Visualizing in the Tidyverse

---

## Visualizing in the Tidyverse

### Applications of Data Science - Class 4

### Giora Simchoni

#### `gsimchoni@gmail.com and add #dsapps in subject`

### Stat. and OR Department, TAU
### 2023-02-23

---

<div class="my-footer">
  <span>
    <a href="https://dsapps-2023.github.io/Class_Slides/" target="_blank">Applications of Data Science
    </a>
  </span>
</div>

---

# Welcome to `ggplot2`

#### Heavily inspired by datascienceinabox.org / @minebocek
---

## `ggplot()`

- **ggplot2** is tidyverse's data visualization package
- The `gg` in "ggplot2" stands for Grammar of Graphics
- It is inspired by the book [Grammar of Graphics](https://www.springer.com/gp/book/9780387245447) by Leland Wilkinson
- A grammar of graphics is a tool that enables us to concisely describe the components of a graphic

---

### Remember the Force, Luke!

```r
sw_tables <- read_rds("../data/sw_tables.rds")
characters <- sw_tables$characters
planets <- sw_tables$planets
films <- sw_tables$films

glimpse(characters)
```

```
## Rows: 173
## Columns: 14
## $ character_id <int> 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 4, …
## $ name         <chr> "Luke Skywalker", "Luke Skywalker", "Luke Skywalker", "Lu…
## $ gender       <chr> "male", "male", "male", "male", "male", NA, NA, NA, NA, N…
## $ homeworld_id <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8, 8, 8, 1, …
## $ height       <dbl> 172, 172, 172, 172, 172, 167, 167, 167, 167, 167, 167, 96…
## $ mass         <dbl> 77, 77, 77, 77, 77, 75, 75, 75, 75, 75, 75, 32, 32, 32, 3…
## $ hair_color   <chr> "blond", "blond", "blond", "blond", "blond", NA, NA, NA, …
## $ skin_color   <chr> "fair", "fair", "fair", "fair", "fair", "gold", "gold", "…
## $ eye_color    <chr> "blue", "blue", "blue", "blue", "blue", "yellow", "yellow…
## $ birth_year   <dbl> 19.0, 19.0, 19.0, 19.0, 19.0, 112.0, 112.0, 112.0, 112.0,…
## $ film_id      <dbl> 1, 2, 3, 6, 7, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 7, 1, …
## $ species      <list> "http://swapi.co/api/species/1/", "http://swapi.co/api/s…
## $ vehicles     <list> <"http://swapi.co/api/vehicles/14/", "http://swapi.co/ap…
## $ starships    <list> <"http://swapi.co/api/starships/12/", "http://swapi.co/a…
```

---

What are the functions doing the plotting? What is the dataset being plotted? 
Which variable is on the x-axis and which variable is on the y-axis? What does the warning mean?

```r
ggplot(data = characters, mapping = aes(x = height, y = mass)) +
  geom_point() +
  labs(title = "Mass vs. Height of Starwars characters",
       x = "Height (cm)", y = "Weight (kg)")
```

<pre style="color: red;"><code>## Warning: Removed 36 rows containing missing values (`geom_point()`).
</code></pre><img src="images/Mass-Height1-1.png" width="70%" />

---

What does `geom_smooth()` do? What else changed between the previous plot and this one?

```r
ggplot(data = characters, mapping = aes(x = height, y = mass)) +
  geom_point() +
* geom_smooth() +
  labs(title = "Mass vs. Height of Starwars characters",
       x = "Height (cm)", y = "Weight (kg)")
```

---

## Who dis!

- `ggplot()` is the main function in ggplot2 and plots are constructed in layers
- The structure of the code for plots can often be summarized as

```r
ggplot + 
  geom_xxx
```

or, more precisely

```r
ggplot(data = [dataset], mapping = aes(x = [x-variable], y = [y-variable])) +
   geom_xxx() +
   other options
```

- To use ggplot2 functions, first load tidyverse

```r
library(tidyverse)
```
- For help with the ggplot2, see [ggplot2.tidyverse.org](http://ggplot2.tidyverse.org/)

---

## Mass vs. Height

```r
ggplot(characters, aes(height, mass)) + geom_point()
```

How would you describe this relationship? What other variables would help us understand data points that don't follow the overall trend? Who is the not so tall but really chubby character?

---

## Jabba!

---

### But seriously, Luke

---

## Additional variables

We can map additional variables to various features of the plot:

- aesthetics
    - shape
    - colour
    - size
    - alpha (transparency)
- faceting: small multiples displaying different subsets

---
class: section-slide

# Aesthetics

---

## Aesthetics options

Visual characteristics of plotting characters that can be **mapped to a specific variable**
in the data are

- `color`
- `fill`
- `size`
- `shape`
- `alpha` (transparency)

---

### Mass vs. Height + Gender

```r
ggplot(data = characters,
       mapping = aes(x = height, y = mass, color = gender)) +
  geom_point()
```

---

### Mass vs. Height + Gender + Birth Year

Let's map the size to `birth_year`:

```r
ggplot(data = characters,
       mapping = aes(x = height, y = mass, color = gender, 
*                    size = birth_year)) +
  geom_point()
```

---

### Mass vs. Height + Gender

Let's now increase the size of all points **not** based on the values of a 
variable in the data:

```r
ggplot(data = characters,
       mapping = aes(x = height, y = mass, color = gender)) +
* geom_point(size = 2)
```

---

## Aesthetics Summary

- Continuous variable are measured on a continuous scale
- Discrete variables are measured (or often counted) on a discrete scale

aesthetics    | discrete                 | continuous
------------- | ------------------------ | ------------
color         | rainbow of colors        | gradient
size          | discrete steps           | linear mapping between radius and value
shape         | different shape for each | shouldn't (and doesn't) work

- Use aesthetics for mapping features of a plot to a variable, define the 
features in the geom for customization **not** mapped to a variable

---
class: section-slide

# Faceting

---

### Faceting options

- Smaller plots that display different subsets of the data
- Useful for exploring conditional relationships and large data

```r
ggplot(data = characters, mapping = aes(x = height, y = mass)) +
* facet_grid(. ~ gender) +
  geom_point() +
  labs(title = "Mass vs. Height of Starwars characters",
*      subtitle = "Faceted by Gender",
       x = "Height (cm)", y = "Weight (kg)")
```

---

### Many ways to facet

```r
ggplot(data = characters, mapping = aes(x = height, y = mass)) +
  geom_point() +
  facet_grid(gender ~ .)
```

---

```r
ggplot(data = characters, mapping = aes(x = height, y = mass)) +
  geom_point() +
  facet_wrap(. ~ gender, scales = "free")
```

---

```r
ggplot(data = characters, mapping = aes(x = height, y = mass)) +
  geom_point(data = transform(characters, gender = NULL), colour = "grey85") +
  geom_point() +
  facet_wrap(. ~ gender)
```

---

## Facet summary

- `facet_grid()`: 
    - 2d grid
    - `rows ~ cols`
    - use `.` for no split
- `facet_wrap()`: 1d ribbon wrapped into 2d

---
class: section-slide

# Take Control

---

```r
okcupid_lgbt_relig <- okcupid %>%
  group_by(religion2) %>%
  summarise(pct_lgbt = mean(orientation %in% c("gay", "bisexual"))) %>%
  mutate(religion = fct_reorder(religion2, desc(pct_lgbt)))

okcupid_lgbt_relig
```

```
## # A tibble: 7 × 3
##   religion2 pct_lgbt religion 
##   <chr>        <dbl> <fct>    
## 1 NA          0.146  NA       
## 2 atheist     0.152  atheist  
## 3 buddhist    0.210  buddhist 
## 4 christian   0.0974 christian
## 5 hindu       0.0778 hindu    
## 6 jewish      0.117  jewish   
## 7 muslim      0.101  muslim
```

---

```r
okcupid_lgbt_relig %>%
  ggplot(aes(religion, pct_lgbt)) +
  geom_bar(stat="identity")
```

---

## Pimp my plot

```r
p <- ggplot(okcupid_lgbt_relig, aes(religion, pct_lgbt)) +
  geom_bar(stat="identity", fill="#f68060", alpha=.6, width=.4) +
  labs(x = "", y = "",
       title = "OkCupid: %Reported LGBTQ+ by Religion") +
  theme(text = element_text(size = 14, color = "#163f59", family = "mono"),
        axis.text.y = element_text(size = 14)) +
  scale_y_continuous(breaks = seq(0.05, 0.2, 0.05),
                     labels=scales::percent_format(accuracy = 1)) +
  coord_flip()
p
```

---

## Pimp my plot

---

## Annotate, Add lines

```r
p + geom_hline(yintercept = 0.139, lty = 2, color = "#163f59") +
  annotate("text", x = 5, y = 0.139 + 0.03, label = "Overall %",
           color = "#163f59", family = "mono") +
  annotate(geom = "curve", x = 5, y = 0.15, xend = 4, yend = 0.139 + 0.001, 
    curvature = .3, arrow = ggplot2::arrow(length = unit(2, "mm")), color = "#163f59")
```

---
class: section-slide

# Themes

---

```r
p + theme_classic()
```

---

```r
p + theme_light()
```

---

```r
p + theme_dark()
```

---

```r
p + theme_linedraw()
```

---

```r
library(tvthemes)
# import_simpsons()
extrafont::loadfonts(device="win")

p + theme_simpsons(title.font = "Akbar",
                 text.font = "Akbar",
                 axis.text.size = 12)
```

---

```r
ggplot(okcupid_lgbt_relig, aes(religion, pct_lgbt)) +
  geom_bar(aes(fill = pct_lgbt), stat="identity", alpha=.6, width=.4) +
  labs(x = "", y = "", title = "This was supposed to be GoT...") +
  scale_y_continuous(breaks = seq(0.05, 0.2, 0.05), labels=scales::percent_format(accuracy = 1)) +
  coord_flip() +
  scale_fill_westeros(palette = "Tyrell", type = "continuous") + theme_minimal() +
  theme(text = element_text(family = "Cinzel", size = 12),
        title = element_text(family = "Cinzel", size = 14)) + guides(fill="none")
```

---
class: section-slide

# Common Plots à la GG

---

## Line Plot

```r
library(hrbrthemes)

characters %>%
  group_by(film_id) %>%
  summarise(n_char = n_distinct(character_id)) %>%
  ggplot(aes(film_id, n_char)) +
* geom_line(linewidth = 2) +
  labs(x = "", y = "No. of Characters",
       title = "Star Wars Films No. of Unique Characters") +
  scale_x_continuous(breaks = 1:7, labels = films$title) +
  theme_ipsum(base_family = "Roboto Condensed") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
```

---

---

## Histogram

```r
library(scales)

sex_names <- list(
  "m" = "men",
  "f" = "women"
)

sex_labeller <- function(variable, value){
  return(sex_names[value])
}

p2 <- okcupid %>%
  ggplot(aes(income)) +
  facet_wrap(. ~ sex, labeller = labeller(sex = sex_labeller)) +
  scale_x_log10(breaks = trans_breaks("log10", function(x) 10^x),
                labels = trans_format("log10", math_format(10^.x))) +
  labs(x = "", y = "", title = "OkCupid: Annual Income Dist. by Gender",
       caption = "Income is presented as log10(Income)") +
  theme_ipsum(base_family = "Roboto Condensed")

*p2 + geom_histogram()
```
---

---

## Density Plot

```r
p2 + geom_density()
```

---

## Box Plot

```r
p3 <- okcupid %>%
  slice_sample(n = 5000) %>%
  ggplot(aes(sex, income)) +
  scale_y_log10(breaks = trans_breaks("log10", function(x) 10^x),
                labels = trans_format("log10", math_format(10^.x))) +
  labs(x = "", y = "", title = "OkCupid: Annual Income Dist. by Gender",
       caption = "Income is presented as log10(Income)") +
  scale_x_discrete(labels = c("women", "men")) +
  theme_ipsum(base_family = "Roboto Condensed")

*p3 + geom_boxplot()
```

---

---

## Violin Plot

```r
p3 + geom_violin(fill = "darkgreen", alpha = 0.6)
```

---

## Swarm Plot

```r
p3 + ggbeeswarm::geom_quasirandom(aes(color = sex)) + guides(color="none")
```

<img src="images/Swarm-1.png" width="80%" />
---

## Bar Plot

```r
p + theme_ipsum(base_family = "Roboto Condensed")
```

<img src="images/Bar-1.png" width="100%" />
---

## Segmented Bar Plots: Counts

```r
p4 <- ggplot(
  okcupid %>%
    mutate(orientation = recode(orientation, "gay" = "lgbt", "bisexual" = "lgbt"),
           religion = fct_relevel(religion2,
                                  count(okcupid, religion2, sort = TRUE)$religion2)),
  aes(religion, fill = orientation)
  ) +
  labs(x = "", y = "",
       title = "OkCupid: Orientation by Religion") +
  scale_y_continuous(breaks = seq(0, 25000, 5000)) +
  coord_flip() +
  theme_ipsum(base_family = "Roboto Condensed") + theme(text = element_text(size = 14))
```

---

```r
p4 + geom_bar(alpha=.6, width=.4)
```

---

## Segmented Bar Plots: Proportions

```r
p4 + geom_bar(position = "fill", alpha=.6, width=.4)
```

## Mosaic Plots

```r
library(ggmosaic)

okcupid %>%
  mutate(orientation = recode(orientation, "gay" = "lgbt", "bisexual" = "lgbt"),
         religion = fct_relevel(religion2, count(okcupid, religion2, sort = TRUE)$religion2)) %>%
ggplot() +
* geom_mosaic(aes(x = product(orientation, religion), fill=orientation)) +
  labs(x = "", y = "", title = "OkCupid: Orientation by Religion") +
  guides(fill="none") +
  theme_ipsum(base_family = "Roboto Condensed") +
  theme(axis.text.y = element_text(size = 14),
        axis.text.x = element_text(angle = 90, vjust = 0.5, size = 12))
```

---

---

## Radar Chart

```r
okcupid %>%
  group_by(religion2) %>%
  summarise(drugs = mean(drugs %in% c("sometimes", "often"), na.rm = TRUE),
            smokes = mean(smokes == "yes", na.rm = TRUE),
            overweight = mean(body_type == "overweight", na.rm = TRUE),
            drinks = mean(drinks %in% c("often", "very often", "desperately"))
            ) -> okcupid_radar
okcupid_radar
```

```
## # A tibble: 7 × 5
##   religion2  drugs smokes overweight drinks
##   <chr>      <dbl>  <dbl>      <dbl>  <dbl>
## 1 NA        0.126  0.0427    0.00634 0.0911
## 2 atheist   0.201  0.0473    0.0110  0.141 
## 3 buddhist  0.160  0.0302    0.00494 0.0775
## 4 christian 0.0649 0.0352    0.00994 0.0759
## 5 hindu     0.0733 0.0236    0.00725 0.0689
## 6 jewish    0.128  0.0197    0.00467 0.0607
## 7 muslim    0.180  0.136     0.0155  0.115
```

---

```r
library(ggradar)

ggradar(okcupid_radar,  grid.max = 0.201, grid.mid = 0.10, values.radar = c("0%", "10%", "20%"),
        plot.title = "OkCupid: Health Indicators by Religion") + theme(plot.title = element_text(size = 18))
```

---

## Sankey Diagram (Alluvial Plot)

```r
library(ggalluvial)

g7 <- c("canada", "france", "germany", "italy", "japan", "united_kingdom", "united_states_of_america")

migration_2019 <- read_rds("../data/migration.rds") %>%
  pivot_longer(cols = -c(1:4), names_to = "country_orig", values_to = "n_migrants") %>%
  filter(year == 2019) %>%
  filter(if_all(c("country_orig", "country_dest"), ~.x %in% g7)) %>%
  mutate(across(c("country_orig", "country_dest"), ~case_when(.x == "united_kingdom" ~ "uk", .x == "united_states_of_america" ~ "usa", TRUE ~ .x))) %>%
  group_by(country_orig, country_dest) %>%
  summarise(n_migrants = sum(n_migrants))

migration_2019 %>% head(4)
```

```
## # A tibble: 4 × 3
## # Groups:   country_orig [1]
##   country_orig country_dest n_migrants
##   <chr>        <chr>             <dbl>
## 1 canada       canada                0
## 2 canada       france            28339
## 3 canada       germany           18211
## 4 canada       italy             25716
```

---

```r
ggplot(migration_2019,
       aes(y = n_migrants, axis1 = country_orig, axis2 = country_dest)) +
* geom_alluvium(aes(fill = country_orig)) +
  geom_stratum(width = 1/12, fill = "black", color = "grey") +
  geom_label(stat = "stratum", aes(label = after_stat(stratum))) +
  scale_x_discrete(limits = c("Origin", "Destination"), expand = c(.05, .05)) +
  scale_fill_brewer(type = "qual", palette = "Set1") +
  guides(fill = "none") +
  labs(y = "", title = "G7 Cross-Country Migration 2019") +
  scale_y_comma() +
  theme_ipsum(base_family = "Roboto Condensed")
```

---

---

## Heatmap

```r
gender_age_height_drink <- okcupid %>%
  mutate(age_group = case_when(
    age < 30 ~ "19-29",
    age < 40 ~ "30-39",
    age < 50 ~ "40-49",
    age < 60 ~ "50-59",
    TRUE ~ "60+"),
    height_group = case_when(
      height_cm < 150 ~ "149-",
      height_cm < 160 ~ "150-159",
      height_cm < 170 ~ "160-169",
      height_cm < 180 ~ "170-179",
      height_cm < 190 ~ "180-189",
      TRUE ~ "190+")) %>%
  group_by(sex, age_group, height_group) %>%
  summarise(n = n(),
            drinks = mean(drinks %in% c("often", "very often", "desperately")))
```

---

```r
gender_age_height_drink
```

```
## # A tibble: 58 × 5
## # Groups:   sex, age_group [10]
##    sex   age_group height_group     n drinks
##    <chr> <chr>     <chr>        <int>  <dbl>
##  1 f     19-29     149-           163 0.0920
##  2 f     19-29     150-159       1963 0.102 
##  3 f     19-29     160-169       5620 0.110 
##  4 f     19-29     170-179       3113 0.130 
##  5 f     19-29     180-189        310 0.145 
##  6 f     19-29     190+            19 0.474 
##  7 f     30-39     149-            80 0.0625
##  8 f     30-39     150-159       1293 0.0673
##  9 f     30-39     160-169       4109 0.0810
## 10 f     30-39     170-179       2231 0.0852
## # … with 48 more rows
```

---

```r
heat <- ggplot(gender_age_height_drink, aes(age_group, height_group, fill = drinks)) +
* geom_tile(aes(text = str_c(percent(drinks, 1), " of ", n, "    "))) +
  facet_wrap(. ~ sex, labeller = labeller(sex = sex_labeller)) +
  labs(x = "Age", y = "Height", title = "OkCupid: %Reported Drinking by Age and Height") +
  scale_fill_gradient(labels = percent_format(accuracy = 1)) +
  theme_ipsum(base_family = "Roboto Condensed")
heat
```

---

## An Actual Heat Map

```r
clean_country_name <- function(name) {
  str_replace_all(str_replace_all(str_to_lower(name), " |-", "_"), "'|,|\$|\$", "")
}

world <- as_tibble(map_data("world"))

world <- world %>%
  mutate(region = clean_country_name(region))

ggplot(world %>%
         left_join(migration_total, "region"),
       aes(x = long, y = lat, group = group)) +
  geom_polygon(aes(fill = n_migrants), colour = "white") +
  scale_fill_gradient(labels = comma, name = "N Migrants", high = "#132B43", low = "#56B1F7") +
  labs(x = "", y = "", title = "No. of Migrants by Country in 2019") +
  theme_ipsum(base_family = "Roboto Condensed")
```

---

---
class: section-slide

# Amazing Extensions

---

## `patchwork`

```r
library(patchwork)

p1 <- ggplot(okcupid %>% filter(between(age, 18, 70))) +
  geom_density(aes(age, fill = sex)) +
  labs(y = "") +
  guides(fill = "none") +
  theme_ipsum(base_family = "Roboto Condensed")

p2 <- ggplot(okcupid %>% filter(between(height_cm, 140, 200))) +
  geom_density(aes(height_cm, fill = sex)) +
  labs(y = "") +
  guides(fill = "none") +
  theme_ipsum(base_family = "Roboto Condensed")

p3 <- ggplot(okcupid) +
  geom_mosaic(aes(x = product(sex, religion2), fill = sex)) +
  labs(x = "", y = "") +
  theme_ipsum(base_family = "Roboto Condensed") +
  theme(axis.text.x = element_text(angle = 90))
```

---

Watch this:

```r
(p1 | p2) / p3
```

---

## `plotly`

Remember those heatmaps? They're now interactive!

```r
library(plotly)
ggplotly(heat, tooltip = "text")
```

<div class="plotly html-widget html-fill-item-overflow-hidden html-fill-item" id="htmlwidget-5814545c93511c6af136" style="width:100%;height:420px;"></div>
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---

## `rayshader`

Or rotatable 3D...

```r
library(rayshader)

plot_gg(heat)
```

---

## `ggfx`

This is literally from February 2021...

```r
library(ggfx)

ggplot(mtcars, aes(mpg, disp)) +
  with_shadow(geom_smooth(alpha = 1), sigma = 4) +
  with_shadow(geom_point(), sigma = 4)
```

---

## `gganimate`

Take a plot and make it better:

```r
library(gganimate)

characters %>%
  group_by(film_id) %>%
  summarise(n_char = n_distinct(character_id)) %>%
  ggplot(aes(film_id, n_char)) +
  geom_line(linewidth = 2) +
  geom_point(aes(group = seq_along(film_id)), size = 5) +
  labs(x = "", y = "No. of Characters",
       title = "No. of Unique Characters: {films$title[frame %/% 15 + 1]}") +
  scale_x_continuous(breaks = 1:7, labels = films$title) +
  theme_ipsum(base_family = "Roboto Condensed") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
* transition_reveal(along = film_id) +
  ease_aes("cubic-in-out")
```

---

---

## `magick`

That Jabba Plot was NOT made by Photoshop...

```r
library(magick)

jabba <- image_read("images/jabba.png")
fig <- image_graph(width = 2400, height = 1200, res = 300)
ggplot(data = characters, mapping = aes(x = height, y = mass)) +
  geom_point(size = 1.5) + 
  labs(title = "Mass vs. Height of Starwars characters",
       x = "Height (cm)", y = "Weight (kg)")
fig %>% image_composite(jabba, offset = "+1000+30")
```

---

## `tidygraph` + `ggraph`

```r
library(tidygraph)

characters_2_plus <- characters %>%
  add_count(name) %>%
  filter(n > 1) %>%
  select(name, film_id) %>%
  mutate(id = group_indices(., name)) # for some reason 1:n ID

nodes <- characters_2_plus %>%
  count(id, name)

edges <- characters_2_plus %>%
  inner_join(characters_2_plus, "film_id") %>%
  rename(from = id.x,
         to = id.y) %>%
  count(from, to) %>%
  rename(weight = n) %>%
  filter(from != to)
  
graph <- tbl_graph(nodes = nodes, edges = edges, directed = FALSE)
```

---

```r
library(ggraph)
ggraph(graph, layout = "fr") + 
  geom_edge_fan(aes(alpha = weight), show.legend = FALSE) + 
  geom_node_point(aes(size = n), color = "lightblue") +
  geom_node_label(aes(label = name), color = "blue", repel = TRUE, fill = "white", size = 3) +
  theme(panel.background = element_rect(fill="white", colour = "white")) +
  labs(size = "N Films", title = "Star Wars: Characters Network", caption = "An Edge between two characters means they appeared in the same film \nEdge transparency means how many films")
```

---

## `ggwithimages`

```r
library(ggwithimages)
  
annapurna <- png::readPNG(system.file("extdata", "annapurna.png", package = "ggwithimages"))
sky <- png::readPNG(system.file("extdata", "sky.png", package = "ggwithimages"))
kathmandu_hourly_aqi <- readr::read_csv(system.file("extdata", "kathmandu_hourly_aqi.csv", package = "ggwithimages"))

ggplot(kathmandu_hourly_aqi, aes(hour, aqi)) +
  geom_line_with_image(annapurna, sky) +
  labs(title = "Air Quality Index in the Thamel, Kathmandu, Nepal",
       subtitle = "Measured in PM2.5 by the US Embassy in Kathmandu",
       y = "Hourly Mean AQI [PM2.5]",
       x = "Hour") +
  ylim(c(50, 200)) +
  theme_ipsum(base_family = "Roboto Condensed")
```

---

---

## `brickr`...

```r
library(brickr)

okcupid %>%
  count(religion2) %>%
  filter(religion2 != "NA") %>%
  mutate(religion =
           fct_relevel(religion2, c("atheist", "christian", "jewish", "buddhist", "hindu", "muslim"))) %>%
  ggplot(aes(religion, n)) +
  geom_brick_col(aes(fill = religion)) +
  scale_fill_brick() +
  coord_brick() +
  theme_brick()
```

(Though this was available only on v0.2.0 😭)
---

<img src = "images/Brickr2-1.png" style = "width: 100%">
]

Get the legos you need with `build_instructions()` 😯
]

---

## And finally some aRt

(No, I mean it, these sell for $)