Lab 9: ggplot2 extension packages for visualization

# Lab 9: ggplot2 extension packages for visualization
### Ramses Llobet

---

## On General Additive Models (HW3)

- I gathered some useful resources on General Additive Models. They all rely on the same package, `mgcv` for GAMs.

- This tutorial from [Noam Ross](https://noamross.github.io/gams-in-r-course/) will provide you with the basic knowledge to do EDA.
  
  
--

- This other tutorial from [Michael Clark](https://m-clark.github.io/generalized-additive-models/) is a little bit more formal, but it provides some theoretical background to complement with the previous one.
  
  
--

- In addition, find [here](https://github.com/gavinsimpson/gratia) the `gratia` package, which provides ‘ggplot’-based graphics and utility functions for working with generalized additive models (GAMs) fitted using the ‘mgcv’ package.

---

## Today's lab

- Many new extension packages for visualization

- `gghighlight`: highlighting specific lines and points while preserving main data. Examples adopted from [Hiroaki Yutani](https://cran.r-project.org/web/packages/gghighlight/vignettes/gghighlight.html) and [Datanovia](https://www.datanovia.com/en/blog/gghighlight-easy-way-to-highlight-a-ggplot-in-r/).

- `geofacet`: arranging plots in a grid that resembles original geographical orientation. Example adopted from [geofacet vignette](https://hafen.github.io/geofacet/).

- `ggridges`: visualizing multiple distributions over time or across groups. Example adopted from [Claus O. Wilke](https://cran.r-project.org/web/packages/ggridges/vignettes/gallery.html).

- `ggforce`: annotating areas by group attributes. Example taken from [Edgar Ruiz](https://rviews.rstudio.com/2019/09/19/intro-to-ggforce/).

- `gganimate`: creating animations from ggplot2 objects. Examples taken from [gganimate vignette](https://gganimate.com/index.html).

- `ggalluvial`: flow diagram to represent changes in network structure over time. Example taken from [Cory Brunson](https://github.com/corybrunson/ggalluvial).

- `flipbookr` + `xaringan`: making flipbook-style HTML slideshows. See the examples from [Evangeline Reynolds](https://evamaerey.github.io/little_flipbooks_library/about/what_the_flipbook).

---

# gghighlight

---

count: false
 
### gghighlight :: highlighting specific lines and points while preserving main data
.panel1-gghighlight-auto[

```r
*as_tibble(iris)
```
]
 
.panel2-gghighlight-auto[

```
# A tibble: 150 x 5
   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
 1          5.1         3.5          1.4         0.2 setosa 
 2          4.9         3            1.4         0.2 setosa 
 3          4.7         3.2          1.3         0.2 setosa 
 4          4.6         3.1          1.5         0.2 setosa 
 5          5           3.6          1.4         0.2 setosa 
 6          5.4         3.9          1.7         0.4 setosa 
 7          4.6         3.4          1.4         0.3 setosa 
 8          5           3.4          1.5         0.2 setosa 
 9          4.4         2.9          1.4         0.2 setosa 
10          4.9         3.1          1.5         0.1 setosa 
# ... with 140 more rows
```
]

---
count: false
 
### gghighlight :: highlighting specific lines and points while preserving main data
.panel1-gghighlight-auto[

```r
as_tibble(iris) %>%
* ggplot(aes(Sepal.Length, fill = Species))
```
]
 
.panel2-gghighlight-auto[
![](lab9_slides_files/figure-html/gghighlight_auto_02_output-1.png)
]

---
count: false
 
### gghighlight :: highlighting specific lines and points while preserving main data
.panel1-gghighlight-auto[

```r
as_tibble(iris) %>%
  ggplot(aes(Sepal.Length, fill = Species)) +
* geom_histogram(bins = 30)
```
]
 
.panel2-gghighlight-auto[
![](lab9_slides_files/figure-html/gghighlight_auto_03_output-1.png)
]

---
count: false
 
### gghighlight :: highlighting specific lines and points while preserving main data
.panel1-gghighlight-auto[

```r
as_tibble(iris) %>%
  ggplot(aes(Sepal.Length, fill = Species)) +
  geom_histogram(bins = 30) +
* scale_fill_viridis_d()
```
]
 
.panel2-gghighlight-auto[
![](lab9_slides_files/figure-html/gghighlight_auto_04_output-1.png)
]

---
count: false
 
### gghighlight :: highlighting specific lines and points while preserving main data
.panel1-gghighlight-auto[

```r
as_tibble(iris) %>%
  ggplot(aes(Sepal.Length, fill = Species)) +
  geom_histogram(bins = 30) +
  scale_fill_viridis_d() +
* gghighlight()
```
]
 
.panel2-gghighlight-auto[
![](lab9_slides_files/figure-html/gghighlight_auto_05_output-1.png)
]

---
count: false
 
### gghighlight :: highlighting specific lines and points while preserving main data
.panel1-gghighlight-auto[

```r
as_tibble(iris) %>%
  ggplot(aes(Sepal.Length, fill = Species)) +
  geom_histogram(bins = 30) +
  scale_fill_viridis_d() +
  gghighlight() +
* facet_wrap(~ Species)
```
]
 
.panel2-gghighlight-auto[
![](lab9_slides_files/figure-html/gghighlight_auto_06_output-1.png)
]

---
count: false
 
### gghighlight :: highlighting specific lines and points while preserving main data
.panel1-gghighlight-auto[

```r
as_tibble(iris) %>%
  ggplot(aes(Sepal.Length, fill = Species)) +
  geom_histogram(bins = 30) +
  scale_fill_viridis_d() +
  gghighlight() +
  facet_wrap(~ Species) +
* scale_y_continuous(expand = c(0, 0))
```
]
 
.panel2-gghighlight-auto[
![](lab9_slides_files/figure-html/gghighlight_auto_07_output-1.png)
]

---
count: false
 
### gghighlight :: highlighting specific lines and points while preserving main data
.panel1-gghighlight-auto[

```r
as_tibble(iris) %>%
  ggplot(aes(Sepal.Length, fill = Species)) +
  geom_histogram(bins = 30) +
  scale_fill_viridis_d() +
  gghighlight() +
  facet_wrap(~ Species) +
  scale_y_continuous(expand = c(0, 0)) +
* theme_minimal()
```
]
 
.panel2-gghighlight-auto[
![](lab9_slides_files/figure-html/gghighlight_auto_08_output-1.png)
]

---

count: false
 
### gghighlight :: highlighting specific lines and points while preserving main data
.panel1-gghighlight2-auto[

```r
## For each continent, find top 3 countries by avg GDP per cap
*gapminder
```
]
 
.panel2-gghighlight2-auto[

```
# A tibble: 1,704 x 6
   country     continent  year lifeExp      pop gdpPercap
   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
 1 Afghanistan Asia       1952    28.8  8425333      779.
 2 Afghanistan Asia       1957    30.3  9240934      821.
 3 Afghanistan Asia       1962    32.0 10267083      853.
 4 Afghanistan Asia       1967    34.0 11537966      836.
 5 Afghanistan Asia       1972    36.1 13079460      740.
 6 Afghanistan Asia       1977    38.4 14880372      786.
 7 Afghanistan Asia       1982    39.9 12881816      978.
 8 Afghanistan Asia       1987    40.8 13867957      852.
 9 Afghanistan Asia       1992    41.7 16317921      649.
10 Afghanistan Asia       1997    41.8 22227415      635.
# ... with 1,694 more rows
```
]

---
count: false
 
### gghighlight :: highlighting specific lines and points while preserving main data
.panel1-gghighlight2-auto[

```r
## For each continent, find top 3 countries by avg GDP per cap
gapminder %>%
* group_by(country, continent)
```
]
 
.panel2-gghighlight2-auto[

```
# A tibble: 1,704 x 6
# Groups:   country, continent [142]
   country     continent  year lifeExp      pop gdpPercap
   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
 1 Afghanistan Asia       1952    28.8  8425333      779.
 2 Afghanistan Asia       1957    30.3  9240934      821.
 3 Afghanistan Asia       1962    32.0 10267083      853.
 4 Afghanistan Asia       1967    34.0 11537966      836.
 5 Afghanistan Asia       1972    36.1 13079460      740.
 6 Afghanistan Asia       1977    38.4 14880372      786.
 7 Afghanistan Asia       1982    39.9 12881816      978.
 8 Afghanistan Asia       1987    40.8 13867957      852.
 9 Afghanistan Asia       1992    41.7 16317921      649.
10 Afghanistan Asia       1997    41.8 22227415      635.
# ... with 1,694 more rows
```
]

---
count: false
 
### gghighlight :: highlighting specific lines and points while preserving main data
.panel1-gghighlight2-auto[

```r
## For each continent, find top 3 countries by avg GDP per cap
gapminder %>%
  group_by(country, continent) %>%
* summarize(mean_gdpPercap = mean(gdpPercap))
```
]
 
.panel2-gghighlight2-auto[

```
# A tibble: 142 x 3
# Groups:   country [142]
   country     continent mean_gdpPercap
   <fct>       <fct>              <dbl>
 1 Afghanistan Asia                803.
 2 Albania     Europe             3255.
 3 Algeria     Africa             4426.
 4 Angola      Africa             3607.
 5 Argentina   Americas           8956.
 6 Australia   Oceania           19981.
 7 Austria     Europe            20412.
 8 Bahrain     Asia              18078.
 9 Bangladesh  Asia                818.
10 Belgium     Europe            19901.
# ... with 132 more rows
```
]

---
count: false
 
### gghighlight :: highlighting specific lines and points while preserving main data
.panel1-gghighlight2-auto[

```r
## For each continent, find top 3 countries by avg GDP per cap
gapminder %>%
  group_by(country, continent) %>%
  summarize(mean_gdpPercap = mean(gdpPercap)) %>%
* group_by(continent)
```
]
 
.panel2-gghighlight2-auto[

```
# A tibble: 142 x 3
# Groups:   continent [5]
   country     continent mean_gdpPercap
   <fct>       <fct>              <dbl>
 1 Afghanistan Asia                803.
 2 Albania     Europe             3255.
 3 Algeria     Africa             4426.
 4 Angola      Africa             3607.
 5 Argentina   Americas           8956.
 6 Australia   Oceania           19981.
 7 Austria     Europe            20412.
 8 Bahrain     Asia              18078.
 9 Bangladesh  Asia                818.
10 Belgium     Europe            19901.
# ... with 132 more rows
```
]

---
count: false
 
### gghighlight :: highlighting specific lines and points while preserving main data
.panel1-gghighlight2-auto[

```r
## For each continent, find top 3 countries by avg GDP per cap
gapminder %>%
  group_by(country, continent) %>%
  summarize(mean_gdpPercap = mean(gdpPercap)) %>%
  group_by(continent) %>%
* slice_max(mean_gdpPercap, n = 3)
```
]
 
.panel2-gghighlight2-auto[

```
# A tibble: 14 x 3
# Groups:   continent [5]
   country       continent mean_gdpPercap
   <fct>         <fct>              <dbl>
 1 Libya         Africa            12014.
 2 Gabon         Africa            11530.
 3 South Africa  Africa             7247.
 4 United States Americas          26261.
 5 Canada        Americas          22411.
 6 Puerto Rico   Americas          10863.
 7 Kuwait        Asia              65333.
 8 Saudi Arabia  Asia              20262.
 9 Bahrain       Asia              18078.
10 Switzerland   Europe            27074.
11 Norway        Europe            26747.
12 Netherlands   Europe            21749.
13 Australia     Oceania           19981.
14 New Zealand   Oceania           17263.
```
]

---
count: false
 
### gghighlight :: highlighting specific lines and points while preserving main data
.panel1-gghighlight2-auto[

```r
## For each continent, find top 3 countries by avg GDP per cap
gapminder %>%
  group_by(country, continent) %>%
  summarize(mean_gdpPercap = mean(gdpPercap)) %>%
  group_by(continent) %>%
  slice_max(mean_gdpPercap, n = 3) ->
* top_gdppc
```
]
 
.panel2-gghighlight2-auto[

]

---
count: false
 
### gghighlight :: highlighting specific lines and points while preserving main data
.panel1-gghighlight2-auto[

*ggplot(gapminder, aes(x = year, y = gdpPercap/1000,
*                     color = country))
```
]
 
.panel2-gghighlight2-auto[
![](lab9_slides_files/figure-html/gghighlight2_auto_07_output-1.png)
]

---
count: false
 
### gghighlight :: highlighting specific lines and points while preserving main data
.panel1-gghighlight2-auto[

ggplot(gapminder, aes(x = year, y = gdpPercap/1000,
                      color = country)) +
* geom_line(show.legend = FALSE)
```
]
 
.panel2-gghighlight2-auto[
![](lab9_slides_files/figure-html/gghighlight2_auto_08_output-1.png)
]

---
count: false
 
### gghighlight :: highlighting specific lines and points while preserving main data
.panel1-gghighlight2-auto[

ggplot(gapminder, aes(x = year, y = gdpPercap/1000,
                      color = country)) +
  geom_line(show.legend = FALSE) +
* scale_y_log10()
```
]
 
.panel2-gghighlight2-auto[
![](lab9_slides_files/figure-html/gghighlight2_auto_09_output-1.png)
]

---
count: false
 
### gghighlight :: highlighting specific lines and points while preserving main data
.panel1-gghighlight2-auto[

ggplot(gapminder, aes(x = year, y = gdpPercap/1000,
                      color = country)) +
  geom_line(show.legend = FALSE) +
  scale_y_log10() +
* facet_wrap(~ continent)
```
]
 
.panel2-gghighlight2-auto[
![](lab9_slides_files/figure-html/gghighlight2_auto_10_output-1.png)
]

---
count: false
 
### gghighlight :: highlighting specific lines and points while preserving main data
.panel1-gghighlight2-auto[

ggplot(gapminder, aes(x = year, y = gdpPercap/1000,
                      color = country)) +
  geom_line(show.legend = FALSE) +
  scale_y_log10() +
  facet_wrap(~ continent) +
* gghighlight(
*   country %in% top_gdppc$country,
*   use_group_by = FALSE,
*   label_params = list(nudge_y = 2, direction = "x",
*                       label.size = 0, label.padding = 0,
*                       box.padding = 0.5,
*                       arrow = arrow(length = unit(0.1, "cm"),
*                                     type = "open"))
* )
```
]
 
.panel2-gghighlight2-auto[
![](lab9_slides_files/figure-html/gghighlight2_auto_11_output-1.png)
]

---
count: false
 
### gghighlight :: highlighting specific lines and points while preserving main data
.panel1-gghighlight2-auto[

ggplot(gapminder, aes(x = year, y = gdpPercap/1000,
                      color = country)) +
  geom_line(show.legend = FALSE) +
  scale_y_log10() +
  facet_wrap(~ continent) +
  gghighlight(
    country %in% top_gdppc$country,
    use_group_by = FALSE,
    label_params = list(nudge_y = 2, direction = "x",
                        label.size = 0, label.padding = 0,
                        box.padding = 0.5,
                        arrow = arrow(length = unit(0.1, "cm"),
                                      type = "open"))
  ) +
* theme_minimal()
```
]
 
.panel2-gghighlight2-auto[
![](lab9_slides_files/figure-html/gghighlight2_auto_12_output-1.png)
]

---

count: false
 
### gghighlight :: highlighting specific lines and points while preserving main data
.panel1-gghighlight2-rotate[

ggplot(gapminder, aes(x = year, y = gdpPercap/1000,
                      color = country)) +
  geom_line(show.legend = FALSE) +
  scale_y_log10() +
  facet_wrap(~ continent) +
  gghighlight(
    country %in% top_gdppc$country,
    use_group_by = FALSE,
*   calculate_per_facet = FALSE,
    label_params = list(nudge_y = 2, direction = "x",
                        label.size = 0, label.padding = 0,
                        box.padding = 0.5,
                        arrow = arrow(length = unit(0.1, "cm"),
                                      type = "open"))
  ) +
  theme_minimal()
```
]
 
.panel2-gghighlight2-rotate[
![](lab9_slides_files/figure-html/gghighlight2_rotate_01_output-1.png)
]

---
count: false
 
### gghighlight :: highlighting specific lines and points while preserving main data
.panel1-gghighlight2-rotate[

ggplot(gapminder, aes(x = year, y = gdpPercap/1000,
                      color = country)) +
  geom_line(show.legend = FALSE) +
  scale_y_log10() +
  facet_wrap(~ continent) +
  gghighlight(
    country %in% top_gdppc$country,
    use_group_by = FALSE,
*   calculate_per_facet = TRUE,
    label_params = list(nudge_y = 2, direction = "x",
                        label.size = 0, label.padding = 0,
                        box.padding = 0.5,
                        arrow = arrow(length = unit(0.1, "cm"),
                                      type = "open"))
  ) +
  theme_minimal()
```
]
 
.panel2-gghighlight2-rotate[
![](lab9_slides_files/figure-html/gghighlight2_rotate_02_output-1.png)
]

---
class: middle, inverse, center

# geofacet

---

count: false
 
### geofacet :: arranging plots in a grid that resembles original geographical orientation
.panel1-geofacet-auto[

```r
*as_tibble(state_unemp)
```
]
 
.panel2-geofacet-auto[

```
# A tibble: 867 x 3
    year  rate state
   <int> <dbl> <chr>
 1  2000   4.6 AL   
 2  2001   6   AL   
 3  2002   5.8 AL   
 4  2003   6   AL   
 5  2004   5.2 AL   
 6  2005   4.2 AL   
 7  2006   3.9 AL   
 8  2007   4.4 AL   
 9  2008   8.3 AL   
10  2009  11.8 AL   
# ... with 857 more rows
```
]

---
count: false
 
### geofacet :: arranging plots in a grid that resembles original geographical orientation
.panel1-geofacet-auto[

```r
as_tibble(state_unemp) %>%
* ggplot(aes(x = year, y = rate))
```
]
 
.panel2-geofacet-auto[
![](lab9_slides_files/figure-html/geofacet_auto_02_output-1.png)
]

---
count: false
 
### geofacet :: arranging plots in a grid that resembles original geographical orientation
.panel1-geofacet-auto[

```r
as_tibble(state_unemp) %>%
  ggplot(aes(x = year, y = rate)) +
* facet_geo(~ state, grid = "us_state_grid1")
```
]
 
.panel2-geofacet-auto[
![](lab9_slides_files/figure-html/geofacet_auto_03_output-1.png)
]

---
count: false
 
### geofacet :: arranging plots in a grid that resembles original geographical orientation
.panel1-geofacet-auto[

```r
as_tibble(state_unemp) %>%
  ggplot(aes(x = year, y = rate)) +
  facet_geo(~ state, grid = "us_state_grid1") +
* geom_line(color = "steelblue")
```
]
 
.panel2-geofacet-auto[
![](lab9_slides_files/figure-html/geofacet_auto_04_output-1.png)
]

---
count: false
 
### geofacet :: arranging plots in a grid that resembles original geographical orientation
.panel1-geofacet-auto[

```r
as_tibble(state_unemp) %>%
  ggplot(aes(x = year, y = rate)) +
  facet_geo(~ state, grid = "us_state_grid1") +
  geom_line(color = "steelblue") +
* scale_x_continuous(
*   labels = function(x) paste0("'", substr(x, 3, 4))
* )
```
]
 
.panel2-geofacet-auto[
![](lab9_slides_files/figure-html/geofacet_auto_05_output-1.png)
]

---
count: false
 
### geofacet :: arranging plots in a grid that resembles original geographical orientation
.panel1-geofacet-auto[

```r
as_tibble(state_unemp) %>%
  ggplot(aes(x = year, y = rate)) +
  facet_geo(~ state, grid = "us_state_grid1") +
  geom_line(color = "steelblue") +
  scale_x_continuous(
    labels = function(x) paste0("'", substr(x, 3, 4))
  ) +
* labs(y = "Unemployment Rate (%)", x = "Year")
```
]
 
.panel2-geofacet-auto[
![](lab9_slides_files/figure-html/geofacet_auto_06_output-1.png)
]

---

count: false
 
### geofacet :: arranging plots in a grid that resembles original geographical orientation
.panel1-geofacet-rotate[

```r
as_tibble(state_unemp) %>%
  ggplot(aes(x = year, y = rate)) +
* facet_geo(~ state, grid = "us_state_grid1") +
  geom_line(color = "steelblue") +
  scale_x_continuous(
    labels = function(x) paste0("'", substr(x, 3, 4))
  ) +
  labs(y = "Unemployment Rate (%)", x = "Year")
```
]
 
.panel2-geofacet-rotate[
![](lab9_slides_files/figure-html/geofacet_rotate_01_output-1.png)
]

---
count: false
 
### geofacet :: arranging plots in a grid that resembles original geographical orientation
.panel1-geofacet-rotate[

```r
as_tibble(state_unemp) %>%
  ggplot(aes(x = year, y = rate)) +
* facet_geo(~ state, grid = "us_state_grid2") +
  geom_line(color = "steelblue") +
  scale_x_continuous(
    labels = function(x) paste0("'", substr(x, 3, 4))
  ) +
  labs(y = "Unemployment Rate (%)", x = "Year")
```
]
 
.panel2-geofacet-rotate[
![](lab9_slides_files/figure-html/geofacet_rotate_02_output-1.png)
]

---

# ggridges

---

count: false
 
### ggridges :: visualizing multiple distributions over time or across groups
.panel1-ggridges-auto[

```r
*dark_colors <- c("#D55E00", "#0072B2")
```
]
 
.panel2-ggridges-auto[

]