R for Archaeologists

class: center, middle, inverse, title-slide

.title[
# R for Archaeologists
]
.subtitle[
## Visualisation
]

---

class: middle
background-image: url("figures/jr-survey.png")
background-size: 67%

.pull-left[
![](figures/Me_Feb2021_square_bw2.jpg)

**Joe Roe**  
University of Copenhagen

* <svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M579.8 267.7c56.5-56.5 56.5-148 0-204.5c-50-50-128.8-56.5-186.3-15.4l-1.6 1.1c-14.4 10.3-17.7 30.3-7.4 44.6s30.3 17.7 44.6 7.4l1.6-1.1c32.1-22.9 76-19.3 103.8 8.6c31.5 31.5 31.5 82.5 0 114L422.3 334.8c-31.5 31.5-82.5 31.5-114 0c-27.9-27.9-31.5-71.8-8.6-103.8l1.1-1.6c10.3-14.4 6.9-34.4-7.4-44.6s-34.4-6.9-44.6 7.4l-1.1 1.6C206.5 251.2 213 330 263 380c56.5 56.5 148 56.5 204.5 0L579.8 267.7zM60.2 244.3c-56.5 56.5-56.5 148 0 204.5c50 50 128.8 56.5 186.3 15.4l1.6-1.1c14.4-10.3 17.7-30.3 7.4-44.6s-30.3-17.7-44.6-7.4l-1.6 1.1c-32.1 22.9-76 19.3-103.8-8.6C74 372 74 321 105.5 289.5L217.7 177.2c31.5-31.5 82.5-31.5 114 0c27.9 27.9 31.5 71.8 8.6 103.9l-1.1 1.6c-10.3 14.4-6.9 34.4 7.4 44.6s34.4 6.9 44.6-7.4l1.1-1.6C433.5 260.8 427 182 377 132c-56.5-56.5-148-56.5-204.5 0L60.2 244.3z"/></svg> [joeroe.io](https://joeroe.io)
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* <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M433 179.11c0-97.2-63.71-125.7-63.71-125.7-62.52-28.7-228.56-28.4-290.48 0 0 0-63.72 28.5-63.72 125.7 0 115.7-6.6 259.4 105.63 289.1 40.51 10.7 75.32 13 103.33 11.4 50.81-2.8 79.32-18.1 79.32-18.1l-1.7-36.9s-36.31 11.4-77.12 10.1c-40.41-1.4-83-4.4-89.63-54a102.54 102.54 0 0 1-.9-13.9c85.63 20.9 158.65 9.1 178.75 6.7 56.12-6.7 105-41.3 111.23-72.9 9.8-49.8 9-121.5 9-121.5zm-75.12 125.2h-46.63v-114.2c0-49.7-64-51.6-64 6.9v62.5h-46.33V197c0-58.5-64-56.6-64-6.9v114.2H90.19c0-122.1-5.2-147.9 18.41-175 25.9-28.9 79.82-30.8 103.83 6.1l11.6 19.5 11.6-19.5c24.11-37.1 78.12-34.8 103.83-6.1 23.71 27.3 18.4 53 18.4 175z"/></svg> [joeroe@archaeo.social](https://archaeo.social/joeroe)
]

.pull-right[
![](figures/jr-palecomod.png)
![](figures/jr-c14.png)
]

???

* Introducing myself

---
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  <tbody class="gt_table_body">
    <tr><td headers="Day" class="gt_row gt_left" style="font-size: small; text-align: left; vertical-align: top; font-weight: bold;"><span class='gt_from_md'>Tuesday</span></td>
<td headers="Time" class="gt_row gt_left" style="font-size: small; text-align: left; vertical-align: top; font-weight: bold;"><span class='gt_from_md'>09–13</span></td>
<td headers="Topics" class="gt_row gt_left" style="font-size: small; text-align: left; vertical-align: top;"><span class='gt_from_md'><strong>Introduction to visualisation in R</strong></p>
<ul>
<li>Visualisation in data science</li>
<li>Overview of R graphics packages</li>
<li>ggplot2: The ‘grammar of graphics’</li>
</ul>
</span></td>
<td headers="Exercises" class="gt_row gt_left" style="font-size: small; text-align: left; vertical-align: top;"><span class='gt_from_md'><em>R for Data Science</em>, ch. 3</span></td></tr>
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<td headers="Time" class="gt_row gt_left" style="font-size: small; text-align: left; vertical-align: top; font-weight: bold;"><span class='gt_from_md'>14–16</span></td>
<td headers="Topics" class="gt_row gt_left" style="font-size: small; text-align: left; vertical-align: top;"><span class='gt_from_md'><strong>Visualisation for Exploratory Data Analysis</strong></p>
<ul>
<li>Applications in archaeology</li>
<li>Visualising ‘tidy data’</li>
<li><code>ggplot2</code> for EDA</li>
</ul>
</span></td>
<td headers="Exercises" class="gt_row gt_left" style="font-size: small; text-align: left; vertical-align: top;"><span class='gt_from_md'><em>R for Data Science</em>, ch. 7 & 11</span></td></tr>
    <tr><td headers="Day" class="gt_row gt_left" style="font-size: small; text-align: left; vertical-align: top; font-weight: bold;"><span class='gt_from_md'>Wednesday</span></td>
<td headers="Time" class="gt_row gt_left" style="font-size: small; text-align: left; vertical-align: top; font-weight: bold;"><span class='gt_from_md'>09–13</span></td>
<td headers="Topics" class="gt_row gt_left" style="font-size: small; text-align: left; vertical-align: top;"><span class='gt_from_md'><strong>Advanced visualisation exercises</strong></p>
<ul>
<li>Tables: <code>gt</code></li>
<li>Radiocarbon: <code>ggridges</code></li>
<li>Cartography: <code>ggspatial</code></li>
<li>3D: <code>rayshader</code></li>
</ul>
</span></td>
<td headers="Exercises" class="gt_row gt_left" style="font-size: small; text-align: left; vertical-align: top;"><span class='gt_from_md'><em>Groupwork</em></span></td></tr>
    <tr><td headers="Day" class="gt_row gt_left" style="font-size: small; text-align: left; vertical-align: top; font-weight: bold;"><span class='gt_from_md'></span></td>
<td headers="Time" class="gt_row gt_left" style="font-size: small; text-align: left; vertical-align: top; font-weight: bold;"><span class='gt_from_md'>14–16</span></td>
<td headers="Topics" class="gt_row gt_left" style="font-size: small; text-align: left; vertical-align: top;"><span class='gt_from_md'><strong>Publication-ready visualisation</strong></p>
<ul>
<li>What makes an effective graphic?</li>
<li>Advanced <code>ggplot2</code>: aesthetics and themes</li>
<li>Tips for the real world</li>
</ul>
</span></td>
<td headers="Exercises" class="gt_row gt_left" style="font-size: small; text-align: left; vertical-align: top;"><span class='gt_from_md'><em>R for Data Science</em>, ch. 28</span></td></tr>
  </tbody>
  
  
</table>
</div>

???

* The "Visualisation" module follows on from the introduction to R you've had so far
* We'll start by outlining the through the two major graphics packages in R: "base" and `ggplot2`
* 
* Then tomorrow, we'll look at some specific "advanced" visualisation techniques relevant to archaeology:
  * First spatial visualisations and mapping, following on from last week's module on spatial analysis
  * Then a selection of other specific applications – subject to change!
* And finally, on Wednesday, we'll discuss preparing plots ready for final publication
* I would like this to be as hands-on as possible, so the format for this module is you doing exercises at your own pace, with me dropping in to explain things as needed

---
class: center, middle

View these slides online:  
<https://joeroe.io/r4r_visualisation/r4r_visualisation_slides.html>

Source code:  
<https://github.com/joeroe/r4r_visualisation>

<small>They're written in RMarkdown!</small>

---
class: inverse, center, middle

# Visualisation with R
## Introduction

???

* Why use R for visualisations? Or: why did we devote a quarter of this course to visualisation?
* Most of you are probably here because, in some way or other, you want to use R to "do statistics"
* As you go up the admittedly steeper learning curve of visualisations in R, you might ask: why not just export the result to a more intuitive programme like Excel, and make the plots there?
* For that I have three answers:
  * (or four: the zeroth being because Excel is bad software, don't use it)

---
class: center, middle

![](figures/data-science.png)

.center[<small>Data science workflow. After Wickham & Grolemund, *R for Data Science*</small>]

???

* First—and this is why we are we looking at visualisations now, rather than at the end—is that visualisation is not just the end product of a statistical analysis, something you use to **communicate**, but right at its centre, as something that helps us **understand**.
* In the exploratory data analysis (EDA) or modern "data science" workflow (illustrated very nicely here in a figure from Hadley Wickham's *R for Data Science*), visualisation is one of the pillars of data analysis
* You should use graphics often, to help you understand data as you go through the iterative cycle of interrogating and modelling it

---
class: center, middle

.pull-left[
![](figures/tukey-eda.jpg)
]

.pull-right[
![](figures/r4ds.jpg)
]

???

* This is an old idea: starting with 'exploratory data analysis', and continued in modern 'data science'
* Computers obviously make it a lot easier to generate plots quickly;
* And R was in fact originally designed to facilitate EDA, so it's very well suited for it: once you've mastered the basic graphics functions, you can generate plots easily and often

---
class: middle

# R: a statistical *programming language*

1. Iterate
2. Scale
3. Reproduce

???

* Second, R is a statisical *programming language*. This has powerful advantages compared to traditional workflows for producing plots:
  * Iteration: generate your final plot and realise you need to change something in the source data? No problem!
  * Scalability: write code to visualise X about Y; then run it on 1000 (or 10000, or 100000) values of Y
  * Reproducibility: everybody can see exactly the steps you took to produce a plot

---
class: center, middle

## `bbplot` <<https://github.com/bbc/bbplot>>

![](figures/bbplot_example_plots.png)

???

* Third, R graphics simply do look very nice. Maybe not always 'out of the box', but the capability is there.
* For example, ... all use R for their statistical graphics

---

# Graphics packages in R

``` r
library("archdata")
data("BACups")
```

.pull-left[
<img src="r4r_visualisation_slides_files/figure-html/plot-demo-base-1.png" width="100%" />

``` r
plot(BACups$H, BACups$RD)
```
]

.pull-right[
<img src="r4r_visualisation_slides_files/figure-html/plot-demo-ggplot-1.png" width="100%" />

``` r
ggplot(BACups, aes(x = H, y = RD)) +
  geom_point()
```
]

???

* There isn't one way to produce plots in R: you have to pick a **graphics package**
* The main options are "base" (actually `graphics`), `ggplot2`, and `lattice` (now rarely used)
* These packages have major differences in syntax, and by extension flexibility and capabilities

---

.pull-left[
<img src="r4r_visualisation_slides_files/figure-html/plot-demo-base-ext-1.png" width="100%" />

``` r
par(mfrow = c(1,2))
plot(BACups[BACups$Phase=="Protoapennine",]$H,
     BACups[BACups$Phase=="Protoapennine",]$RD,
     cex = BACups[BACups$Phase=="Protoapennine",]$ND / 10,
     main = "Bronze Age cups",
     sub = "Protoapennine",
     xlab = "Height",
     ylab = "Rim Diameter")
plot(BACups[BACups$Phase=="Subapennine",]$H,
     BACups[BACups$Phase=="Subapennine",]$RD,
     cex = BACups[BACups$Phase=="Subapennine",]$ND / 10,
     main = "(Data from Lukesh and Howe 1978)",
     sub = "Subapennine",
     xlab = "Height",
     ylab = "Rim Diameter")
legend("bottomright", 
       title = "Neck Diameter", 
       pch = 1,
       legend = c(10, 15, 20, 25), 
       pt.cex = c(1, 1.5, 2, 2.5))
```
]

.pull-right[
<img src="r4r_visualisation_slides_files/figure-html/plot-demo-ggplot-ext-1.png" width="100%" />

``` r
ggplot(BACups, aes(x = H, y = RD, size = ND)) +
  geom_point(shape = 1) +
  facet_wrap(vars(Phase)) +
  labs(x = "Height", 
       y = "Rim Diameter", 
       size = "Neck Diameter",
       title = "Bronze Age cups", 
       subtitle = "Data from Lukesh and Howe (1978)")
```
]

???

* These differences are highlighted when you try to do more complex plots
* Don't get hung up on the aesthetic differences – these are defaults, you can easily make one look like the other (or anything else)
* There is some debate over the pros and cons of each – I see ggplot2 as a straightforward upgrade and from this afternoon we will focus on its approach
* But it's helpful to be familiar with base as the 'default' in a lot of cases, e.g. used by many packages to plot complex data structures, so we'll start with that

---
class: inverse, center, middle

# `ggplot2`: the 'grammar of graphics'

???

* `ggplot2` is based on the "grammar of graphics"
* This is a somewhat theoretical concept. Further reading: 
  * Wickham 2010, *A Layered Grammar of Graphics*
  * Wilkinson, Anand & Grossman 2005, *The Grammar of Graphics*
* But it's helpful to have some idea of that theory, to make sense of random prefixes like "aes" or "geom"

---
class: center, middle

# The Grammar of Graphics

![](figures/grammar-of-graphics.png)

???

* The grammer is a structured way of *describing* the relationship between data and graphical elements (declarative) – as opposed to `base` graphics, which is you *telling* R what to plot (imperative)
* Specifically, `ggplot` introduces a *layered* grammer of graphics, allowing you to combine multiple mappings into a single plot
* This makes it much more flexible than base and allows us to maintain readable code for even very complicated plots
* Downsides: data does have to be in a roughly tabular (if not fully "tidy") structure to work well with ggplot. Complex objects from other packages can be a challenge to work with.
  * For EDA purposes, it's often better to fall back on `base::plot()` with these objects
  * Though packages like `broom` can help

---

# Plotting with `ggplot2`: exercises

**Wickham & Grolemund, *R for data science***

https://r4ds.had.co.nz/

Chapter 3: *Data visualisation*

Chapter 7: *Exploratory Data Analysis* (if you have time)

* Install the `tidyverse` metapackage
* Run Hadley's code examples
* Answer questions as comments (`# Here is my answer...`)
* Do the 'exercises' sections – they're very informative!

---

# Plotting with `ggplot2`: applications?

.pull-left[
<img src="r4r_visualisation_slides_files/figure-html/ggplot-scatter-1.png" width="100%" />
<img src="r4r_visualisation_slides_files/figure-html/ggplot-smooth-1.png" width="100%" />
]

.pull-right[
<img src="r4r_visualisation_slides_files/figure-html/ggplot-bar-1.png" width="100%" />
<img src="r4r_visualisation_slides_files/figure-html/ggplot-boxplot-1.png" width="100%" />
]

???

* We've seen how to do a few fundamental plots with ``ggplot2` this morning, but using "dummy" datasets like `cars` and `diamonds`. 
* What are some archaeological applications of these plots?
* Tomorrow we will look at some more niche types of visualisation in archaeology;
* For now, we continue to work on the fundamentals of using visualisation EDA

---
class: middle

# Tidy data

![](figures/r4rds-tidy-1.png)

???

* As I mentioned this morning, a big part of producing graphics effectively is having the tools to transform data to the right format
* And `ggplot2` is generally super-charged when used as part of a "tidy data analysis" workflow
* With the example data you've used so far, this has been done for you. With "real world" archaeology data, it won't be!

---

# Plotting with tidy data

.pull-left[

``` r
data("emedyd") # From rcarbon
emedyd %>% 
  filter(SiteName == "Wadi Jilat 13") %>% 
  arrange(CRA) %>% 
  mutate(LabID = as_factor(LabID)) %>% 
  mutate(CalDate = cal(CRA, Error, verbose = FALSE)) %>% 
  unnest_legacy() %>% 
  ggplot(aes(calBP, PrDens)) +
  facet_wrap(~LabID, ncol = 1, 
             scales = "free_y", 
             strip.position = "left") +
  geom_area() + 
  scale_x_reverse(name = "cal BP") +
  scale_y_continuous(labels = NULL, name = NULL) +
  labs(title = "Wadi Jilat 13",
      subtitle = "Radiocarbon chronology")
```
]

.pull-right[
<img src="r4r_visualisation_slides_files/figure-html/tidy-rcarbon-1.png" width="100%" />
]

???

* For example, *pipes* allow us to elegantly and concisely load data, clean and filter, and plot in a single code block
* Half of the code here—the most important half—is *reshaping* the data before even giving it to the plot. Often, it's more than half!
* You can read more about reshaping or "wrangling" data in *R for Data Science*

---

# `ggplot2` for EDA: exercises

**Wickham & Grolemund, *R for data science***

https://r4ds.had.co.nz/

Chapter 3: *Data visualisation* (if you haven't finished)  
Chapter 7: *Exploratory Data Analysis*  
Chapter 12: *Tidy data* (if you have time)

* Run Hadley's code examples
* Answer questions as comments (`# Here is my answer...`)
* Do the 'exercises' sections – they're very informative!

---
class: inverse, center, middle

# Advanced visualisations
## Plotting archaeological data

---
class: inverse, center, middle

# CartogRaphy
## Mapping with `ggplot2` and friends

---
class: center, middle

# R as a GIS
## (R isn't a GIS)

---

# R spatial ecosystem

.pull-left[
* Spatial data
  * `rgdal`, `rgeos`, `rgrass7`, `link2gi`
  * `sf`, `sp`
  * `raster`, `stars`, `terra`
* Geostatistics
  * `spatstat`, `gstat`, `spdep`, `nlme`
  * `landsat`, `dismo`
]
.pull-right[
* Base maps + open data
  * `maps`, `maptools`, `rnaturalearth`
  * `ggmap`, `leaflet`, `mapview`
  * `rOpenSci`
* Cartography
  * `ggplot2`
  * `ggspatial`
  * `rasterVis`
  * `tmap`
]

---

# Mapping with `ggplot2`: exercises (1/2)

**Wickham, Navarro & Pedersen, *ggplot2: Elegant Graphics for Data Analysis***

https://ggplot2-book.org/

Chapter 6: *Maps*

* Run the code examples
* Reproduce the following using the 'tavo-a27' dataset.
  * Hint: In addition to `ggplot2`, you will need the `raster` and `ggspatial` packages.
  
---
class: center, middle

---

# Tables (`gt`)
## The forgotten visualisation

<div id="npzqtpzxze" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
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  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}

#npzqtpzxze thead, #npzqtpzxze tbody, #npzqtpzxze tfoot, #npzqtpzxze tr, #npzqtpzxze td, #npzqtpzxze th {
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}

#npzqtpzxze p {
  margin: 0;
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#npzqtpzxze .gt_table {
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  font-weight: normal;
  font-style: normal;
  background-color: #FFFFFF;
  width: auto;
  border-top-style: solid;
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  border-top-color: #A8A8A8;
  border-right-style: none;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #A8A8A8;
  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
}

#npzqtpzxze .gt_caption {
  padding-top: 4px;
  padding-bottom: 4px;
}

#npzqtpzxze .gt_title {
  color: #333333;
  font-size: 125%;
  font-weight: initial;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 5px;
  padding-right: 5px;
  border-bottom-color: #FFFFFF;
  border-bottom-width: 0;
}

#npzqtpzxze .gt_subtitle {
  color: #333333;
  font-size: 85%;
  font-weight: initial;
  padding-top: 3px;
  padding-bottom: 5px;
  padding-left: 5px;
  padding-right: 5px;
  border-top-color: #FFFFFF;
  border-top-width: 0;
}

#npzqtpzxze .gt_heading {
  background-color: #FFFFFF;
  text-align: left;
  border-bottom-color: #FFFFFF;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
}

#npzqtpzxze .gt_bottom_border {
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
}

#npzqtpzxze .gt_col_headings {
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
}

#npzqtpzxze .gt_col_heading {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: normal;
  text-transform: inherit;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: bottom;
  padding-top: 5px;
  padding-bottom: 6px;
  padding-left: 5px;
  padding-right: 5px;
  overflow-x: hidden;
}

#npzqtpzxze .gt_column_spanner_outer {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: normal;
  text-transform: inherit;
  padding-top: 0;
  padding-bottom: 0;
  padding-left: 4px;
  padding-right: 4px;
}

#npzqtpzxze .gt_column_spanner_outer:first-child {
  padding-left: 0;
}

#npzqtpzxze .gt_column_spanner_outer:last-child {
  padding-right: 0;
}

#npzqtpzxze .gt_column_spanner {
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  vertical-align: bottom;
  padding-top: 5px;
  padding-bottom: 5px;
  overflow-x: hidden;
  display: inline-block;
  width: 100%;
}

#npzqtpzxze .gt_spanner_row {
  border-bottom-style: hidden;
}

#npzqtpzxze .gt_group_heading {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  text-transform: inherit;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: middle;
  text-align: left;
}

#npzqtpzxze .gt_empty_group_heading {
  padding: 0.5px;
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  vertical-align: middle;
}

#npzqtpzxze .gt_from_md > :first-child {
  margin-top: 0;
}

#npzqtpzxze .gt_from_md > :last-child {
  margin-bottom: 0;
}

#npzqtpzxze .gt_row {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  margin: 10px;
  border-top-style: solid;
  border-top-width: 1px;
  border-top-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: middle;
  overflow-x: hidden;
}

#npzqtpzxze .gt_stub {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  text-transform: inherit;
  border-right-style: solid;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
  padding-left: 5px;
  padding-right: 5px;
}

#npzqtpzxze .gt_stub_row_group {
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  font-weight: initial;
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  border-right-width: 2px;
  border-right-color: #D3D3D3;
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  vertical-align: top;
}

#npzqtpzxze .gt_row_group_first td {
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#npzqtpzxze .gt_row_group_first th {
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  text-transform: inherit;
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
}

#npzqtpzxze .gt_first_summary_row {
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}

#npzqtpzxze .gt_first_summary_row.thick {
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}

#npzqtpzxze .gt_last_summary_row {
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  padding-left: 5px;
  padding-right: 5px;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
}

#npzqtpzxze .gt_grand_summary_row {
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}

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  border-bottom-color: #D3D3D3;
}

#npzqtpzxze .gt_striped {
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}

#npzqtpzxze .gt_table_body {
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  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
}

#npzqtpzxze .gt_footnotes {
  color: #333333;
  background-color: #FFFFFF;
  border-bottom-style: none;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
}

#npzqtpzxze .gt_footnote {
  margin: 0px;
  font-size: 90%;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 5px;
  padding-right: 5px;
}

#npzqtpzxze .gt_sourcenotes {
  color: #333333;
  background-color: #FFFFFF;
  border-bottom-style: none;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
}

#npzqtpzxze .gt_sourcenote {
  font-size: 90%;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 5px;
  padding-right: 5px;
}

#npzqtpzxze .gt_left {
  text-align: left;
}

#npzqtpzxze .gt_center {
  text-align: center;
}

#npzqtpzxze .gt_right {
  text-align: right;
  font-variant-numeric: tabular-nums;
}

#npzqtpzxze .gt_font_normal {
  font-weight: normal;
}

#npzqtpzxze .gt_font_bold {
  font-weight: bold;
}

#npzqtpzxze .gt_font_italic {
  font-style: italic;
}

#npzqtpzxze .gt_super {
  font-size: 65%;
}

#npzqtpzxze .gt_footnote_marks {
  font-size: 75%;
  vertical-align: 0.4em;
  position: initial;
}

#npzqtpzxze .gt_asterisk {
  font-size: 100%;
  vertical-align: 0;
}

#npzqtpzxze .gt_indent_1 {
  text-indent: 5px;
}

#npzqtpzxze .gt_indent_2 {
  text-indent: 10px;
}

#npzqtpzxze .gt_indent_3 {
  text-indent: 15px;
}

#npzqtpzxze .gt_indent_4 {
  text-indent: 20px;
}

#npzqtpzxze .gt_indent_5 {
  text-indent: 25px;
}

#npzqtpzxze .katex-display {
  display: inline-flex !important;
  margin-bottom: 0.75em !important;
}

#npzqtpzxze div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after {
  height: 0px !important;
}
</style>
<table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
  <thead>
    <tr class="gt_heading">
      <td colspan="11" class="gt_heading gt_title gt_font_normal" style>Composition of Romano-British Pottery</td>
    </tr>
    <tr class="gt_heading">
      <td colspan="11" class="gt_heading gt_subtitle gt_font_normal gt_bottom_border" style>Data: Tubb et al. (1980) in Baxter (2003)</td>
    </tr>
    <tr class="gt_col_headings gt_spanner_row">
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="2" colspan="1" scope="col" id="Kiln">Kiln</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="2" colspan="1" scope="col" id="Sites">Sites</th>
      <th class="gt_center gt_columns_top_border gt_column_spanner_outer" rowspan="1" colspan="9" scope="colgroup" id="Mean composition">
        <div class="gt_column_spanner">Mean composition</div>
      </th>
    </tr>
    <tr class="gt_col_headings">
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Al2O3">Al2O3</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Fe2O3">Fe2O3</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="MgO">MgO</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="CaO">CaO</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Na2O">Na2O</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="K2O">K2O</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="TiO2">TiO2</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="MnO">MnO</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="BaO">BaO</th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr class="gt_group_heading_row">
      <th colspan="11" class="gt_group_heading" scope="colgroup" id="Gloucester">Gloucester</th>
    </tr>
    <tr class="gt_row_group_first"><td headers="Gloucester  Kiln" class="gt_row gt_left">Gloucester</td>
<td headers="Gloucester  Sites" class="gt_row gt_right">22</td>
<td headers="Gloucester  Al2O3" class="gt_row gt_right">16.94%</td>
<td headers="Gloucester  Fe2O3" class="gt_row gt_right">7.43%</td>
<td headers="Gloucester  MgO" class="gt_row gt_right">1.84%</td>
<td headers="Gloucester  CaO" class="gt_row gt_right">0.94%</td>
<td headers="Gloucester  Na2O" class="gt_row gt_right">0.35%</td>
<td headers="Gloucester  K2O" class="gt_row gt_right">3.11%</td>
<td headers="Gloucester  TiO2" class="gt_row gt_right"><span class="gt_footnote_marks gt_asterisk" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>*</sup></span> 0.90%</td>
<td headers="Gloucester  MnO" class="gt_row gt_right">0.07%</td>
<td headers="Gloucester  BaO" class="gt_row gt_right">0.02%</td></tr>
    <tr class="gt_group_heading_row">
      <th colspan="11" class="gt_group_heading" scope="colgroup" id="Wales">Wales</th>
    </tr>
    <tr class="gt_row_group_first"><td headers="Wales  Kiln" class="gt_row gt_left" style="font-weight: bold;">Llanedeyrn</td>
<td headers="Wales  Sites" class="gt_row gt_right" style="font-weight: bold;">14</td>
<td headers="Wales  Al2O3" class="gt_row gt_right" style="font-weight: bold;">12.56%</td>
<td headers="Wales  Fe2O3" class="gt_row gt_right" style="font-weight: bold;">6.37%</td>
<td headers="Wales  MgO" class="gt_row gt_right" style="font-weight: bold;">4.83%</td>
<td headers="Wales  CaO" class="gt_row gt_right" style="font-weight: bold;">0.20%</td>
<td headers="Wales  Na2O" class="gt_row gt_right" style="font-weight: bold;">0.25%</td>
<td headers="Wales  K2O" class="gt_row gt_right" style="font-weight: bold;"><span class="gt_footnote_marks gt_asterisk" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>*</sup></span> 3.93%</td>
<td headers="Wales  TiO2" class="gt_row gt_right" style="font-weight: bold;">0.71%</td>
<td headers="Wales  MnO" class="gt_row gt_right" style="font-weight: bold;"><span class="gt_footnote_marks gt_asterisk" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>*</sup></span> 0.14%</td>
<td headers="Wales  BaO" class="gt_row gt_right" style="font-weight: bold;">0.02%</td></tr>
    <tr><td headers="Wales  Kiln" class="gt_row gt_left">Caldicot</td>
<td headers="Wales  Sites" class="gt_row gt_right">2</td>
<td headers="Wales  Al2O3" class="gt_row gt_right">11.70%</td>
<td headers="Wales  Fe2O3" class="gt_row gt_right">5.42%</td>
<td headers="Wales  MgO" class="gt_row gt_right">3.85%</td>
<td headers="Wales  CaO" class="gt_row gt_right">0.29%</td>
<td headers="Wales  Na2O" class="gt_row gt_right">0.05%</td>
<td headers="Wales  K2O" class="gt_row gt_right">4.57%</td>
<td headers="Wales  TiO2" class="gt_row gt_right">0.57%</td>
<td headers="Wales  MnO" class="gt_row gt_right">0.10%</td>
<td headers="Wales  BaO" class="gt_row gt_right">0.01%</td></tr>
    <tr class="gt_group_heading_row">
      <th colspan="11" class="gt_group_heading" scope="colgroup" id="New Forest">New Forest</th>
    </tr>
    <tr class="gt_row_group_first"><td headers="New Forest  Kiln" class="gt_row gt_left">Islands Thorns</td>
<td headers="New Forest  Sites" class="gt_row gt_right">5</td>
<td headers="New Forest  Al2O3" class="gt_row gt_right">18.18%</td>
<td headers="New Forest  Fe2O3" class="gt_row gt_right">1.71%</td>
<td headers="New Forest  MgO" class="gt_row gt_right">0.67%</td>
<td headers="New Forest  CaO" class="gt_row gt_right">0.03%</td>
<td headers="New Forest  Na2O" class="gt_row gt_right">0.05%</td>
<td headers="New Forest  K2O" class="gt_row gt_right">2.08%</td>
<td headers="New Forest  TiO2" class="gt_row gt_right">1.05%</td>
<td headers="New Forest  MnO" class="gt_row gt_right">0.00%</td>
<td headers="New Forest  BaO" class="gt_row gt_right">0.02%</td></tr>
    <tr><td headers="New Forest  Kiln" class="gt_row gt_left">Ashley Rails</td>
<td headers="New Forest  Sites" class="gt_row gt_right">5</td>
<td headers="New Forest  Al2O3" class="gt_row gt_right">17.32%</td>
<td headers="New Forest  Fe2O3" class="gt_row gt_right">1.51%</td>
<td headers="New Forest  MgO" class="gt_row gt_right">0.61%</td>
<td headers="New Forest  CaO" class="gt_row gt_right">0.05%</td>
<td headers="New Forest  Na2O" class="gt_row gt_right">0.05%</td>
<td headers="New Forest  K2O" class="gt_row gt_right">1.97%</td>
<td headers="New Forest  TiO2" class="gt_row gt_right">0.99%</td>
<td headers="New Forest  MnO" class="gt_row gt_right">0.00%</td>
<td headers="New Forest  BaO" class="gt_row gt_right">0.02%</td></tr>
  </tbody>
  
  <tfoot class="gt_footnotes">
    <tr>
      <td class="gt_footnote" colspan="11"><span class="gt_footnote_marks gt_asterisk" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>*</sup></span> May contain an error.</td>
    </tr>
  </tfoot>
</table>
</div>

???

* Tables are often overlooked as a substitute for a plot, but actually they can be a very effective visualisation.
* For example Tufte advises that if you have a bar plot with less than 5 (?) bars... use a table!
* Or a 
* R is table-oriented, but for publication we might want to break the "tidy" rules and do things like merge cells, add style, etc.
* The new `gt` package from RStudio is excellent for this, and uses a ggplot2-like grammar

---

# Radiocarbon

.pull-left[
![:scale 70%](figures/shubayqa1-radiocarbon.jpg)
.center[<small>Richter et al. 2017</small>]
]

.pull-right[
![](figures/bronk-ramsey-spd.jpeg)
.center[<small>Bronk Ramsey 2017</small>]
]

---

# Radiocarbon packages in R

* Databases
  * `c14bazAAR`
* Calibration
  * `rcarbon`
  * `oxcAAR` (interface for OxCal)
  * `Bchron`
* Modelling
  * `rcarbon`
  * `stratigraphr`
  * `ArchaeoPhases`

---

```
## # A tibble: 1,915 × 10
##    LabID   CRA Error Material Species SiteName Country Longitude Latitude Region
##    <chr> <int> <int> <chr>    <chr>   <chr>    <chr>       <dbl>    <dbl> <chr> 
##  1 AA-1…  9100   140 charcoal Pisum   'Ain Gh… JO           36.0     32.0 1     
##  2 AA-1…  8570   180 charcoal <NA>    'Ain Gh… JO           36.0     32.0 1     
##  3 AA-2…  8775    75 charcoal <NA>    'Ain Gh… JO           36.0     32.0 1     
##  4 AA-2…  8030    65 charcoa… <NA>    'Ain Gh… JO           36.0     32.0 1     
##  5 AA-2…  8080    65 charcoal <NA>    'Ain Gh… JO           36.0     32.0 1     
##  6 AA-2…  8205    65 charcoal <NA>    'Ain Gh… JO           36.0     32.0 1     
##  7 AA-2…  7910    60 charcoal <NA>    'Ain Gh… JO           36.0     32.0 1     
##  8 AA-2…  7910    60 charcoal <NA>    'Ain Gh… JO           36.0     32.0 1     
##  9 AA-2…  7980    55 seeds (… Lens    'Ain Gh… JO           36.0     32.0 1     
## 10 AA-5…  7670   100 charcoal <NA>    'Ain Gh… JO           36.0     32.0 1     
## # ℹ 1,905 more rows
```

.pull-left[

``` r
emedyd %>% 
  filter(SiteName == "Wadi Jilat 13") %>% 
  arrange(CRA) %>% 
  mutate(LabID = as_factor(LabID)) %>% 
  mutate(CalDate = c14_calibrate(CRA, Error, verbose = FALSE)) %>% 
  unnest_legacy() %>% 
  ggplot(aes(year, p)) +
  facet_wrap(~LabID, ncol = 1, 
             scales = "free_y", 
             strip.position = "left") +
  geom_area() + 
  scale_x_reverse() +
  labs(title = "Wadi Jilat 13",
       subtitle = "Radiocarbon chronology",
       x = "cal BP") +
  theme(axis.title.y = element_blank(),
        axis.text.y = element_blank(),
        axis.ticks.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        panel.grid.major.y = element_blank(),
        strip.text.y.left = element_text(angle = 0, hjust = 0))
```
]
.pull-right[
<img src="r4r_visualisation_slides_files/figure-html/eg-rcarbon-1.png" width="100%" />
]

---
class: center, middle

# 3D (`rayshader` & `rayrender`)

![:scale 50%](figures/rayrendersmall.jpg)

???

* These two packages are introducing rayshaded 3D models to R
* 3D can be a 'touchy subject' in data viz., but there are different kins:
  * Bad 3D: purely decorative
  * Dodgy 3D: functional, but usually better options, e.g. 3D scatterplots
  * Good 3D: things that are 3D, e.g. objects, topography
* E.g. ...
  
---
class: center, middle

![](figures/raisz-cuba.jpeg)

.center[<small>Gerardo Canet & Erwin Raisz, *Atlas de Cuba* (1949)</small>]

???

* ... these beautiful topographic maps by Erwin Raisz
* Which can now be modernised with rayrender ...

---
class: center, middle
![](figures/rzvr-3d.png)

---

.pull-left[
<img src="r4r_visualisation_slides_files/figure-html/eg-tavo-1.png" width="100%" />
<img src="r4r_visualisation_slides_files/figure-html/tidy-rcarbon-1.png" width="100%" />
]

.pull-right[
![](figures/archdata_table.png)
![](figures/rzvr-3d.png)
]

???

* Today's exercise: form groups and try to recreate one of these visualisations.

---
class: inverse, center, middle

# Publication-ready visualisations
## With `ggplot2` and friends

---
class: center, middle

# Effective visualisations: Tufte's principles

Above all else show data.

Maximize the data-ink ratio.

Erase non-data-ink.

Erase redundant data-ink.

Revise and edit.

---
class: center, middle

![:scale 30%](figures/tufte-worst-chart.jpg)
> The worst graphic ever to find its way into print
> – Tufte

---

``` r
tufte_data %>% 
  filter(age_group == "<25") %>% 
  ggplot(aes(x = year, y = enrollment)) +
  geom_line() +
  scale_y_continuous(limits = c(0, 1), labels = percent) +
  labs(title = "Enrollment under 25") +
  theme_minimal()
```

???

R nudges you into making good graphics (as opposed to Excel!)

---

# Publication-ready visualisation: exercises

**Wickham & Grolemund, *R for data science***

https://r4ds.had.co.nz/

Chapter 28: *Graphics for communication*

* Run Hadley's code examples
* Answer questions as comments (`# Here is my answer...`)
* Do the 'exercises' sections – they're very informative!

---

# Tips for the real world

.pull-left[
* Getting your data in the right shape is half the battle (at least)
* (Semantic) colour is dangerous; try facets
  * Five colours max; three is better
  * Pretend you have to use black and white, even if you don't
* Don't worry about aesthetics and output formats until you need to
* ...but don't ignore them either!
]
.pull-right[
* Tables are great, but `gt` support for PDF is still limited
* `cowplot` for publication-quality figures
* Colour schemes: `RColorBrewer` <<https://colorbrewer2.org/>>, `khroma`, `rcartocolor`
* `patchwork` for composing multi-panel figures
* If you can use it, animation is powerful (`gganimate`)
* Annotating ggplots is frustrating, but `ggrepel`, `directlabels` and `ggforce` help
]

---

# Further reading

.pull-left[
### R/tidyverse

* Wickham & Grolemund, *R for Data Science*, <https://r4ds.had.co.nz/>
  * Also recommends lots of good books!
* RStudio cheatsheets, <https://rstudio.com/resources/cheatsheets/>

### ggplot2

* Wickham, Navarro & Pedersen, *ggplot2: Elegant Graphics for Data Analysis* (3rd ed.), <https://ggplot2-book.org/>
* Chang, *R Graphics Cookbook*, <https://r-graphics.org/>
* The R Graph Gallery, <https://www.r-graph-gallery.com/>
* Wickham 2010, *A Layered Grammar of Graphics*
* Wilkinson, Anand & Grossman 2005, *The Grammar of Graphics*
]
 
.pull-right[
* Tufte 1983, *The Visual Display of Quantitative Information*

### Cartography

* Lovelace, Nowosad & Muenchow, *Geocomputation with R*, <https://geocompr.robinlovelace.net/index.html>
* Robinson 1953, *Elements of Cartography*

### Extensions

* Marwick et al. 2017, *Packaging Data Analytical Work Reproducibly Using R (and Friends)*, https://doi.org/10.1080/00031305.2017.1375986
* Wickham 2020, *Mastering `Shiny`*, https://mastering-shiny.org/
]