8 Exercises

8.1 Getting started

8.1.1 Exploring RStudio

1 . Find the keyboard shortcuts menu:

Tools > Keyboard shortcuts help

Change the appearance to something different.

8.1.2 Installing packages and using functions

Loading packages

What function loads a package that is already on your computer?

8.1.3 Creating folders

Assuming we have created a project called library-r:

Create a folder called R and folder called outputs in your project folder

8.2 Palmer penguins

Load the Palmer Penguins library if it’s not already loaded.

8.2.1 Palmer penguins dataset

Find out about the penguins dataset:

what is it?
and what data types does it contain?

8.2.2 Vectors and assignment

Code

cat("Type the assignment operator ", fitb('<-'))

Type the assignment operator

Create a character vector of your name and assign it to an object called my_name
Code
```
my_name <- "Alistair"
```
Pass my_name to cowsay as an argument. Chose whatever animal you wish
Code
```
say(my_name, by = "monkey")
```
Create a sequence of numbers from 1 to 10 and assign it to an object called my_seq
Code
```
my_seq <- seq(1:10)
```

8.2.3 Data frames/tibble

Make a character vector of three names
Make a numeric vector of three numbers
Make a factor vector of three fruit
Combine into a data frame.

Code

# For example
char_vec <- c("James","Hannah","Matt")
num_vec <- c(5,1792,23)
factor_vec <- factor(x = c("oranges","apples","grapes"))

# Combine into data.frame, the names of the variables will
# come from the objects.
df <- data.frame(char_vec,
                 num_vec,
                 factor_vec)

8.3 dplyr

All these exercises use dplyr from the tidyverse, but in addition to reading the book, you may have to Google or ask an AI assistance for help with some of these exercises.

8.3.1 Importing and inspecting data

Exercise 1: Import the data contained in csv file to an object called books from: https://raw.githubusercontent.com/ab604/library-r/main/data/books-2024-04-30.csv

Tip

You can read data directly from paths to on-line datasets with read_csv() as you would for locally stored csv files.

Code

# Exercise 1: Import the data contained in csv file to an object called `books`
books <- read_csv("https://raw.githubusercontent.com/ab604/library-r/main/data/books-2024-04-30.csv")