ggplot2 컬러링 가이드

# ggplot2 컬러링 가이드
----
## **서울 R 미트업**
### 방태모
### 2022-08-12

---

## .brand-blue[준비하기]

```r
library(tidyverse)
library(showtext)
font_add_google("Fira Sans", "firasans")
showtext_auto()

theme_customs <- theme(
  text = element_text(family = 'firasans', size = 16),
  plot.title.position = 'plot',
  plot.title = element_text(
    face = 'bold', 
    colour = thematic::okabe_ito(8)[6],
    margin = margin(t = 2, r = 0, b = 7, l = 0, unit = "mm")
  )
)

theme_set(theme_minimal() + theme_customs)
```

---

# 음영을 활용하자

---

## .brand-blue[막대그래프 예제]

- 1998년과 2008년 인기 차량 모델 38개에 관한 연비 데이터 `mpg` 이용

- 연도별(`year`) 7개 차종(`class`)의 빈도는 어떻게 되는가?

```r
mpg |> 
    select(manufacturer:model, year, class) |> 
    str()
```

```
## tibble [234 × 4] (S3: tbl_df/tbl/data.frame)
##  $ manufacturer: chr [1:234] "audi" "audi" "audi" "audi" ...
##  $ model       : chr [1:234] "a4" "a4" "a4" "a4" ...
##  $ year        : int [1:234] 1999 1999 2008 2008 1999 1999 2008 1999 1999 2008 ...
##  $ class       : chr [1:234] "compact" "compact" "compact" "compact" ...
```

---

## .brand-blue[흔히들 하는 시각화]

]

```r
mpg %>% 
  ggplot(aes(x = factor(year), 
             fill = class)) +
  geom_bar() +
  labs(x = "year")
```

]

---

## .brand-blue[음영 활용하기 I - Preprocessing]

```r
# Group classes into three groups (to reduce colors to 3)
dat <- mpg %>% 
  mutate(
    year = factor(year),
    class_group = case_when(
      class %in% c('2seater', 'compact', 'midsize') ~ "grp1",
      class == 'minivan' ~ "grp2",
      T ~ "grp3"
    )
  )
```

]

---

## .brand-blue[음영 활용하기 I - Visualization]

]

```r
shades_plt <- dat %>% 
  ggplot(aes(x = year, 
*            fill = class_group,
*            alpha = class)) +
  geom_bar() +
  labs(
    x = 'Year',
    y = 'Counts',
    alpha = 'Class',
    title = 'Show shades, not hues'
  )
shades_plt 
```

]

---

## .brand-blue[음영 활용하기 II - Adjusting]

```r
# Color-blind safe colors
colors <-  thematic::okabe_ito(3)
# Possible levels of transparency (one for each class)
alpha_max <- 1
alpha_min <- 0.7
alpha_vals <- c(
  seq(alpha_max, alpha_min, length.out = 4), 
  seq(alpha_min, alpha_max, length.out = 4)[-1]
)
alpha_vals
```

```
## [1] 1.0 0.9 0.8 0.7 0.8 0.9 1.0
```

]

---

## .brand-blue[음영 활용하기 II - Visualization]

]

```r
# Tweak previous plot
shades_plt <- shades_plt +
* scale_fill_manual(values = colors) +
* scale_alpha_manual(values = alpha_vals)
shades_plt
```

]

---

## .brand-blue[음영 활용하기 III - Adjusting and visualization]

]

- `guides()` 이용

```r
shades_plt <- shades_plt +
* guides(
*   fill = guide_none(),
*   alpha = guide_legend(
*     override.aes =
*         list(fill = colors[c(1, 1, 1, 2, 3, 3, 3)])
*   )
* )
shades_plt
```

]

---

# 음영만으로는 부족해

---

## .brand-blue[컬러블록에 줄 추가하기]

]

```r
dat %>% 
  ggplot(aes(x = year, fill = class_group, alpha = class)) +
* geom_bar(col = 'white') +
  scale_fill_manual(values = colors) +
  scale_alpha_manual(values = alpha_vals) +
  guides(
    fill = guide_none(),
    alpha = guide_legend(override.aes = list(fill = colors[c(1, 1, 1, 2, 3, 3, 3)]))
  ) +
  labs(
    x = 'Year', y = 'Counts', alpha = 'Class',
    title = 'Group categories together by color, \nbut keep showing them'
  )
```

]

---

# 전달하고 싶은 내용만 강조하자

---

## .brand-blue[스파게티 플롯 예제]

- [Our World in Data](https://ourworldindata.org/time-with-others-lifetime)의 미국인 설문조사 자료 이용

- 우리는 일생동안 누구(`person`)와 시간(`minutes`)을 보내는가?

```r
time_data <- read_csv("./time-spent-with-relationships-by-age-us.csv") %>% 
  rename_with(
    ~c('Entitity', 'Code', 'Age', 
       'alone', 'friends', 'children', 
       'parents', 'partner', 'coworkers')
  ) %>% 
  pivot_longer(
    cols = alone:coworkers, names_to = 'person',
    values_to = 'minutes'
  ) %>% 
  janitor::clean_names() %>% 
  filter(age <= 80)
```
]

```r
str(time_data)
```

```
## tibble [396 × 5] (S3: tbl_df/tbl/data.frame)
##  $ entitity: chr [1:396] "United States" "United States" "United States" "United States" ...
##  $ code    : chr [1:396] "USA" "USA" "USA" "USA" ...
##  $ age     : num [1:396] 15 15 15 15 15 15 16 16 16 16 ...
##  $ person  : chr [1:396] "alone" "friends" "children" "parents" ...
##  $ minutes : num [1:396] 193.3 109.4 23.5 267.1 0 ...
```

]

---
## .brand-blue[흔히들 하는 시각화]

]

```r
# Color-blind safe colors
colors <- thematic::okabe_ito(7)[-6]

# Line plot
p <- time_data %>% 
  ggplot(aes(x = age, y = minutes, col = person)) +
  geom_line(size = 1.5) +
  scale_color_manual(values = colors) +
  coord_cartesian(xlim = c(15, 81), expand = F) +
  scale_y_continuous(minor_breaks = NULL) +
  labs(x = 'Age (in years)', y = 'Minutes', col = 'Time spent')
p
```

]
---
## .brand-blue[전달력 높이기 I]

]

- `gghighlight()` 이용

```r
library(gghighlight)
alone_plt <- p + 
* gghighlight(person == 'alone',
*             use_direct_label = F) +
  labs(title = 'Emphasize just one or a few categories')
alone_plt
```

]

---

## .brand-blue[전달력 높이기 II]

]

```r
alone_plt +
  annotate(
    'text',
    x = 15,
    y = 455,
    label = 'We spend a lot of time alone...',
    hjust = 0,
    vjust = 0,
    family = 'firasans',
    size = 7
  )
```

]

---

## .brand-blue[전달력 높이기 III]

---
## .brand-blue[전달력 높이기 III]

- `ggphighlight()`를 이용해 다중 하이라이팅

```r
age_40_plt <- p + 
* gghighlight(person %in% c('alone', 'children'), age >= 38,  use_direct_label = F) +
  geom_segment(x = 38, xend = 38, y = -Inf, yend = 300, linetype = 2, col = 'grey20') +
  labs(title = 'Emphasize just one or a few categories')

age_40_plt +
  annotate(
    'text', x = 15, y = 403,
    label = 'Around the age of 40, we spend \nless time with children and \nmore time alone.',
    hjust = 0, vjust = 0,
    family = 'firasans',
    lineheight = 0.85,
    size = 5.5
  )
```

---

# 라벨링 활용하기

---

## .brand-blue[라벨링 활용하기 I]

]

```r
alone_plt +
  annotate(
    'text', x = 15, y = 455,
    label = 'We spend a lot of time alone...',
    hjust = 0, vjust = 0,
    family = 'firasans',
    size = 7) +
* annotate(
*   'text',  x = 70,  y = 420,
*   label = 'alone',
*   hjust = 0, vjust = 0, size = 7,
*   family = 'firasans', color = colors[1]) +
  labs(title = 'Label directly') +
* theme(legend.position = 'none')
```

]

---

## .brand-blue[라벨링 활용하기 II - Adjusting]

- `ggtext` 패키지를 통해 richtext로 변환 및 HTML 문법 활용

```r
library(ggtext)
color_alone <- glue::glue(
* "We spend a lot of time <span style = 'color:{colors[1]};'>alone</span>..."
)
color_alone
```

```
## We spend a lot of time <span style = 'color:#E69F00;'>alone</span>...
```

---

## .brand-blue[라벨링 활용하기 II - Visualization]

]

```r
alone_plt +
  labs(title = 'Label directly') +
  annotate(
*   'richtext',
    x = 15,
    y = 400,
*   label = color_alone,
    hjust = 0, vjust = 0,
    family = 'firasans',
    size = 6,
*   label.color = NA
  ) +
* theme(legend.position = 'none')
```

]

---

## .brand-blue[라벨링 활용하기 II - Adjusting]

```r
age_40_text <- glue::glue(
  "Around the age of 40, we spent <br> less time 
  with <span style = 'color:{colors[2]};'>children</span> 
  and <br> more time <span style = 'color:{colors[1]};'>alone</span>."
)
age_40_text
```

```
## Around the age of 40, we spent <br> less time 
## with <span style = 'color:#009E73;'>children</span> 
## and <br> more time <span style = 'color:#E69F00;'>alone</span>.
```

---

## .brand-blue[라벨링 활용하기 II - Visualization]

]

```r
age_40_plt +
  labs(title = 'Label directly') +
  annotate(
*   'richtext',
    x = 15, y = 350, 
*   label = age_40_text,
    hjust = 0, vjust = 0,
    family = 'firasans',
    lineheight = 1.25,
    size = 5,
*   label.color = NA
  ) +
* theme(legend.position = 'none')
```
  
]

---

## .brand-blue[라벨링 활용하기 III - Preprocessing]

- 빈도 계산

```r
manual_counts <- mpg %>% 
  count(year, class) %>% 
  mutate(
    year = factor(year),
    class_group = case_when(
      class %in% c('2seater', 'compact', 'midsize') ~ "grp1",
      class == 'minivan' ~ "grp2",
      T ~ "grp3"
    )
  ) 
head(manual_counts)
```
]

```
## # A tibble: 6 × 4
##   year  class          n class_group
##   <fct> <chr>      <int> <chr>      
## 1 1999  2seater        2 grp1       
## 2 1999  compact       25 grp1       
## 3 1999  midsize       20 grp1       
## 4 1999  minivan        6 grp2       
## 5 1999  pickup        16 grp3       
## 6 1999  subcompact    19 grp3
```
]

---

## .brand-blue[라벨링 활용하기 III - Preprocessing]

- 레이블 높이 계산

```r
labels <- manual_counts %>% 
  mutate(class = factor(class)) %>%  
  group_by(year) %>% 
  arrange(year, desc(class)) %>% 
  mutate(
*   csum = cumsum(n),
*   n = (lag(csum, default = 0) + csum) / 2
  )
head(labels)
```
]

```
## # A tibble: 6 × 5
## # Groups:   year [1]
##   year  class          n class_group  csum
##   <fct> <fct>      <dbl> <chr>       <int>
## 1 1999  suv         14.5 grp3           29
## 2 1999  subcompact  38.5 grp3           48
## 3 1999  pickup      56   grp3           64
## 4 1999  minivan     67   grp2           70
## 5 1999  midsize     80   grp1           90
## 6 1999  compact    102.  grp1          115
```
]

---

## .brand-blue[라벨링 활용하기 III - Visualization]

.pull-left[
<div class="figure">
<img src="coloring_guide_files/figure-html/mpg-add-1.png" alt="그림 3.3" width="504" />
<p class="caption">그림 3.3</p>
</div>
]

```r
*shades_plt2 <- manual_counts %>%
  ggplot(aes(x = year, y = n, fill = class_group)) +
* geom_col(aes(alpha = class), col = 'white') +
  scale_fill_manual(values = colors) +
  scale_alpha_manual(values = alpha_vals) +
  labs(
    x = 'Year', y = 'Counts',
    alpha = 'Class', title = 'Label directly'
  )
shades_plt2
```
]

---

## .brand-blue[라벨링 활용하기 III - Visualization]

---

## .brand-blue[라벨링 활용하기 III - Visualization]

```r
shades_plt2 +
  # Add all but one label
  geom_text(
*   data = labels %>% filter(class != '2seater'), aes(label = class),
    col = 'white', family = 'firasans', size = 5, fontface = 'bold'
  ) +
  # Add 2seater label
  geom_text(
*   data = labels %>% filter(class == '2seater'), aes(y = n + 3, label = class),
    col = 'black', family = 'firasans', size = 5, fontface = 'bold'
  ) +
  theme(legend.position = 'none')
```

---

## .brand-blue[Before & After - Bar chart]

---

## .brand-blue[Before & After - Spaghetti plot]

---

## .brand-blue[Before & After - Spaghetti plot]

---
class: inverse, middle, center

# References

---

## .brand-blue[References]

[1] Rapp, Albert. “4 Ways to Use Colors in Ggplot More Efficiently.” Blog. Albert Rapp, February 19, 2022. https://albert-rapp.de/posts/ggplot2-tips/07_four_ways_colors_more_efficiently/07_four_ways_colors_more_efficiently.html.

### .brand-blue[Theme and CSS in xaringan slides]

- https://github.com/statisticsplaybook/statisticsplaybook

- https://github.com/garthtarr/sydney_xaringan

---

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]]