**Usage:** This tutorial accompanies [Discovering Statistics Using R and RStudio](https://www.discovr.rocks/) [@fielddsr22026] by [Andy Field](https://profandyfield.com/). It contains material from the book so there are some copyright considerations but I offer them under a [Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License](http://creativecommons.org/licenses/by-nc-nd/4.0/). Tl;dr: you can use this tutorial for teaching and non-profit activities but please don't meddle with it or claim it as your own work.
r cat_space(fill = blu) Welcome to the discovr space pirate academy
Hi, welcome to discovr space pirate academy. Well done on embarking on this brave mission to planet r rproj()s, which is a bit like Mars, but a less red and more hostile environment. That's right, more hostile than a planet without water. Fear not though, the fact you are here means that you can master r rproj(), and before you know it you'll be as brilliant as our pirate leader Mae Jemstone (she's the badass with the gun). I am the space cat-det, and I will pop up to offer you tips along your journey.
On your way you will face many challenges, but follow Mae's system to keep yourself on track:
r bmu(height = 1.5) This icon flags materials for teleporters. That's what we like to call the new cat-dets, you know, the ones who have just teleported into the academy. This material is the core knowledge that everyone arriving at space academy must learn and practice. For accessibility, these sections will also be labelled with [(A)]{.lbl}.
r user_visor(height = 1.5) Once you have been at space pirate academy for a while, you get your own funky visor. It has various modes. My favourite is the one that allows you to see everything as a large plate of tuna. More important, sections marked for cat-dets with visors goes beyond the core material but is still important and should be studied by all cat-dets. However, try not to be disheartened if you find it difficult. For accessibility, these sections will also be labelled with [(B)]{.lbl}.
r user_astronaut(height = 1.5) Those almost as brilliant as Mae (because no-one is quite as brilliant as her) get their own space suits so that they can go on space pirate adventures. They get to shout RRRRRR really loudly too. Actually, everyone here gets to should RRRRRR really loudly. Try it now. Go on. It feels good. Anyway, this material is the most advanced and you can consider it optional unless you are a postgraduate cat-det. For accessibility, these sections will also be labelled with [(C)]{.lbl}.
It's not just me that's here to help though, you will meet other characters along the way:
r alien(height = 1.5) aliens love dropping down onto the planet and probing humanoids. Unfortunately you'll find them probing you quite a lot with little coding challenges. Helps is at hand though.
r robot(height = 1.5)bend-R is our coding robot. She will help you to try out bits of r rproj() by writing the code for you before you encounter each coding challenge.
r bug(height = 1.5) we also have our friendly alien bugs that will, erm, help you to avoid bugs in your code by highlighting common mistakes that even Mae Jemstone sometimes makes (but don't tell her I said that or my tuna supply will end).
Also, use hints and solutions to guide you through the exercises (Figure 1).
Figure 1: In a code exercise click the hints button to guide you through the exercise.
By for now and good luck - you'll be amazing!
Workflow
If you are unfamilar with r rproj(), r rstudio() and r quarto(), it's a good idea to work through the first tutorial (discovr_01) before attempting this one.
The tutorials are self-contained (you practice code in code boxes). However, so you get practice at working in r rstudio() I strongly recommend that you create an r quarto() document within an r rstudio() project and practice everything you do in the tutorial in the r quarto() document, make notes on things that confused you or that you want to remember, and save it. Within your r quarto() document you will need to load the relevant packages and data.
Coding style
There are (broadly) two styles of coding:
Explicit: Using this style you declare the package when using a function: package::function(). For example, if I want to use the mutate() function from the package [dplyr]{.pkg}, I will type dplyr::mutate(). If you adopt an explicit style, you don't need to load packages at the start of your Quarto document but code can get busy.
Concise: Using this style you load all of the packages at the start of your r quarto() document using library(package_name), and then refer to functions without their package. For example, if I want to use the mutate() function from the package [dplyr]{.pkg}, I will use library(dplyr) in my first code chunk and type the function as mutate() when I use it subsequently. The resulting code is less busy, but you can can stumble across clashes with functions.
Coding style is a personal choice, and neither approach is 'correct'. I recommend the best of both worlds, which is:
Use concise style for [tidyverse]{.pkg} and [easystats]{.pkg} because by loading these in your first code chunk most functions you need will be loaded and so your code will be succinct.
Use explicit style for packages that you'll use rarely.
**Usage:** This tutorial accompanies [Discovering Statistics Using R and RStudio](https://www.discovr.rocks/) [@fielddsr22026] by [Andy Field](https://profandyfield.com/). It contains material from the book so there are some copyright considerations but I offer them under a [Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License](http://creativecommons.org/licenses/by-nc-nd/4.0/). Tl;dr: you can use this tutorial for teaching and non-profit activities but please don't meddle with it or claim it as your own work.
