One (gg)plot(2) to rule them all

class: center, middle, inverse, title-slide

# One (gg)plot(2) to rule them all
## <html>
<div style="float:left">

</div>
<hr color='#EB811B' size=1px width=796px>
</html>
### Marcus Becker

---

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# Outline
<br>
## [Introduction](#intro)

<br>
####Some musings from me about learning data vis in {ggplot2}.

--
<br>
## [We're going to build a plot.](#plot)

<br>
####It will be fun.

---

class: inverse, center, middle
name: intro

# Introduction

<html><div style='float:left'></div><hr color='#EB811B' size=1px width=796px></html>

---

# What is ggplot2?

.pull-left[
+ The **gg** stands for the [**grammar of graphics**](https://link.springer.com/book/10.1007/0-387-28695-0).

+ **ggplot2** is an R package written by Hadley Wickham, and is an implementation of the book written by Leland Irving.

+ All the cool kids use it. 
]
--
.pull-right[
<img src="pics/hadley-tweet.PNG" height = 475 width = 400>
]
---

# Here's the grammar (in brief ...)

## 1. **Data** - without data, you don't have a plot!

---
count:false
# Here's the grammar (in brief ...)

#### 1. **Data** - without data, you don't have a plot!
## 2. <span style="color:#D24C39">**Mapping**</span> - linking variables to graphical properties.

---
count:false
# Here's the grammar (in brief ...)

#### 1. **Data** - without data, you don't have a plot!
#### 2. <span style="color:#D24C39">**Mapping**</span> - linking variables to graphical properties.
## 3. <span style="color:#268DC7">**Statistics**</span> - compute/transform numbers for us.

---
count:false
# Here's the grammar (in brief ...)

#### 1. **Data** - without data, you don't have a plot!
#### 2. <span style="color:#D24C39">**Mapping**</span> - linking variables to graphical properties.
#### 3. <span style="color:#268DC7">**Statistics**</span> - compute/transform numbers for us.
## 4. <span style="color:#F79A2F">**Scales**</span> - interpret values in data to graphical properties.

---
count:false
# Here's the grammar (in brief ...)

#### 1. **Data** - without data, you don't have a plot!
#### 2. <span style="color:#D24C39">**Mapping**</span> - linking variables to graphical properties.
#### 3. <span style="color:#268DC7">**Statistics**</span> - compute/transform numbers for us.
#### 4. <span style="color:#F79A2F">**Scales**</span> - interpret values in data to graphical properties.
## 5. <span style="color:#86B359">**Geometries**</span> - interpret aesthetics as graphical representations.

---
count:false
# Here's the grammar (in brief ...)

#### 1. **Data** - without data, you don't have a plot!
#### 2. <span style="color:#D24C39">**Mapping**</span> - linking variables to graphical properties.
#### 3. <span style="color:#268DC7">**Statistics**</span> - compute/transform numbers for us.
#### 4. <span style="color:#F79A2F">**Scales**</span> - interpret values in data to graphical properties.
#### 5. <span style="color:#86B359">**Geometries**</span> - interpret aesthetics as graphical representations.
## 6. <span style="color:#FFE784">**Facets**</span> - split plot into panels.

---
count:false
# Here's the grammar (in brief ...)

#### 1. **Data** - without data, you don't have a plot!
#### 2. <span style="color:#D24C39">**Mapping**</span> - linking variables to graphical properties.
#### 3. <span style="color:#268DC7">**Statistics**</span> - compute/transform numbers for us.
#### 4. <span style="color:#F79A2F">**Scales**</span> - interpret values in data to graphical properties.
#### 5. <span style="color:#86B359">**Geometries**</span> - interpret aesthetics as graphical representations.
#### 6. <span style="color:#FFE784">**Facets**</span> - split plot into panels.
## 7. <span style="color:#D03461">**Coordinates**</span> - define physical mapping.

---
count:false
# Here's the grammar (in brief ...)

#### 1. **Data** - without data, you don't have a plot!
#### 2. <span style="color:#D24C39">**Mapping**</span> - linking variables to graphical properties.
#### 3. <span style="color:#268DC7">**Statistics**</span> - compute/transform numbers for us.
#### 4. <span style="color:#F79A2F">**Scales**</span> - interpret values in data to graphical properties.
#### 5. <span style="color:#86B359">**Geometries**</span> - interpret aesthetics as graphical representations.
#### 6. <span style="color:#FFE784">**Facets**</span> - split plot into panels.
#### 7. <span style="color:#D03461">**Coordinates**</span> - define physical mapping.
## 8. <span style="color:#398E9A">**Theme**</span> - what does your plot look like?

---
count:false
# Here's the grammar (in brief ...)

#### 1. **Data** - without data, you don't have a plot!
####2. <span style="color:#D24C39">**Mapping**</span> - linking variables to graphical properties.
#### 3. <span style="color:#268DC7">**Statistics**</span> - compute/transform numbers for us.
#### 4. <span style="color:#F79A2F">**Scales**</span> - interpret values in data to graphical properties.
#### 5. <span style="color:#86B359">**Geometries**</span> - interpret aesthetics as graphical representations.
#### 6. <span style="color:#FFE784">**Facets**</span> - split plot into panels.
#### 7. <span style="color:#D03461">**Coordinates**</span> - define physical mapping.
#### 8. <span style="color:#398E9A">**Theme**</span> - what does your plot look like?

---
# My advice

### Find data you're interested in.

<br>
### Find code for plots you like, then experiment.

---
# My advice

### [You're going to suck.](https://www.youtube.com/watch?v=8SGif63VW6E&t=246s)

--
<br>
<div align="center">
<img src="pics/hadley_suck.gif" height = 400>
</div>

---
class: inverse, center, middle
name: plot

# Let's plot.

<html><div style='float:left'></div><hr color='#EB811B' size=1px width=796px></html>

---

# First, a wholesome meme.
<div align="center">

<img src="pics/mister-rogers.PNG" height = 550>
</div>
---

# Tidy Tuesday

### We'll need some data as a starting point.

--

+ Let's use a dataset from the [Tidy Tuesday](https://github.com/rfordatascience/tidytuesday)

+ Remember, find something interesting to **you**!
--

+ I'm going to choose the data on [Ultra Trail Running](https://github.com/rfordatascience/tidytuesday/blob/master/data/2021/2021-10-26/readme.md)

---

# Obtain data

```r
library(tidytuesdayR)

# Obtain data
*ultra_running <- tidytuesdayR::tt_load(x = 2021, week = 44)
```

```
## 
## 	Downloading file 1 of 2: `ultra_rankings.csv`
## 	Downloading file 2 of 2: `race.csv`
```

```r
# It's a list object, with two elements: "ultra_rankings" and "race"
names(ultra_running)
```

```
## [1] "ultra_rankings" "race"
```

```r
# Pull those two elements out into separate objects
ultra_rankings <- ultra_running[["ultra_rankings"]]
race <- ultra_running[["race"]]
```

---

# What are our questions?

1. Which countries have the most ultra trail runners?

--

2. How has participation increased over time?

--

3. Have there been increases in female participation over time?

--

4. How does the gender pace gap decrease over distance?

---
count:false
# What are our questions?

1. **Which countries have the most ultra trail runners?**

<br>
<br>
### We're going to make the data simple for this session.

---

# A quick aside

### What is [**Tidy** data](https://r4ds.had.co.nz/tidy-data.html)?

<br>
A few basic rules:
1. Each variable must have its own column.
2. Each observation must have its own row.
3. Each value must have its own cell.

--

### ggplot2 is going to expect to be fed with tidy data.

---

# Top Countries

.code[

```r
# We'll use the dplyr package for some basic wrangling.
library(dplyr)

# Using the ultra_rankings dataframe
runners_per_nation <- ultra_rankings %>%
  # Only care about the runner and nationality columns
  select(runner, nationality) %>%
  # Obtain distinct combinations
  distinct() %>%
  # Count the number of runners from each country
  group_by(nationality) %>%
  tally(name = "number") %>%
  arrange(desc(number)) %>%
  ungroup()
```
]
---

# Top Countries

.pull-left[

```r
*head(runners_per_nation, n = 15)
```
]

.pull-right[

```
## # A tibble: 15 x 2
##    nationality number
##    <chr>        <int>
##  1 USA          20345
##  2 FRA          17420
##  3 GBR           5778
##  4 JPN           3817
##  5 ESP           3587
##  6 CHN           3187
##  7 ITA           1796
##  8 CAN           1427
##  9 POL           1073
## 10 ARG           1060
## 11 SWE           1055
## 12 AUS           1024
## 13 GER            973
## 14 RUS            886
## 15 BEL            756
```

]

---

# First crack at it

.left-code[

```r
library(ggplot2)

*ggplot(
  data = runners_per_nation,
  # geom_bar() requires both 
  # and y mapped
  mapping = aes(x = nationality, 
                y = number)) +
  # Raw counts
  geom_bar(stat = "identity")
```
]
---

# First crack at it

.left-code[

```r
library(ggplot2)

ggplot(
* data = runners_per_nation,
  # geom_bar() requires both 
  # and y mapped
  mapping = aes(x = nationality, 
                y = number)) +
  # Raw counts
  geom_bar(stat = "identity")
```
]

---

# First crack at it

.left-code[

```r
library(ggplot2)

ggplot(
  data = runners_per_nation,
  # geom_bar() requires both 
  # and y mapped
* mapping = aes(x = nationality,
*               y = number)) +
  # Raw counts
  geom_bar(stat = "identity")
```
]

---

# First crack at it

.left-code[

```r
library(ggplot2)

ggplot(
  data = runners_per_nation,
  # geom_bar() requires both 
  # and y mapped
  mapping = aes(x = nationality, 
                y = number)) +
  # Raw counts 
* geom_bar(stat = "identity")
```
]

--
.right-plot[
<img src="ggplot2-intro_2021-12-16_files/figure-html/unnamed-chunk-8-1.png" width="100%" style="display: block; margin: auto;" />

]

---

# Global vs. geom-specific mapping

.left-code[

```r
ggplot(
  data = runners_per_nation) +
  geom_bar(
    mapping = aes(
*     x = nationality,
*     y = number),
    stat = "identity") 
```
]

.right-plot[
<img src="ggplot2-intro_2021-12-16_files/figure-html/unnamed-chunk-9-1.png" width="100%" style="display: block; margin: auto;" />

]

---

# Let's use geom_col() instead

.left-code[

```r
ggplot(
  data = runners_per_nation) +
* geom_col(
    mapping = aes(
      x = nationality,
      y = number))
  # Shortcut 
```
]

.right-plot[
<img src="ggplot2-intro_2021-12-16_files/figure-html/unnamed-chunk-10-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Let's look at the top 10

```r
top10 <- runners_per_nation %>%
  slice_max(number, n = 10)

*# Aside: how many runners is this of the total?

runners_per_nation %>%
  mutate(prop = round(prop.table(number), digits = 2)) %>%
  slice_max(number, n = 10) %>%
  summarise(prop_top_10 = sum(prop))
```

```
## # A tibble: 1 x 1
##   prop_top_10
##         <dbl>
## 1        0.78
```

--
<br>

78% of the runners hail from the top 10 countries.

---

# Let's look at the top 10

.left-code[

```r
*ggplot(data = top10) +
  geom_col( 
    mapping = aes(
      x = nationality,
      y = number))

# More informative!
```
]

.right-plot[
<img src="ggplot2-intro_2021-12-16_files/figure-html/unnamed-chunk-12-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Order can be a visual aid

.left-code[

```r
*library(forcats)

top10 <- top10 %>%
  mutate(
*   nationality = fct_reorder(
*     nationality,
*     number,
*     .desc = TRUE
    )
  )

ggplot(data = top10) +
  geom_col(
    mapping = aes(
      x = nationality,
      y = number))
```
]

.right-plot[
<img src="ggplot2-intro_2021-12-16_files/figure-html/unnamed-chunk-13-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Colour!

<br>
### Do we want to map a **specific property of the data** onto the *colour aesthetic*?

--

<br>
### Or, do we just want to add colour because we like it, not because the data dictates it?

--

<br>
### Both are valid!

---

# Colour aesthetic

.left-code[

```r
ggplot(data = top10) +
  geom_col( 
    mapping = aes(
      x = nationality,
      y = number,
      # Within aes()
      # Simple expression
*     colour = number > 10000))
```
]

--

.right-plot[
<img src="ggplot2-intro_2021-12-16_files/figure-html/unnamed-chunk-14-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Fill (sorry!) aesthetic

.left-code[

```r
ggplot(data = top10) +
  geom_col( 
    mapping = aes(
      x = nationality,
      y = number,
# We actually wanted fill 
# in this case
*     fill = number > 10000))
```
]

--

.right-plot[
<img src="ggplot2-intro_2021-12-16_files/figure-html/unnamed-chunk-15-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Fill aesthetic

.left-code[

```r
ggplot(data = top10) +
  geom_col( 
    mapping = aes(
      x = nationality,
      y = number,
      fill = number > 10000)) +
* scale_fill_manual(
*   values = c("steelblue",
*              "darkred")
  )
```
]

.right-plot[
<img src="ggplot2-intro_2021-12-16_files/figure-html/unnamed-chunk-16-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Fill aesthetic - continuous

.left-code[

```r
ggplot(data = top10) +
  geom_col( 
    mapping = aes(
      x = nationality,
      y = number,
*     fill = number))
```
]

--

.right-plot[
<img src="ggplot2-intro_2021-12-16_files/figure-html/unnamed-chunk-17-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Fill aesthetic - continuous

.left-code[

```r
ggplot(data = top10) +
  geom_col( 
    mapping = aes(
      x = nationality,
      y = number,
      fill = number)) +
* scale_fill_gradient(
*   low = "#c6dbef",
*   high = "#08306b"
  )
```
]

--

.right-plot[
<img src="ggplot2-intro_2021-12-16_files/figure-html/unnamed-chunk-18-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Fill aesthetic - continuous

.left-code[

```r
ggplot(data = top10) +
  geom_col( 
    mapping = aes(
      x = nationality,
      y = number,
      fill = number)) +
  # More exotic
* scale_fill_viridis_c(
*   direction = -1
  )
```
]

--

.right-plot[
<img src="ggplot2-intro_2021-12-16_files/figure-html/unnamed-chunk-19-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Considerations

<br>
### Is this colour (fill) an effective visual aid?

<br>
### Is it communicating something important to the audience?

<br>
### Is there redundancy in the plot?

---

# Fill aesthetic - discrete

.left-code[

```r
ggplot(data = top10) +
  geom_col( 
    mapping = aes(
      x = nationality,
      y = number,
      # No longer continuous
*     fill = nationality)
  )
```
]

--

.right-plot[
<img src="ggplot2-intro_2021-12-16_files/figure-html/unnamed-chunk-20-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Fill aesthetic - discrete

.left-code[

```r
ggplot(data = top10) +
  geom_col( 
    mapping = aes(
      x = nationality,
      y = number,
      fill = nationality)) +
* theme(legend.position = "none")
```
]

.right-plot[
<img src="ggplot2-intro_2021-12-16_files/figure-html/unnamed-chunk-21-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Fill aesthetic - discrete

.left-code[

```r
library(RColorBrewer)

ggplot(
  filter(top10, 
         number < 10000)) +
  geom_col( 
    mapping = aes(
      x = nationality,
      y = number,
      fill = nationality)) +
* scale_fill_brewer(
*   palette = "Dark2") +
  theme(legend.position = "none")
```
]

.right-plot[
<img src="ggplot2-intro_2021-12-16_files/figure-html/unnamed-chunk-22-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Single (fill) colour

.left-code[

```r
ggplot(data = top10) +
  geom_col( 
    mapping = aes(
      x = nationality,
      y = number,
*     fill = "steelblue"))
```
]

--

.right-plot[
<img src="ggplot2-intro_2021-12-16_files/figure-html/unnamed-chunk-23-1.png" width="100%" style="display: block; margin: auto;" />
]

--

**Crap.**

---

# Single (fill) colour - not related to data

.left-code[

```r
ggplot(data = top10) +
  geom_col( 
    mapping = aes(
      x = nationality,
*     y = number),
*     fill = "steelblue")
# No legend in this case.

# Nothing to communicate
# about the fill.
```
]

.right-plot[
<img src="ggplot2-intro_2021-12-16_files/figure-html/unnamed-chunk-24-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Part 2

OK, we have the basics of the plot. What else can we do?

--

Here are some ideas to run through:

+ Flip coordinates
+ Re-jig the axis labels and text
+ Add a title, maybe a caption
+ Explore different themes
+ Use more informative colours to tell a story
+ Add text labels
+ Improve various aesthetics

---

# Better labeling

```r
library(countrycode)

top10 <- top10 %>%
  mutate(full_nm = countrycode(nationality,
                               origin = "iso3c",
                               destination = "country.name"),
         full_nm = fct_reorder(full_nm,
                               number,
                               .desc = FALSE))

head(top10)
```

```
## # A tibble: 6 x 3
##   nationality number full_nm       
##   <fct>        <int> <fct>         
## 1 USA          20345 United States 
## 2 FRA          17420 France        
## 3 GBR           5778 United Kingdom
## 4 JPN           3817 Japan         
## 5 ESP           3587 Spain         
## 6 CHN           3187 China
```

---

# Base plot

.left-code[

```r
plot1 <- ggplot(data = top10) +
  geom_col( 
    mapping = aes(
*     x = full_nm,
      y = number))

plot1
```
]

--

.right-plot[
<img src="ggplot2-intro_2021-12-16_files/figure-html/unnamed-chunk-26-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Flip coordinates

.left-code[

```r
plot1 +
  coord_flip()
```
]

.right-plot[
<img src="ggplot2-intro_2021-12-16_files/figure-html/unnamed-chunk-27-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Add title(s), remove axis labels

.left-code[

```r
plot1 +
  coord_flip() +
* labs(
*   title = "Top 10 countries with most ultra runners",
*   subtitle = "In races held between 2012-2021.") +
* scale_y_continuous(
*   labels = scales::comma) +
* theme(
*   axis.title.x = element_blank(),
*   axis.title.y = element_blank())
```
]

.right-plot[
<img src="ggplot2-intro_2021-12-16_files/figure-html/unnamed-chunk-28-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Informative (fill) colours

```r
library(stringr)

# Maybe we want to highlight certain countries.
top10 <- top10 %>%
  mutate(group = case_when(
    str_detect(nationality, "FRA|GBR|ESP|ITA|POL") ~ "Europe",
    nationality == "CAN" ~ "Canada",
    TRUE ~ "Other"
  ))

# New base plot ...
plot2 <- ggplot(top10) +
* geom_col(aes(x = full_nm, y = number, fill = group)) +
  coord_flip() +
  labs(title = "Top 10 countries with most ultra runners",
       subtitle = "In races held between 2012-2021.") + 
  scale_y_continuous(labels = scales::comma)
```

---

# Informative (fill) colours

.left-code[

```r
plot2 + 
* scale_fill_manual(
*   values = c(
*     "Europe" = "steelblue",
*     "Canada" = "#FF0000",
*     "Other" = "grey60"),
*   name = NULL) +
  theme(
*   legend.position = "bottom",
*   axis.title = element_blank()
  )
```
]

.right-plot[
<img src="ggplot2-intro_2021-12-16_files/figure-html/unnamed-chunk-29-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# We can do better than a legend.

```r
# Let's write some cool text.
library(ggtext)

# About what?
top10 %>%
  group_by(group) %>%
  summarise(total_runners = sum(number)) %>%
  mutate(prop = prop.table(total_runners))
```

```
## # A tibble: 3 x 3
##   group  total_runners   prop
##   <chr>          <int>  <dbl>
## 1 Canada          1427 0.0240
## 2 Europe         29654 0.498 
## 3 Other          28409 0.478
```

```r
cool_text <- '<span style="color:#4682b4;">**European**</span> countries<br>account for 49.8% of all runners<br>among the top 10 countries.<br><br>There have been<br>1,427 ultra runners from <span style="color:#FF0000;">**Canada**</span>.'
```

---

# We can do better than a legend.

.left-code[

```r
plot2 + 
* annotate(
*   geom = "richtext",
*   x = 6, y = 14500,
*   label = cool_text,
*   label.colour = NA, fill = NA,
*   fontface = "bold",
*   color = "grey20",
*   size = 3.5
  ) +
  scale_fill_manual(
    values = c( 
      "Europe" = "steelblue",
      "Canada" = "#FF0000",
      "Other" = "grey60"),
    name = NULL) +
* theme_minimal() +
  theme(
*   legend.position = "none",
    axis.title = element_blank())
```
]

.right-plot[
<img src="ggplot2-intro_2021-12-16_files/figure-html/unnamed-chunk-31-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Let's try text labels.

```r
# Going to re-do the whole plot ... getting complicated!!
plot1 <- ggplot(top10) +
  geom_col(aes(x = full_nm, y = number, fill = group)) +
* geom_text(aes(y = number + 1000, x = full_nm, colour = group,
*               label = scales::comma(number)), size = 3) +
  annotate(geom = "richtext", x = 6, y = 14500, label = cool_text, label.colour = NA, 
           fill = NA, fontface = "bold", color = "grey20", size = 3.5) +
  labs(title = "Top 10 countries with most ultra runners",
       subtitle = "In races held between 2012-2021.") + 
* scale_y_continuous(expand = c(0, 150)) +
  scale_fill_manual(values = c("Europe" = "steelblue", "Canada" = "#FF0000",
                               "Other" = "grey50"), name = NULL) +
  scale_color_manual(values = c("Europe" = "steelblue", "Canada" = "#FF0000",
                                "Other" = "grey50"), name = NULL) +
  coord_flip() +
  theme_classic() +
  theme(axis.title = element_blank(),
        axis.text.x = element_blank(), axis.text.y = element_text(face = "bold"),
        legend.position = "none",
*       panel.grid = element_blank(),
*       axis.ticks = element_blank(), axis.line = element_blank())
```

---

# The product

.left-code-less[

```r
plot1
```
]

.right-plot-more[
<img src="ggplot2-intro_2021-12-16_files/figure-html/unnamed-chunk-33-1.png" width="100%" style="display: block; margin: auto;" />

]

---

# Final options

.left-code[

```r
# Let's change the fonts and add a caption
# sysfonts::font_add_google("Montserrat")
showtext::showtext_auto()

plot1 +
* labs(
*   caption = "Data from #TidyTuesday project. Plot by M.Becker.") +
* theme(
    text = element_text(
*     family = "Montserrat"))
```
]

.right-plot[
<img src="ggplot2-intro_2021-12-16_files/figure-html/unnamed-chunk-34-1.png" width="100%" style="display: block; margin: auto;" />

]

---

# gg after dark

```r
library(ggdark)
last_plot1 <- ggplot(top10) +
  geom_col(aes(x = full_nm, y = number, fill = group)) +
  geom_text(aes(y = number + 1000, x = full_nm, label = scales::comma(number)), 
*           size = 3, family = "Montserrat", color = "white") +
  annotate(geom = "richtext", x = 6, y = 14500, label = cool_text, label.colour = NA, 
*          fill = NA, fontface = "bold", color = "white", size = 3.5,
*          family = "Montserrat")
```

---

# gg after dark

```r
last_plot2 <- last_plot1 +
  labs(title = "Top 10 countries with most ultra runners",
       subtitle = "In races held between 2012-2021.",
*      caption = "Data from #TidyTuesday project. Plot by M.Becker.") +
  scale_y_continuous(expand = c(0, 150)) +
  scale_fill_manual(values = c("Europe" = "steelblue", "Canada" = "#FF0000",
                               "Other" = "grey50"), name = NULL) +
  scale_color_manual(values = c("Europe" = "steelblue", "Canada" = "#FF0000",
                                "Other" = "grey50"), name = NULL) +
  coord_flip() +
* dark_theme_classic() +
  theme(axis.title = element_blank(),
        axis.text.x = element_blank(), axis.text.y = element_text(face = "bold"),
        legend.position = "none",
        panel.grid = element_blank(),
        axis.ticks = element_blank(), axis.line = element_blank(),
*       text = element_text(family = "Montserrat", color = "white"))
```

---

# gg after dark

.left-code-less[

```r
last_plot2
```
]

.right-plot-more[
<img src="ggplot2-intro_2021-12-16_files/figure-html/unnamed-chunk-37-1.png" width="100%" style="display: block; margin: auto;" />

]