Polishing Charts

]

# Week 10: .fancy[Polishing Charts]

### <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:white;overflow:visible;position:relative;"><path d="M243.4 2.6l-224 96c-14 6-21.8 21-18.7 35.8S16.8 160 32 160v8c0 13.3 10.7 24 24 24H456c13.3 0 24-10.7 24-24v-8c15.2 0 28.3-10.7 31.3-25.6s-4.8-29.9-18.7-35.8l-224-96c-8-3.4-17.2-3.4-25.2 0zM128 224H64V420.3c-.6 .3-1.2 .7-1.8 1.1l-48 32c-11.7 7.8-17 22.4-12.9 35.9S17.9 512 32 512H480c14.1 0 26.5-9.2 30.6-22.7s-1.1-28.1-12.9-35.9l-48-32c-.6-.4-1.2-.7-1.8-1.1V224H384V416H344V224H280V416H232V224H168V416H128V224zM256 64a32 32 0 1 1 0 64 32 32 0 1 1 0-64z"/></svg> EMSE 4572 / 6572: Exploratory Data Analysis
### <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:white;overflow:visible;position:relative;"><path d="M304 128a80 80 0 1 0 -160 0 80 80 0 1 0 160 0zM96 128a128 128 0 1 1 256 0A128 128 0 1 1 96 128zM49.3 464H398.7c-8.9-63.3-63.3-112-129-112H178.3c-65.7 0-120.1 48.7-129 112zM0 482.3C0 383.8 79.8 304 178.3 304h91.4C368.2 304 448 383.8 448 482.3c0 16.4-13.3 29.7-29.7 29.7H29.7C13.3 512 0 498.7 0 482.3z"/></svg> John Paul Helveston
### <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:white;overflow:visible;position:relative;"><path d="M152 24c0-13.3-10.7-24-24-24s-24 10.7-24 24V64H64C28.7 64 0 92.7 0 128v16 48V448c0 35.3 28.7 64 64 64H384c35.3 0 64-28.7 64-64V192 144 128c0-35.3-28.7-64-64-64H344V24c0-13.3-10.7-24-24-24s-24 10.7-24 24V64H152V24zM48 192H400V448c0 8.8-7.2 16-16 16H64c-8.8 0-16-7.2-16-16V192z"/></svg> October 30, 2024

]

---

# .center[.fancy[.blue[Tip of the week]]]

### .center[Ever wondered what colors look like _before_ you plot them?]

Use `scales::show_col()` to preview them

Example:

``` r
colors <- c('lightblue', 'sienna', 'forestgreen', '#eae9ea')
scales::show_col(colors)
```

]]

]

---

## Today's data

``` r
wildlife_impacts <- read_csv(here::here('data', 'wildlife_impacts.csv'))
federal_spending <- read_csv(here::here('data', 'federal_spending_long.csv'))
milk_production  <- read_csv(here::here('data', 'milk_production.csv'))
lotr_words       <- read_csv(here::here('data', 'lotr_words.csv'))
msleep           <- read_csv(here::here('data', 'msleep.csv'))
```

## New (?) package:

``` r
install.packages('hrbrthemes')
```

---

# Week 10: .fancy[Polishing Charts]

### 1. Scales
### 2. Annotations

### BREAK

### 3. Colors
### 4. Fonts
### 5. qmd tricks

---

# Week 10: .fancy[Polishing Charts]

### 1. .orange[Scales]
### 2. Annotations

### BREAK

### 3. Colors
### 4. Fonts
### 5. qmd tricks

---

## When is it okay to to truncate an axis?

- **When small movements matter**

]

]

---

## When is it okay to to truncate an axis?

- When small movements matter
- **When zero values are impossible**

]

]

---

## When is it okay to to truncate an axis?

- When small movements matter
- When zero values are impossible
- **When it's normal / a convention**

]

]

---

## When is it okay to to truncate an axis?

- When small movements matter
- When zero values are impossible
- When it's normal / a convention
- **.red[Never on a bar chart]**

]

]

---

# You are most sensitive to changes<br>in angles close to 45 degrees

---

## You are most sensitive to changes<br>in angles close to 45 degrees

]

]

]

---

## You are most sensitive to changes<br>in angles close to 45 degrees

Bad

]

Better

Set image dimensions in R chunk header:

````markdown
```{r}
#| fig.width: 5
#| fig.height: 3.1

plot
```
````

]

---

### .center[Consider setting dimensions to "Golden Ratio" (1 : 1.618)]

<br>

]

Approx. to golden ratio:

width  |  height
-------|---------
5      | 3.1 or 3
6      | 3.7 or 4
7      | 4.3

<br>

[Also check out Donald Duck in Mathemagic Land](https://youtu.be/U_ZHsk0-eF0?t=512)
]]

---

## Adjust axes with `scale_*` functions

Continous variables

``` r
scale_x_continuous()
scale_y_continuous()
```

Discrete variables

``` r
scale_x_discrete()
scale_y_discrete()
```

Others

``` r
scale_x_log10()
scale_y_log10()
scale_x_date()
```

]

Common arguments for **continuous** variables

``` r
scale_y_continuous(
  # Set the lower & upper boundaries
  limits = c(lower, upper),

# Explicitly set the break points
  breaks = c(break1, break2, etc.)

# Adjust the axis so bars start at 0
  expand = expand_scale(mult = c(0, 0.05))
)
```

]

---

## Adjusting **continuous** scales

``` r
milk_bars <- milk_production %>%
  filter(year == 2017) %>%
  arrange(desc(milk_produced)) %>%
  slice(1:10) %>%
  mutate(
      milk_produced = milk_produced / 10^9,
      state = fct_reorder(state, milk_produced)) %>%
  ggplot() +
  geom_col(aes(x = milk_produced, y = state)) +
  theme_minimal_vgrid(font_size = 18) +
  labs(x = 'Milk produced (billions of lbs)',
       y = 'State')

milk_bars
```

]]

]

---

## Adjusting **continuous** scales

``` r
milk_bars +
* scale_x_continuous(
*   breaks = c(0, 15, 30, 45),
*   limits = c(0 , 45),
*   expand = expand_scale(mult = c(0, 0.05)))
```

]]

]

---

## Adjusting **discrete** scales

``` r
milk_bars +
* scale_y_discrete(
*   breaks = c('California', 'Wisconsin', 'Idaho'))
```

]]

]

---

## Adjusting **log** scales

Regular scaling

``` r
plot <- ggplot(msleep) +
  geom_point(aes(x = brainwt, y = bodywt)) +
  theme_half_open(font_size = 20) +
  labs(x = 'Brain weight (kg)',
       y = 'Body weight (kg)')

plot
```

]]

]

---

## Adjusting **log** scales

Log scaling

``` r
plot +
* scale_x_log10() +
* scale_y_log10() +
  labs(x = 'Log brain weight (kg)',
       y = 'Log body weight (kg)')
```

]

Log-log relationship:

`$$y = x^n$$`

`$$\log(y) = n\log(x)$$`

]

]

---

## Example from Mini Project 2

Regular scaling

``` r
plot <- transit_cost %>%
  filter(!is.na(length)) %>%
  filter(length < 2500) %>%
  mutate(cost = as.numeric(real_cost)) %>%
* ggplot(aes(x = length, y = cost)) +
  geom_point() +
  geom_smooth(method = 'lm', se = FALSE) +
  theme_half_open(font_size = 20) +
  labs(x = "Length of Proposed Line",
       y = "Real Cost in Millions USD")

plot
```

]]

]

---

## Example from Mini Project 2

Log scaling

``` r
plot +
* scale_x_log10() +
* scale_y_log10() +
  labs(x = 'Log length of proposed line',
       y = 'Log cost')
```

]

Log-log relationship:

`$$y = x^n$$`

`$$\log(y) = n\log(x)$$`

]

]

---

# Date scales can be confusing

---

# What's wrong with this chart?

---

# What's wrong with this chart?

---

# What's wrong with this chart?

---

# What's wrong with this chart?

---

## Adjusting **date** scales

Summarise the data

``` r
*library(lubridate)

plot <- wildlife_impacts %>%
  filter(incident_year == 2016) %>%
  count(operator, incident_date) %>%
* mutate(incident_date = ymd(incident_date)) %>%
  ggplot() +
  geom_col(
    aes(x = incident_date, y = n,
        color = operator)) +
  facet_wrap(~operator, ncol = 1) +
  theme_minimal_grid(font_size = 16) +
  panel_border() +
  theme(legend.position = 'none') +
  labs(x = 'Incident date (2016)',
       y = 'Number of incidents')

plot
```

]]

<br>

]

---

## Adjusting **date** scales

``` r
plot +
* scale_x_date(
*   date_breaks = '1 month',
*   date_labels = '%b')
```

]]

<br>

]

---

## Adjusting **date** scales

``` r
scale_x_date(
  date_breaks = '1 month',
  date_labels = '%b')
```

``` r
date_breaks = '1 month'
```

- '1 day'
- '10 days'
- '1 month'
- '3 months'
- '1 year'
- '3 years'

]

``` r
date_labels = '%b'
```

Example date: March 04, 2020

- `%Y` = 2020
- `%y` = 20
- `%B` = March
- `%b` = Mar
- `%D` = 03/04/2020
- `%d` = 03

]

---

## Use **scales** library to modify scale **text**

---

## `scales` converts numbers to formatted characters

``` r
scales::comma(200000)
```

```
#> [1] "200,000"
```

``` r
scales::dollar(200000)
```

```
#> [1] "$200,000"
```

``` r
scales::percent(0.5)
```

```
#> [1] "50%"
```

---

## Use **scales** library to modify scale text

``` r
federal_spending %>%
  filter(year == 2017) %>%
  mutate(
    department = fct_reorder(
      department, rd_budget)) %>%
  ggplot() +
  geom_col(aes(x = rd_budget, y = department)) +
  scale_x_continuous(
    expand = expand_scale(mult = c(0, 0.05))) +
  theme_minimal_vgrid(font_size = 16) +
  labs(x = 'Department',
       y = 'R&D spending ($ Millions)')
```

]]

]

---

## Use **scales** library to modify scale text

``` r
federal_spending %>%
  filter(year == 2017) %>%
  mutate(
    department = fct_reorder(
      department, rd_budget)) %>%
  ggplot() +
  geom_col(aes(x = rd_budget, y = department)) +
  scale_x_continuous(
*   labels = scales::comma,
    expand = expand_scale(mult = c(0, 0.05))) +
  theme_minimal_vgrid(font_size = 16) +
  labs(x = 'Department',
       y = 'R&D spending ($ Millions)')
```

]]

]

---

## Use **scales** library to modify scale text

``` r
federal_spending %>%
  filter(year == 2017) %>%
  mutate(
    department = fct_reorder(
      department, rd_budget)) %>%
  ggplot() +
  geom_col(aes(x = rd_budget, y = department)) +
  scale_x_continuous(
*   labels = scales::dollar,
    expand = expand_scale(mult = c(0, 0.05))) +
  theme_minimal_vgrid(font_size = 16) +
  labs(x = 'Department',
       y = 'R&D spending ($ Millions)')
```

]]

]

---

# Check out this guide to scales: https://ggplot2tor.com/scales

---

## Your turn

Adjust the scales in the code chunk provided to match the chart on the slides.

]

]

---

# Week 10: .fancy[Polishing Charts]

### 1. Scales
### 2. .orange[Annotations]

### BREAK

### 3. Colors
### 4. Fonts
### 5. qmd tricks

---

# Text is usually the single most important component on your chart

---

.font80[Michelle Borkin, et al. (2015) [Beyond Memorability: Visualization Recognition and Recall](https://ieeexplore.ieee.org/document/7192646)]

---

]

## Titles matter

Source: https://www.reddit.com/r/dataisugly/comments/odk65x/this_was_not_the_right_headline_to_go_along_with

]

---

]

## Good annotations should tell a story

Source: https://ourworldindata.org/less-meat-or-sustainable-meat

]

---

``` r
labs(
  x = 'Year',
  y = 'Cost of incident repairs ($ millions)',
  title = 'Repair costs of aircraft impacts with wildlife',
  subtitle = 'Top 4 airlines',
  caption = 'Source: https://wildlife.faa.gov/home')
```

---

Use mapped variables in `aes()` in `labs()`

``` r
milk_production %>%
  filter(year %in% c(1970, 2017)) %>%
  group_by(year, region) %>%
  summarise(milk_produced = sum(milk_produced) / 10^9) %>%
  ungroup() %>%
  mutate(
    region = fct_reorder2(region, year, desc(milk_produced))) %>%
  ggplot() +
  geom_col(
*   aes(x = milk_produced,
*       y = region,
        fill = as.factor(year)),
    position = "dodge") +
  scale_x_continuous(expand = expansion(mult = c(0, 0.05))) +
  theme_minimal_vgrid() +
  labs(
*   x = 'Milk produced (billions lbs)',
*   y = 'Region',
    title = 'Milk production by region',
    subtitle = '1970 & 2017')
```

]]

]

---

Use mapped variables in `aes()` in `labs()`

``` r
milk_production %>%
  filter(year %in% c(1970, 2017)) %>%
  group_by(year, region) %>%
  summarise(milk_produced = sum(milk_produced) / 10^9) %>%
  ungroup() %>%
  mutate(
    region = fct_reorder2(region, year, desc(milk_produced))) %>%
  ggplot() +
  geom_col(
    aes(x = milk_produced,
        y = region,
*       fill = as.factor(year)),
    position = "dodge") +
  scale_x_continuous(expand = expansion(mult = c(0, 0.05))) +
  theme_minimal_vgrid() +
  labs(
    x = 'Milk produced (billions lbs)',
    y = 'Region',
    title = 'Milk production by region',
    subtitle = '1970 & 2017',
*   fill = "Year")
```

]]

]

---

# Legends suck

---

# Legends suck

### Legends require look-up task

]

### Direct labeling is much better

]

---

# Legends suck

### Legends require look-up task

]

### Direct labeling is much better

]

---

## Use `annotate()` to add text to chart

``` r
dod_spending <- ggplot(federal_spending_summary) +
    geom_area(aes(x = year, y = rd_budget,
                  fill = department)) +
*   annotate(geom = 'text', x = 1995, y = 85,
*            label = 'Other', size = 6, color = 'black') +
*   annotate(geom = 'text', x = 1995, y = 25,
*            label = 'DOD', size = 6, color = 'white') +
    scale_y_continuous(
        expand = expand_scale(mult = c(0, 0.05))) +
    scale_fill_manual(values = c('grey', 'sienna')) +
    theme_minimal_hgrid() +
*   theme(legend.position = 'none') +
    labs(x = NULL,
         y = 'R&D Budget ($ Billions)',
         title = 'Federal R&D spending, 1976 - 2017',
         subtitle = 'Source: AAAS Historical Trends')

dod_spending
```

]]

<br>

]

---

## Use `geom_text()` to add text to chart

``` r
dod_spending <- ggplot(federal_spending_summary) +
    geom_area(aes(x = year, y = rd_budget,
                  fill = department)) +
*   geom_text(
*     data = data.frame(x = 1995, y = 85, label = 'Other'),
*     aes(x = x, y = y, label = label),
*     size = 6, color = 'black') +
*   geom_text(
*     data = data.frame(x = 1995, y = 25, label = 'DOD'),
*     aes(x = x, y = y, label = label),
*     size = 6, color = 'white') +
    scale_y_continuous(
        expand = expand_scale(mult = c(0, 0.05))) +
    scale_fill_manual(values = c('grey', 'sienna')) +
    theme_minimal_hgrid() +
    theme(legend.position = 'none') +
    labs(x = NULL,
         y = 'R&D Budget ($ Billions)',
         title = 'Federal R&D spending, 1976 - 2017',
         subtitle = 'Source: AAAS Historical Trends')

dod_spending
```

]]

<br>

]

---

### Use `geom_label()` to add text to chart **with a background**

``` r
dod_spending <- ggplot(federal_spending_summary) +
    geom_area(aes(x = year, y = rd_budget,
                  fill = department)) +
*   geom_label(
      data = data.frame(x = 1995, y = 85, label = 'Other'),
      aes(x = x, y = y, label = label),
      size = 6) +
*   geom_label(
      data = data.frame(x = 1995, y = 25, label = 'DOD'),
      aes(x = x, y = y, label = label),
*     size = 6, fill = "black", color = "white") +
    scale_y_continuous(
        expand = expand_scale(mult = c(0, 0.05))) +
    scale_fill_manual(values = c('grey', 'sienna')) +
    theme_minimal_hgrid() +
    theme(legend.position = 'none') +
    labs(x = NULL,
         y = 'R&D Budget ($ Billions)',
         title = 'Federal R&D spending, 1976 - 2017',
         subtitle = 'Source: AAAS Historical Trends')

dod_spending
```

]]

<br>

]

---

### Use `geom_curve()` + `geom_label()` to direct attention

``` r
label <- "The Dept. of Defense R&D Budget
has made up nearly half of all
federal R&D spending"

dod_spending +
* geom_curve(
*   data = data.frame(
*     x = 1981, xend = 1987, y = 160, yend = 25),
*   mapping = aes(x = x, xend = xend, y = y, yend = yend),
*   color = 'grey75', size = 0.5, curvature = 0.1,
*   arrow = arrow(length = unit(0.01, "npc"),
*                 type = "closed")) +
  geom_label(
    data = data.frame(x = 1977, y = 160, label = label),
    mapping = aes(x = x, y = y, label = label),
    hjust = 0, lineheight = 0.8)
```

]]

]

---

## Use `annotate()` to direct attention

Use `geom = "rect"` for box, `geom = "text"` for label

``` r
ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point(
    aes(fill = as.factor(cyl)),
    color = 'white', alpha = 0.8,
    size = 3.5, shape = 21) +
* annotate(geom = "rect",
    xmin = 5, xmax = 7.5,
    ymin = 21, ymax = 28,
    fill = "grey55", alpha = 0.2) +
* annotate(geom = "text",
    x = 5, y = 29, label = "Hybrid vehicles",
    hjust = 0, size = 5) +
  theme_half_open(font_size = 15) +
  labs(x = "Engine displacement",
       y = "Fuel efficiency (mpg)",
       fill = '# cylinders',
       title = "Vehicle fuel efficiency vs. engine displacement",
       caption = "Source: U.S. EPA.")
```

]]

]

---

## Find where to put annotations with [`ggannotate`](https://github.com/MattCowgill/ggannotate)

Install:

``` r
remotes::install_github("mattcowgill/ggannotate")
```

Use:

``` r
library(ggannotate)

plot <- ggplot(mpg) +
  geom_point(aes(x = displ, y = hwy, color = as.factor(cyl))) +
  theme_half_open()

*ggannotate(plot)
```

]

---

## Your turn

Use the `lotr_summary` data frame to create the following chart.

Hints:
- For the `geom_label()`, use these points:
    - `x = 0.6`
    - `y = 2100`
- For the `geom_curve()`, use these points:
    - `x = 1.2`
    - `xend = 1`
    - `y = 1300`
    - `yend = 200`

]

]

---

# Intermission

---

# Week 10: .fancy[Polishing Charts]

### 1. Scales
### 2. Annotations

### BREAK

### 3. .orange[Colors]
### 4. Fonts
### 5. qmd tricks

---

# Color is hard

---

## How do I know what colors look good together?

### .red[Use the color wheel]

]

---

## How do I know what colors look good together?

### .red[Use the color wheel]

### 1. **Complementary**: High contrast

]

]

---

## How do I know what colors look good together?

### .red[Use the color wheel]

### 1. Complementary: High contrast
### 2. **Analogous**: Calm, harmonious

]

]

---

## How do I know what colors look good together?

### .red[Use the color wheel]

### 1. Complementary: High contrast
### 2. Analogous: Calm, harmonious
### 3. **Triadic**: Vibrant, contrast

]

]

---

# [Artists use color theory too!](https://twitter.com/emeldraws/status/1151835561415495681?s=19)

---

# Steal colors with the [eye dropper tool](https://chrome.google.com/webstore/detail/eye-dropper/hmdcmlfkchdmnmnmheododdhjedfccka?hl=en)

]

---

## Using your own colors

Map color to variable

``` r
mpg_plot <- ggplot(mtcars, aes(x = mpg, y = hp)) +
  geom_point(
*   aes(color = as.factor(cyl)),
    alpha = 0.8, size = 3) +
  theme_half_open(font_size = 16) +
  labs(x = "Fuel efficiency (mpg)",
       y = "Power (hp)",
       color = '# cylinders',
       title = "Vehicle fuel efficiency vs. power",
       caption = "Source: 1974 Motor Trend U.S. magazine.")
```

]]

<br>

]

---

## Using your own colors

Map color to variable

Manually change colors

``` r
mpg_plot +
*   scale_color_manual(values = c(
*       '#a0522d', '#522da0', '#2da052'))
```

]]

<br>

]

---

## Consider color blind friendly colors

Manually change colors

``` r
mpg_plot_mycolors <- mpg_plot +
    theme_half_open(font_size = 10) +
*   scale_color_manual(values = c(
*       '#a0522d', '#522da0', '#2da052'))
```

Simulate color blindness with [colorblindr](https://github.com/clauswilke/colorblindr)

``` r
# remotes::install_github("clauswilke/colorblindr")
library(colorblindr)

cvd_grid(mpg_plot_mycolors)
```

]]

]

---

class:center

# Use palettes

### [Viridis](https://cran.r-project.org/web/packages/viridis/vignettes/intro-to-viridis.html)

]

### [ColorBrewer](https://www.datanovia.com/en/blog/the-a-z-of-rcolorbrewer-palette/)

]

---

### 3 types of palettes

### 1. Sequential
### 2. Diverging
### 3. Categorical

]

---

### 3 types of palettes

### 1. **Sequential**
### 2. Diverging
### 3. Categorical

]

Image from [betterfigures.org](https://betterfigures.org/2015/06/23/picking-a-colour-scale-for-scientific-graphics/)

]

---

### 3 types of palettes

### 1. Sequential
### 2. **Diverging**
### 3. Categorical

]

Image from [betterfigures.org](https://betterfigures.org/2015/06/23/picking-a-colour-scale-for-scientific-graphics/)

]

---

### 3 types of palettes

### 1. Sequential
### 2. Diverging
### 3. **Categorical**

]

Image from [betterfigures.org](https://betterfigures.org/2015/06/23/picking-a-colour-scale-for-scientific-graphics/)

]

---

## **ColorBrewer** palettes

Map color to variable

``` r
mpg_plot <- ggplot(mtcars, aes(x = mpg, y = hp)) +
*   geom_point(aes(color = as.factor(cyl)),
               alpha = 0.8, size = 3) +
    theme_half_open(font_size = 16) +
    labs(x = "Fuel efficiency (mpg)",
         y = "Power (hp)",
         color = '# cylinders',
         title = "Vehicle fuel efficiency vs. power",
         caption = "Source: 1974 Motor Trend U.S. magazine.")
```

Use "Dark2" palette

``` r
mpg_plot +
*   scale_color_brewer(palette = 'Dark2')
```

]]

<br>

]

---

## **ColorBrewer** palettes

``` r
mpg_plot +
    scale_color_brewer(
*       palette = 'Set1')
```

]]

``` r
mpg_plot +
    scale_color_brewer(
*       palette = 'Set2')
```

]]

``` r
mpg_plot +
    scale_color_brewer(
*       palette = 'Accent')
```

]]

---

## **viridis** palettes

Map color to variable

Use viridis colors

``` r
mpg_plot +
*   scale_color_viridis(discrete = TRUE)
```

]]

<br>

]

---

## **viridis** palettes

``` r
mpg_plot +
    scale_color_viridis(
        discrete = TRUE,
*       option = 'viridis')
```

]]

``` r
mpg_plot +
    scale_color_viridis(
        discrete = TRUE,
*       option = 'inferno')
```

]]

``` r
mpg_plot +
    scale_color_viridis(
        discrete = TRUE,
*       option = 'plasma')
```

]]

---

Fun custom palettes

[Inauguration palette](https://github.com/ciannabp/inauguration)

]

[PNWColors](https://github.com/jakelawlor/PNWColors)
<center>
<img src="images/PNWColors.jpg" width=500>

]

---

## Consider using `color` + `fill` for points

``` r
ggplot(mtcars, aes(x = mpg, y = hp)) +
  geom_point(
*   aes(fill = as.factor(cyl)),
*   color = 'white', shape = 21,
    size = 3.5, alpha = 0.8) +
* scale_fill_brewer(palette = 'Dark2') +
  theme_half_open(font_size = 15) +
  labs(
    x = "Fuel efficiency (mpg)",
    y = "Power (hp)",
    fill = '# cylinders',
    title = "Vehicle fuel efficiency vs. power",
    caption = "Source: 1974 Motor Trend U.S. magazine.")
```

]]

]

---

## .center[`grey` = "Other"]

``` r
dod_spending_plot <- federal_spending %>%
  mutate(department = fct_other(
    department, keep = 'DOD')) %>%
  group_by(department, year) %>%
  summarise(rd_budget = sum(rd_budget) / 10^3) %>%
  ungroup() %>%
  mutate(department = fct_relevel(
  department, c('Other', 'DOD'))) %>%
  ggplot() +
  geom_area(aes(x = year, y = rd_budget,
                fill = department)) +
  scale_y_continuous(
      expand = expand_scale(mult = c(0, 0.05))) +
* scale_fill_manual(
*   values = c('grey', 'sienna')) +
  theme_minimal_hgrid() +
  labs(x = NULL,
       y = 'R&D Budget ($ Billions)',
       fill = 'Department',
       caption = 'Source: AAAS')

dod_spending_plot
```

]]

]

---

# .center[`grey` = "Other"]

``` r
dod_spending_plot +
    scale_fill_manual(
*       values = c('grey40', 'sienna'))
```

]]

``` r
dod_spending_plot +
    scale_fill_manual(
*       values = c('grey60', 'sienna'))
```

]]

``` r
dod_spending_plot +
    scale_fill_manual(
*       values = c('grey80', 'sienna'))
```

]]

---

# Your turn

Make 3 different versions of this chart:

1. Change the colors to the `"RdYlBu"` ColorBrewer palette.
2. Change the colors to the `"inferno"` palette from the **viridis** library.
3. Change the colors to your custom triadic palette:

- Use the ["eye dropper"](https://chrome.google.com/webstore/detail/eye-dropper/hmdcmlfkchdmnmnmheododdhjedfccka?hl=en) tool in Google Chrome to select a color from a website, then
    - Use your color and the [color wheel tool](https://www.sessions.edu/color-calculator/) to find a triadic color palette.

]

]

---

# Week 10: .fancy[Polishing Charts]

### 1. Scales
### 2. Annotations

### BREAK

### 3. Colors
### 4. .orange[Fonts]
### 5. qmd tricks

<!--
Source of the content on how to change fonts in ggplot2:

https://www.andrewheiss.com/blog/2017/09/27/working-with-r-cairo-graphics-custom-fonts-and-ggplot/

Source of a lot of the slide content on choosing good fonts:
https://resources.rstudio.com/rstudio-conf-2020/the-glamour-of-graphics-william-chase
-->

---

# Fonts matter

"Fast Taco"

]

"Mega Flicks"

]

---

---

## Best resource on fonts:

## [practicaltypography.com](https://practicaltypography.com/)

---

## .center[Font families you should consider using]

<p style="font-family: Roboto;">Roboto</p>

<p style="font-family: Source Code Pro;">Source</p>

<p style="font-family: Alegreya;">Alegreya</p>

]]

Download:

- Individually from https://fonts.google.com/
- All of these with [this zip file](https://github.com/emse-eda-gwu/2022-Fall/raw/main/content/fonts.zip)

]

---

## Use fonts to create **hierarchy**

```
# Hierarchy

## Hierarchy

### Hierarchy

#### Hierarchy
```

]

# Hierarchy

## Hierarchy

### Hierarchy

#### Hierarchy

]

---

Title<br>This is some text that goes into detail and explains a lot more about the topic described in the title. Here's some random Latin words: Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.

]

---

# Size

This is some text that goes into detail and explains a lot more about the topic described in the title. Here's some random Latin words: Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.

]

---

**Weight**

]

---

]

---

Spacing

<br>

]

---

Typeface

<p style="font-family: Alegreya;">
This is some text that goes into detail and explains a lot more about the topic described in the title. Here's some random Latin words: Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
</p>

]

---

<h2 style="font-family: Roboto Condensed; color: #a5442b;">Title</h2>
<h3 style="font-family: Alegreya;">Subtitle</h3>

<p style="font-family: Roboto;">
This is some text that goes into detail and explains a lot more about the topic described in the title. Here's some random Latin words: Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
</p>

]

---

# .center[Use fonts with **same-height** numbers]

<br>

## "Oldstyle" (bad)

<br>

## Lining (good)

]]

]

---

# .center[Use fonts with **same-width** numbers]

<br>

## "Proportional" (bad)

<br>

## "Tabular" (good)

]]

]

---

# How to customize fonts in **ggplot**

---

## 1. Change the whole theme

For "Base R" themes, use `base_family`:

``` r
theme_minimal(base_family = "Roboto Condensed")
```

``` r
theme_bw(base_family = "Roboto Condensed")
```

<br>

For "cowplot" themes, use `font_family`:

``` r
theme_half_open(font_family = "Roboto Condensed")
```

``` r
theme_minimal_grid(font_family = "Roboto Condensed")
```

---

## 1. Change the whole theme font

Make the base plot

``` r
mpg_plot <- ggplot(mtcars) +
  geom_point(aes(x = mpg, y = hp)) +
* theme_minimal(base_size = 15) +
  labs(
    x = "Fuel efficiency (mpg)",
    y = "Power (hp)",
    title = "Vehicle fuel efficiency vs. power",
    subtitle = "Select makes and models",
    caption = "Source: 1974 Motor Trend U.S. magazine.")
```

]]

]

---

## 1. Change the whole theme font

Use `base_family` with base themes

``` r
mpg_plot <- ggplot(mtcars) +
  geom_point(aes(x = mpg, y = hp)) +
* theme_minimal(
*   base_family = 'Source Sans Pro',
    base_size = 15) +
  labs(
    x = "Fuel efficiency (mpg)",
    y = "Power (hp)",
    title = "Vehicle fuel efficiency vs. power",
    subtitle = "Select makes and models",
    caption = "Source: 1974 Motor Trend U.S. magazine.")
```

]]

]

---

## 1. Change the whole theme font

Use `font_family` with cowplot themes

``` r
mpg_plot <- ggplot(mtcars) +
  geom_point(aes(x = mpg, y = hp)) +
* theme_minimal_grid(
*   font_family = 'Source Sans Pro',
    font_size = 15) +
  labs(
    x = "Fuel efficiency (mpg)",
    y = "Power (hp)",
    title = "Vehicle fuel efficiency vs. power",
    subtitle = "Select makes and models",
    caption = "Source: 1974 Motor Trend U.S. magazine.")
```

]]

]

---

## 2. Adjust theme elements with `element_text()`

``` r
mpg_plot <- ggplot(mtcars) +
  geom_point(aes(x = mpg, y = hp)) +
  theme_minimal_grid(
    font_family = 'Source Sans Pro',
    font_size = 15) +
  theme(
*   plot.title = element_text(
*     family = "Roboto Condensed",
*     size = 20)) +
  labs(
    x = "Fuel efficiency (mpg)",
    y = "Power (hp)",
    title = "Vehicle fuel efficiency vs. power",
    subtitle = "Select makes and models",
    caption = "Source: 1974 Motor Trend U.S. magazine.")
```

]

See theme components [here](https://ggplot2.tidyverse.org/reference/theme.html)

]

]

---

### 3. Adjust annotations:

### `geom_text()`, `geom_label()`, and `annotate()`

``` r
label <- "Higher power engines,
often come at the expense,
of fuel economy."

mpg_plot +
    geom_label(
      data = data.frame(
        x = 17, y = 270, label = label),
      aes(x = x, y = y, label = label),
      lineheight = .8, hjust = 0,
*     family = 'Roboto Condensed')
```

]]

]

---

## The [hrbrthemes](https://github.com/hrbrmstr/hrbrthemes) package:

### Great themes + great fonts

``` r
*library(hrbrthemes)

mpg_plot <- ggplot(mtcars) +
  geom_point(aes(x = mpg, y = hp)) +
  labs(
    x = "Fuel efficiency (mpg)",
    y = "Power (hp)",
    title = "Vehicle fuel efficiency vs. power",
    subtitle = "Select makes and models",
    caption = "Source: 1974 Motor Trend U.S. magazine.")

mpg_plot +
* theme_ipsum()
```

]

]

---

## The [hrbrthemes](https://github.com/hrbrmstr/hrbrthemes) package:

### Great themes + great fonts

``` r
mpg_plot +
  theme_ipsum()
```

]

``` r
mpg_plot +
  theme_ipsum_rc()
```

]

``` r
mpg_plot +
  theme_ft_rc()
```

]

---

## Your turn

Modify the fonts and annotations in the dumbbell chart to match the chart shown here. The main font is `'Roboto Condensed'`.

Once you've recreated the plot, try other fonts and themes, such as:

- The `'Source Sans Pro'` font.
- The `'Lato'` font.
- The `theme_ipsum()` theme from the `hrbrthemes` library.

Hint: Use `annotate()` to insert the year labels at the top:

- 1970: `x = 9`, `y = 10.5`
  - 2017: `x = 40`, `y = 10.5`

]]

]

---

# Week 10: .fancy[Polishing Charts]

### 1. Scales
### 2. Annotations

### BREAK

### 3. Colors
### 4. Fonts
### 5. .orange[qmd tricks]

---

## .center[Use themes to change global look]

## Change theme in YAML header

```
---
title: Your title
author: Author name
toc: true
format:
  html:
    theme: united
---
```

]

## List of themes

https://quarto.org/docs/output-formats/html-themes.html

]

---

## Use Quarto extensions

https://quarto.org/docs/extensions/

Some personal favoriates:

- [lightbox](https://quarto-ext.github.io/lightbox/)
- [elevator](https://m.canouil.dev/quarto-elevator/)
- [webR](https://quarto-webr.thecoatlessprofessor.com/)

---

# Set a global chart theme

``` r
theme_set(theme_minimal_grid())
```

---

# Check out [these Quarto tricks](https://www.productive-r-workflow.com/quarto-tricks), by Yan Holtz