ref-visualizing-data.Rmd

---
title: "Visualizing Data"
output:
  html_document:
    number_sections: false
    toc: no
---

```{r setup, echo=FALSE, message=FALSE, warning=FALSE}
rm(list=objects()) # start with a clean workspace
source("knitr_setup.R")
```

# Principles of data visualization

- Healy, Kieran. "Data Visualization: A practical introduction"
[[free online](http://socviz.co/)],
[[buy on amazon](https://www.amazon.com/Data-Visualization-Introduction-Kieran-Healy/dp/0691181624)]
- Wilke, Claus O. "Fundamentals of Data Visualization"
[[free online](https://serialmentor.com/dataviz/)],
[[buy on amazon](https://www.amazon.com/Fundamentals-Data-Visualization-Informative-Compelling/dp/1492031089)]
- Video: [How Humans See Data](https://youtu.be/fSgEeI2Xpdc), by John Rauser. This is probably the best overviews I've seen on how to exploit the psychology of how our brains interpret images to make effective visualizations.

# Choosing the right chart

You will want to choose different chart types depending on the relationship or message you want to convey. Fortunately, we live in the future and awesome people have created wonderful tools to help you find the most appropriate graph for your data. Take a look through some of these resources - these will come in handy when you start working on your final project:

- <i class="fas fa-file-pdf"></i> [Stephen Few's Graph Selection Matrix](content/Graph_Selection_Matrix.pdf)
- [The Data Visualisation Catalogue](https://datavizcatalogue.com/)
- [Data-to-Viz](https://www.data-to-viz.com/)
- [R Graph Gallery](http://r-graph-gallery.com/)
- [Visual Vocabulary](https://gramener.github.io/visual-vocabulary-vega/)
- [The Data Viz Project](https://datavizproject.com/)

# Making plots with **ggplot2**

- [The R Graph Catalog](http://shinyapps.stat.ubc.ca/r-graph-catalog/): 124 examples (with code) for making common ggplots.
- [The ggplot2 Extensions gallery](http://www.ggplot2-exts.org/gallery/): 64 registered libraries that extend the functionality of ggplot2.
- [Top 50 `ggplot2` visualizations](http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html)
- [R Cookbook for `ggplot2`](http://www.cookbook-r.com/Graphs/)
- [RStudio `ggplot2` Cheatsheet](https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf)
- [Tidyverse `ggplot2` reference guide](https://ggplot2.tidyverse.org/reference/)
- [ggThemeAssist](https://github.com/calligross/ggthemeassist): Package for customizing plot themes and layout
- [16 HOWTO's](https://bookdown.org/lyzhang10/lzhang_r_tips_book/how-to-plot-data.html), by Lingyun Zhang - super useful for making specific types of charts

# Polishing **ggplot2** plots

- [ggplot2: Elegant Graphics for Data Analysis](https://ggplot2-book.org/index.html)
- [Follow good design rules](https://www.data-to-viz.com/caveats.html)

# Choosing colors

- [Color wheel tool](https://www.sessions.edu/color-calculator/)
- [ggplot2 book chapter 12](https://ggplot2-book.org/scales.html#scale-colour)
- [R graph gallery](http://r-graph-gallery.com/ggplot2-color)
- [Coolors.co](https://coolors.co/) - super nice color palette generator

# Animating **ggplot2** plots

- [Animate your ggplots with gganimate](https://gganimate.com/articles/gganimate.html)
- [How to make a bar chart race](https://emilykuehler.github.io/bar-chart-race/)