##################################################################
# BEGINNER'S R for SB1a
# You may find these exercises useful to work through.
# I anticipate your doing the practicals without the
# need for functions and loops etc, using just the small
# range of commands I present in the Lab sessions.
# However you may find this 'broadening' training helpful
# for context. It should take about 2 hours to work through
# the entire set (including solving the problems).
##################################################################
##################################################################
#Getting started - variables, functions, graphs
print('hello world')
1+1
x=3/7
x
exp(-x^2)
pi
cos(pi)
#exercises
#1. Use R to calculate 1.1 to the power of 100
#2. calculate the sine of pi/2 (using R!)
#3. pi is defined, but "e" the base of the natural logarithm, is not. Calculate it.
#4. in R, log(x) is the logarithm function. Is it base 10 or base e?
#here is a simple R function to plot functions
curve(exp(-x^2),from=-3,to=3,col=2)
#add horizontal and vertical lines for axes
abline(h=0)
abline(v=0)
#add a legend explaining which line is which
legend('topright','exp(-x^2)',lty=1,col=2)
#exercises
#5. plot log10(x) from 0.1 to 10
#6. plot log(x) from 1/e to e
#7. plot cos(x^2) from 0 to sqrt(4*pi)
####################################################################
#vectors
#we can collect things together in a vector
#and take functions of all the numbers at once
theta=c(0,pi/2,pi)
theta
sin(theta)
#functions to make special vectors
n=-4:4
n
#to access elements of a vector use []
n[1]
n[1:3]
m=rep(1,9)
m
m[1:3]=0
m
m[7:9]=m[7:9]-1
m
#element by element multiplication, and addition
n*2
n*n
m+n
#some functions on vectors
length(n)
sum(n)
max(n)
min(n)
#regular sequences (useful for plotting)
x=seq(from=-4,to=4,by=2)
x
x=seq(from=0,to=1,length.out=11)
x
#exercises
#8. use the methods above to make a vector x=c(0,pi/2,pi,3*pi/2,...,8*pi)
#dont just type in x=c(0,pi/2,pi,3*pi/2, etc etc )! Use seq().
#9. calculate cos(a*pi) for a=0,0.5*pi,pi,...,8*pi
#10. create a vector x of 101 equally spaced values from 0 to 2
#11. create a vector y=x^2 for x=0...1 and y=x^2+1 for x=1.02 to 2 (hint x[51] should equal 1 so x[52:101]...)
#12. enter the command "plot(x,y)" (without the quotes)
#13. create a vector 1,1/4,1/9,1/16...,1/10000 and sum the values (hint start with 1:100, square, invert and sum)
#(you should find the answer is about pi^2/6)
################################################################
#matrices
#When we make a matrix R fulls the columns from left to right with the vector of values
M=matrix(1:9,3,3)
M
#to access an entry
M[3,3]
#or a group of entries
M[1:2,1:2]
#to access the first row
M[1,]
#or the third column
M[,3]
#matrices have dimensions
d=dim(M)
d
#number of rows and columns
d[1]
d[2]
#we can apply functions to matrices, just like vectors
L=matrix(c(-1,0,1,1,-1,0,0,1,-1),3,3)
L
L+M
M+1
2*L+M
cos(L*pi/2)
round(cos(L*pi/2),2) #rounded to 2 decimal places - easier to read
#exercises
#14. make this matrix
# [,1] [,2] [,3]
#[1,] -4 -1 2
#[2,] -3 0 3
#[3,] -2 1 4
#15. This matrix is a transition probability matrix for a markov chain
# [,1] [,2] [,3]
#[1,] 0.1 0.4 A
#[2,] 0.2 0.5 B
#[3,] 0.3 0.6 C
#Work out A,B and C and create the matrix. Call it P
#"sum" is a function that sums all the elements in a matrix
sum(M)
#We often want to just sum along the rows or down the columns of a matrix
#apply(Matrix,row/col,function) applies a function to the rows/cols of Matrix
M
#direction 2 is the column direction, this sums down the columns
apply(M,2,sum)
#direction 1 is the row direction, this sums along the rows
apply(M,1,sum)
#exercise
#16. check that M and M+L have the same row and colum sums
#we can multiply matrices
L=matrix(c(-1,0,1,1,-1,0,0,1,-1),3,3)
L
L%*%M
#notice that L*M is not the same thing!
L*M
#same with taking powers
L*L
L^2
#are both just squaring all the elements whereas
L%*%L
#is the matrix multiplication square.
#diag() is a useful function. We can use it to make an identity matrix,
#and also to extract and change diagonal elements all at once
diag(1,c(3,3))
M
d=diag(M)
d
diag(M)=0
M
diag(M)=d
M
#exercises
#17. Does matrix multiplication commute? Evaluate L%*%M and M%*%L - are they equal?
#18. Work out P%*%P, P%*%P%*%P etc out to P^n with n=8 (that's 8 matrix multiplications)
#where P=matrix(c(0.1,0.2,0.3,0.4,0.5,0.6,0.5,0.3,0.1),3,3) is the transition matrix from Question 15.
#When n is large the resulting matrix should have 3 near-identical rows. Why is that?
##################################################################
#for-loops
#handy when we want to carry out the same operation many times on a list of things.
for (k in 1:10) {
print('hello world')
}
tot=0
for (j in 1:10) {
tot=tot+j
}
#That last one starts with tot=0 and accumulates 1+2+3+...+10
#same as sum(1:10) (!)
#we can use the loop to calculate P%*%P...P%*%P without alot of typing
Q=P
n=8
for (i in 2:n) {
Q=Q%*%P
}
Q
#exercise
#19. Let P be the transition matrix for the Sunny/Rainy chain from yesterday
# [,1] [,2]
#[1,] 0.9 0.1
#[2,] 0.5 0.5
#State one is sunny state 2 is rainy.
#Calculate P^100 (matrix multiplication 100 times). What are the approximate
#long-term chances of it being sunny?
############################################################################
#functions
#if we have a calculation we need to do often (for example, taking matrix powers)
#we can make our own function to save typing it all out each time.
#first a very simple example to give the idea
f=function(x) {
d=x^2
return(d)
}
f(3)
f(-3)
#this function will act on vectors
y=-3:3
y
f(y)
#and matrices
M=matrix(c(1,2,3,4,5,6,7,8,9),3,3)
f(M)
#but notice that this function just does element-by-element squaring.
#our function behaves like an R function
curve(f(x),from=-3,to=3,main='y=x^2')
#exercises
#20. Change the function f() so that it calculates cos(x). Give your function a new name.
#21. suppose x is a vector, say (x_1,x_2,x_3). The euclidean norm of x is sqrt(x_1^2+x_2^2+x_3^2).
#For example, if x=c(1,0,2) the norm is sqrt(5) and in R this would be sqrt(sum(x^2))).
#Write an R function that takes as input a vector x and calculates and returns the norm.
#functions can have more than one argument
#this function lets us input the power - and defaults to 2 if we
#omit the 2nd argument
g=function(x,p=2) {
d=x^p
return(d)
}
g(3,3)
#same thing but easier to read
g(x=3,p=3)
#R uses the default if we omit the 2nd argument
g(3)
#exercises
#22. change the function g() so that it calculates cos(p*x) defaulting to p=1.
#Give your function a new name.
#23. suppose x is a vector, say (x_1,x_2,x_3). The p-norm of x is (|x_1|^p+|x_2|^p+|x_3|^p))^(1/p)
#where |x| is the absolute value of x. For example, if x=c(1,0,2) the 3-norm is (1+0+8)^(1/3) ~ 2.08.
#In R this would be (sum(abs(x)^p)))^(1/p). Write an R function that takes as input a vector x
#and a value for p and calculates and returns the p-norm of x.
#Now for something a bit more complicated: a function to calculate positive
#integer matrix powers
my.matrix.power=function(M,p) {
#calculate M^p (matrix-power)
Q=diag(1,dim(M))
for (i in 1:p) {
Q=Q%*%M
}
return(Q)
}
my.matrix.power(P,1)
my.matrix.power(P,2)
#exercises
#24. Use the function to calculate P^100 (matrix multiplication)
#for P the transition matrix of the sunny rainy Markov chain.
################################################################
#comparisons and boolean variables
#TRUE (corresponding to 1) and FALSE (corresponding to 0) are
#boolean variables recognised by R. We can make comparisons
#with TRUE/FALSE outcomes
x=1; y=4; z=2^2;
x==y
y==z
y=y)}
x=z
if (x=y")}
#here is another example. %% is the remainder function
#so 10%%2 is 0 and 10%%3 is 1 (since 10=3*3+1 remainder).
#sort numbers into odd and even
for (k in 1:10) {
if (k%%2==0) print(paste(k,'is even'))
}
#25. Write a function is.even() that takes as input a number and
#returns TRUE if the number is even and FALSE if it is odd. Here is a start
#is.even=function(x) {
# if ???
# return(???)
#}
#try your function out - here is mine
#> is.even(10)
#[1] TRUE
#> is.even(9)
#[1] FALSE
#These operations work on vectors
x=-4:4
x>0
#going back to exercise 10
x=seq(from=0,to=2,length.out=101)
y=x^2+(x>1)
plot(x,y,type='l')
#this works because x>1 is FALSE (ie 0) for x<=1 and TRUE (ie 1) for x>=1
#26. f(x) is sin(x) for x<=pi/2 and sin^4(x) for x>pi/2 - plot x from 0 to pi - here is a start
#x=seq(from=0,to=pi,length.out=101)
#y=WHAT.FUNCTION.GOES.HERE?*(x<=pi/2)+sin(x)^2*(WHAT.TEST.GOES.HERE)
#plot(x,y,type='l')
#Advanced topic - rounding errors
#A warning! Computers have 'rounding errors'. They dont always represent
#fractions and large numbers exactly. Here is an example...
#TRUE or FALSE?
0.3==0.1+0.1+0.1
sprintf('%1.60f',0.3)
sprintf('%1.60f',0.1+0.1+0.1)
#Advanced topic - recursion
#Functions can call themselves!
#For example the factorial function - if f(n)=n! then f(1)=1 and f(n)=n*f(n-1).
my.factorial=function(n) {
if (n==1) return(1)
return(n*my.factorial(n-1))
}
my.factorial(3)
#################################################################################
#random numbers
#R has lots of functions to generate 'random' or 'pseudo-random' numbers
#for example sample() samples numbers at random from a list
x=-4:4
#sampling without replacement
sample(x,6)
#sampling with replacement
sample(x,6,replace=TRUE)
#we can also sample with non-equal probabilities. For example the long-run
#probability for a sunny day is 5/6 and 1/6 for rainy.
#here are 20 independent random days worth of weather
sample(c(1,2),size=20,prob=c(5/6,1/6),replace=TRUE)
#(you should see about three 2's and about 17 ones - but it is random after all)
#27. Use sample to simulate a die throw
#28. Use sample to simulate the weather tomorrow, if today is sunny - the transition matrix was
# [,1] [,2]
#[1,] 0.9 0.1
#[2,] 0.5 0.5
#we use uniform random numbers alot in simulation work
#one random number from 0-1
runif(1)
#one random number from 9-11
runif(1,9,11)
#eight random numbers from 0-1
runif(10)
#normal (or gaussian) random numbers are often useful
#here are eight normal random variables, each mean 0 and standard deviation 1
rnorm(8)
#we can change the mean and variance - for example, mean 10 standard deviation 3
rnorm(8,10,3)
#how can we check they are normal? Try generating 1000 of them
x=rnorm(1000)
x
#we can see the numbers on a plot
plot(1:1000,x)
#plotting a histogram we can see the bell-curve shape
hist(x)
#the sample mean should be about 0 and the sample std. dev should be about 1
mean(x)
sd(x)
#what is the probability that x>1 or less than -1 - about 30% right?
#we can estimate that using the proportion of samples that fall outside +/- 1
x>1
sum(x>1)
tot=sum(x>1)+sum(x<(-1))
av=tot/1000
av
#or more simply
mean(abs(x)>1)
#exercises
#29. the ratio of two normal random numbers rnorm(1)/rnorm(1)
#has a special distribution called a t(1)-distribution (with one
#degree of freedom). Simulate 1000 t-distributed numbers
#using rnorm(1000)/rnorm(1000). Make a histogram of you numbers.
#30. Estimate the probability that a t(1) random number is bigger
#than one or less than -1.
####################################################################
#Random walks
#Finally, for today, we will plot some random walks in different colors
#a random walk is a sequnce of numbers, at each step we add a random jump.
#We will use normal random jumps, mean zero, std dev 1. At each step the
#next X-value is equal the last X-value plus a normal random number.
n=1000
X=rep(0,n)
for (i in 2:n) {
X[i]=X[i-1]+rnorm(1)
}
plot(1:n,X,type='l',ylim=c(-3*sqrt(n),3*sqrt(n)))
for (k in 1:10) {
for (i in 2:n) {
X[i]=X[i-1]+rnorm(1)
}
lines(1:n,X,type='l',col=k)
}
#exercise
#31. Replace X[i]=X[i-1]+rnorm(1) with X[i]=X[i-1]+rnorm(1)/rnorm(1) (the t-dbn)
#and see what happens! You may need to replace
#ylim=c(-3*sqrt(n),3*sqrt(n)) with ylim=c(-3*n,3*n) or just remove ylim completely