Customizing your ggplots

class: center, middle, inverse, title-slide

# Customizing your ggplots

---

class: center, middle

---
count: false

.panel1-clean-data-auto[

```r
*earn
```
]
 
.panel2-clean-data-auto[

```
## # A tibble: 4,224 x 8
##    sex     race   ethnic_origin age      year quarter n_persons median_weekly_e…
##    <chr>   <chr>  <chr>         <chr>   <dbl>   <dbl>     <dbl>            <dbl>
##  1 Both S… All R… All Origins   16 yea…  2010       1  96821000              754
##  2 Both S… All R… All Origins   16 yea…  2010       2  99798000              740
##  3 Both S… All R… All Origins   16 yea…  2010       3 101385000              740
##  4 Both S… All R… All Origins   16 yea…  2010       4 100120000              752
##  5 Both S… All R… All Origins   16 yea…  2011       1  98329000              755
##  6 Both S… All R… All Origins   16 yea…  2011       2 100593000              753
##  7 Both S… All R… All Origins   16 yea…  2011       3 101447000              753
##  8 Both S… All R… All Origins   16 yea…  2011       4 101458000              764
##  9 Both S… All R… All Origins   16 yea…  2012       1 100830000              769
## 10 Both S… All R… All Origins   16 yea…  2012       2 102769000              771
## # … with 4,214 more rows
```
]

---
count: false

.panel1-clean-data-auto[

```r
earn %>%
* mutate(median_yearly = median_weekly_earn * 52)
```
]
 
.panel2-clean-data-auto[

```
## # A tibble: 4,224 x 9
##    sex     race   ethnic_origin age      year quarter n_persons median_weekly_e…
##    <chr>   <chr>  <chr>         <chr>   <dbl>   <dbl>     <dbl>            <dbl>
##  1 Both S… All R… All Origins   16 yea…  2010       1  96821000              754
##  2 Both S… All R… All Origins   16 yea…  2010       2  99798000              740
##  3 Both S… All R… All Origins   16 yea…  2010       3 101385000              740
##  4 Both S… All R… All Origins   16 yea…  2010       4 100120000              752
##  5 Both S… All R… All Origins   16 yea…  2011       1  98329000              755
##  6 Both S… All R… All Origins   16 yea…  2011       2 100593000              753
##  7 Both S… All R… All Origins   16 yea…  2011       3 101447000              753
##  8 Both S… All R… All Origins   16 yea…  2011       4 101458000              764
##  9 Both S… All R… All Origins   16 yea…  2012       1 100830000              769
## 10 Both S… All R… All Origins   16 yea…  2012       2 102769000              771
## # … with 4,214 more rows, and 1 more variable: median_yearly <dbl>
```
]

---
count: false

.panel1-clean-data-auto[

```r
earn %>%
  mutate(median_yearly = median_weekly_earn * 52) %>%
* filter(race != "All Races")
```
]
 
.panel2-clean-data-auto[

```
## # A tibble: 1,980 x 9
##    sex     race  ethnic_origin age       year quarter n_persons median_weekly_e…
##    <chr>   <chr> <chr>         <chr>    <dbl>   <dbl>     <dbl>            <dbl>
##  1 Both S… White All Origins   16 year…  2010       1  78418000              772
##  2 Both S… White All Origins   16 year…  2010       2  80890000              756
##  3 Both S… White All Origins   16 year…  2010       3  82280000              759
##  4 Both S… White All Origins   16 year…  2010       4  81038000              772
##  5 Both S… White All Origins   16 year…  2011       1  79349000              774
##  6 Both S… White All Origins   16 year…  2011       2  81516000              770
##  7 Both S… White All Origins   16 year…  2011       3  82357000              772
##  8 Both S… White All Origins   16 year…  2011       4  82124000              786
##  9 Both S… White All Origins   16 year…  2012       1  80278000              793
## 10 Both S… White All Origins   16 year…  2012       2  82045000              792
## # … with 1,970 more rows, and 1 more variable: median_yearly <dbl>
```
]

---
count: false

.panel1-clean-data-auto[

```r
earn %>%
  mutate(median_yearly = median_weekly_earn * 52) %>%
  filter(race != "All Races") %>%
* filter(sex != "Both Sexes")
```
]
 
.panel2-clean-data-auto[

```
## # A tibble: 1,320 x 9
##    sex   race  ethnic_origin age         year quarter n_persons median_weekly_e…
##    <chr> <chr> <chr>         <chr>      <dbl>   <dbl>     <dbl>            <dbl>
##  1 Men   White All Origins   16 years …  2010       1  44040000              869
##  2 Men   White All Origins   16 years …  2010       2  45771000              838
##  3 Men   White All Origins   16 years …  2010       3  47013000              839
##  4 Men   White All Origins   16 years …  2010       4  45917000              857
##  5 Men   White All Origins   16 years …  2011       1  44712000              856
##  6 Men   White All Origins   16 years …  2011       2  46572000              850
##  7 Men   White All Origins   16 years …  2011       3  47275000              847
##  8 Men   White All Origins   16 years …  2011       4  46881000              871
##  9 Men   White All Origins   16 years …  2012       1  45755000              874
## 10 Men   White All Origins   16 years …  2012       2  46765000              891
## # … with 1,310 more rows, and 1 more variable: median_yearly <dbl>
```
]

---
count: false

.panel1-clean-data-auto[

```r
earn %>%
  mutate(median_yearly = median_weekly_earn * 52) %>%
  filter(race != "All Races") %>%
  filter(sex != "Both Sexes") %>%
* group_by(race, sex, year, quarter)
```
]
 
.panel2-clean-data-auto[

```
## # A tibble: 1,320 x 9
## # Groups:   race, sex, year, quarter [264]
##    sex   race  ethnic_origin age         year quarter n_persons median_weekly_e…
##    <chr> <chr> <chr>         <chr>      <dbl>   <dbl>     <dbl>            <dbl>
##  1 Men   White All Origins   16 years …  2010       1  44040000              869
##  2 Men   White All Origins   16 years …  2010       2  45771000              838
##  3 Men   White All Origins   16 years …  2010       3  47013000              839
##  4 Men   White All Origins   16 years …  2010       4  45917000              857
##  5 Men   White All Origins   16 years …  2011       1  44712000              856
##  6 Men   White All Origins   16 years …  2011       2  46572000              850
##  7 Men   White All Origins   16 years …  2011       3  47275000              847
##  8 Men   White All Origins   16 years …  2011       4  46881000              871
##  9 Men   White All Origins   16 years …  2012       1  45755000              874
## 10 Men   White All Origins   16 years …  2012       2  46765000              891
## # … with 1,310 more rows, and 1 more variable: median_yearly <dbl>
```
]

---
count: false

.panel1-clean-data-auto[

```r
earn %>%
  mutate(median_yearly = median_weekly_earn * 52) %>%
  filter(race != "All Races") %>%
  filter(sex != "Both Sexes") %>%
  group_by(race, sex, year, quarter) %>%
* summarize(
*   median_salary = median(median_yearly)
* )
```
]
 
.panel2-clean-data-auto[

```
## # A tibble: 264 x 5
## # Groups:   race, sex, year [66]
##    race  sex    year quarter median_salary
##    <chr> <chr> <dbl>   <dbl>         <dbl>
##  1 Asian Men    2010       1         48880
##  2 Asian Men    2010       2         46852
##  3 Asian Men    2010       3         50076
##  4 Asian Men    2010       4         49244
##  5 Asian Men    2011       1         49296
##  6 Asian Men    2011       2         50544
##  7 Asian Men    2011       3         52780
##  8 Asian Men    2011       4         49920
##  9 Asian Men    2012       1         53404
## 10 Asian Men    2012       2         56784
## # … with 254 more rows
```
]

---
count: false

.panel1-clean-data-auto[

```
## # A tibble: 132 x 5
## # Groups:   race, year [33]
##    race   year quarter   Men Women
##    <chr> <dbl>   <dbl> <dbl> <dbl>
##  1 Asian  2010       1 48880 40716
##  2 Asian  2010       2 46852 44408
##  3 Asian  2010       3 50076 40196
##  4 Asian  2010       4 49244 37388
##  5 Asian  2011       1 49296 39624
##  6 Asian  2011       2 50544 38896
##  7 Asian  2011       3 52780 37024
##  8 Asian  2011       4 49920 40092
##  9 Asian  2012       1 53404 43160
## 10 Asian  2012       2 56784 40092
## # … with 122 more rows
```
]

---
count: false

.panel1-clean-data-auto[

```
## # A tibble: 132 x 5
## # Groups:   race [3]
##    race   year quarter   Men Women
##    <chr> <dbl>   <dbl> <dbl> <dbl>
##  1 Asian  2010       1 48880 40716
##  2 Asian  2010       2 46852 44408
##  3 Asian  2010       3 50076 40196
##  4 Asian  2010       4 49244 37388
##  5 Asian  2011       1 49296 39624
##  6 Asian  2011       2 50544 38896
##  7 Asian  2011       3 52780 37024
##  8 Asian  2011       4 49920 40092
##  9 Asian  2012       1 53404 43160
## 10 Asian  2012       2 56784 40092
## # … with 122 more rows
```
]

---
count: false

.panel1-clean-data-auto[

```r
earn %>%
  mutate(median_yearly = median_weekly_earn * 52) %>%
  filter(race != "All Races") %>%
  filter(sex != "Both Sexes") %>%
  group_by(race, sex, year, quarter) %>%
  summarize(
    median_salary = median(median_yearly)
  ) %>%
  pivot_wider(id_cols = c("race", "year", "quarter"), names_from = sex, values_from = median_salary) %>%
  group_by(race) %>%
* mutate(
*   time = paste(year, quarter, sep = "-Q"),
*   men_dev = Men - (Men + Women)/2,
*   women_dev = Women - (Men + Women)/2,
*   race = factor(race, levels = c("Asian", "White", "Black or African American"))
* )
```
]
 
.panel2-clean-data-auto[

```
## # A tibble: 132 x 8
## # Groups:   race [3]
##    race   year quarter   Men Women time    men_dev women_dev
##    <fct> <dbl>   <dbl> <dbl> <dbl> <chr>     <dbl>     <dbl>
##  1 Asian  2010       1 48880 40716 2010-Q1    4082     -4082
##  2 Asian  2010       2 46852 44408 2010-Q2    1222     -1222
##  3 Asian  2010       3 50076 40196 2010-Q3    4940     -4940
##  4 Asian  2010       4 49244 37388 2010-Q4    5928     -5928
##  5 Asian  2011       1 49296 39624 2011-Q1    4836     -4836
##  6 Asian  2011       2 50544 38896 2011-Q2    5824     -5824
##  7 Asian  2011       3 52780 37024 2011-Q3    7878     -7878
##  8 Asian  2011       4 49920 40092 2011-Q4    4914     -4914
##  9 Asian  2012       1 53404 43160 2012-Q1    5122     -5122
## 10 Asian  2012       2 56784 40092 2012-Q2    8346     -8346
## # … with 122 more rows
```
]

---
count: false

.panel1-clean-data-auto[

```r
earn %>%
  mutate(median_yearly = median_weekly_earn * 52) %>%
  filter(race != "All Races") %>%
  filter(sex != "Both Sexes") %>%
  group_by(race, sex, year, quarter) %>%
  summarize(
    median_salary = median(median_yearly)
  ) %>%
  pivot_wider(id_cols = c("race", "year", "quarter"), names_from = sex, values_from = median_salary) %>%
  group_by(race) %>%
  mutate(
    time = paste(year, quarter, sep = "-Q"),
    men_dev = Men - (Men + Women)/2,
    women_dev = Women - (Men + Women)/2,
    race = factor(race, levels = c("Asian", "White", "Black or African American"))
  ) ->
*earn_clean
```
]
 
.panel2-clean-data-auto[

]

---
class: center, inverse, middle

# Customization Basics

---
count: false

.panel1-base-plot-rotate[

```r
ggplot(earn_clean, aes(x = time, ymin = women_dev, ymax = men_dev, fill = race, group = race)) +
  geom_ribbon() +
* theme_bw()
```
]
 
.panel2-base-plot-rotate[
![](plots/base-plot_rotate_01_output-1.png)
]

---
count: false

.panel1-base-plot-rotate[

```r
ggplot(earn_clean, aes(x = time, ymin = women_dev, ymax = men_dev, fill = race, group = race)) +
  geom_ribbon() +
* theme_dark()
```
]
 
.panel2-base-plot-rotate[
![](plots/base-plot_rotate_02_output-1.png)
]

---
count: false

.panel1-base-plot-rotate[

```r
ggplot(earn_clean, aes(x = time, ymin = women_dev, ymax = men_dev, fill = race, group = race)) +
  geom_ribbon() +
* theme_classic()
```
]
 
.panel2-base-plot-rotate[
![](plots/base-plot_rotate_03_output-1.png)
]

---
count: false

.panel1-base-plot-rotate[

```r
ggplot(earn_clean, aes(x = time, ymin = women_dev, ymax = men_dev, fill = race, group = race)) +
  geom_ribbon() +
* theme_calc()
```
]
 
.panel2-base-plot-rotate[
![](plots/base-plot_rotate_04_output-1.png)
]

---
count: false

.panel1-base-plot-rotate[

```r
ggplot(earn_clean, aes(x = time, ymin = women_dev, ymax = men_dev, fill = race, group = race)) +
  geom_ribbon() +
* theme_clean()
```
]
 
.panel2-base-plot-rotate[
![](plots/base-plot_rotate_05_output-1.png)
]

---
count: false

.panel1-base-plot-rotate[

```r
ggplot(earn_clean, aes(x = time, ymin = women_dev, ymax = men_dev, fill = race, group = race)) +
  geom_ribbon() +
* theme_economist()
```
]
 
.panel2-base-plot-rotate[
![](plots/base-plot_rotate_06_output-1.png)
]

---
count: false

.panel1-base-plot-rotate[

```r
ggplot(earn_clean, aes(x = time, ymin = women_dev, ymax = men_dev, fill = race, group = race)) +
  geom_ribbon() +
* theme_few()
```
]
 
.panel2-base-plot-rotate[
![](plots/base-plot_rotate_07_output-1.png)
]

---
count: false

.panel1-base-plot-rotate[

```r
ggplot(earn_clean, aes(x = time, ymin = women_dev, ymax = men_dev, fill = race, group = race)) +
  geom_ribbon() +
* theme_gdocs()
```
]
 
.panel2-base-plot-rotate[
![](plots/base-plot_rotate_08_output-1.png)
]

---
count: false

.panel1-base-plot-rotate[

```r
ggplot(earn_clean, aes(x = time, ymin = women_dev, ymax = men_dev, fill = race, group = race)) +
  geom_ribbon() +
* theme_hc()
```
]
 
.panel2-base-plot-rotate[
![](plots/base-plot_rotate_09_output-1.png)
]

---
count: false

.panel1-base-plot-rotate[

```r
ggplot(earn_clean, aes(x = time, ymin = women_dev, ymax = men_dev, fill = race, group = race)) +
  geom_ribbon() +
* theme_pander()
```
]
 
.panel2-base-plot-rotate[
![](plots/base-plot_rotate_10_output-1.png)
]

---
count: false

.panel1-base-plot-rotate[

```r
ggplot(earn_clean, aes(x = time, ymin = women_dev, ymax = men_dev, fill = race, group = race)) +
  geom_ribbon() +
* theme_solarized()
```
]
 
.panel2-base-plot-rotate[
![](plots/base-plot_rotate_11_output-1.png)
]

---
count: false

.panel1-base-plot-rotate[

```r
ggplot(earn_clean, aes(x = time, ymin = women_dev, ymax = men_dev, fill = race, group = race)) +
  geom_ribbon() +
* theme_solarized(light = FALSE)
```
]
 
.panel2-base-plot-rotate[
![](plots/base-plot_rotate_12_output-1.png)
]

---
count: false

.panel1-base-plot-rotate[

```r
ggplot(earn_clean, aes(x = time, ymin = women_dev, ymax = men_dev, fill = race, group = race)) +
  geom_ribbon() +
* theme_tufte()
```
]
 
.panel2-base-plot-rotate[
![](plots/base-plot_rotate_13_output-1.png)
]

---
count: false

.panel1-base-plot-rotate[

```r
ggplot(earn_clean, aes(x = time, ymin = women_dev, ymax = men_dev, fill = race, group = race)) +
  geom_ribbon() +
* theme_wsj()
```
]
 
.panel2-base-plot-rotate[
![](plots/base-plot_rotate_14_output-1.png)
]

---
count: false

.panel1-base-plot-rotate[

```r
ggplot(earn_clean, aes(x = time, ymin = women_dev, ymax = men_dev, fill = race, group = race)) +
  geom_ribbon() +
* theme_fivethirtyeight()
```
]
 
.panel2-base-plot-rotate[
![](plots/base-plot_rotate_15_output-1.png)
]

---
count: false

.panel1-base-plot-rotate[

```r
ggplot(earn_clean, aes(x = time, ymin = women_dev, ymax = men_dev, fill = race, group = race)) +
  geom_ribbon() +
* theme_excel()
```
]
 
.panel2-base-plot-rotate[
![](plots/base-plot_rotate_16_output-1.png)
]

---
count: false

.panel1-base-plot-rotate[

```r
ggplot(earn_clean, aes(x = time, ymin = women_dev, ymax = men_dev, fill = race, group = race)) +
  geom_ribbon() +
* theme_minimal()
```
]
 
.panel2-base-plot-rotate[
![](plots/base-plot_rotate_17_output-1.png)
]

---
count: false

.panel1-change-color-rotate[

```r
ggplot(earn_clean, aes(x = time, ymin = women_dev, ymax = men_dev, fill = race, group = race)) +
  geom_ribbon() +
  theme_minimal() +
* scale_fill_brewer()
```
]
 
.panel2-change-color-rotate[
![](plots/change-color_rotate_01_output-1.png)
]

---
count: false

.panel1-change-color-rotate[

```r
ggplot(earn_clean, aes(x = time, ymin = women_dev, ymax = men_dev, fill = race, group = race)) +
  geom_ribbon() +
  theme_minimal() +
* scale_fill_brewer(palette = "Greens")
```
]
 
.panel2-change-color-rotate[
![](plots/change-color_rotate_02_output-1.png)
]

---
count: false

.panel1-change-color-rotate[

```r
ggplot(earn_clean, aes(x = time, ymin = women_dev, ymax = men_dev, fill = race, group = race)) +
  geom_ribbon() +
  theme_minimal() +
* scale_fill_brewer(palette = "Set1")
```
]
 
.panel2-change-color-rotate[
![](plots/change-color_rotate_03_output-1.png)
]

---
count: false

.panel1-change-color-rotate[

```r
ggplot(earn_clean, aes(x = time, ymin = women_dev, ymax = men_dev, fill = race, group = race)) +
  geom_ribbon() +
  theme_minimal() +
* scale_fill_brewer(palette = "Spectral")
```
]
 
.panel2-change-color-rotate[
![](plots/change-color_rotate_04_output-1.png)
]

---
count: false

.panel1-change-color-rotate[

```r
ggplot(earn_clean, aes(x = time, ymin = women_dev, ymax = men_dev, fill = race, group = race)) +
  geom_ribbon() +
  theme_minimal() +
* scale_fill_brewer(palette = "Dark2")
```
]
 
.panel2-change-color-rotate[
![](plots/change-color_rotate_05_output-1.png)
]

---
count: false

.panel1-change-color-rotate[

```r
ggplot(earn_clean, aes(x = time, ymin = women_dev, ymax = men_dev, fill = race, group = race)) +
  geom_ribbon() +
  theme_minimal() +
* scale_fill_viridis_d()
```
]
 
.panel2-change-color-rotate[
![](plots/change-color_rotate_06_output-1.png)
]

---
count: false

.panel1-change-color-rotate[

```r
ggplot(earn_clean, aes(x = time, ymin = women_dev, ymax = men_dev, fill = race, group = race)) +
  geom_ribbon() +
  theme_minimal() +
* scale_fill_viridis_d(option = "plasma")
```
]
 
.panel2-change-color-rotate[
![](plots/change-color_rotate_07_output-1.png)
]

---
count: false

.panel1-change-color-rotate[

```r
ggplot(earn_clean, aes(x = time, ymin = women_dev, ymax = men_dev, fill = race, group = race)) +
  geom_ribbon() +
  theme_minimal() +
* scale_fill_viridis_d(option = "inferno")
```
]
 
.panel2-change-color-rotate[
![](plots/change-color_rotate_08_output-1.png)
]

---
count: false

.panel1-change-color-rotate[

```r
ggplot(earn_clean, aes(x = time, ymin = women_dev, ymax = men_dev, fill = race, group = race)) +
  geom_ribbon() +
  theme_minimal() +
* scale_fill_viridis_d(option = "magma")
```
]
 
.panel2-change-color-rotate[
![](plots/change-color_rotate_09_output-1.png)
]

---
count: false

.panel1-change-color-rotate[

```r
ggplot(earn_clean, aes(x = time, ymin = women_dev, ymax = men_dev, fill = race, group = race)) +
  geom_ribbon() +
  theme_minimal() +
* scale_fill_viridis_d(option = "magma", end = .75)
```
]
 
.panel2-change-color-rotate[
![](plots/change-color_rotate_10_output-1.png)
]

---
count: false

.panel1-change-color-rotate[

```r
ggplot(earn_clean, aes(x = time, ymin = women_dev, ymax = men_dev, fill = race, group = race)) +
  geom_ribbon() +
  theme_minimal() +
* scale_fill_manual(values = c("#73434B", "#2F3659", "#96ACD9"))
```
]
 
.panel2-change-color-rotate[
![](plots/change-color_rotate_11_output-1.png)
]

---
class: center, inverse, middle

# Even MORE customization

---
count: false

.panel1-even-more-auto[

```r
*ggplot(earn_clean, aes(x = time, ymin = women_dev, ymax = men_dev, fill = race, group = race))
```
]
 
.panel2-even-more-auto[
![](plots/even-more_auto_01_output-1.png)
]

---
count: false

.panel1-even-more-auto[

```r
ggplot(earn_clean, aes(x = time, ymin = women_dev, ymax = men_dev, fill = race, group = race)) +
* geom_ribbon()
```
]
 
.panel2-even-more-auto[
![](plots/even-more_auto_02_output-1.png)
]

---
count: false

.panel1-even-more-auto[

```r
ggplot(earn_clean, aes(x = time, ymin = women_dev, ymax = men_dev, fill = race, group = race)) +
  geom_ribbon() +
* theme_minimal()
```
]
 
.panel2-even-more-auto[
![](plots/even-more_auto_03_output-1.png)
]

---
count: false

.panel1-even-more-auto[

```r
ggplot(earn_clean, aes(x = time, ymin = women_dev, ymax = men_dev, fill = race, group = race)) +
  geom_ribbon() +
  theme_minimal() +
* scale_fill_manual(values = c("#73434B", "#2F3659", "#96ACD9"))
```
]
 
.panel2-even-more-auto[
![](plots/even-more_auto_04_output-1.png)
]

---
count: false

.panel1-even-more-auto[

---
count: false

.panel1-even-more-auto[

---
count: false

.panel1-even-more-auto[

---
count: false

.panel1-even-more-auto[

---
count: false

.panel1-even-more-auto[

---
count: false

.panel1-even-more-auto[

---
count: false

.panel1-even-more-auto[

---
count: false

.panel1-even-more-auto[

---
count: false

.panel1-even-more-auto[

```r
ggplot(earn_clean, aes(x = time, ymin = women_dev, ymax = men_dev, fill = race, group = race)) +
  geom_ribbon() +
  theme_minimal() +
  scale_fill_manual(values = c("#73434B", "#2F3659", "#96ACD9")) +
  labs(
    title = "The gender wage gap persists across <span style='color:#73434B;'>**Asian**</span>, <span style='color:#2F3659;'>**White**</span>, and <span style='color:#96ACD9;'>**Black**</span> Americans",
    x = "",
    y = "",
    caption = "Source: BLS Earnings Data"
  ) +
  theme(legend.position = "none") +
  theme(axis.text.x = element_blank()) +
  theme(axis.text.y = element_blank()) +
  theme(plot.title = element_markdown()) +
  theme(panel.grid.minor = element_blank()) +
  theme(panel.grid.major.x = element_blank()) +
  theme(text = element_text(family = "serif")) +
* annotate("errorbar", x = "2009",  ymin = -5000, ymax = 5000, col = "darkgray")
```
]
 
.panel2-even-more-auto[
![](plots/even-more_auto_13_output-1.png)
]

---
count: false

.panel1-even-more-auto[

```r
ggplot(earn_clean, aes(x = time, ymin = women_dev, ymax = men_dev, fill = race, group = race)) +
  geom_ribbon() +
  theme_minimal() +
  scale_fill_manual(values = c("#73434B", "#2F3659", "#96ACD9")) +
  labs(
    title = "The gender wage gap persists across <span style='color:#73434B;'>**Asian**</span>, <span style='color:#2F3659;'>**White**</span>, and <span style='color:#96ACD9;'>**Black**</span> Americans",
    x = "",
    y = "",
    caption = "Source: BLS Earnings Data"
  ) +
  theme(legend.position = "none") +
  theme(axis.text.x = element_blank()) +
  theme(axis.text.y = element_blank()) +
  theme(plot.title = element_markdown()) +
  theme(panel.grid.minor = element_blank()) +
  theme(panel.grid.major.x = element_blank()) +
  theme(text = element_text(family = "serif")) +
  annotate("errorbar", x = "2009",  ymin = -5000, ymax = 5000, col = "darkgray") +
* annotate("text", x = "2002", y = 0, label = "$10K", col = "darkgray", angle = 90, family = "serif")
```
]
 
.panel2-even-more-auto[
![](plots/even-more_auto_14_output-1.png)
]

---
count: false

.panel1-even-more-auto[

```r
ggplot(earn_clean, aes(x = time, ymin = women_dev, ymax = men_dev, fill = race, group = race)) +
  geom_ribbon() +
  theme_minimal() +
  scale_fill_manual(values = c("#73434B", "#2F3659", "#96ACD9")) +
  labs(
    title = "The gender wage gap persists across <span style='color:#73434B;'>**Asian**</span>, <span style='color:#2F3659;'>**White**</span>, and <span style='color:#96ACD9;'>**Black**</span> Americans",
    x = "",
    y = "",
    caption = "Source: BLS Earnings Data"
  ) +
  theme(legend.position = "none") +
  theme(axis.text.x = element_blank()) +
  theme(axis.text.y = element_blank()) +
  theme(plot.title = element_markdown()) +
  theme(panel.grid.minor = element_blank()) +
  theme(panel.grid.major.x = element_blank()) +
  theme(text = element_text(family = "serif")) +
  annotate("errorbar", x = "2009",  ymin = -5000, ymax = 5000, col = "darkgray") +
  annotate("text", x = "2002", y = 0, label = "$10K", col = "darkgray", angle = 90, family = "serif") +
* annotate("text", x = "2000", y = 0, label = "")
```
]
 
.panel2-even-more-auto[
![](plots/even-more_auto_15_output-1.png)
]

---
count: false

.panel1-even-more-auto[

```r
ggplot(earn_clean, aes(x = time, ymin = women_dev, ymax = men_dev, fill = race, group = race)) +
  geom_ribbon() +
  theme_minimal() +
  scale_fill_manual(values = c("#73434B", "#2F3659", "#96ACD9")) +
  labs(
    title = "The gender wage gap persists across <span style='color:#73434B;'>**Asian**</span>, <span style='color:#2F3659;'>**White**</span>, and <span style='color:#96ACD9;'>**Black**</span> Americans",
    x = "",
    y = "",
    caption = "Source: BLS Earnings Data"
  ) +
  theme(legend.position = "none") +
  theme(axis.text.x = element_blank()) +
  theme(axis.text.y = element_blank()) +
  theme(plot.title = element_markdown()) +
  theme(panel.grid.minor = element_blank()) +
  theme(panel.grid.major.x = element_blank()) +
  theme(text = element_text(family = "serif")) +
  annotate("errorbar", x = "2009",  ymin = -5000, ymax = 5000, col = "darkgray") +
  annotate("text", x = "2002", y = 0, label = "$10K", col = "darkgray", angle = 90, family = "serif") +
  annotate("text", x = "2000", y = 0, label = "") +
* annotate("text", x = "2010-Q2", y = -10500, label = "2010", col = "darkgray", family = "serif")
```
]
 
.panel2-even-more-auto[
![](plots/even-more_auto_16_output-1.png)
]

---
count: false

.panel1-even-more-auto[

```r
ggplot(earn_clean, aes(x = time, ymin = women_dev, ymax = men_dev, fill = race, group = race)) +
  geom_ribbon() +
  theme_minimal() +
  scale_fill_manual(values = c("#73434B", "#2F3659", "#96ACD9")) +
  labs(
    title = "The gender wage gap persists across <span style='color:#73434B;'>**Asian**</span>, <span style='color:#2F3659;'>**White**</span>, and <span style='color:#96ACD9;'>**Black**</span> Americans",
    x = "",
    y = "",
    caption = "Source: BLS Earnings Data"
  ) +
  theme(legend.position = "none") +
  theme(axis.text.x = element_blank()) +
  theme(axis.text.y = element_blank()) +
  theme(plot.title = element_markdown()) +
  theme(panel.grid.minor = element_blank()) +
  theme(panel.grid.major.x = element_blank()) +
  theme(text = element_text(family = "serif")) +
  annotate("errorbar", x = "2009",  ymin = -5000, ymax = 5000, col = "darkgray") +
  annotate("text", x = "2002", y = 0, label = "$10K", col = "darkgray", angle = 90, family = "serif") +
  annotate("text", x = "2000", y = 0, label = "") +
  annotate("text", x = "2010-Q2", y = -10500, label = "2010", col = "darkgray", family = "serif") +
* annotate("text", x = "2020-Q2", y = -10500, label = "2020", col = "darkgray", family = "serif")
```
]
 
.panel2-even-more-auto[
![](plots/even-more_auto_17_output-1.png)
]