Note: We are going to use random number functions and random process functions in R such as
runif, a problem with these functions is that every time you run them you will obtain a different value. To make your results reproducible you can specify the value of the seed using
set.seed(‘any number’) before calling a random function. (If you are not familiar with seeds, think of them as the tracking number of your random numbers). For this set of exercises we will use
set.seed(1), don’t forget to specify it before every random exercise.
Answers to the exercises are available here
If you obtained a different (correct) answer than those listed on the solutions page, please feel free to post your answer as a comment on that page.
Generating random numbers. Set your seed to 1 and generate 10 random numbers using
runif and save it in an object called
Using the function
ifelse and the object
random_numbers simulate coin tosses. Hint: If
random_numbers is bigger than .5 then the result is head, otherwise is tail.
Another way of generating random coin tosses is by using the
rbinom function. Set the seed again to 1 and simulate with this function 10 coin tosses. Note: The value you will obtain is the total number of heads of those 10 coin tosses.
Using the function
rbinom to generate 10 unfair coin tosses with probability success of 0.3. Set the seed to 1.
We can simulate rolling a die in R with
runif. Save in an object called
die_roll 1 random number with
min = 1 and
max = 6. This mean that we will generate a random number between 1 and 6.
Apply the function
die_roll. Don’t forget to set the seed to 1 before calling
Simulate normal distribution values. Imagine a population in which the average height is 1.70 m with an standard deviation of 0.1, using
rnorm simulate the height of 100 people and save it in an object called
To get an idea of the values of heights applying the function
a) What’s the probability that a person will be smaller than 1.90? Use
b) What’s the probability that a person will be taller than 1.60? Use
The waiting time (in minutes) at a doctor’s clinic follows an exponential distribution with a rate parameter of 1/50. Use the function
rexp to simulate the waiting time of 30 people at the doctor’s office.
What’s the probability that a person will wait less than 10 minutes? Use
What’s the waiting time average?
Let’s assume that patients with a waiting time bigger than 60 minutes leave. Out of 30 patients that arrive to the clinic how many are expected to leave? Use