04-viz.Rmd

# (PART) Visualization {-}

# ggplot2

## Slides {#gg-slides}
Workshop: Introduction to ggplot2

Date: November 2 2017

[Slides](https://slides.robitalec.ca/intro-ggplot.html) and  [Resources](https://gitlab.com/robit.a/workshops/-/archive/master/workshops-master.zip?path=intro-ggplot)

```{r, echo = FALSE}
knitr::include_url('https://slides.robitalec.ca/intro-ggplot.html')
```


## Resources  {#gg-resources}

* [ggplot2 book](https://ggplot2-book.org/)
* Claus Wilke's course [Data Visualization in R](https://wilkelab.org/SDS375/syllabus.html), book [Fundamentals of Data Visualization](https://clauswilke.com/dataviz/)
* [What makes bad figures bad](https://socviz.co/lookatdata.html#what-makes-bad-figures-bad)

## Facets


```{r, include = FALSE}
library(ggplot2)
data(diamonds)

ggplot(diamonds, aes(depth, price)) + 
  geom_point()
```

Facet wrap using categorical columns:

```{r}
ggplot(diamonds, aes(depth, price)) + 
  geom_point() + 
  facet_wrap(~cut)
```


Facet wrap using automatically binned values:

"What if I look at four separate chunks of the data, with approximately the same number of points"

```{r}
ggplot(diamonds, aes(depth, price)) + 
  geom_point() + 
  facet_wrap(~ cut_number(carat, 4))
```

"What if I look at four separate chunks of the data, with the equal ranges in each group?"

```{r}
ggplot(diamonds, aes(depth, price)) + 
  geom_point() + 
  facet_wrap(~ cut_interval(carat, 4))
```

"What if I look at four separate chunks of the data, with a specific width of values in each group?"
```{r}
ggplot(diamonds, aes(depth, price)) + 
  geom_point() + 
  facet_wrap(~ cut_width(carat, 0.5))
```


## Combining plots
![](https://img.shields.io/badge/-WIP-yellow.svg)

<!-- **TODO: ALR - examples, use ##C layout, & theme** -->

The one and only: [`patchwork`](https://patchwork.data-imaginist.com/).


# Spatial plotting

![](https://img.shields.io/badge/-WIP-yellow.svg)


Lots of examples of spatial plotting of vector data in the [study-area-figure](https://gitlab.com/WEEL_grp/study-area-figures) repository. 

Also check out [`tmap`](https://r-tmap.github.io/tmap-book/).

Note: plotting large rasters can be really intensive, so start small or 
reduce the resolution. It also might help to write the plot out to a PDF, 
instead of trying to view it directly in the RStudio window. 


# Cool Packages {#viz-cool}
![](https://img.shields.io/badge/-WIP-yellow.svg)

<!-- **TODO: links, fill, grab from https://ropensci.org/packages/ under vis** -->


## ggplot extensions

* Themes: `ggthemes`
* Annotations: `ggannotate`
* Distribution geometries: `ggdist`
* Labels that repel: `ggrepel`

* Turn ggplots into interactive plots: [`plotly`](https://github.com/plotly/plotly.R#getting-started)

## Spatial plotting

* `mapview`
* `leaflet`
* Coarse scale/simple geometries for plots: `rnaturalearth`