Lab 01b: BMI 5/625

class: center, middle, inverse, title-slide

.title[
# Lab 01b: BMI 5/625
]
.subtitle[
## Introducing ggplot
]
.author[
### Steven Bedrick
]

---

## Introducing `ggplot`

* Goal for this session: a very quick `ggplot` refresher

* Meet our dataset:

* [Palmer Penguins](https://allisonhorst.github.io/palmerpenguins/)

```r
glimpse(penguins)
```

```
Rows: 344
Columns: 8
$ species           <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…
$ island            <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…
$ bill_length_mm    <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …
$ bill_depth_mm     <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …
$ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…
$ body_mass_g       <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …
$ sex               <fct> male, female, female, NA, female, male, female, male…
$ year              <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…
```

---

## Sidebar: Why not Fisher's Irises?

* Hint: The original citation for that data is:

* R. A. Fisher (1936) "The use of multiple measurements in taxonomic problems." _Annals of Eugenics_ 7(2): 179-188

* 🤦

---

## So: Penguins!

```r
glimpse(penguins)
```

---

## Core `ggplot` concepts

* _Aesthetics_ map dimensions of our data to visual properties of the plot

* _Geometries_ ("geoms") actually put "ink on the page"

* Each type of graph (scatterplot, etc.) has a corresponding geom

* Different geoms attend to different aesthetics
  
--

* e.g., `geom_point` cares about `x` and `y`
--

* Multiple geoms can be combined on the same plot

* _Scales_ control axes, fills, etc.

* _Themes_ control visual properties (fonts, background colors, etc.)

---
class: center, middle

## `ggplot` is an _opinionated_ tool!

---

## Data considerations

* By default, `ggplot` assumes "tidy" data

* Think: one "row" per mark on the graph...

* ... and then the various properties for each mark in columns.

---

## Our first plot

```r
penguins %>% 
  ggplot(mapping=aes(
    x=bill_length_mm, y=bill_depth_mm
  )) 
  + geom_point()
```
--
<img src="01b-slides_files/figure-html/unnamed-chunk-5-1.png" width="80%" style="display: block; margin: auto;" />

---

## What about color?

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

--
<img src="01b-slides_files/figure-html/unnamed-chunk-6-1.png" width="80%" style="display: block; margin: auto;" />

---

## Want a different plot? Try a different geom

```r
penguins %>% ggplot(mapping=aes(
  x=bill_length_mm, fill=species
)) + geom_histogram()
```

---

## We can specify aesthetic values by hand:

```r
penguins %>% ggplot(
  mapping=aes(x=bill_length_mm, fill=species)
  ) + geom_histogram(alpha=0.6)
```

---

## Different geoms have different options:

```r
penguins %>% ggplot(
  mapping=aes(x=bill_length_mm, fill=species)
  ) + geom_histogram(alpha=0.6)
```

---

## Different geoms have different options:

```r
penguins %>% ggplot(
  mapping=aes(x=bill_length_mm, fill=species)
  ) + geom_histogram(alpha=0.6, position="identity")
```

---

## Customizing other aspects of the plot

```r
penguins %>% ggplot(
  mapping=aes(x=bill_length_mm, fill=species)
  ) + geom_histogram(alpha=0.6, position="identity") +
  labs(x="Bill Length (mm)", y="Frequency") +
  ggtitle("Bill Length, by species")
```

---

## Workflow tip: Save a plot for later

```r
basic.plot <- penguins %>% 
  ggplot(aes(x=bill_length_mm, y=bill_depth_mm)) + geom_point()
```

---

## Workflow tip: Save a plot for later

```r
basic.plot + labs(
    x="Bill Length (mm)", y="Bill Depth (mm)"
  )
```

* This is useful when building a complex plot!