[3,] 3 1 2 3 1
[4,] 1 2 3 1 2

in which rows 1 and 4 are similar, I want to find that vector c(1,2,3,1,2).

library(cluster)
x <- array(1:3,dim=c(4,5))
dissim <- as.matrix(daisy(as.data.frame(x)))
dissim[!upper.tri(dissim)] <- NA
unique(x[which(dissim == 0, arr.ind=TRUE), ])

or

count <- table(apply(x, 1, paste, collapse=" "))
count[which.max(count)]

R number output format

I'd like to save the number 0.0000012 to a file just as it appears:

?formatC
formatC(.000000012, format='fg')
[1] "0.000000012"

also

?sprintf
sprintf("%.10f", 0.0000000012)
[1] "0.0000000012"

or

format(.0000012, scientific=FALSE)
[1] "0.0000012"

Detecting outliers through boxplots of the features

This function detects univariate outliers simultaneously using boxplots
of the features:

require(dprep)

data(diabetes)

outbox(diabetes,nclass=1)

Selecting “contrasting” colors

From the R-list (as usual):

What I want to be able to do is to place text on a background of arbitrary
(but known RGB) colour so that the text is legible.
I guess that this is better described as a "contrasting" than a "complementary" colour.
Since luminance contrasts are necessary and sufficient for readable text,
you could use white for dark colors and black for light colors.
Luminance is roughly proportional to 0.2*(R^2.4)+0.6*(G^2.4),
suggesting something like

lightdark <-function (color){
rgb <- col2rgb(color)/255
L <- c(0.2, 0.6, 0) %*% rgb
ifelse(L >= 0.2, "#000060", "#FFFFA0")
}

This uses a pale yellow for dark backgrounds and a dark blue for light
backgrounds, and it seems to work reasonably well.

Scatter plot with axes drawn on the same scale

I'd like to produce some scatter plots where N units on the X axis are > equal to N units on the Y axis (as measured with a ruler, on screen or paper).
x <- sample(10:200,40)
y <- sample(20:100,40)
windows(width = max(x),height = max(y))
plot(x,y)
# try:
plot(x, y, asp = 1)
# or, better:
library(MASS)
eqscplot(x,y)
#or
library(lattice)
xyplot(y ~ x, aspect = "iso")

How to draw a plot with two Y axises and one X axis

plot(1:10)
par("usr")
# [1] 0.64 10.36 0.64 10.36
# Now resetting y axis' usr coordinates:
par(usr=c(par("usr")[1:2], 101, 105))
points(1:5, 105:101, col="red")
axis(4)

Conversion of column matrix into a vector without duplicate

A = matrix(c("a","b","c","d","e","u","v",rep("x",3)),5,2,byrow=F)

unique(A[, 2])

Running R Programs on clusters

1. Syntax for running R programs in BATCH mode from the command-line

$ R CMD BATCH [options] my_script.R [outfile]
$ nohup nice -n 14 R CMD BATCH myRfile.R &

The output file lists the commands from the script file and their outputs. If no outfile is specified, the name used is that of 'infile' and '.Rout' is appended to outfile. To stop all the usual R command line information from being written to the outfile, add this as first line to my_script.R file: 'options(echo=FALSE)'. If the command is run like this 'R CMD BATCH --no-save my_script.R', then nothing will be saved in the .Rdata file which can get often very large. More on this can be found on the help pages: '$ R CMD BATCH --help' or '> ?BATCH'.

2. Submitting R script to Linux cluster via Torque Create the following shell script 'my_script.sh'

##################################
#!/bin/sh
cd $PBS_O_WORKDIR
R CMD BATCH --no-save my_script.R
##################################

This script doesn't need to have executable permissons. Use the following 'qsub' command to send this shell script to the Linux cluster from the directory where the R script 'my_script.R' is located. To utilize several CPUs on the Linux cluster, one can divide the input data into several smaller subsets and execute for each subset a separate process from a dedicated directory.

$ qsub my_script.sh

Quick and dirty function for descriptive statistics

desc <- function(mydata) {
require(e1071)
quantls <- quantile(x=mydata, probs=seq(from=0, to=1, by=0.25))
themean <- mean(mydata)
thesd <- sd(mydata)
kurt <- kurtosis(mydata)
skew <- skewness(mydata)
retlist <- list(Quantiles=quantls, Mean=themean,
StandDev=thesd,Skewness=skew, Kurtosis=kurt)
return(retlist)
}
# example
exampledata <- rnorm(10000)
summary(exampledata)
desc(exampledata)

How can I turn a string into a variable?

If you have

varname <- c("a", "b", "d")

you can do

get(varname[1]) + 2

for a + 2

or

assign(varname[1], 2 + 2)

for a <- 2 + 2

or

eval(substitute(lm(y ~ x + variable), list(variable = as.name(varname[1]))

for lm(y ~ x + a)

At least in the first two cases it is often easier to just use a list, and then you can easily index it by name

vars <- list(a = 1:10, b = rnorm(100), d = LETTERS) vars[["a"]]

without any of this messing about.

Large Matrix into a vector

mx <- matrix(rnorm(100,1:100),10,10)
vec <- c(mx)

or

vec <- c(as.matrix(mx))

or

dim(mx) <- NULL

Comparing two matrices, row by row

ar1 <- array(data=c(1:16),dim=c(4,4))
ar2 <- array(data=c(1,2,3,3,5:16),dim=c(4,4))
z<-ar1==ar2
ar1
[,1] [,2] [,3] [,4]
[1,] 1 5 9 13
[2,] 2 6 10 14
[3,] 3 7 11 15
[4,] 4 8 12 16
ar2
[,1] [,2] [,3] [,4]
[1,] 1 5 9 13
[2,] 2 6 10 14
[3,] 3 7 11 15
[4,] 3 8 12 16
z
[,1] [,2] [,3] [,4]
[1,] TRUE TRUE TRUE TRUE
[2,] TRUE TRUE TRUE TRUE
[3,] TRUE TRUE TRUE TRUE
[4,] FALSE TRUE TRUE TRUE
which(z==FALSE)
[1] 4

Or

apply(ar1==ar2,1,all)

Density curve over a histogram

This post was updated.

x <- rnorm(1000)
hist(x, freq = FALSE, col = "grey")
curve(dnorm, col = 2, add = TRUE)

This thread (specifically the Ted Harding answer) from the r-help-list augments the usefulness of this simple tip.

This kind of plots cab be easily produced using the lessR package.

For example, from ?color.density:

library(lessR)
# generate 100 random normal data values
y <- rnorm(100)
# normal curve and general density curves superimposed over histogram
# all defaults
color.density(y)

Barplots of two sets

x <- c(0.0001, 0.0059, 0.0855, 0.9082)
y <- c(0.54, 0.813, 0.379, 0.35)
# create a two row matrix with x and y
height <- rbind(x, y)
# Use height and set 'beside = TRUE' to get pairs
# save the bar midpoints in 'mp'
# Set the bar pair labels to A:D
mp <- barplot(height, beside = TRUE,
ylim = c(0, 1), names.arg = LETTERS[1:4])
# Nel caso generale, i.e., che si usa di
# solito (height MUST be a matrix)
mp <- barplot(height, beside = TRUE)
# Draw the bar values above the bars
text(mp, height, labels = format(height, 4),
pos = 3, cex = .75)

Fill a matrix with vectors of different lengths

v <- c(1,1,2,3,4,6)
binc <- function(x){
l <- sum(x)+1
y <- c(1,rep(0,l-1))
for (i in x) y <- y+c(rep(0,i),y)[1:l]
}
out <- lapply(1:length(v), function(i) binc(v[1:i]))
nout <- max(sapply(out, length))
sapply(out, function(x) c(x, rep(0, nout - length(x))))

Make many barplot into one plot

# I have 4 tables like this:
satu <- array(c(5,15,20,68,29,54,84,119), dim=c(2,4), dimnames=list(c("Negative", "Positive"), c("Black", "Brown", "Red", "Blond")))
dua <- array(c(50,105,30,8,29,25,84,9), dim=c(2,4), dimnames=list(c("Negative", "Positive"), c("Black", "Brown", "Red", "Blond")))
tiga <- array(c(9,16,26,68,12,4,84,12), dim=c(2,4), dimnames=list(c("Negative", "Positive"), c("Black", "Brown", "Red", "Blond")))
empat <- array(c(25,13,50,78,19,34,84,101), dim=c(2,4), dimnames=list(c("Negative", "Positive"), c("Black", "Brown", "Red", "Blond")))
# rbind() the tables together
TAB <- rbind(satu, dua, tiga, empat)
# Do the barplot and save the bar midpoints
mp <- barplot(TAB, beside = TRUE, axisnames = FALSE)
# Add the individual bar labels
mtext(1, at = mp, text = c("N", "P"),
line = 0, cex = 0.5)
# Get the midpoints of each sequential pair of bars
# within each of the four groups
at <- t(sapply(seq(1, nrow(TAB), by = 2),
function(x) colMeans(mp[c(x, x+1), ])))
# Add the group labels for each pair
mtext(1, at = at, text = rep(c("satu", "dua", "tiga", "empat"), 4),
line = 1, cex = 0.75)
# Add the color labels for each group
mtext(1, at = colMeans(mp), text = c("Black", "Brown", "Red", "Blond"), line = 2)

How to clear screen in R (Windows)

This tip (taken from this old thread) does work only under Windows:

cls <- function() {
       require(rcom)
       wsh <- comCreateObject("Wscript.Shell")
       comInvoke(wsh, "SendKeys", "\014")
       invisible(wsh)
}
cls()

or

# An R function to clear the screen on RGui:
cls <- function() {
if (.Platform$GUI[1] != "Rgui")
return(invisible(FALSE))
if (!require(rcom, quietly = TRUE)) # Not shown any way!
stop("Package rcom is required for 'cls()'")
wsh <- comCreateObject("Wscript.Shell")
if (is.null(wsh)) {
    return(invisible(FALSE))
} else {
comInvoke(wsh, "SendKeys", "\014")
    return(invisible(TRUE))
}
}
cls()

See this StackOverflow Question for other solutions.

Fill a matrix with vectors of different lengths

V <- c(1,1,2,3,4,6)
binc <- function(x){
     l <- sum(x)+1
     y <- c(1,rep(0,l-1))
     for (i in x) y <- y+c(rep(0,i),y)[1:l]
}

out <- lapply(1:length(v), function(i) binc(v[1:i]))
nout <- max(sapply(out, length))
sapply(out, function(x) c(x, rep(0, nout - length(x))))

Rotating a distribution plot by 90 degrees

x <- c( rnorm(50,10,2), rnorm(30,20,2) )
y <- 2+3*x + rnorm(80)
d.x <- density(x)
d.y <- density(y)
layout( matrix( c(0,2,2,1,3,3,1,3,3),ncol=3) )
plot(d.x$x, d.x$y, xlim=range(x), type='l')
plot(d.y$y, d.y$x, ylim=range(y), xlim=rev(range(d.y$y)), type='l')
plot(x,y, xlim=range(x), ylim=range(y) )

ls() improved!

This marvelous little function shows all objects in the current workspace
by mode, class and 'size'! Thanks to Bendix Carstensen!

lls <- function (pos = 1, pat = "")

{

dimx <- function(dd) if (is.null(dim(dd)))

length(dd)

else dim(dd)

lll <- ls(pos = pos, pat = pat)

cat(formatC("mode", 1, 15), formatC("class", 1, 18),

formatC("name",1, max(nchar(lll)) + 1), "size\n-----------------------------------------------------------------\n")

if (length(lll) > 0)

{

for (i in 1:length(lll))

{

cat(formatC(eval(parse(t = paste("mode(", lll[i],

")"))), 1, 15), formatC(paste(eval(parse(t = paste("class(",

lll[i], ")"))), collapse = " "), 1, 18), formatC(lll[i],

1, max(nchar(lll)) + 1), " ", eval(parse(t = paste("dimx(", lll[i], ")"))), "\n")

}

}

}

What's the best way to upgrade R on Windows©?

Copied and pasted from R for Windows FAQ - Version for R-2.5.0 - FAQ 2.8:

That's a matter of taste. For most people the best thing to do is to uninstall R (see the previous Q), install the new version, copy any installed packages to the library folder in the new installation, run update.packages() in the new R (`Update packages...' from the Packages menu, if you prefer) and then delete anything left of the old installation. Different versions of R are quite deliberately installed in parallel folders so you can keep old versions around if you wish.

Upgrading from R 1.x.y to R 2.x.y is special as all the packages need to be reinstalled. Rather than copy them across, make a note of their names and re-install them from CRAN.

How to Superimpose Histograms

Function inspired by the code of Martin Maechler found on the R-List at http://tolstoy.newcastle.edu.au/R/help/06/06/30059.html

superhist2pdf <- function(x, filename = "super_histograms.pdf",
dev = "pdf", title = "Superimposed Histograms", nbreaks ="Sturges") {
junk = NULL
grouping = NULL
for(i in 1:length(x)) {
junk = c(junk,x[[i]])
grouping <- c(grouping, rep(i,length(x[[i]]))) }
grouping <- factor(grouping)
n.gr <- length(table(grouping))
xr <- range(junk)
histL <- tapply(junk, grouping, hist, breaks=nbreaks, plot = FALSE)
maxC <- max(sapply(lapply(histL, "[[", "counts"), max))
if(dev == "pdf") { pdf(filename, version = "1.4") } else{}
if((TC <- transparent.cols <- .Device %in% c("pdf", "png"))) {
cols <- hcl(h = seq(30, by=360 / n.gr, length = n.gr), l = 65, alpha = 0.5) }
else {
h.den <- c(10, 15, 20)
h.ang <- c(45, 15, -30) }
if(TC) {
plot(histL[[1]], xlim = xr, ylim= c(0, maxC), col = cols[1], xlab = "x", main = title) }
else { plot(histL[[1]], xlim = xr, ylim= c(0, maxC), density = h.den[1], angle = h.ang[1], xlab = "x") }
if(!transparent.cols) {
for(j in 2:n.gr) plot(histL[[j]], add = TRUE, density = h.den[j], angle = h.ang[j]) } else {
for(j in 2:n.gr) plot(histL[[j]], add = TRUE, col = cols[j]) }
invisible()
if( dev == "pdf") {
dev.off() }
}

# How to use the function:
d1 = rnorm(1:100)
d2 = rnorm(1:100) + 4
# the input object MUST be a list!
l1 = list(d1,d2)
superhist2pdf(l1, nbreaks="Sturges")

Plotting multiple smooth lines on the same graph

d1 <- cbind(rnorm(100), rnorm(100,3,1))
d2 <- cbind(rnorm(100), rnorm(100,1,1))
plot(d1[,1], d1[,2], xlim=range(c(d1[,1], d2[,1])), ylim=range(c(d1[,2], d2[,2])), col="blue", xlab="X", ylab="Y")
points(d2[,1], d2[,2], col="red")
points(loess.smooth(d1[,1], d1[,2]), type="l", col="blue")
points(loess.smooth(d2[,1], d2[,2]), type="l", col="red")

How to convert the lower triangle of a matrix to a symmetric matrix

See this thread.

This post was updated.

How to join two arrays using their column names intersection

ar1 <- array(data = c(1:16), dim = c(4,4))
ar2 <- array(data = c(1:16), dim = c(4,4))
colnames(ar1) <- c("A","B","D","E")
colnames(ar2) <- c("C","A","E","B")
# get the common names between the matrices
same <- intersect(colnames(ar1), colnames(ar2))
# join them together
rbind(ar1[,same], ar2[,same])

Highlight overlapping area between two curves

This post was updated thanks to Blaž Triglav contribution.
p <- seq(0.2,1.4,0.01)
x1 <- dnorm(p, 0.70, 0.12)
x2 <- dnorm(p, 0.90, 0.12) 
plot(range(p), range(x1,x2), type = "n") 
lines(p, x1, col = "red", lwd = 4, lty = 2) 
lines(p, x2, col = "blue", lwd = 4)
polygon(c(p,p[1]), c(pmin(x1,x2),0), col = "grey")

Below an advanced example (Thanks to Blaž):

p <- seq(54,71,0.1)
x1 <- dnorm(p, 60, 1.5)
x2 <- dnorm(p, 65, 1.5)
par(mar=c(5.5, 4, 0.5, 0.5))
plot(range(p), range(x1,x2), type = "n", xlab="", ylab="", axes=FALSE)
box()
mtext(expression(bar(y)), side=1, line=1, adj=1.0, cex=2, col="black")
mtext("f"~(bar(y)), at=0.265, side=2, line=0.5, adj=1.0, las=2.0, cex=2, col="black")
axis(1, at=60, lab=mu[H[0]]~"=60", cex.axis=1.5, pos=-0.0165, tck=0.02)
axis(1, at=65, lab=mu[H[1]]~"=65", cex.axis=1.5, pos=-0.0165, tck=0.02)
axis(1, at=63.489, lab=y[k][","][zg]~"=63,489", pos=-0.035, tck=0.2)
axis(1, at=63.489, lab="", pos=-0.035, tck=-0.02)
lines(p, x1, col = "black")
lines(p, x2, col = "black")
line3 <- 63.5
polygon(c(line3, p[p<=line3], line3), c(0,x2[p<=line3],0), col = "grey23")
lines(p, x1, col = "black")
line <- 63.489
line1 <- 60
line2 <- 65
abline(v=line, col="black", lwd=1.5, lty = "dashed")
abline(v=line1, col="black", lwd=0.3, lty = "dashed")
abline(v=line2, col="black", lwd=0.3, lty = "dashed")
xt3 <- c(line,p[p>line],line)
yt3 <- c(0,x1[p>line],0)
polygon(xt3, yt3, col="grey")
legend("topright", inset=.05, c(expression(alpha), expression(beta)), fill=c("grey", "grey23"), cex=2)

It's not R but It's VERY useful

On the chance that you are talking about replacing a word, say, 500 times in a large text file, you might be interested in sed.

Example (you must be in a unix environment with GNU free software):

sed 's/old_word/new_word/g' text_file > new_text_file

After which, new_text_file will contain the contents of text_file, except with the new_word values in place of the old_word values. For those interested in more detailed information, as usual, man (in this case man sed) is your friend.

From Similarity To Distance matrix


# This function returns an object of class "dist"
sim2dist <- function(mx) as.dist(sqrt(outer(diag(mx), diag(mx), "+") - 2*mx)) # from similarity to distance matrix d.mx = as.matrix(d.mx) d.mx = sim2dist(d.mx) # The distance matrix can be used to visualize # hierarchical clustering results as dendrograms hc = hclust(d.mx) plot(hc)

See Multivariate Analysis (Probability and Mathematical Statistics) for the statistical theory.