Lecture 201

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.title[
# Lecture 201
]
.author[
### Dr Stefano De Sabbata School of Geography, Geology, and the Env., University of Leicester <a href="https://github.com/sdesabbata/r-for-geographic-data-science" style="color: white">github.com/sdesabbata/r-for-geographic-data-science</a> <a href="mailto:s.desabbata@le.ac.uk" style="color: white">s.desabbata@le.ac.uk</a> | <a href="https://twitter.com/maps4thought" style="color: white">@maps4thought</a> text licensed under <a href="https://creativecommons.org/licenses/by-sa/4.0/" style="color: white">CC BY-SA 4.0</a>, code licensed under <a href="https://www.gnu.org/licenses/gpl-3.0.html" style="color: white">GNU GPL v3.0</a>
]

---

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# Data visualisation

---
## Recap

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**Previously**: Table operations

- Long and wide table formats
- Pivot operations *(not as in Excel)*
- Join operations

**Today**: Exploratory visualisation

- Grammar of graphics
- Visualising amounts and proportions
- Visualising variable distributions and relationships

]
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![](data:image/png;base64,#https://raw.githubusercontent.com/tidyverse/ggplot2/main/man/figures/logo.png)

.right[
.referencenote[
by ggplot2 authors 
via [ggplot2 GitHub repository](https://github.com/tidyverse/ggplot2/), MIT License
]
]

]

---
## Grammar of graphics

Grammars provide rules for languages

*"The grammar of graphics takes us beyond a limited set of charts (words) to an almost unlimited world of graphical forms (statements)"* (Wilkinson, 2005)

Statistical graphic specifications are expressed in six statements:

1. **Data** manipulation 
2. **Variable** transformations (e.g., rank),
3. **Scale** transformations (e.g., log),
4. **Coordinate system** transformations (e.g., polar),
5. **Element**: mark (e.g., points) and visual variables (e.g., color)
6. **Guides** (axes, legends, etc.).

---
## Data manipulation (1) and variable trans. (2)

It might be necessary to manipulate (wrangle) the data before they are suitable for visualisation.

For instance, using functions available in `dplyr`

- `select`: to select specific columns
- `filter`: to select specific rows
- `join`: to merge different but related sets of data

Variable transformations are used:

- Make statistical operations on variables appropriate
- Create new variables
    - Including aggregates, or other types of summaries

For instance, using functions available in `dplyr`

- `mutate`: to create new columns (e.g., combining values from different columns)
- `group_by` and `summarise`: to calculate aggregated values (e.g. mean, sum)

---
## Scale tranformation (3) and guides (4)

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Scales map variables to dimensions.

Categorical dimensions:

- one unit/tick per category
- commonly ordered alphabetically by default
- common transformation: ordered by another variable

Numeric dimension:

- select a range for our numbers and intervals to mark ticks
- common transformation: logarithmic

]
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The term "guide" refers to all the elements that provide information useful to *interpret* the graphics

- Scale guides
    - axis labels
    - legends
        - size
        - shape
        - colour
- Annotations
    - title
    - footnotes
    - data sources
    - north arrows

]

---
## Elements (5) and ggplot2

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The `ggplot2` library offers a series of functions for creating graphics **declaratively**, based on the Grammar of Graphics.

To create a graph in `ggplot2`:

- provide the data
- specify elements
    - which visual variables (`aes`)
    - which marks (e.g., `geom_point`)
- apply transformations
- guides

]
.pull-right[

![](data:image/png;base64,#https://raw.githubusercontent.com/tidyverse/ggplot2/main/man/figures/logo.png)

.right[
.referencenote[
by ggplot2 authors 
via [ggplot2 GitHub repository](https://github.com/tidyverse/ggplot2/), MIT License
]
]

]

---
## Marks (5.1)

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A mark is a sign positioned on the plane of the graph

- that represents an entity
- using geometric elements
    - geometric primitives
    - or combinations thereof

In most information visualisation tools:

- point and/or symbols (e.g. stars)
- line
- area (including bars, pie slices, etc)
- text

]
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Marks (graphical primitives) can be specified through a series of functions, such as:

- `geom_point` is used to create scatterplots
- `geom_line` connects points in order of the variable on the x-axis
- `geom_text` adds text directly to the plot
- `geom_col` or `geom_bar` to create barcharts
- `geom_boxplot` to create boxplots
- .. and many more

These can be added to the construction of the graph using `+`

]

---
## Visual variables (5.2)

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A **visual variable** is an aspect of a **mark** that can be controlled to change its appearance.

Visual variables include:

- Size
- Shape 
- Orientation
- Colour (hue)
- Colour value (brightness)
- Texture
- Position (2 dimensions)

]
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The `aes` element  provides a *"mapping"* from the data *columns* (attributes) to the graphic's *visual variables*, including:

- `x` and `y`
- `fill` (fill colour) and `colour` (border colour)
- `shape`
- `size`

```r
data %>%
  ggplot(
    aes(
      x = column_1,
      y = column_2
    )
  ) +
  geom_...()
```

]

---
## Example

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```r
library(tidyverse)
library(knitr)

leicester_2011OAC <- 
 read_csv(
 "2011_OAC_Raw_uVariables_Leicester.csv"
 )
```

- `x`: a column to *"map"* to the x-axis, e.g. days (category)
- `y`: a column to *"map"* to the y-axis, e.g. delay (continuous)
- `geom_point`: point mark (graphical primitive)

```r
leicester_2011OAC %>%
  ggplot(
    aes(
      x = u005,
      y = Total_Population
    )
  ) +
  geom_point()
```

]
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![](data:image/png;base64,#/home/rstudio/r-for-geographic-data-science/docs/slides/201-slides-data-visualisation_files/figure-html/unnamed-chunk-6-1.png)

]

---
## Adding more visual variables

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... then, why not add some colour?

- `colour`: a column to *"map"* to the visual variable *colour* as colour of the point mark (border colour for area marks), e.g. origin (category)
  - `fill` can be used to *"map"* a column to the visual variable *colour* as the fill of an area mark

```r
leicester_2011OAC %>%
  ggplot(
    aes(
      x = u005,
      y = Total_Population,
      colour = supgrpname
    )
  ) +
  geom_point()
```

]
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![](data:image/png;base64,#/home/rstudio/r-for-geographic-data-science/docs/slides/201-slides-data-visualisation_files/figure-html/unnamed-chunk-8-1.png)

]

---
##  Editing scales and guides

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```r
leicester_2011OAC %>%
  ggplot(aes(
    x = u005,
    y = Total_Population,
    colour = fct_reorder(supgrpname, supgrpcode)
  )) +
  geom_point() +
  ggtitle("Leicester's population density") +
  labs(
    colour = paste0(
      "2011 Output Area\n",
      "Classification (OAC)\n(supergroups)")
  ) +
  xlab("OA area (hectars)") +
  ylab("Resident population") +
  scale_x_log10() +
  scale_y_log10() +
  scale_colour_manual(
    values = 
      c(
        "#e41a1c", "#f781bf", "#ff7f00", 
        "#a65628", "#984ea3", "#377eb8", 
        "#ffff33"
      )
  ) +
  theme_bw()
```

]
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![](data:image/png;base64,#/home/rstudio/r-for-geographic-data-science/docs/slides/201-slides-data-visualisation_files/figure-html/unnamed-chunk-10-1.png)

]

---
## Histograms

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```r
leicester_2011OAC %>%
  ggplot(
    aes(
      x = u011,
      fill = fct_reorder(supgrpname, supgrpcode)
    )
  ) +
  geom_histogram(binwidth = 5) +
  ggtitle("Leicester's young adults") +
  labs(
    fill = paste0(
      "2011 Output Area\n",
      "Classification (OAC)\n(supergroups)")
  ) +
  xlab("Residents aged 20 to 24") +
  ylab("Count") +
  scale_fill_manual(
    values = 
      c(
        "#e41a1c", "#f781bf", "#ff7f00", 
        "#a65628", "#984ea3", "#377eb8", 
        "#ffff33"
      )
  ) +
  theme_bw()
```

]
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---
## Boxplots

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```r
leicester_2011OAC %>%
  ggplot(
    aes(
      x = fct_reorder(supgrpname, supgrpcode),
      y = u011,
      fill = fct_reorder(supgrpname, supgrpcode)
    )
  ) +
  geom_boxplot() +
  ggtitle("Leicester's young adults") +
  labs(
    fill = paste0(
      "2011 Output Area\n",
      "Classification (OAC)\n(supergroups)")
  ) +
  xlab("2011 OAC (supergroups)") +
  ylab("Residents aged 20 to 24") +
  scale_fill_manual(
    values = 
      c(
        "#e41a1c", "#f781bf", "#ff7f00", 
        "#a65628", "#984ea3", "#377eb8", 
        "#ffff33"
      )
  ) +
  theme_bw() +
  theme(axis.text.x = 
    element_text(angle = 90, hjust = 1))
```

]
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---
## Jittered points

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```r
leicester_2011OAC %>%
  ggplot(
    aes(
      x = fct_reorder(supgrpname, supgrpcode),
      y = u011,
      color = fct_reorder(supgrpname, supgrpcode)
    )
  ) +
  geom_jitter() +
  ggtitle("Leicester's young adults") +
  labs(
    colour = paste0(
      "2011 Output Area\n",
      "Classification (OAC)\n(supergroups)")
  ) +
  xlab("2011 OAC (supergroups)") +
  ylab("Residents aged 20 to 24") +
  scale_colour_manual(
    values = 
      c(
        "#e41a1c", "#f781bf", "#ff7f00", 
        "#a65628", "#984ea3", "#377eb8", 
        "#ffff33"
      )
  ) +
  theme_bw() +
  theme(axis.text.x = 
    element_text(angle = 90, hjust = 1))
```

]
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![](data:image/png;base64,#/home/rstudio/r-for-geographic-data-science/docs/slides/201-slides-data-visualisation_files/figure-html/unnamed-chunk-16-1.png)

]

---
## Coordinate system transformation (6)

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```r
leicester_2011OAC %>%
  ggplot(
    aes(
      x = factor(1),
      y = Total_Population,
      fill = fct_reorder(supgrpname, supgrpcode)
    )
  ) +
  geom_col() +
  ggtitle("Leicester's population") +
  labs(
    colour = paste0(
      "2011 Output Area\n",
      "Classification (OAC)\n(supergroups)")
  ) +
  xlab("Leicester") +
  ylab("Residents") +
  scale_fill_manual(
    values =
      c(
        "#e41a1c", "#f781bf", "#ff7f00", 
        "#a65628", "#984ea3", "#377eb8", 
        "#ffff33"
      )
  ) +
  theme_bw()
```

]
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![](data:image/png;base64,#/home/rstudio/r-for-geographic-data-science/docs/slides/201-slides-data-visualisation_files/figure-html/unnamed-chunk-18-1.png)

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---
## Coordinate system transformation (6)

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]
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---
## Best practices

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- To represent numerical data:
    - **location**
        - x-axis and y-axis, easily identified, put into order and quantity estimated)
    - **size** 
        - easily identified, put into order and quantity estimated
    - **colour value** 
        - brightness -- easily identified and put into order
- To represent categories:
    - **colour hue**
        - easily identified and grouped together
    - **shape** 
        - grouped together
    - **texture**
]
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- Colour
  - use [ColorBrewer](https://colorbrewer2.org/) (e.g., `scale_color_brewer`)
- Marks
  - dots and non-contiguous areas suggest discrete objects
  - lines and contiguous areas suggest continuous phenomena
- Scale transformations
  - `log` transformation can help unskew distributions
- Guides (axes, legends, etc.)
  - where size is used (e.g., barcharts) axise must always start as zero
  - keep labels horizontal (e.g., use horizontal barcharts)
  - alphabetical orders of axes are rarely the optimal choice

]

.referencenote[
See also: Roth, R.E. (2017). Visual Variables. In International Encyclopedia of Geography: People, the Earth, Environment and Technology. https://doi.org/10.1002/9781118786352.wbieg0761
]

---
## Summary

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**Today**: Exploratory visualisation

- Grammar of graphics
- Visualising amounts and proportions
- Visualising variable distributions and relationships

**Next week**: Exploratory statistics

- Descriptive statistics
- Exploring assumptions
    - Normality
    - Skewness and kurtosis
    - Homogeneity of variance

.referencenote[
Slides created via the R package [**xaringan**](https://github.com/yihui/xaringan). The chakra comes from [remark.js](https://remarkjs.com), [**knitr**](https://yihui.org/knitr), and [R Markdown](https://rmarkdown.rstudio.com).
]

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