ODSC: Data Visualization with ggplot2

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<div class="footer-small"><span>https://github.com/mjfrigaard/talks/tree/main/odsc-eda-2022-04</span></div>

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# ODSC: Data Visualization with ggplot2
#.fancy[Part 2: Creating graphs with ggplot2]

### https://bit.ly/odsc-ggplot2

<br>

.large[by Martin Frigaard & Peter Spangler]

Written: February 08 2022

Updated: July 23 2022

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# Resources

<br>

.leftcol[

## Links:

### - [Conference Website](https://odsc.com/boston/)

### - [Website](https://mjfrigaard.github.io/odsc-ggplot2-2022/)

### - [Part 1](https://mjfrigaard.github.io/odsc-ggplot2-2022/eda-slides-01.html#1)

### - [Part 2](https://mjfrigaard.github.io/odsc-ggplot2-2022/eda-slides-02.html#1)

]

.rightcol[

## Materials:

### - [RStudio.Cloud](https://rstudio.cloud/project/3941178)

### - [Github Repo](https://github.com/mjfrigaard/odsc-ggplot2-2022/tree/gh-pages)

]

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## Outline

.leftcol[

### Part 1

.font70[

**Exploratory data analysis**

- *What is it, who does it, and why it's important*

**A Bayesian mindset**

- *Priors -> new information -> posteriors*

**The grammar of graphics**

- *Layers, aesthetics, and geoms*

]

--
 
.rightcol[

### .red[Part 2]

.font70[

**Build labels first**

- *Set expectations*

**Exercises & solutions**

- *RStudio.Cloud*

**Creating graphs**

- *Building graphs layer-by-layer, global vs. local mapping, visual encodings*

**Applying the grammar**

- *Mapping vs. setting aesthetics, combining layers, facets*

]

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# .fancy[.large[PART 2]]

#### .left[*Tip: writing code can be frustrating, especially in the beginning...*]

#### .center[*...it doesn't always produce a tangible result...*]

#### .right[*...but creating visualizations is rewarding!!!*]

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# `ggplot2`: before we start

<br>

### **Build the labels first!**

### - Create a title, subtitle (with data source), and x/y axis labels

.leftcol60[.code60[

```r
labs_pengiuns <- ggplot2::labs(
              title = "Flipper vs. Bill Length", 
              subtitle = "source: palmerpenguins::penguins", 
              x = "flipper length (mm)", 
              y = "bill length (mm)")
```

]]

.rightcol40[

.center[.large[.red[<- our expectations]]]

]

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# `ggplot2`: build graph, check labels

.leftcol40[.code50[

```r
labs_pengiuns <- ggplot2::labs(
    title = "Flipper vs. Bill Length", 
    subtitle = "source: palmerpenguins::penguins", 
    x = "flipper length (mm)", 
    y = "bill length (mm)")

ggplot(data = penguins, 
       mapping = aes(x = bill_length_mm, 
                     y = flipper_length_mm) 
       ) + 
* labs_pengiuns
```

]]

.rightcol60[

### .red[What's wrong here?]

]

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## `ggplot2`: build graph, check labels, .red[revise]

.leftcol40[.code50[

```r
labs_pengiuns <- ggplot2::labs(
    title = "Flipper vs. Bill Length", 
    subtitle = "source: palmerpenguins::penguins", 
*   x = "flipper length (mm)",
*   y = "bill length (mm)")

ggplot(data = penguins, 
*      mapping = aes(x = flipper_length_mm,
*                    y = bill_length_mm)
       ) + 
  labs_pengiuns
```

]]

.rightcol60[

## .green[FIXED!!!]

]

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## `ggplot2`: build graph, check labels, .red[REVISE]

> .blue[*Revision Sharpens Thinking: More particularly, rewriting is the key to improved thinking.*]

> .blue[*It demands a real open-mindedness and objectivity. It demands a willingness to cull verbiage so that ideas stand out clearly. And it demands a willingness to meet logical contradictions head on and trace them to the premises that have created them.*]

> .red[*In short, it forces a writer to get up his courage and expose his thinking process to his own intelligence.* — Marvin H. Swift, HBR [Clear Writing Means Clear Thinking Means…](https://hbr.org/1973/01/clear-writing-means-clear-thinking-means)]

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# **.fancy[.darkblue[.large[Exercises & Solutions]]]**

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# RStudio.Cloud: Set up

.cols3[

### In your RStudio.Cloud, you will see the following:

]

.cols3[

]

.cols3[

### Log in with your .red[GitHub] credentials

]

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# RStudio.Cloud: Set up

### On the top of the RStudio IDE, you will see the following:

<br>

<br>

### Click on .red[Save a Permanent Copy] to add this project to your workspace

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# RStudio.Cloud: Set up

### In the .blue[Files] pane, click on the `inst.R` file to open it

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# RStudio.Cloud: Set up

### In the .blue[Source] pane, click on the *Source* icon to run `inst.R`

### This sends the code in the .blue[Source] pane to the .blue[Console]

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# RStudio.Cloud: Exercises

### The exercises are in the `graphs/` folder

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# RStudio.Cloud: Solutions

### Each exercise has a solution file in `solutions/` folder

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# The data

### We're going to use the `palmerpenguins::penguins`

### - Below are three options for viewing a dataset in RStudio:

.cols3[.code90[

```r
View(penguins)
```

]]

.cols3[.code90[

```r
glimpse(penguins)
```

]]

.cols3[.code90[

```r
str(penguins)
```

]]

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# Viewing data

### `View()` opens the RStudio data viewer

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# Viewing data

### `glimpse()` and `str()` are displayed in the console

.leftcol[

]

.rightcol[

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# **.fancy[.darkblue[.large[Build from scratch, layer-by-layer]]]**

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# .red[graph 01] Step 0 = LABELS!

We want to build the labels **first**:

- title = "Bill and flipper length of Palmer penguins"
- subtitle = "Size measurements for adult foraging penguins"
- x = "Bill length (mm)"
- y = "Flipper length (mm)"

```r
# build labels 
labs_bill_vs_flippper <- ggplot2::labs(
  title = "Bill and flipper length of Palmer penguins", 
  subtitle = "Size measurements for adult foraging penguins",
  x = "Bill length (mm)", 
  y = "Flipper length (mm)")
```

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# .red[graph 01] Step 1: Initialize plot with data

.leftcol[

.small[The `ggplot2::ggplot()` function initiates the plot]

.code60[

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

]

.small[Place `penguins` in the data argument]

.code60[

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

]
]

.rightcol[

.small[This gives us a blank canvas!]

.code60[

]
]

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# .red[graph 02] Step 2: Map variables to positions

We have our data and labels--we just need to add our variables

.leftcol[

.small[Map `bill_length_mm` to `x`]

.code60[

```r
ggplot(data = penguins, 
*      mapping = aes(x = bill_length_mm,
                     )) 
```

]

.small[Map `flipper_length_mm` to `y`]

.code60[

```r
ggplot(data = penguins, 
*      mapping = aes(x = bill_length_mm,
*                    y = flipper_length_mm))
```

]
]

.rightcol[

.small[Now our canvas has `x` and `y` axes]

]

Now we have our variables on our graph!

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# .red[graph 03] Step 3: Adding geoms

.leftcol[

.small[Add the `geom_point()` function with the `+` symbol]

.code60[

```r
ggplot(data = penguins, 
       mapping = aes(x = bill_length_mm, 
                     y = flipper_length_mm)) + 
*      geom_point()
```

]
]

.rightcol[

.small[The `geom_point()` function tells R we want to see the points (or dots) on our canvas]

]

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# .red[graph 04] Step 4: Don't forget the labels!

.leftcol[

.small[Finally, we want to add the labels we created (`labs_bill_vs_flippper`)]

.code60[

```r
ggplot(data = penguins, 
       mapping = aes(x = bill_length_mm, 
                     y = flipper_length_mm)) + 
  geom_point() + 
* labs_bill_vs_flippper
```

]
]

.rightcol[

.small[And we have our first graph!]

]

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# **.fancy[.darkblue[.large[Global vs. local mapping]]]**

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# Global vs. local mapping

We've just created a graph by setting .green[global] aesthetics

.green[Global] means aesthetic mappings are set when the graph is initialized with the `ggplot()` function

.leftcol[

.code60[

```r
ggplot(data = penguins, 
*      mapping = aes(x = bill_length_mm,
*                    y = flipper_length_mm)) +
  geom_point() + 
  labs_bill_vs_flippper
```
]

.small[If we map aesthetics `ggplot()`, all the following `geom_*()` layers will inherit these aesthetics.]

]

.rightcol[

.small[Recall what goes into each layer from Part 1]

]

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# Global vs. local mapping

Mapping aesthetics .green[globally] and then adding the `geom_*()` function results in the same graph as when we map aesthetics .green[locally] (*inside* the `geom_*()` function)

.leftcol[
.code40[

```r
ggplot(data = penguins,
*      mapping = aes(x = bill_length_mm,
*                    y = flipper_length_mm)) +
* geom_point() +
  labs_bill_vs_flippper
```

]]

.rightcol[ 
.code40[

```r
ggplot(data = penguins) + 
* geom_point(
*   mapping = aes(x = bill_length_mm,
*                 y = flipper_length_mm)) +
  labs_bill_vs_flippper
```

]

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# Our `ggplot2` templates

The template from part 1 uses .green[local] mappings (i.e. aesthetic mappings are set *inside* the `geom_*` function).

```r
# Recall our template from Part 1
ggplot(data = <DATA>) +
     geom_*(mapping = aes(<AESTHETIC MAPPINGS>)) 
```

We could adjust this template to include .green[global] mappings (and the option to include aesthetic mappings .green[locally])

```r
# Adjusted template
ggplot(data = <DATA>, 
*      mapping = aes(<AESTHETIC MAPPINGS>)) + # global mappings
*      geom_*(mapping = aes(<AESTHETIC MAPPINGS>)) # local mappings
```

.small[Read more [here](https://ggplot2-book.org/collective-geoms.html#different-groups-on-different-layers).]

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# .red[graph 05] Convert .green[global] to .green[local] mappings

For .red[graph 05.R], convert the global aesthetics to local aesthetics inside the `geom_point()` function

.leftcol[

Global

.code80[

```r
ggplot(data = penguins, 
* mapping = aes(x = bill_length_mm,
*            y = flipper_length_mm)) +
  geom_point() + 
  labs_bill_vs_flippper
```

]
]

.rightcol[

Local

.code80[

```r
ggplot(data = penguins) + 
  geom_point(
*   mapping = aes(x = bill_length_mm,
*            y = flipper_length_mm)) +
  labs_bill_vs_flippper
```

]
]

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# **.fancy[.darkblue[.large[Visual encodings]]]**

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# What are visual encodings?

### Visual encodings are what we see on the graph

### Things like position, size, shape, color, etc.

### Ranked by accuracy ("generally speaking"):

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# .red[graph 06] Adding color (global)

Map `color` to the `species` variable in the scatter plot using .green[global] aesthetic mapping

.leftcol[

.code60[

```r
ggplot(data = penguins,
       mapping = 
*        aes(x = bill_length_mm,
*            y = flipper_length_mm,
*            color = species)) +
  geom_point() + 
  labs_bill_vs_flippper
```

]
]

.rightcol[

]

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# .red[graph 07] Adding color (local)

Map `color` to the `species` variable in the scatter plot using .green[local] aesthetic mapping

.leftcol[

.code60[

```r
ggplot(data = penguins,
       mapping = 
         aes(x = bill_length_mm, 
             y = flipper_length_mm)) + 
* geom_point(aes(color = species)) +
  labs_bill_vs_flippper
```

]
]

.rightcol[

.small[The `x` and `y` aesthetics are inherited from the `ggplot()` function, but the `color` aesthetic comes from the `geom_point()` layer]

]

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# .red[graph 08] Color vs. Fill

Below we'll look at the counts of `sex` vs. `species` of Palmer penguins

.leftcol[

.small[Create labels]

.code60[

```r
labs_sex_vs_species <- ggplot2::labs(
  title = "Sex by species of Palmer penguins", 
  subtitle = "Counts for adult foraging penguins",
  x = "Sex", 
  fill = "Species")
```

.small[Create `penguins_no_miss` by removing missing values]

```r
penguins_no_miss <- drop_na(data = penguins)
```

]
]

.rightcol[

.code60[

```r
penguins_no_miss
```

.small[

<div data-pagedtable="false">
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  </script>
</div>

]

]
]

---
class: left, top, inverse
background-image: url(images/ODSC_Logo_2020.png)
background-position: 97% 6%
background-size: 8%

# .red[graph 08] Color vs. Fill

Some `geom_*()` functions take the `fill` argument instead of `color`

.leftcol[

.small[Build a bar-graph using `geom_bar()` using .green[local] aesthetic mapping]

.small[Map `sex` to the `x` axis and `y` to `fill`]

.code60[

```r
ggplot(data = penguins_no_miss) + 
  geom_bar(mapping = aes(x = sex, 
*                        fill = species)) +
  labs_sex_vs_species
```

]

.small[Don't forget the labels!]

]

.rightcol[

]

---
class: left, top, inverse
background-image: url(images/ODSC_Logo_2020.png)
background-position: 97% 6%
background-size: 8%

# .red[graph 09] Bar position

Stacked bar-graphs make it difficult to do side-by-side comparisons  using the `y` axis

.leftcol[

.small[Using the same code as .red[graph 08], add the `position = "dodge"` argument *outside* the `aes()` function]

.code60[

```r
ggplot(data = penguins_no_miss) + 
  geom_bar(mapping = aes(x = sex, 
                         fill = species), 
*          position = "dodge") +
  labs_sex_vs_species
```

]
]

.rightcol[

]

---
class: left, top, inverse
background-image: url(images/ODSC_Logo_2020.png)
background-position: 97% 6%
background-size: 8%

# .red[graph 10] Histograms (special bar-graphs)

The `geom_histogram()` function uses 'bins' to represent counts for each value

.leftcol[

.small[Create new labels]

.code50[

```r
labs_bodymass_vs_species <- ggplot2::labs(
  title = "Body mass by species of Palmer penguins", 
  subtitle = "Counts for adult foraging penguins",
  x = "Body Mass (grams)", 
  fill = "Species")
```

]

.small[Create a histogram of `body_mass_g`, colored (filled) by `species`]

.code50[

```r
ggplot(data = penguins) + 
  geom_histogram(
*   mapping = aes(x = body_mass_g,
*                 fill = species)) +
  labs_bodymass_vs_species
```

]
]

.rightcol[

.small[Notice the overlapping distributions of `body_mass_g`]

]

---
class: left, top, inverse
background-image: url(images/ODSC_Logo_2020.png)
background-position: 97% 6%
background-size: 8%

# .red[graph 11] Density plots

Density plots are also great for comparing overlapping distributions

.leftcol[

.small[Create a density plot with `geom_density()`]

.small[Change the color saturation by setting .red[alpha] to `1/2`]

.code60[

```r
ggplot(data = penguins) + 
  geom_density(
    mapping = aes(x = body_mass_g, 
                  fill = species),
*   alpha = 1/2) +
  labs_bodymass_vs_species
```

]
]

.rightcol[

.small[Also check out [ridgeline plots](https://mjfrigaard.github.io/odsc-ggplot2-2022/ggplot2-graph gallery.html#74)]

]

---
class: center, middle
background-image: url(images/ODSC_Logo_2020.png)
background-position: 95% 8%
background-size: 12%

# **.fancy[.darkblue[.large[Mapping vs. setting aesthetics]]]**

---
class: left, top, inverse
background-image: url(images/ODSC_Logo_2020.png)
background-position: 97% 6%
background-size: 8%

# Mapping vs. setting

.leftcol[

.small[
The last few graphs have mapped aesthetics inside and outside the `aes()` function
]

.code40[

```r
ggplot(data = penguins_no_miss) + 
  geom_point(
    mapping = aes(x = bill_length_mm, 
                  y = flipper_length_mm,
*                 color = sex)) + # inside
  labs_bill_vs_flippper
```

]
]

.rightcol[

.small[
Inside the `aes()` function is 'mapping', outside the `aes()` function is 'setting'
]

.code40[

```r
ggplot(data = penguins_no_miss) + 
  geom_point(
    mapping = aes(x = bill_length_mm, 
                  y = flipper_length_mm),
*                 color = "dodgerblue") + # outside
  labs_bill_vs_flippper
```

]
]

---
class: left, top, inverse
background-image: url(images/ODSC_Logo_2020.png)
background-position: 97% 6%
background-size: 8%

# Mapping vs. setting

<br>

### From [`ggplot2` book](https://ggplot2-book.org/layers.html#setting-mapping)

> .large[.blue[*If you want appearance to be governed by a variable, put the specification inside `aes()`; if you want override the default size or colour, put the value outside of `aes()`.*]]

---
class: left, top, inverse
background-image: url(images/ODSC_Logo_2020.png)
background-position: 97% 6%
background-size: 8%

# .red[graph 12] Setting graph aesthetics

Change the code below to make the points `"firebrick"` red

.leftcol[

.small[Create labels]

.code50[

```r
labs_body_mass_vs_bill_depth <- ggplot2::labs(
  title = "Body mass and bill depth of Palmer penguins", 
  subtitle = "Size measurements for adult foraging penguins",
  x = "Body mass (mm)", 
  y = "Bill depth (mm)")
```

]

.small[What color will the points be on this graph?]

.code50[

```r
ggplot(data = penguins) + 
  geom_point(
    mapping = aes(x = body_mass_g, 
                  y = bill_depth_mm,
                  color = "firebrick")) + 
  labs_body_mass_vs_bill_depth
```

]
]

.rightcol[

.small[TIP: the legend tells us `geom_point()` is looking for a mapped variable in the penguins dataset named `"firebrick"`]

]

---
class: center, middle
background-image: url(images/ODSC_Logo_2020.png)
background-position: 95% 8%
background-size: 12%

# **.fancy[.darkblue[.large[Combining layers]]]**

---
class: left, top, inverse
background-image: url(images/ODSC_Logo_2020.png)
background-position: 97% 6%
background-size: 8%

# .red[graph 13] New layer, new data, no problem

Each `geom_*()` function also has a `data` argument, so we can supply new data at each layer

Create a small dataset with max values from `bill_length_mm`, `bill_depth_mm`, `flipper_length_mm` and `body_mass_g`

.leftcol[

.small[Create a dataset of the max body mass and bill depth (`big_penguins`)]

.code50[

```r
big_penguins <- bind_rows(
  slice_max(.data = penguins, bill_length_mm, n = 1),
  slice_max(.data = penguins, bill_depth_mm, n = 1),
  slice_max(.data = penguins, flipper_length_mm, n = 1),
  slice_max(.data = penguins, body_mass_g, n = 1))
```

]

.rightcol[

.small[Create data `label` and `source`]

.code40[

```r
big_penguins <- mutate(.data = big_penguins, 
        label = case_when(
   bill_length_mm == 59.6 ~ paste0("long bill = ", bill_length_mm),
   bill_depth_mm == 21.5 ~ paste0("deep bill = ", bill_depth_mm),
   flipper_length_mm == 231 ~ paste0("big flipper = ", flipper_length_mm),
   body_mass_g == 6300 ~ paste0("big bird = ", body_mass_g)),
        source = case_when(
   bill_length_mm == 59.6 ~ "max bill length",
   bill_depth_mm == 21.5 ~ "max bill depth",
   flipper_length_mm == 231 ~ "max flipper length",
   body_mass_g == 6300 ~ "max body mass"))
```

]

---
class: left, top, inverse
background-image: url(images/ODSC_Logo_2020.png)
background-position: 97% 6%
background-size: 8%

# Our label dataset

### View `label` and `source` from `big_penguins`

<div data-pagedtable="false">
  <script data-pagedtable-source type="application/json">
{"columns":[{"label":["label"],"name":[1],"type":["chr"],"align":["left"]},{"label":["source"],"name":[2],"type":["chr"],"align":["left"]}],"data":[{"1":"long bill = 59.6","2":"max bill length"},{"1":"deep bill = 21.5","2":"max bill depth"},{"1":"big flipper = 231","2":"max flipper length"},{"1":"big bird = 6300","2":"max body mass"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
  </script>
</div>

### **Objective:** Create a scatter-plot to show the relationship between body mass, flipper length, and bill length.

---
class: left, top, inverse
background-image: url(images/ODSC_Logo_2020.png)
background-position: 97% 6%
background-size: 8%

# .red[graph 13] Layer 1

Create layer 1 with `geom_point()` using `size`

.leftcol40[

.small[Create labels]

.code50[

```r
labs_bodymass_bill_depth_flipper_length <- labs(
  title = "Body mass, flipper length & bill depth", 
  subtitle = "Size measures Palmer penguins",
  x = "Bill depth (mm)", 
  y = "Flipper length (mm)", 
  size = "Body mass (g)")
```

]

.small[Add new layer with new data]

.code50[

```r
ggplot(data = penguins_no_miss) + 
  # layer 1
  geom_point(
    mapping = aes(x = bill_depth_mm, 
                  y = flipper_length_mm, 
                  size = body_mass_g), 
    alpha = 1/2)  +
  # labels
  labs_bodymass_bill_depth_flipper_length
```

]

.rightcol60[

]

---
class: left, top, inverse
background-image: url(images/ODSC_Logo_2020.png)
background-position: 97% 6%
background-size: 8%

# .red[graph 14] Layer 2

Create layer 2 with another `geom_point()` using `color` and `size`

.leftcol40[

.small[Set the `data` argument to `big_penguins`]

.small[Use `scale_size()` to adjust point scaling]

.code50[

```r
ggplot(data = penguins_no_miss) + 
  geom_point(
    mapping = aes(x = bill_depth_mm, 
                  y = flipper_length_mm, 
                  size = body_mass_g), 
    alpha = 1/2) + 
  # layer 2 
  geom_point(data = big_penguins,
             mapping = aes(x = bill_depth_mm, 
                           y = flipper_length_mm,
                           # color by source
                           color = source,
                           size = body_mass_g)) + 
  # re-scale
  scale_size(range = c(1, 5)) +
  # labels
  labs_bodymass_bill_depth_flipper_length
```

]
]

.rightcol60[

]

---
class: left, top, inverse
background-image: url(images/ODSC_Logo_2020.png)
background-position: 97% 6%
background-size: 8%

# .red[graph 15] Label 3 (max values)

Add layer 3 with the `geom_label_repel()` function from [`ggrepel`](https://cran.r-project.org/web/packages/ggrepel/vignettes/ggrepel.html)

.leftcol40[

.small[Add layer for `label`s in `big_penguins`]

.code40[

```r
ggplot(data = penguins_no_miss) + 
  geom_point(
    mapping = aes(x = bill_depth_mm, 
                  y = flipper_length_mm, 
                  size = body_mass_g), 
    alpha = 1/2) + 
  geom_point(data = big_penguins,
             mapping = aes(x = bill_depth_mm, 
                           y = flipper_length_mm,
                           color = source,
                           size = body_mass_g),
             # remove legend
             show.legend = FALSE) + 
  # rescale
  scale_size(range = c(1, 5)) +
  # layer 3 
  ggrepel::geom_label_repel(
    data = big_penguins,
    mapping = aes(x = bill_depth_mm,
                  y = flipper_length_mm,
                  label = label)) +
  # labels
  labs_bodymass_bill_depth_flipper_length
```

]
]

.rightcol60[

]

---
class: center, middle
background-image: url(images/ODSC_Logo_2020.png)
background-position: 95% 8%
background-size: 12%

# **.fancy[.darkblue[.large[Facets]]]**

---
class: left, top, inverse
background-image: url(images/ODSC_Logo_2020.png)
background-position: 97% 6%
background-size: 8%

# Facets

From [`ggplot2` book](https://ggplot2-book.org/facet.html)

> .large[.blue[*Small multiples are a powerful tool for exploratory data analysis: you can rapidly compare patterns in different parts of the data and see whether they are the same or different.*]]

---
class: left, top, inverse
background-image: url(images/ODSC_Logo_2020.png)
background-position: 97% 6%
background-size: 8%

# Facets = small multiples

In the previous graph, we used multiple aesthetics (`color`, `size`, `shape`)

Can we explore these relationships by `sex` or `species`?

.leftcol[

.small[Store graph 15 in `ggp_penguin_measures`]

.code60[

```r
ggp_penguin_measures <- ggplot(data = penguins_no_miss) + 
  geom_point(
    mapping = aes(x = bill_depth_mm, 
                  y = flipper_length_mm, 
                  size = body_mass_g), 
    alpha = 1/3) + 
  geom_point(data = big_penguins,
             mapping = aes(x = bill_depth_mm, 
                           y = flipper_length_mm,
                           color = source,
                           size = body_mass_g),
             show.legend = FALSE) + 
  scale_size(range = c(1, 5)) 
```

]
]

.rightcol[

```r
ggp_penguin_measures
```

]

---
class: left, top, inverse
background-image: url(images/ODSC_Logo_2020.png)
background-position: 97% 6%
background-size: 8%

# .red[graph 16] Facet by `sex`

Use `facet_wrap()` to view our previous graph by `sex`

.leftcol40[

.small[`facet_wrap()` uses `~ [var]` ]

.code50[

```r
ggp_penguin_measures + 
  ggrepel::geom_label_repel(
    data = big_penguins,
    mapping = aes(x = bill_depth_mm,
                  y = flipper_length_mm,
                  label = label), 
  # adjust size
              size = 2) +
  # facet by sex
  facet_wrap(. ~ sex) + 
  # labels
  labs_bodymass_bill_depth_flipper_length
```

]
]

.rightcol60[

]

---
class: left, top, inverse
background-image: url(images/ODSC_Logo_2020.png)
background-position: 97% 6%
background-size: 8%

# .red[graph 17] Facet by `species`

Change `facet_wrap()` to build graphs by `species` and add theme

.leftcol40[

.small[Change `facet_wrap()` to `~ species`]

.small[Add `theme_minimal()`]

.code50[

```r
ggp_penguin_measures +
  ggrepel::geom_label_repel(
    data = big_penguins,
    mapping = aes(x = bill_depth_mm,
                  y = flipper_length_mm,
                  label = label), 
    size = 2) +
  # change to species
  facet_wrap(. ~ species) + 
  # add theme
  theme_minimal() + 
  # labels
  labs_bodymass_bill_depth_flipper_length
```

]
]

.rightcol60[

]

---
class: center, middle
background-image: url(images/ODSC_Logo_2020.png)
background-position: 95% 8%
background-size: 12%

# **.fancy[.darkblue[.large[Recap]]]**

---
class: left, top, inverse
background-image: url(images/ODSC_Logo_2020.png)
background-position: 97% 6%
background-size: 8%

# What we've covered

.leftcol[

### 1. Build labels (set your expectations)

### 2. View data before building any graphs

### 3. Building graphs layer-by-layer (data, mapping, geom)

]

.rightcol[

### 4. Mapping variables to graph elements (color, position, size, etc)

### 5. Extending graphs by combining layers

### 6. Using facets to explore relationships

]

---
class: center, middle, inverse, no-slide-number
background-image: url(images/ODSC_Logo_2020.png)
background-position: 95% 8%
background-size: 12%

# Thanks!

.footer-large[
.right[
[@mjfrigaard <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:white;overflow:visible;position:relative;"><path d="M459.37 151.716c.325 4.548.325 9.097.325 13.645 0 138.72-105.583 298.558-298.558 298.558-59.452 0-114.68-17.219-161.137-47.106 8.447.974 16.568 1.299 25.34 1.299 49.055 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.112-72.772 6.498.974 12.995 1.624 19.818 1.624 9.421 0 18.843-1.3 27.614-3.573-48.081-9.747-84.143-51.98-84.143-102.985v-1.299c13.969 7.797 30.214 12.67 47.431 13.319-28.264-18.843-46.781-51.005-46.781-87.391 0-19.492 5.197-37.36 14.294-52.954 51.655 63.675 129.3 105.258 216.365 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.828 46.782-104.934 104.934-104.934 30.213 0 57.502 12.67 76.67 33.137 23.715-4.548 46.456-13.32 66.599-25.34-7.798 24.366-24.366 44.833-46.132 57.827 21.117-2.273 41.584-8.122 60.426-16.243-14.292 20.791-32.161 39.308-52.628 54.253z"/></svg>](http://twitter.com/mjfrigaard)<br>
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[mjfrigaard@pm.e <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:white;overflow:visible;position:relative;"><path d="M476 3.2L12.5 270.6c-18.1 10.4-15.8 35.6 2.2 43.2L121 358.4l287.3-253.2c5.5-4.9 13.3 2.6 8.6 8.3L176 407v80.5c0 23.6 28.5 32.9 42.5 15.8L282 426l124.6 52.2c14.2 6 30.4-2.9 33-18.2l72-432C515 7.8 493.3-6.8 476 3.2z"/></svg>](mailto:mjfrigaard@pm.me)<br>
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]]