Visualizing Data in R

class: title-slide, nobar

![:img right: 25px, bottom: 50px, 65%, , ](figures/viz_fig.png)

## Workshop: Dealing with Data in R
# Visualizing Data in R
## A primer on `ggplot2`

.footnote[Steffi LaZerte <https://steffilazerte.ca> | *Compiled: 2022-01-26*]

---
class: section

# First things first

Save previous script (consider names like `intro.R` or `1_getting_started.R`)

Open New File .medium[(make sure you're in the RStudio Project)]

![:spacer 10px]()

Add `library(tidyverse)` to the top

Save this new script

.medium[consider names like `figs.R` or `2_figures.R`]

---
# Outline

## 1. Figures with `ggplot2` .small[(A `tidyverse` package)]
- Basic plot
- Common plot types
- Plotting by categories
- Adding statistics
- Customizing plots
- Annotating plots

## 2. Combining figures with `patchwork`

## 3. Saving figures

---
class: nobar
# `ggplot2`

![:img center, bottom: 0, 78%, ,](https://github.com/allisonhorst/stats-illustrations/raw/master/rstats-artwork/ggplot2_masterpiece.png)

.footnote[Artwork by [@allison_horst](https://github.com/allisonhorst/stats-illustrations)]

---

# Our data set: Palmer Penguins!

![:img center, bottom: 0, 78%, ,](https://raw.githubusercontent.com/allisonhorst/palmerpenguins/master/man/figures/lter_penguins.png)

.footnote[Artwork by [@allison_horst](https://github.com/allisonhorst/stats-illustrations)]

![:img right: 1%, top: 1%, 10%, ,](https://raw.githubusercontent.com/allisonhorst/palmerpenguins/master/man/figures/logo.png)

---
# Our data set: Palmer Penguins!

.small[

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

```
## # A tibble: 344 × 8
## species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex year
## <fct> <fct> <dbl> <dbl> <int> <int> <fct> <int>
## 1 Adelie Torgersen 39.1 18.7 181 3750 male 2007
## 2 Adelie Torgersen 39.5 17.4 186 3800 female 2007
## 3 Adelie Torgersen 40.3 18 195 3250 female 2007
## 4 Adelie Torgersen NA NA NA NA <NA> 2007
## 5 Adelie Torgersen 36.7 19.3 193 3450 female 2007
## 6 Adelie Torgersen 39.3 20.6 190 3650 male 2007
## 7 Adelie Torgersen 38.9 17.8 181 3625 female 2007
## 8 Adelie Torgersen 39.2 19.6 195 4675 male 2007
## # … with 336 more rows
```
]

![:img right: 10%, bottom: 0, 25%, ,](https://raw.githubusercontent.com/allisonhorst/palmerpenguins/master/man/figures/lter_penguins.png)

.footnote[Artwork by [@allison_horst](https://github.com/allisonhorst/stats-illustrations)]

![:img right: 1%, top: 1%, 10%, ,](https://raw.githubusercontent.com/allisonhorst/palmerpenguins/master/man/figures/logo.png)

--
.medium[
> **Your turn!**  
> 1. Run this code and look at the output in the console
> 2. Run `view(penguins)` to see the output in the built-in spreadsheet viewer
]

---
# Side Note

### Where did the `penguins` data set come from?

- Sometimes R packages contain data
- If you load a package (i.e. `library(palmerpenguins)`) you can use the data
- **Note** that here the data object is called `penguins` (not `palmerpenguins`)
- **Note** this is NOT how you'll load your own data

---
# A basic plot

<code class ='r hljs remark-code'>library(palmerpenguins) library(tidyverse) ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm)) + &nbsp;&nbsp;geom_point()</code>

---
# Break it down

<code class ='r hljs remark-code'>library(palmerpenguins) library(tidyverse) ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm)) + &nbsp;&nbsp;geom_point()</code>

![:spacer 10px]()

### `library(palmerpenguins)`
- Load the `palmerguins` package so we have access to `penguins` data

---
# Break it down

<code class ='r hljs remark-code'>library(palmerpenguins) library(tidyverse) ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm)) + &nbsp;&nbsp;geom_point()</code>

![:spacer 10px]()

### `library(tidyverse)`
- Load the `tidyverse` package (which loads the `ggplot2` package and gives us access to the `ggplot()` function among others)

---
# Break it down

<code class ='r hljs remark-code'>library(palmerpenguins) library(tidyverse) ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm)) + &nbsp;&nbsp;geom_point()</code>

![:spacer 10px]()

### `ggplot()`
- Set the attributes of your plot
- **`data`** = Dataset
- **`aes`** = Aesthetics (how the data are used)
- Think of this as your plot defaults

---

# Break it down

<code class ='r hljs remark-code'>library(palmerpenguins) library(tidyverse) ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm)) + &nbsp;&nbsp;geom_point()</code>

![:spacer 10px]()

### `geom_point()`

- Choose a `geom` function to display the data
- Always *added* to a `ggplot()` call with `+`

> ggplots are essentially layered objects, starting with a call to `ggplot()`

---
class: split-50
# Plots are layered

.columnl[
.small[

```r
ggplot(data = penguins, aes(x = sex, y = body_mass_g))
```

<img src="2 Visualizing Data_files/figure-html/unnamed-chunk-8-1.png" width="100%" style="display: block; margin: auto;" />
]]

.columnr[
.small[

```r
ggplot(data = penguins, aes(x = sex, y = body_mass_g)) +
* geom_boxplot()
```

<img src="2 Visualizing Data_files/figure-html/unnamed-chunk-9-1.png" width="100%" style="display: block; margin: auto;" />
]]

---
class: split-50
# Plots are layered

.columnl[
.small[

```r
ggplot(data = penguins, aes(x = sex, y = body_mass_g))
```

<img src="2 Visualizing Data_files/figure-html/unnamed-chunk-10-1.png" width="100%" style="display: block; margin: auto;" />
]]

.columnr[
.small[

```r
ggplot(data = penguins, aes(x = sex, y = body_mass_g)) +
* geom_point()
```

<img src="2 Visualizing Data_files/figure-html/unnamed-chunk-11-1.png" width="100%" style="display: block; margin: auto;" />
]]

---
class:split-50
# Plots are layered

.columnl[
.small[

```r
ggplot(data = penguins, aes(x = sex, y = body_mass_g))
```

<img src="2 Visualizing Data_files/figure-html/unnamed-chunk-12-1.png" width="100%" style="display: block; margin: auto;" />
]]

.columnr[
.small[

```r
ggplot(data = penguins, aes(x = sex, y = body_mass_g)) +
* geom_violin()
```

<img src="2 Visualizing Data_files/figure-html/unnamed-chunk-13-1.png" width="100%" style="display: block; margin: auto;" />
]]

---
class: split-50
# Plots are layered

.columnl[
### You can add multiple layers

.small[

```r
ggplot(data = penguins, aes(x = sex, y = body_mass_g)) +
  geom_boxplot()
```

<img src="2 Visualizing Data_files/figure-html/unnamed-chunk-14-1.png" width="100%" style="display: block; margin: auto;" />
]]

---
class: split-50
# Plots are layered

.columnl[
### You can add multiple layers

.small[

```r
ggplot(data = penguins, aes(x = sex, y = body_mass_g)) +
  geom_boxplot() + 
* geom_point(size = 2, colour = "red")
```

<img src="2 Visualizing Data_files/figure-html/unnamed-chunk-15-1.png" width="100%" style="display: block; margin: auto;" />
]]

.columnr[

### Order matters

.small[

```r
ggplot(data = penguins, aes(x = sex, y = body_mass_g)) +
* geom_point(size = 2, colour = "red") +
  geom_boxplot()
```

<img src="2 Visualizing Data_files/figure-html/unnamed-chunk-16-1.png" width="100%" style="display: block; margin: auto;" />
]]

---
class: split-50
# Plots are objects

### Any ggplot can be saved as an object

```r
g <- ggplot(data = penguins, aes(x = sex, y = body_mass_g))
```

.columnl[

```r
g
```

<img src="2 Visualizing Data_files/figure-html/unnamed-chunk-18-1.png" width="100%" style="display: block; margin: auto;" />
]

.columnr[

```r
g + geom_boxplot()
```

<img src="2 Visualizing Data_files/figure-html/unnamed-chunk-19-1.png" width="100%" style="display: block; margin: auto;" />
]

---
class: section

# More Geoms

### (Plot types)

---
# Geoms: Lines

```r
ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm)) +
* geom_line()
```

---
# Geoms: Boxplots

```r
ggplot(data = penguins, aes(x = species, y = flipper_length_mm)) +
* geom_boxplot()
```

---
# Geoms: Histogram

```r
ggplot(data = penguins, aes(x = body_mass_g)) +
* geom_histogram(binwidth = 100)
```

![:label 50%, 18%, 25%](Note: We only need 1 aesthetic here (x&rpar;)

---
# Geoms: Barplots

### Let `ggplot` count your data

```r
ggplot(data = penguins, aes(x = sex)) +
* geom_bar()
```

---
# Geoms: Barplots

### You can also provide the counts

<code class ='r hljs remark-code'># Create our own data frame of counts species <- count(penguins, species) ggplot(data = species, aes(x = species, y = n)) + &nbsp;&nbsp;geom_bar(stat = "identity")</code>

---
# Your Turn: Create this plot

<code class ='r hljs remark-code'>library(tidyverse) ggplot(data = &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;, aes(x = &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;, y = &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;)) + &nbsp;&nbsp;geom_ &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;( &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;)</code>

![:box 25%, 15%, 25%](Extra Challenge Plot points on top)

---
exclude: TRUE
# Your Turn: Create this plot

<code class ='r hljs remark-code'>library(tidyverse) ggplot(data = penguins, aes(x = island, y = bill_depth_mm)) + &nbsp;&nbsp;geom_boxplot(colour = "blue")</code>

![:box 25%, 15%, 25%](Extra Challenge Plot points on top)

---
exclude: TRUE
# Your Turn: Create this plot (Extra Challenge)

<code class ='r hljs remark-code'>library(tidyverse) ggplot(data = penguins, aes(x = island, y = bill_depth_mm)) + &nbsp;&nbsp;geom_boxplot(colour = "blue") + &nbsp;&nbsp;geom_point()</code>

![:box 25%, 15%, 25%](Extra Challenge Plot points on top)

---
exclude: TRUE
# Your Turn: Create this plot (Extra Challenge)

![:box 25%, 15%, 25%](Extra Challenge Plot points on top)

---
class: section
# Showing data by group

---
# Mapping aesthetics

```r
ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm)) +
  geom_point()
```

---
# Mapping aesthetics

<code class ='r hljs remark-code'>ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm, colour = sex)) + &nbsp;&nbsp;geom_point()</code>

![:box 15%, 35%, 25%](<code>ggplot(&rpar;</code> automatically creates legends)

---
# Mapping aesthetics

<code class ='r hljs remark-code'>ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm, size = flipper_length_mm)) + &nbsp;&nbsp;geom_point()</code>

---
# Mapping aesthetics

<code class ='r hljs remark-code'>ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm, alpha = sex)) + &nbsp;&nbsp;geom_point()</code>

---
# Mapping aesthetics

<code class ='r hljs remark-code'>ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm, shape = species)) + &nbsp;&nbsp;geom_point()</code>

---
# Mapping aesthetics

<code class ='r hljs remark-code'>ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm, colour = sex, shape = sex)) + &nbsp;&nbsp;geom_point()</code>

![:box 15%, 35%, 25%](<code>ggplot(&rpar;</code> combines legends where it can)

---
# Faceting: `facet_wrap()`

```r
ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm, colour = sex)) +
  geom_point() +
* facet_wrap(~ species)
```

![:box 15%, 25%, 20%](Split plots by wrapping Generally for one grouping variable)

---
# Faceting: `facet_grid()`

```r
ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm, colour = sex)) +
  geom_point() +
* facet_grid(sex ~ species)
```

![:box 15%, 25%, 20%](Split plots by two grouping variables on a grid)

---
# Your Turn: Create this plot

<code class ='r hljs remark-code'>ggplot(data = &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;, aes( &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;)) + &nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; + &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</code>

![:spacer 5px]()

.medium[
> **Hint:** `colour` is for outlining with a colour, `fill` is for 'filling' with a colour  
> **Extra Challenge:** Split boxplots by sex **and** island
]

---
exclude: TRUE
# Your Turn: Create this plot

<code class ='r hljs remark-code'>ggplot(data = penguins, aes(x = sex, y = flipper_length_mm, fill = sex)) + &nbsp;&nbsp;geom_boxplot() + &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;facet_wrap(~ species)</code>

<img src="2 Visualizing Data_files/figure-html/yt_facet-flaired-a9z3qju-1.png" width="80%" style="display: block; margin: auto;" />
![:spacer 5px]()

.medium[
> **Hint:** `colour` is for outlining with a colour, `fill` is for 'filling' with a colour  
> **Extra Challenge:** Split boxplots by sex **and** island
]

---
exclude: TRUE
# Your Turn: Create this plot (Extra Challenge)

<code class ='r hljs remark-code'>ggplot(data = penguins, aes(x = sex, y = flipper_length_mm, fill = island)) + &nbsp;&nbsp;geom_boxplot() + &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;facet_wrap(~ species)</code>

<img src="2 Visualizing Data_files/figure-html/yt_facet_extra-flaired-1.png" width="80%" style="display: block; margin: auto;" />
![:spacer 5px]()

.medium[
> Small change (`fill = sex` to `fill = island`) results in completely different plot
]

---
exclude: true
class: section

# Adding Statistics to Plots

---
# Summarizing data

### Add data means as points

```r
ggplot(data = penguins, aes(x = sex, y = body_mass_g)) +
* stat_summary(geom = "point", fun = mean)
```

---
# Summarizing data

### Add error bars, calculated from the data

```r
ggplot(data = penguins, aes(x = sex, y = body_mass_g)) +
  stat_summary(geom = "point", fun = mean) +
* stat_summary(geom = "errorbar", width = 0.05, fun.data = mean_se)
```

---
class: section

# Trendlines / Regression Lines

---
# Trendlines / Regression lines

### `geom_line()` is connect-the-dots, not a trend or linear model

```r
ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm)) +
  geom_point() +
  geom_line()
```

![:box 25%, 25%, 20%](Not what we're looking for)

---
# Trendlines / Regression lines
### Let's add a trend line properly

Start with basic plot:

```r
g <- ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm)) +
 geom_point()
g
```

---
class: split-45
# Trendlines / Regression lines

.columnl[
### Add the `stat_smooth()`
- `lm` is for "linear model" (i.e. trendline)
- grey ribbon = standard error
]

.columnr[

<code class ='r hljs remark-code'>g + stat_smooth(method = "lm")</code>
]

![:spacer 55px]()

---
class: split-45
# Trendlines / Regression lines

.columnl[
### Add the `stat_smooth()`
- remove the grey ribbon `se = FALSE`
]

.columnr[

<code class ='r hljs remark-code'>g + stat_smooth(method = "lm", se = FALSE)</code>

]

![:spacer 55px]()

---
# Trendlines / Regression lines

### A line for each group
- Specify group (here we use `colour` to specify `species`)

```r
g <- ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm, colour = species)) +
 geom_point()
g
```

---
# Trendlines / Regression lines

### A line for each group
- `stat_smooth()` automatically uses the same grouping

```r
g + stat_smooth(method = "lm", se = FALSE)
```

---
# Trendlines / Regression lines

### A line for each group AND overall

```r
g +  
  stat_smooth(method = "lm", se = FALSE) + 
  stat_smooth(method = "lm", se = FALSE, colour = "black")
```

---
# Your Turn: Create this plot

- A scatter plot: **Flipper Length** by **Body Mass** grouped by **Species**
- With *a single regression line for the overall trend*

> **Extra Challenge:** Create a separate plot for each sex as well

---
exclude: TRUE

# Your Turn: Create this plot

- A scatter plot: **Flipper Length** by **Body Mass** grouped by **Species**
- With *a single regression line for the overall trend*

```r
ggplot(data = penguins, aes(x = body_mass_g, y = flipper_length_mm, colour = species)) + 
  geom_point() +
  stat_smooth(se = FALSE, colour = "black", method = "lm")
```

---
exclude: TRUE

# Your Turn: Create this plot (Extra Challenge)

```r
ggplot(data = penguins, aes(x = body_mass_g, y = flipper_length_mm, colour = species)) + 
  geom_point() +
  stat_smooth(se = FALSE, colour = "black", method = "lm") +
* facet_wrap(~sex)
```

---
class: section

# Customizing plots

---
# Customizing: Starting plot

### Let's work with this plot

```r
g <- ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm, colour = species)) +
 geom_point()
```

---
# Customizing: Labels

```r
g + labs(title = "Bill Length vs. Body Mass", 
         x = "Body Mass (g)", 
         y = "Bill Length (mm)",
         colour = "Species", tag = "A")
```

![:box 30%, 20%, 40%](Your Turn: Add proper labels to one of your previous plots)

---
class: split-50
# Customizing: Built-in themes

.columnl[
<img src="2 Visualizing Data_files/figure-html/unnamed-chunk-56-1.png" width="95%" style="display: block; margin: auto;" /><img src="2 Visualizing Data_files/figure-html/unnamed-chunk-56-2.png" width="95%" style="display: block; margin: auto;" />
]

.columnr[
<img src="2 Visualizing Data_files/figure-html/unnamed-chunk-57-1.png" width="95%" style="display: block; margin: auto;" /><img src="2 Visualizing Data_files/figure-html/unnamed-chunk-57-2.png" width="95%" style="display: block; margin: auto;" />
]

---
# Customizing: Axes

`scale_` + (`x` or `y`) + type (`contiuous`, `discrete`, `date`, `datetime`)

- `scale_x_continuous()`
- `scale_y_discrete()`
- etc.

### Common arguments

```r
g + scale_x_continuous(breaks = seq(0, 20, 10)) # Tick breaks
g + scale_x_continuous(limits = c(0, 15))       # xlim() is a shortcut for this
g + scale_x_continuous(expand = c(0, 0))        # Space between axis and data
```

---
# Customizing: Axes

### Breaks

```r
g + scale_x_continuous(breaks = seq(2500, 6500, 500))
```

---
# Customizing: Axes

### Limits

```r
g + scale_x_continuous(limits = c(3000, 4000))
```

---
# Customizing: Axes

### Space between origin and axis start

```r
g + scale_x_continuous(expand = c(0, 0))
```

---
# Customizing: Aesthetics

### Using scales

`scale_` + aesthetic (`colour`, `fill`, `size`, etc.) + type (`manual`, `continuous`, `datetime`, etc.)

```r
g + scale_colour_manual(name = "Type", values = c("green", "purple", "yellow"))
```

---
# Customizing: Aesthetics

### Using scales

Or be very explicit:

```r
g + scale_colour_manual(name = "Type", na.value = "black", values = c("Adelie" = "green", 
                                                                      "Gentoo" = "purple", 
                                                                      "Chinstrap" = "yellow"))
```

---
# Customizing: Aesthetics

### For colours, consider colour-blind-friendly scale

**`viridis_d` for "discrete" data**

<code class ='r hljs remark-code'>ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm, colour = species)) + &nbsp;&nbsp;geom_point() + &nbsp;&nbsp;scale_colour_viridis_d(name = "Type")</code>

---
# Customizing: Aesthetics

### For colours, consider colour-blind-friendly scale

**`viridis_c` for "continuous" data**

<code class ='r hljs remark-code'>ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm, colour = bill_length_mm)) + &nbsp;&nbsp;geom_point() + &nbsp;&nbsp;scale_colour_viridis_c(name = "Bill Length (mm)")</code>

---
# Customizing: Aesthetics

### Forcing

Remove the association between a variable and an aesthetic

<code class ='r hljs remark-code'>ggplot(data = penguins, aes(x = body_mass_g, y = bill_length_mm, colour = sex)) + &nbsp;&nbsp;geom_point(colour = "green", size = 5) + &nbsp;&nbsp;stat_smooth(method = "lm", se = FALSE, colour = "red")</code>

![:box 15%, 60%, 20%](Note: When forcing, aesthetic is not inside <code>aes(&rpar;<code>)

---
class: split-50
# Customizing: Legends placement

.columnl[
### At the: top, bottom, left, right

```r
g + theme(legend.position = "top")
```

<img src="2 Visualizing Data_files/figure-html/unnamed-chunk-67-1.png" width="100%" style="display: block; margin: auto;" />
]

.columnr[
### Exactly here

```r
g + theme(legend.position = c(0.15, 0.7))
```

<img src="2 Visualizing Data_files/figure-html/unnamed-chunk-68-1.png" width="100%" style="display: block; margin: auto;" />
]

---
# Your Turn: Create this plot

![:spacer 20px]()

![:box 20%, 8%, 25%](Extra Challenge Create a plot of your own data)

---
exclude: TRUE
# Your Turn: Create this plot

.small[

```r
ggplot(penguins, aes(x = body_mass_g, y = flipper_length_mm, colour = species)) +
  theme_bw() + 
  geom_point() +
  stat_smooth(method = "lm", se = FALSE, colour = "black") + 
  scale_colour_viridis_d() +
  facet_wrap(~ sex) +
  labs(x = "Body Mass (g)",
       y = "Flipper Length (mm)",
       colour = "Species")
```

<img src="2 Visualizing Data_files/figure-html/unnamed-chunk-69-1.png" width="60%" style="display: block; margin: auto;" />
]

---
class: section

# Side note: Order of operations

---
class: split-50
# Order of operations

## Remember...
- `ggplot()` is the default line (all options passed down)
- The other lines are *added* with the `+` (options only apply to this line)

---
class: split-50
# Order of operations

## Where to put the `aes()`?

**Sometimes it doesn't matter...**
.columnl[
.small[

```r
ggplot(penguins, aes(x = body_mass_g, 
                     y = flipper_length_mm, 
*                    colour = species)) +
  geom_point()
```

<img src="2 Visualizing Data_files/figure-html/unnamed-chunk-70-1.png" width="100%" style="display: block; margin: auto;" />
]]

.columnr[
.small[

```r
ggplot(penguins, aes(x = body_mass_g, 
                     y = flipper_length_mm)) +
* geom_point(aes(colour = species))
```

<img src="2 Visualizing Data_files/figure-html/unnamed-chunk-71-1.png" width="100%" style="display: block; margin: auto;" />
]]

---
class: split-50
# Order of operations

## Where to put the `aes()`?

**Sometimes it DOES matter...**
.columnl[
.small[

```r
ggplot(penguins, aes(x = body_mass_g, 
                     y = flipper_length_mm, 
*                    colour = species)) +
  geom_point() +
  stat_smooth(method = "lm")
```

<img src="2 Visualizing Data_files/figure-html/unnamed-chunk-72-1.png" width="100%" style="display: block; margin: auto;" />
]]

.columnr[
.small[

```r
ggplot(penguins, aes(x = body_mass_g, 
                     y = flipper_length_mm)) +
* geom_point(aes(colour = species)) +
  stat_smooth(method = "lm")
```

<img src="2 Visualizing Data_files/figure-html/unnamed-chunk-73-1.png" width="100%" style="display: block; margin: auto;" />
]]

![:box 75%, 25%, 45%](Applies to ALL lines in the ggplot including <code>stat_smooth</code>)

![:box 25%, 25%, 45%](Applies to only the geom_point in the ggplot not <code>stat_smooth</code>)

---
class: section

# Annotating plots

---
# Annotating

### Plot to be annotated: Let's add sample sizes

```r
ggplot(data = penguins, aes(x = body_mass_g, y = flipper_length_mm, colour = species)) +
  geom_point() +
  facet_grid(~ species)
```

---
# Annotating

### Create data to use in our annotations
(`mutate()` is covered tomorrow!)

```r
library(tidyverse)
n <- count(penguins, species)
n <- mutate(n, text = paste0("n = ", n))
n
```

```
## # A tibble: 3 × 3
## species n text 
## <fct> <int> <chr> 
## 1 Adelie 152 n = 152
## 2 Chinstrap 68 n = 68 
## 3 Gentoo 124 n = 124
```

---
# Annotating

.small[

```r
ggplot(data = penguins, aes(x = body_mass_g, y = flipper_length_mm, colour = species)) +
  geom_point() +
  facet_grid(~ species) +
* geom_text(data = n,                  # Use 'n' data set
*           x = +Inf, y = -Inf,        # Location relative to plot (right, bottom)
*           aes(label = text),         # Map 'text' to label
*           hjust = 1, vjust = 0,      # Adjust horizontal and vertical placement
*           colour = "black")          # black text
```

<img src="2 Visualizing Data_files/figure-html/unnamed-chunk-76-1.png" width="100%" style="display: block; margin: auto;" />
]

---
class: section
# Combining plots