01-intro.qmd

---
title: "Data Visualisation - Introduction"
author: "Eugene"
date: "`r format(Sys.Date(), '%B %d %Y')`"
format: revealjs
editor: visual
---


## Course Contents


1. System Configuration - installing software
2. Using RStudio
3. Introduction to R
4. Getting and Cleaning Data
5. Exploratory Analysis - making rough plots
6. Different Types of Plots
7. Playing with Aesthetics
8. Using Plotting Themes
9. Advanced Topics - Maps, Networks


## Why We're Here

- Alternative to Excel, and Tableau
- Enables Reproducible Research
- Can Make Lots of Plots Quickly
    - Good for Exploratory Analysis
- Publication Ready Figures


## And.... a gateway to so much more

- data capture
- statistical analysis
- machine learning
- artificial intelligence
- writing your thesis
- writing a blog

## Not Why We're Here

- Won't discuss choices for data presentation
- Nor good practices in visualisations
    - but these are sort of in the background
- This isn't a machine learning course
    - but lots of the techniques we'll use are relevant
- So, this course it about skills development, how you use these is up to you.


## We said we wouldn't discuss this....but

- Graphics are important, overlooked, and inconsistent
    - the last mile of analysis
- Need to tell a story
- Can be misleading, almost always by accident
- Choice of colours - we'll spend some time on this
- Choice of fonts
- Keep it simple - reduce amount of ink
- Increasing number of options for showcasing your data

----

```{r bar_plot}
#| message: false
#| echo: false
#| fig-height: 8


library(tidyverse)
library(ggtext)

title = "Sales of Jeans in Wakanda by Year.<br>Either <b style='color:#AE404D'> Bootcut </b> or <b style='color:#2E4B71'> Slimfit</b>"
jeans <- data.frame(year = 2000:2012, 
                    bootcut = c(1873, 1984, 2020, 2180, 2187, 2280, 2410, 2420, 2559, 2602, 2725, 2844, 2755),
                    slimfit = c(1750, 1740, 1710, 1954, 1994, 2067, 2290, 2547, 2541, 2801, 2855, 3007, 3076))

jeans %>% 
  pivot_longer(names_to = "jean_type", cols = -year, values_to = "sales") %>% 
  ggplot(aes(year, sales-1500, fill = jean_type)) + 
  geom_col(position = position_dodge()) +
  scale_fill_manual(values = c('#AE404D', '#2E4B71')) +
  labs(title = title,
       caption = "barplot by @eugene100hickey, data: Marvel Corporation") +
  ylab("") +
  xlab("Year") +
  theme_minimal() +
  theme(
    plot.title = element_markdown(vjust = 7, size = 24),
    legend.position = "none",
    axis.text.y = element_blank(),
    axis.ticks.y = element_blank(),
    axis.title.x = element_text(size = 18),
    axis.text.x = element_text(size = 16),
    panel.grid = element_blank())
```

---  

```{r line_plot}
#| message: false
#| echo: false
#| fig-height: 8

jeans %>% 
  pivot_longer(names_to = "jean_type", cols = -year, values_to = "sales") %>% 
  ggplot(aes(year, sales-1500, col = jean_type)) + 
  geom_line(size = 5) +
  geom_point(size = 5) + 
  scale_color_manual(values = c('#AE404D', '#2E4B71')) +
  labs(title = title,
       caption = "lineplot by @eugene100hickey, data: Marvel Corporation") +
  ylab("") +
  xlab("Year") + 
  scale_x_continuous(labels = scales::number_format(accuracy = 1)) + 
  xlim(2000, 2015) +
  theme_minimal() +
  theme(
    plot.title = element_markdown(vjust = 7, size = 24),
    legend.position = "none",
    axis.text.y = element_blank(),
    axis.ticks.y = element_blank(),
    axis.title.x = element_text(size = 18),
    axis.text.x = element_text(size = 16),
    panel.grid = element_blank())
```