1. variables numeric character string logical > a = 24 > b<-25
> sqrt(a+b)
> a = "The dog ate my homework"
> sub("dog","cat",a)
> a = (1+1==3)
> a
numeric
character string
logical

2. variables paste("X", "Y") paste("X", "Y", sep = " + ")
paste("Fig", 1:4)
paste(c("X", "Y"), 1:4, sep = "", collapse = " + ")
x<-2.17
y<-as.character(x)
z<-as.numeric(y)
a = c(1,2,3)
a*2

3. Vector/list a = c(7,5,1) a[2] doe = list(name="john",age=28,married=F)
doe$name

4. id locallization tumorsize progress 1 xx348 proximal 6.3 FALSE
id<-c("xx348", "xx234", "xx987")
locallization<-c("proximal", "distal", "proximal")
progress<-c(F, T, F)
tumorsize<-c(6.3, 8.0, 10.0)
results<-data.frame(id, locallization , tumorsize, progress)
> results
id locallization tumorsize progress
1 xx proximal FALSE
2 xx distal TRUE
3 xx proximal FALSE

5. id locallization tumorsize progress 1 xx348 proximal 6.3 FALSE
results[3,2]
results[1,]
results[c(1,3),]
results[c(T,F,T),]
results$locallization
results[ results$locallization=="proximal", ]
> results
id locallization tumorsize progress
1 xx proximal FALSE
2 xx distal TRUE
3 xx proximal FALSE

6. results<-edit(results)

7. x = c(1, 1, 2, 3, 5, 8)
x[c(TRUE, TRUE, FALSE, FALSE, TRUE, TRUE)]
x[c(TRUE, FALSE)]
x == 1
x[x == 1]
x[x%%2 == 0]
y = c(1, 2, 3)
y[]=3 y

8. a=matrix(1:9, ncol = 3, nrow = 3)
b=matrix(c(TRUE, FALSE, TRUE), ncol = 3, nrow = 3) b
x=1:10
y=11:20
z=matrix(c(x,y)) z
z=matrix(c(x,y),nrow=2)
z=matrix(c(x,y),nrow=4)

9. R code
max(z) min(z) length(z) mean(z) sd(z) sum(z)

10. expression<-factor(c("over","under","over","unchanged","under","under"))
levels(expression)
expression[6]="x"
expression

11. Working directory setwd("D:/data")
x<-read.table("profiles.csv", sep=",", header=TRUE)
matplot(x, type="l")
matplot(x, type="l", xlab="fraction", ylab="quantity", col=1:6, lty=1:5, lwd=2)
write.table(x, file="test.txt", sep="\t")

12. Apply the max function on columns (2) or rows (1) of matrix x
xmax<-apply(x, 2, max)
xmax
ymax<-apply(x, 1, max)
ymax
Apply the max function on columns (2) or rows (1) of matrix x

13. Legend
matplot(x,type="l",xlab="fraction",ylab="quantity",col=1:6,lty=1:5, lwd=2)
legend(x=600,legend=names(x),col=1:6,lty=1:5,lwd=2,bg="snow")

14. > x
x p1 p2 p3 p4 p5 p6 p7 p8 p9 p10 p11 p12
xmax <- apply(x, 2, max)
xscaled = scale(x, scale=xmax, center=FALSE)

15. Writing a Program Can save a file as do in perl
Use “source” command to load the file

16. Write this to a file named “profile.r” and save to D:\data
setwd("D:/data")
x<-read.table("profiles.csv", sep=",", header=TRUE)
xmax <- apply(x, 2, max)
xscaled = scale(x, scale=xmax, center=FALSE)
matplot(xscaled, type="l")
Write this to a file named “profile.r” and save to D:\data

17. Write this to a file named “profile.r” and save to D:\data
setwd("D:/data")
source("profile.r")
Write this to a file named “profile.r” and save to D:\data

18. Function
Functions in R are dened using the function keyword. Curly braces are used to dene the body of the function.
The value / object returned by the function is indicated by the return keyword.

19. square = function(x) {
return(x^2) }
z=square(4) z

20. plus= function(x,y) {
return(x+y) }
z=plus(4,5) z

21. z=10
z=sqrt(z)
if (z<5) print("<5") else if (z == 5) print ("z==5") else print("z>5")

22. Conditions

23. evenodd = function(x) {
if (!is.numeric(x)) {
print("neither") }
else if (x%%2 == 0) {
print("even")
else {
print("odd")
evenodd(10)

24. Loop
for
while
repeat

25. for (x in 1:3) {
print(x) }
for (x in c("hello", "goodbye")) {

26. for (x in 1:4) {
if (x%%2==0) next
print(x) }
for (x in 1:3) {
if (x==2) break

27. #test
x = 1
while (x < 3) {
print(x)
x = x + 1 }

28. cars <- c(1, 3, 6, 4, 9)
plot(cars)
barplot(cars)
suvs <- c(4,4,6,6,16)
hist(suvs)
pie(suvs)

29. x <- c(1,3,6,9,12)
y <- c(1.5,2,7,8,15)
plot(x,y)
myline.fit <- lm(y ~ x)
abline(myline.fit)
x2 <- c(0.5, 3, 5, 8, 12)
y2 <- c(0.8, 1, 2, 4, 6)
points(x2, y2, pch=16, col="green")

30. d<-dist(t(xscaled),method="euclidean")
dendrogram=hclust(t(d),method="complete",members=NULL)
plot(dendrogram)
rect(1,0,2,1)

31. cummean = function(x){
n = length(x)
y = numeric(n)
z = c(1:n)
y = cumsum(x)
y = y/z
return(y) }
n = 10000
z = rnorm(n)
x = seq(1,n,1)
y = cummean(z)
X11()
plot(x,y,type= 'l',main= 'Convergence Plot')