1. R in Action Obtaining and installing R and RStudio
Obtaining and installing R and RStudio
R is freely available from the Comprehensive R Archive Network (CRAN) at
Y R studio:
#Working with R
set.seed(34)
x <- rnorm(5)
Comments are preceded by the # symbol
set.seed:  Set the seed of R's random number generator, which is useful for creating random objects that can be reproduced
Creates a vector object named x containing five random deviates from a standard normal distribución

2. Working with R
#The function c()combines arguments into a vector
age <- c(1,3,5,2,11,9,3,9,12,3)
weight <- c(4.4,5.3,7.2,5.2,8.5,7.3,6.0,10.4,10.2,6.1)
mean(weight) #arithmetic mean
sd(weight) #standard deviation
cor(age,weight) #correlation of x and y
plot(age,weight) #plot of x and y
#Randon uniform distribution
x <- runif(20)
summary(x) #summary
hist(x) # histogram
#Help
help.start() #General help
data() #List all available example datasets

3. Installing a package (with internet)
Packages
Packages are collections of R functions, data, and compiled code in a well-defined format
#Packages
library() #shows you what packages you’ve saved in your library
search() #tells you which packages are loaded and ready to use
R comes with a standard set of packages (including base, datasets, utils, grDevices, graphics, stats, and methods).
Installing a package (with internet)
#Installing a package
install.packages("mlbench") # install the package mlbench
Load a package
#Load a package
library(mlbench) # load and attach the package mlbench

4. Using output as input—reusing results
Mtcars # 1974 Motor Trend US magazine
head(mtcars) # Returns the first parts of a vector, matrix, table, or data frame
fit <- lm(mpg~wt, data=mtcars) # lm(): fit linear models, regression
summary(fit)
plot(mtcars$wt,mtcars$mpg)
title("Regression of MPG on Weight")
abline(lmfit) # Add Straight Lines to a Plot

5. Vectors
Vectors are one-dimensional arrays that can hold numeric data, character data, or logical data. The combine function c() is used to form the vector
#Vectors
a <- c(1, 2, 5, 3, 6, -2, 4)
b <- c("one", "two", "three")
c <- c(TRUE, TRUE, TRUE, FALSE, TRUE, FALSE)
The data in a vector must only be one type or mode (numeric, character, or logical). You can’t mix modes in the same vector
You can refer to elements of a vector using a numeric vector of positions within brackets
# Refer to elements of a vector
a <- c(1, 2, 5, 3, 6, -2, 4)
a[3]
a[c(1, 3, 5)]
a[2:6]

6. y <- matrix(1:20, nrow=5, ncol=4) y
Matrices
A matrix is a two-dimensional array where each element has the same mode (numeric, character, or logical)
# Matrices
y <- matrix(1:20, nrow=5, ncol=4) y
You can identify elements of a matrix
X[i,] refers to the ith row
X[,j] refers to jth column
X[i, j] refers to the ijth element
# identify elements
x <- matrix(1:10, nrow=2) X
x[2,]
x[,2]
x[1,4]
x[1, c(4,5)]

7. Data frames
A data frame is more general than a matrix in that different columns can contain different modes of data (numeric, character, etc.)
#Data frames
patientID <- c(1, 2, 3, 4)
age <- c(25, 34, 28, 52)
diabetes <- c("Type1", "Type2", "Type1", "Type1")
status <- c("Poor", "Improved", "Excellent", "Poor")
patientdata <- data.frame(patientID, age, diabetes, status)
patientdata
Specifying elements of a data frame
# Specifying elements
patientdata[1:2]
patientdata[c(3,4)]
patientdata$age
# Contingency table of the counts at each combination of factor levels
table(patientdata$diabetes, patientdata$status)

8. Attach, Detach, and With
#Attach
attach(mtcars)
summary(mpg)
plot(mpg, disp)
detach(mtcars)
#With
with(mtcars, {
summary(disp)
plot(mpg, wt) })

9. Using factors
#Factors
patientID <- c(1, 2, 3, 4)
age <- c(25, 34, 28, 52)
diabetes <- c("Type1", "Type2", "Type1", "Type1")
status <- c("Poor", "Improved", "Excellent", "Poor")
diabetes <- factor(diabetes)
status <- factor(status, order=TRUE)
patientdata <- data.frame(patientID, age, diabetes, status)
str(patientdata)
summary(patientdata)

10. Lists
#List
g <- "My First List"
h <- c(25, 26, 18, 39)
j <- matrix(1:10, nrow=5)
k <- c("one", "two", "three")
mylist <- list(title=g, ages=h, j, k)
Mylist
mylist[[2]]
mylist[["ages"]]

11. Entering data from the keyboard
The edit() function invoke a text editor
# Text editor
mydata <- data.frame(age=numeric(0),
gender=character(0), weight=numeric(0))
mydata <- edit(mydata)
Entering data from Excel
# Data from clipboard
gastos<-read.table("clipboard",header=TRUE)
# Data from csv file
Gastos<-read.csv(“Gastos.cvs",head=T)

12. Working with graphs
# Graph example
dose <- c(20, 30, 40, 45, 60)
drugA <- c(16, 20, 27, 40, 60)
drugB <- c(15, 18, 25, 31, 40)
plot(dose, drugA, type="b")
title("Drug A")
Use help(plot) to view other options

13. opar <- par(no.readonly=TRUE) #opar: current graphical settings
Graphical parameters
# Graphical settings
par() #list of the current graphical settings
par(no.readonly=TRUE) #list of current graphical settings that can be modified
# Graphical settings example
opar <- par(no.readonly=TRUE) #opar: current graphical settings
par(lty=2, pch=17)
plot(dose, drugA, type="b") # or: plot(dose, drugA, type="b", lty=2, pch=17)
par(opar) #reset graphical settings

14. Parameters for specifying symbols and lines
pch Specifies the symbol to use when plotting points
cex Specifies the symbol size. 1=default, 1.5 is 50% larger, 0.5 is 50% smaller
lty Specifies the line type
lwd Specifies the line width. lwd=2 generates a line twice as wide as the default
# symbols and lines
plot(dose, drugA, type="b", lty=3, lwd=3, pch=15, cex=2)

15. Parameters for specifying color
col Default plotting color. col=“red”
col.axis Color for axis text
col.lab Color for axis labels
col.main Color for titles
col.sub Color for subtitles
fg The plot’s foreground color
bg The plot’s background color

16. Functions that can be used to create vectors of contiguous colors
rainbow(), heat.colors(), terrain.colors(), topo.colors(), and cm.colors()
# contiguous rainbow colors
n <- 10
mycolors <- rainbow(n)
pie(rep(1, n), labels=mycolors, col=mycolors)
Parameters specifying text size
cex plotted text. 1=default, 1.5 is 50% larger, 0.5 is 50% smaller
cex.axis Magnification of axis text relative to cex
cex.lab Magnification of axis labels relative to cex
cex.main Magnification of titles relative to cex
cex.sub Magnification of subtitles relative to cex
par(font.lab=3, cex.lab=1.5, font.main=4, cex.main=2)

17. Parameters specifying font family, size, and style
font Integer specifying font to use for plotted text.. 1=plain, 2=bold, 3=italic, 4=bold italic
font.axis Font for axis text
font.lab Font for axis labels
font.main Font for titles
font.sub Font for subtitles
family Font family for drawing text. Standard values are serif, sans, and mono.
Parameters for graph and margin dimensions
pin Plot dimensions (width, height) in inches
mai Numerical vector indicating margin size where c(bottom, left, top, right) is expressed in inches
mar margin size where c(bottom, left, top, right) is expressed in lines. The default is c(5, 4, 4, 2)
par(pin=c(4,3), mai=c(1,.5, 1, .2))

18. Using graphical parameters to control graph appearance
#Two plots
dose <- c(20, 30, 40, 45, 60)
drugA <- c(16, 20, 27, 40, 60)
drugB <- c(15, 18, 25, 31, 40)
opar <- par(no.readonly=TRUE)
par(pin=c(2, 3))
par(lwd=2, cex=1.5)
par(cex.axis=.75, font.axis=3)
plot(dose, drugA, type="b", pch=19, lty=2, col="red")
plot(dose, drugB, type="b", pch=23, lty=6, col="blue", bg="green")
#Two graphs in same plot
lines(dose, drugB, type="b", pch=23, lty=6, col="blue", bg="green")
par(opar)

19. Adding text, customized axes, and legends
plot(dose, drugA, type="b",
col="red", lty=2, pch=2, lwd=2,
main="Clinical Trials for Drug A",
sub="This is hypothetical data",
xlab="Dosage", ylab="Drug Response",
xlim=c(0, 60), ylim=c(0, 70))
Axis options
Side (1=bottom, 2=left, 3=top, 4=right)
at A numeric vector indicating where tick marks should be drawn
labels A character vector of labels to be placed at the tick marks
pos The coordinate at which the axis line is to be drawn
lty Line type.
col The line and tick mark color.
las Labels are parallel (=0) or perpendicular (=2) to the axis.

20. #An example of custom axes
x <- c(1:10)
y <- x
z <- 10/x
opar <- par(no.readonly=TRUE)
par(mar=c(4, 4, 2, 6) + 0.1)
plot(x, y, type="b",
pch=21, col="red",
yaxt="n", lty=3, ann=FALSE)
lines(x, z, type="b", pch=22, col="blue", lty=2)
axis(2, at=x, labels=x, col.axis="red", las=2)
axis(4, at=z, labels=round(z, digits=2),
col.axis="blue", las=2, cex.axis=0.7, tck=-.01)
mtext("y=1/x", side=4, line=3, cex.lab=1, las=2, col="blue")
title("An Example of Creative Axes",
xlab="X values",
ylab="Y=X")
par(opar)

21. Reference lines
The abline() function is used to add reference lines to our graph. The format is abline(h=yvalues, v=xvalues)
#Add reference lines
abline(h=c(1,5,7))
abline(v=c(1, 3, 5, 7, 9), lty=2, col="blue")
Legend
legend(location, title, legend, ...)
location You can give an x, y coordinate for the upper-left corner of the legend. You can use
locator(1), in which case you use the mouse to indicate the location of the legend. You can also use the keywords bottom, bottomleft, left, topleft, top, topright, right, bottomright, or center to place the legend in the graph.
title A character string for the legend title (optional).
legend A character vector with the labels.

22. Comparing Drug A and Drug B response by dose
dose <- c(20, 30, 40, 45, 60)
drugA <- c(16, 20, 27, 40, 60)
drugB <- c(15, 18, 25, 31, 40)
opar <- par(no.readonly=TRUE)
par(lwd=2, cex=1.5, font.lab=2)
plot(dose, drugA, type="b",
pch=15, lty=1, col="red", ylim=c(0, 60),
main="Drug A vs. Drug B",
xlab="Drug Dosage", ylab="Drug Response")
lines(dose, drugB, type="b",
pch=17, lty=2, col="blue")
abline(h=c(30), lwd=1.5, lty=2, col="gray")
library(Hmisc)
minor.tick(nx=3, ny=3, tick.ratio=0.5)
legend("topleft", inset=.05, title="Drug Type", c("A","B"),
lty=c(1, 2), pch=c(15, 17), col=c("red", "blue"))
par(opar)

23. text(location, "text to place", pos, ...)
Text annotations
text() places text within the graph
text(location, "text to place", pos, ...)
mtext() places text in one of the four margins
mtext("text to place", side, line=n, ...)
location Location can be an (x, y) coordinate, or via mouse by specifying location as locator(1)
pos Position relative to location. 1 = below, 2 = left, 3 = above, 4 = right.
side Which margin to place text in, where 1 = bottom, 2 = left, 3 = top, 4 = right
You can also specify adj=0 for left/bottom alignment or adj=1 for top/right alignment.

24. Example of a scatter plot (car weight vs
Example of a scatter plot (car weight vs. mileage) with labeled points (car make)
# scatter plot with labeled points
attach(mtcars)
plot(wt, mpg,
main="Mileage vs. Car Weight",
xlab="Weight", ylab="Mileage",
pch=18, col="blue")
text(wt, mpg,
row.names(mtcars),
cex=0.6, pos=4, col="red")
detach(mtcars)
#Display font families
opar <- par(no.readonly=TRUE)
par(cex=1.5)
plot(1:7,1:7,type="n")
text(3,3,"Example of default text")
text(4,4,family="mono","Example of mono-spaced text")
text(5,5,family="serif","Example of serif text")
par(opar)

25. opar <- par(no.readonly=TRUE) par(mfrow=c(2,2))
Combining graphs
R makes it easy to combine several graphs into one overall graph, using either the par() or layout() function
With par()
mfrow=c(nrows, ncols) to create a matrix of nrows x ncols plots that are filled in by row. mfcol=c(nrows, ncols) to fill the matrix by columns.
# plots filled by row
attach(mtcars)
opar <- par(no.readonly=TRUE)
par(mfrow=c(2,2))
plot(wt,mpg, main="Scatterplot of wt vs. mpg")
plot(wt,disp, main="Scatterplot of wt vs disp")
hist(wt, main="Histogram of wt")
boxplot(wt, main="Boxplot of wt")
par(opar)
detach(mtcars)

26. opar <- par(no.readonly=TRUE) par(mfrow=c(3,1)) hist(wt) hist(mpg)
#plots filled by row
attach(mtcars)
opar <- par(no.readonly=TRUE)
par(mfrow=c(3,1))
hist(wt)
hist(mpg)
hist(disp)
par(opar)
detach(mtcars)
#Creating a figure arrangement with fine control
opar <- par(no.readonly=TRUE)
par(mar = c(4, 4, 2, 2))
par(fig=c(0, 0.8, 0, 0.8))
plot(mtcars$wt, mtcars$mpg,
Xlab=”miles Per Gallon”,
ylab=“Car Weigth”)
par(fig=c(0, 0.8, 0.55, 1), new=TRUE
boxplot(mtcars$wt, horizontal=TRUE, axes=FALSE)
par(fig=c(0.65, 1, 0, 0.8), new=TRUE)
boxplot(mtcars$mpg, axes=FALSE)
mtext("Enhanced Scatterplot", side=3, outer=TRUE, line=-3)
par(opar)
The full graph area as going from (0,0) in the lower-left corner to (1,1) in the upper-right corner
fig= parameter is a numerical vector of the form
c(x1, x2, y1, y2)

27. Useful functions for working with data objects
length(object) Number of elements/components.
dim(object) Dimensions of an object.
str(object) Structure of an object.
class(object) Class or type of an object.
mode(object) How an object is stored.
names(object) Names of components in an object.
c(object, object,...) Combines objects into a vector.
cbind(object, object, ...) Combines objects as columns.
rbind(object, object, ...) Combines objects as rows.
object Prints the object.
head(object) Lists the first part of the object.
tail(object) Lists the last part of the object.
ls() Lists current objects.
rm(object, object, ...) Deletes one or more objects. The statement rm(list = ls()) will remove most objects from the working environment.
newobject <- edit(object) Edits object and saves as newobject.
fix(object) Edits in place.