Week 1 Exercises: Intro R

First things

The very first things to do are to open RStudio and get a blank script ready for writing your code.

Our recommendation is that you have an R project for this course, and use a new script for each week of work. See the tip about “R projects” in Section 1A.

Pet Data

We’re going to play with some data on a sample of licensed pets from the city of Seattle, USA. It can be downloaded (or read directly into R) from https://uoepsy.github.io/data/pets_seattle.csv. It contains information on the license ID, year of issue, as well as the species, breeds and weights of each pet. You can find a data dictionary in @tab-petdict

Seattle Pets: Data dictionary
Variable Description
license_year Year in which license was issued
license_number Unique license ID number
animals_name Full name of pet
species Species of pet
primary_breed Primary breed of pet
secondary_breed Secondary breed of pet
weight_kg Weight in kilograms
Question 1

Write a line of code that reads in the data to your R session. Then examine the dimensions of the dataset, and take a look at the first few lines lines.

Hints: You’ll need the read.csv() function. Remember to assign it a name to store it in your environment. 1B #basic-data-wrangling contains an example of reading in data from a URL. You’ll then want to play with functions like dim() and head().

Solution

Question 2

What are the names of the 47th and the 200th animals in the dataset?

Hints: You’ll probably want to make use of the square brackets data[rows, columns].

Solution

Question 3

Subset the data to only the data for the dogs, and store this object as another named object in your environment. Subset the data to only the data for the cats, and store this object as another named object in your environment.

Hints: You’ll want to think about how we access data via asking for those entries that meet a specific condition (see 1B #accessing-by-a-condition)

Solution

Question 4

Find the name and weight of the heaviest cat, and of the lightest dog.

Hints: You could do this using the original data you read in from question 1, or use the subsets you created in question 3. You’ll again want to supply a condition within square brackets. That condition may well have something to do with being equal to the min() or the max() of some variable.

Solution

Question 5

Does the data contain only dogs and cats?

Hints: Given what you did in question 3, you might be able to answer this by just looking at your environment.

Solution

Question 6

Extract the entries of the dataset for which the species is neither “Dog” nor “Cat”? What are the names and species of these animals?

Hints: This is a slightly complex one. 1B #more-complex-conditions might help you here.

Solution

Question 7

Create a new variable in the data, which contains the weights of all the animals, but rounded to the nearest kg.

Hints: Try looking up the help documentation for the function round(). Try playing with it in the console, e.g. round(c(3.5, 4.257, 1.1111)). You may find it helpful to look back at 1B #adding/changing-a-variable.

Solution

Question 8

Try giving the dataset to the function summary(). You’ll get out some information on each of the variables. It is likely that you’ll get more useful information for the variables containing information on the animal’s weights than for those containing their names, breeds etc because these variables are vectors of “characters”. We’ll start to look more about different types of data next week.

Solution

Simulating Dice

Question 9

Copy the code from the lecture which creates a custom function called dice() (copied below). Be sure to run the code (highlight it all with your cursor, and hit “run” in the top right, or press Ctrl/Cmd+Enter).

dice <- function(num = 1) {
  sum(sample(1:6, num, replace=TRUE))
}

What did that code do?

In a sense, this code does nothing: It won’t give you any output when you run it. What it is actually doing, though, is defining a function called dice(). If you look at your environment panel (top right), you’ll see dice appear when you run the code.

To produce some output, we have to call the function dice() (by writing it into code: dice(4), for example). dice() wants to be supplied with some information (in the argument num). If no information is supplied, num will take a default value of 1. (So writing dice() is equivalent to writing dice(1)).

What does dice() do with num? It calls another function, sample(), with 3 arguments. We didn’t write sample(): it’s a function that’s “supplied with” R. To find out more about what sample() does:

  • click inside the brackets just after sample() in your R script;

  • press TAB (⇥), then F1

  • you should see some help appear in the bottom right-hand panel of RStudio.

You will find that “sample() takes a sample … from the elements of x …” If you compare the code in RStudio to the code under “Usage” you’ll see that where the help has x, we have 1:6. So what does 1:6 mean? One way to find out is to open the console in RStudio (bottom left) and just type stuff in. What happens when you type 1:6? What about 2:17? (What about 6:1?)

Remember: The console is the place to “try stuff out” (don’t worry, you can’t break it).

What you will discover is that 1:6 creates a vector (list of similar things, in this case numbers) of the numbers 1-6. The next bit of the sample() function is size. In the dice() function, the num passes down to the size of the sample(): Looking through the help, size is the number of items to choose. So sample(1:6, 1) would choose one number from the numbers 1-6 at random; sample(1:6, 3) would choose 3, and so on. The last argument, replace=TRUE, tells sample() what to do with a number once it’s been picked: Does it go ‘back into the bag’ to be picked again (TRUE) or not? (FALSE)?

Around the outside is sum() which simply sums the numbers on however many (num) dice you “rolled”.

Putting it all together, our dice() function “throws num dice” by sample()ing from the numbers 1-6 num times, replaceing each number when it’s been picked, and sums the numbers of all the dice.

Question 10

Look up the function replicate(). We can use it to do something in R lots of times! For instance, replicate(20, 1+1) will evaluate 1+1 twenty times.

Use replicate() to simulate 20 rolls of a single dice, and store the results in an object in your environment. Give it an easily identifiable name.

Hints: A single dice means num = 1

Solution

Question 11

Create a barplot showing the frequency with which each number was landed on in the 20 rolls.

Hints: the functions table() and barplot() were used to do this in the lecture.

Solution

Question 12

Do the same for 1,000 rolls, and then for 10,000. What do you notice?

Solution

Question 12

Copy the code below into your script and run it. It creates a new function called wdice() which simulates the rolling of num dice which are slightly weighted.

Roll a single weighted die 20 times and plot the frequency distribution. Do the same for 1,000 and 10,000 rolls of a single die. Does a pattern emerge? At how many rolls?

wdice <- function(num = 1){
    sum(sample(1:6, num, replace=TRUE, prob = c(0.15,0.15,0.15,0.15,0.15,0.25)))
}

Solution

Question 14

Remember, wdice() and dice() are really just relying on different functions, like sample(). Try playing around with sample() in the console again - what does the prob = c(....) bit do?

Solution

Question 15

Let’s try to modify the wdice() function. Edit the code for wdice() so that 50% of the time it lands on number 6.

Hints:

  • To test out your modified function, you will need to re-run the code which defines the function. When we use wdice() we use the function which is in our environment. If we want to edit the function, we need to then overwrite (or “replace”/“reassign”) the object in our environment.
  • We need to be careful to remember that the probability of different outcomes should sum to 1 (i.e., it’s not possible to “50% of the time land on 6” as well as “70% of the time land on 5”!).

Solution

Question 16

Can you observe the weighting in your new die (the one which 50% of the time lands on number 6) in only 20 rolls?

Solution

Question 17

Conceptually, what can we learn from this toy example?

Solution