Data Visualization with {ggplot2}

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<img src="images/uo-logo2.png" width="100%" />
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<img src="images/psy-logo.png" width="100%" />
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# Data Visualization with {ggplot2}

UO R Bootcamp 2025

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# ggplot

`{ggplot2}` is the tidyverse package for **data visualization** that can make beautiful, customizable, publication-ready plots

It follows the **"grammar of graphics"**, creating graphics one layer at a time (like a painting)

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# Palmer penguins

We're going to use the **Palmer Penguins** dataset as an example throughout our discussion of `{ggplot}`.

.center[
<img src="images/penguins.png" width="60%" />
]

The dataset **penguins** comes from the `{palmerpenugins}` package, which you can download with `install.packages("palmerpenguins")` and load with `library(palmerpenguins)`.

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# Palmer penguins

``` r
library(palmerpenguins)
penguins
```

```
## # A tibble: 344 × 8
##    species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
##    <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
##  1 Adelie  Torgersen           39.1          18.7               181        3750
##  2 Adelie  Torgersen           39.5          17.4               186        3800
##  3 Adelie  Torgersen           40.3          18                 195        3250
##  4 Adelie  Torgersen           NA            NA                  NA          NA
##  5 Adelie  Torgersen           36.7          19.3               193        3450
##  6 Adelie  Torgersen           39.3          20.6               190        3650
##  7 Adelie  Torgersen           38.9          17.8               181        3625
##  8 Adelie  Torgersen           39.2          19.6               195        4675
##  9 Adelie  Torgersen           34.1          18.1               193        3475
## 10 Adelie  Torgersen           42            20.2               190        4250
## # ℹ 334 more rows
## # ℹ 2 more variables: sex <fct>, year <int>
```

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# {ggplot2}

There are 3 components to making any plot.

1. The data set, including variables you want visually represent

2. Geoms or geometric shapes (e.g., bars, points, lines)

3. Aesthetic mappings (e.g., color, shape, transparency, location)

.center[
<img src="images/ggplot_summary.png" width="40%" />

]
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# Data set

When creating a plot in `{ggplot2}`, the first thing you have to do is call the `ggplot()` function and tell it what data you want to graph.

The function `ggplot()` takes `data` as its first argument.

.panelset[
.panel[.panel-name[Code]

``` r
ggplot(data = penguins)
```

]

.panel[.panel-name[Plot]

![](09-slides_files/figure-html/plot-1-out-1.png)

]
]

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background-position: 90% 5%
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# Geoms

**Geoms** are geometric objects like points, bars, histograms, boxplots

In order to paint a geom on our blank canvas, we need to tell `ggplot()` which geom we want to map

Some popular geoms are `geom_histogram()` for histograms, `geom_bar()` for bar charts, `geom_point()` for points (e.g., for scatter plots), and`geom_boxplot()` for boxplots

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# Aesthetic mapping

Each `geom()` function in `ggplot` takes a **mapping** argument.

**Aesthetic mapping** allows us to assign aesthetic properties to the geom, e.g., location, color, size, transparency (different geoms have different options)

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# Geoms and aesthetic mapping

We're going to call `ggplot()` again, and this time add the `geom_histogram()` **layer**, telling `ggplot()` to map the variable `flipper_length_mm` on the x-axis

.panelset[
.panel[.panel-name[Code]

``` r
penguins %>% 
  ggplot() + 
  geom_histogram(mapping = aes(x = flipper_length_mm)) 
```
]

.panel[.panel-name[Plot]

![](09-slides_files/figure-html/penguins-hist-basic-out-1.png)

]
]
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background-position: 90% 5%
background-size: 10%

# Mapping with geoms

If we want to add a static property to a `geom()`, we need to do so outside of the `mapping` parameter. For example, we could change the number of bins...

.panelset[
.panel[.panel-name[Code]

``` r
penguins %>% 
  ggplot() + 
  geom_histogram(mapping = aes(x = flipper_length_mm),
                 bins    = 10)
```

]

.panel[.panel-name[Plot]

![](09-slides_files/figure-html/penguins-hist-bins-out-1.png)
]
]

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background-image: url(images/hex/ggplot2.png)
background-position: 90% 5%
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# Mapping with geoms

...or make them a different color. We can change the color of 2D objects with the `fill` aesthetic

.panelset[
.panel[.panel-name[Code]

``` r
penguins %>% 
  ggplot() + 
  geom_histogram(mapping = aes(x = flipper_length_mm), 
                 fill    = "turquoise")
```

]

.panel[.panel-name[Plot]

![](09-slides_files/figure-html/penguins-hist-fill-out-1.png)
]
]

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# Mapping with geoms

Now we'll use a different `geom`---we'll add a layer of points to our plot using `geom_point()`

``` r
penguins %>% 
  ggplot() + 
  geom_point(mapping = aes(x = flipper_length_mm))
```

```
## Error in `geom_point()`:
## ! Problem while setting up geom.
## ℹ Error occurred in the 1st layer.
## Caused by error in `compute_geom_1()`:
## ! `geom_point()` requires the following missing aesthetics: y.
```

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# Mapping with geoms

.smaller-font[We get an error, telling us that `geom_point()` requires the y-aesthetic. This makes sense---we need an x- and y-axis to define where points belong on a scatter plot. Let's add `bill_length_mm` as the y-axis]

.panelset[
.panel[.panel-name[Code]

``` r
penguins %>% 
  ggplot() + 
  geom_point(mapping = aes(x = flipper_length_mm,
                           y = bill_length_mm))
```
]

.panel[.panel-name[Plot]

![](09-slides_files/figure-html/penguins-point-fixed-out-1.png)

]
]

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background-image: url(images/hex/ggplot2.png)
background-position: 90% 5%
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# Mapping with geoms

Let's find out if the relationship between `flipper_length_mm` and `bill_length_mm` relates to the species of penguin. We'll map `species` to the `color` aesthetic (similar to `fill`, but for 1D objects).

.panelset[
.panel[.panel-name[Code]

``` r
penguins %>% 
  ggplot() + 
  geom_point(
    mapping = aes(x     = flipper_length_mm,
                  y     = bill_length_mm,
                  color = species))
```
]

.panel[.panel-name[Plot]

![](09-slides_files/figure-html/penguins-point-color-out-1.png)
]
]

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background-image: url(images/hex/ggplot2.png)
background-position: 90% 5%
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# Mapping with geoms

Notice that we included `color` **inside** our aesthetic mapping call (`mapping = aes()`) here, but not when we filled our histogram with the color turquoise earlier.

This is the difference between mapping an aesthetic to **data** and just setting an aesthetic to some **value** (e.g., "turquoise").

This is a fairly common mistake, so let's take a look at an example

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# Mapping with geoms

What happens if we tell `ggplot` to make our points turquoise but accidentally include that **inside** the `aes()` call?

.panelset[
.panel[.panel-name[Code]

``` r
penguins %>% 
  ggplot() + 
  geom_point(mapping = aes(x     = flipper_length_mm, 
                           y     = bill_length_mm,
                           color = "turquoise"))
```

]

.panel[.panel-name[Plot]

.pull-left[

![](09-slides_files/figure-html/penguins-point-turq-out-1.png)
]

.pull-right[
This is not what we want! `ggplot` is treating the value "turquoise" as if it were part of our data, which it isn't.
]

]
]

---
class: yourturn
# Your Turn 1

1. Create a scatter plot to visualize the relationship between `flipper_length_mm` and `bill_length_mm`.

1. Build on your plot above by adding an aesthetic to visualize the effect of `species`. Choose any aesthetic you’d like or play around with a few. What do they do? How might you use more than one aesthetic? *Note:* Options for aesthetics include `color`, `shape`, `size`, and `alpha`.

---
class: solution

# Solution

## Q1

.panelset[
.panel[.panel-name[Code]

``` r
penguins %>% 
  ggplot() + 
  geom_point(aes(x = flipper_length_mm, 
                 y = bill_length_mm))
```
]

.panel[.panel-name[Plot]

![](09-slides_files/figure-html/penguins-yt-q1-out-1.png)

]
]

---
class: solution
# Solution
## Q2 (answers will vary...)

.panelset[
.panel[.panel-name[Code]

``` r
penguins %>% 
  ggplot() + 
  geom_point(aes(x     = flipper_length_mm, 
                 y     = bill_length_mm,
                 color = species, 
                 shape = species),
             alpha = 0.5,
             size  = 3)
```
]

.panel[.panel-name[Plot]

![](09-slides_files/figure-html/penguins-yt-q2-out-1.png)
]
]

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background-image: url(images/hex/ggplot2.png)
background-position: 90% 5%
background-size: 10%

# Mapping with geoms

We could also make separate graphs for each `species` using `facet_wrap()`. We do this by passing a one-sided formula to `facet_wrap()`.

.panelset[
.panel[.panel-name[Code]

``` r
penguins %>% 
  ggplot() + 
  geom_point(aes(x = flipper_length_mm, 
                 y = bill_length_mm)) + 
  facet_wrap(~species)
```
]

.panel[.panel-name[Plot]

![](09-slides_files/figure-html/penguins-point-facet-out-1.png)
]
]

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background-image: url(images/hex/ggplot2.png)
background-position: 90% 5%
background-size: 10%

# Mapping with geoms

Another thing we often want to do is to add a line over our scatterplot to describe the linear relationship between variables. We can do this by adding a `geom_smooth()` layer to our plot.

.panelset[
.panel[.panel-name[Code 1]

``` r
penguins %>% 
  ggplot() + 
  geom_point(aes(x     = flipper_length_mm, 
                 y     = bill_length_mm,
                 color = species)) + 
  geom_smooth(aes(x    = flipper_length_mm, 
                  y    = bill_length_mm),
              color = "black")
```

]

.panel[.panel-name[Plot 1]

.pull-left[
![](09-slides_files/figure-html/penguins-smooth-out-1.png)
]

.pull-right[
Note that "loess" is the default function for `geom_smooth()`.

Learn more on that [here](http://www.statisticshowto.com/lowess-smoothing/).
]

]

.panelset[
.panel[.panel-name[Code 2]

.smaller-font[You can change that by setting the `method` argument in `geom_smooth()`. Let's change it to our old friend linear regression or "lm".]

]

.panel[.panel-name[Plot 2]

![](09-slides_files/figure-html/point-smooth-lm-out-1.png)

]
]

]

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background-image: url(images/hex/ggplot2.png)
background-position: 90% 5%
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# Global aesthetic mapping

Our code so far has been getting rather inefficient. We're specifying the x and y axis for each `geom_*` call.

Instead, we can use **global** aesthetic mappings, which are specified in the `ggplot()` call.

Global mappings are inherited by each layer unless they're overwritten.

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# Global aesthetic mapping

Let's re-make our previous plot using global aesthetic mapping.

.panelset[
.panel[.panel-name[Code]

``` r
penguins %>% 
  ggplot(aes(x = flipper_length_mm, 
             y = bill_length_mm))+ 
  geom_point(aes(color = species)) + 
  geom_smooth(color = "black",
              method = "lm")
```
]

.panel[.panel-name[Plot]

![](09-slides_files/figure-html/point-smooth-global-out-1.png)
]

.panel[.panel-name[Explanation]

So...what do we put in global aesthetic mapping and what do we put in the aesthetic mapping of specific geoms?

You want to put anything in the global mapping that you want *every layer to inherit* (or at least the majority of them).

In the code above, I defined the `x` and `y` aesthetics globally because I want those the same in every `geom`.

However, I *don't* define the `color` aesthetic globally, because `color` is geom-specific in this case.
]
]

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# Global aesthetic mapping

Let's take a look at the previous example again, but this time with `color` in the global aesthetic...

.panelset[
.panel[.panel-name[Code 1]

``` r
penguins %>% 
  ggplot(aes(x     = flipper_length_mm, 
             y     = bill_length_mm,
             color = species))+ 
  geom_point() +  # inherit global
  geom_smooth(method = "lm") #inherit global
```
]

.panel[.panel-name[Plot 1]

.pull-left[
![](09-slides_files/figure-html/smooth-color-global-out-1.png)
]

.pull-right[
As you can see, global aesthetic mapping gets inherited by every layer. We can override this by providing a different aesthetic mapping in individual `geom()` calls...
]
]

.panel[.panel-name[Code 2]

``` r
penguins %>% 
  ggplot(aes(x     = flipper_length_mm, 
             y     = bill_length_mm,
             color = species))+ 
  geom_point() +  #inherit global 
  geom_smooth(method = "lm",
              color  = "black") #override global `color`
```
]

.panel[.panel-name[Plot 2]

![](09-slides_files/figure-html/smooth-color-global-override-out-1.png)
]
]

---
class: yourturn
# Your Turn 2

1. Take a look at the `diamonds` data set that is loaded as part of the `{ggplot2}` package. Use `glimpse()`, `str()`, `head()`, or any other data viewing function we've previously discussed.

1. Fill in the blanks in the code to re-create the plot below. *Note*: This plot uses a geom we haven't seen yet called `geom_bar()`, which I've filled in for you.

---
class: solution
# Solution

.panelset[

.panel[.panel-name[Q1]

``` r
glimpse(diamonds)
```

```
## Rows: 53,940
## Columns: 10
## $ carat   <dbl> 0.23, 0.21, 0.23, 0.29, 0.31, 0.24, 0.24, 0.26, 0.22, 0.23, 0.…
## $ cut     <ord> Ideal, Premium, Good, Premium, Good, Very Good, Very Good, Ver…
## $ color   <ord> E, E, E, I, J, J, I, H, E, H, J, J, F, J, E, E, I, J, J, J, I,…
## $ clarity <ord> SI2, SI1, VS1, VS2, SI2, VVS2, VVS1, SI1, VS2, VS1, SI1, VS1, …
## $ depth   <dbl> 61.5, 59.8, 56.9, 62.4, 63.3, 62.8, 62.3, 61.9, 65.1, 59.4, 64…
## $ table   <dbl> 55, 61, 65, 58, 58, 57, 57, 55, 61, 61, 55, 56, 61, 54, 62, 58…
## $ price   <int> 326, 326, 327, 334, 335, 336, 336, 337, 337, 338, 339, 340, 34…
## $ x       <dbl> 3.95, 3.89, 4.05, 4.20, 4.34, 3.94, 3.95, 4.07, 3.87, 4.00, 4.…
## $ y       <dbl> 3.98, 3.84, 4.07, 4.23, 4.35, 3.96, 3.98, 4.11, 3.78, 4.05, 4.…
## $ z       <dbl> 2.43, 2.31, 2.31, 2.63, 2.75, 2.48, 2.47, 2.53, 2.49, 2.39, 2.…
```
]

.panel[.panel-name[Q2]

.pull-left[

``` r
diamonds %>%
  ggplot(aes(x = cut, y = price, fill = color)) +
    geom_bar(position = "dodge", stat = "summary", fun = "mean") +
    facet_wrap(~clarity) +
    labs(title = "Diamond Price by Cut, Color, and Clarity",
         x     = "Cut", 
         y     = "Price",
         fill  = "Color") 
```
]

.pull-right[

![](09-slides_files/figure-html/unnamed-chunk-11-1.png)
]
]
]

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# Labels and themes

You can do a TON more customization of your plots than what we've covered so far. The possibilities with `{ggplot2}` really are endless!

For example, you can change your axis labels, change the theme of the plot, etc...

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# Examples 🤯

.footnote[Image from [Eric Ekholm](https://github.com/ekholme/tidytuesday#most-recent-finished-contribution)]
<img src="images/ggplot_ex_1.png" width="60%" style="display: block; margin: auto;" />

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background-position: 90% 5%
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# Examples 🤯

.footnote[Image from [Eric Ekholm](https://github.com/ekholme/tidytuesday#most-recent-finished-contribution)]

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background-image: url(images/hex/ggplot2.png)
background-position: 90% 5%
background-size: 10%

# Examples 🤯

.footnote[Image from [Georgios Karamanis](https://github.com/gkaramanis/tidytuesday#highlights-click-on-image-to-go-to-code-)]

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background-image: url(images/hex/ggplot2.png)
background-position: 90% 5%
background-size: 10%

# Examples 🤯

.footnote[Image from [Georgios Karamanis](https://github.com/gkaramanis/tidytuesday#highlights-click-on-image-to-go-to-code-)]

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background-image: url(images/hex/ggplot2.png)
background-position: 90% 5%
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# Examples 🤯

.footnote[Image from [Jake Kaupp](https://github.com/jkaupp/tidytuesdays)]

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background-image: url(images/hex/ggplot2.png)
background-position: 90% 5%
background-size: 10%

# Examples 🤯

.footnote[Image from [Georgios Karamanis](https://github.com/gkaramanis/tidytuesday#highlights-click-on-image-to-go-to-code-)]

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background-image: url(images/hex/ggplot2.png)
background-position: 90% 5%
background-size: 10%

# Examples 🤯

.footnote[Image from [Torsten Sprenger](https://github.com/spren9er/tidytuesday)]

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background-image: url(images/hex/ggplot2.png)
background-position: 90% 5%
background-size: 10%

# Examples 🤯

.footnote[Image from [Ariane Aumaitre](https://twitter.com/ariamsita?s=20&t=e0M_tAtBxKKKdjlWpykazw)]

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background-image: url(images/hex/ggplot2.png)
background-position: 90% 5%
background-size: 10%

# Examples 🤯

.footnote[Image from [Cara Thompson](https://github.com/cararthompson/tidytuesdays)]

---
background-image: url(images/hex/ggplot2.png)
background-position: 90% 5%
background-size: 10%

# Examples 🤯

.footnote[Image from [Cameron S. Kay](https://twitter.com/cameronskay/status/1552003928295817217/photo/1) 🙃]

---
class: yourturn, center, middle
# Q & A

---
class: yourturn, center, middle
# Break!