Tutorial 1: Introduction to Data Visualisation

Author

Dr. Kate Saunders

Learning Objectives

  • Get you started using R
  • Learn your way around RStudio.
  • Become familiar with R, RStudio, RStudio projects and Quarto files.
  • Become confident doing basic computation in R.

Preparation

  • Install R and RStudio

  • Watch/Read the Introductory materials on R provided

Exercise 1: R Packages

R has many standard functions, like mean, sum and sqrt. Other functions are stored in R packages that can be loaded as needed for specific tasks.

One of the common packages is the tidyverse it contains lots of useful functions for data wrangling and visualisation. In fact the tidyverse is actually a collection of packages.

The below code checks if the package is already downloaded. If the package is not there if then it uses the install.package function to download it.

if(!require(tidyverse)){
    install.packages("tidyverse")
}

You can’t use function in the tidyverse yet though. When you start a new session in RStudio, you will need to load the package into R using the library function.

library(tidyverse)

A useful was to think of it is install.packages() is like screwing in a lightbulb, and running the library() function is light turning the light when you want to use it.

To check the package has installed correctly try calling ?ggplot. This will bring up the help menu for this function. If you are ever unsure of what a function does scroll down the bottom you’ll see some examples that you can run.

Your turn:

  1. Change the above code to install the cowsay package
  2. Load cowsay package using the library() function
  3. Try running cowsay::say()
  4. Now try running cowsay::say(what = "I'm learning R", by = "alligator")
  5. Call the help menu to read more about the ?sayfunction. Then change the function inputs to your name, your favourite animal and your favourite colour (based on the options available).
install.package("cowsay")
library("cowsay")
cowsay::say()
cowsay::say(what = "I'm learning R", by = "alligator")
cowsay::say(what = "Kate", by = "dragon", what_color = "purple", by_color = "red")

Exercise 2: Assign a Variable

R is more than just as a calculator, you can store things in a variable. Here the word variable has a slightly different meaning to the usual statistical meaning.

Let us start with a simple command, type in a <- 5. Here we assign the value 5 to the variable a.

a <- 5

Note that you can use either <- or = to assign variables.

Your turn:

  1. Let try b = 4

  2. We can store more than just numbers in a variable. Try storing your name. e.g. name = 'Jane Doe'.

  3. We can use str(name), str(a) to check the structure of the variable, it will show you that variable name contains character and variable a contains numeric.

  4. R is case sensitive. We create a variable name in previous step, try type NAME

b = 4
name = 'Jane Doe'
str(name)
str(a)
NAME

It will returns an Error: object 'NAME' not found, this is because name and NAME are two totally different variables where NAME has not been created.

Exercise 3: Basic Computation

Let continue with: x = 5, y = sqrt(16), z = -3, and w = x + y + z

x = 5 
y = sqrt(16)
z = -3
w = x + y + z

sqrt(16) is a function for square root of 16, therefore the value for y is 4 To find the value of w, type it in. The answer should be 6.

w

R can be used as a calculator, to subtract use -, to multiply use *, to divide use / and to take powers use ^.

To see all assigned variables:

ls()

You can also look in the environment tab in the top right hand panel of Rstudio to see what variables have been stored.

Your turn:

  1. Try h = x^2 and f = z/y.
h = x^2
f = z/y

Exercise 4: Vectors

A vector is a list of similar type objects and is a basic data structure in R that can hold values. Previously w had only one value, but we can store multiple values in w by using the command c(). Here c stands for “combine” or “concatenate”.

For example try:

litres_drank <- c(50, 40, 25, 0)
litres_drank
str(litres_drank)

To create a vector of characters:

type_of_drink <- c('Coke', 'Pepsi', 'Coke', 'Homebrand')
type_of_drink
str(type_of_drink)

We can also access different elements of our vector using square brackets []

litres_drank[2]
type_of_drink[2]

Sometimes when we apply a function to a vector it will work on each element in the vector

litres_drank*2
log(litres_drank)

Or the function might summarise something about our vector

table(type_of_drink)
max(litres_drank)

Your turn:

  1. Create two variables: x1 contains “1,2,3,4,5” and y1 contains “-1,-2,-3,-4,-5”

  2. Compute the mean for x1 and y1 by using mean()

  3. We can find the variance of the vectors x1 and y1 using the var function.

  4. We can find the correlation between the two vectors x1 and y1 using the cor function.

x1 <- c(1,2,3,4,5)
y1 <- c(-1,-2,-3,-4,-5)

or try using the : symbol which will create a vector of everything between two numbers

x1 <- 1:5
y1 <- -1:-5
mean(x1)
mean(y1)
var(x1)
var(y1)
cor(x1, y1)