36-315-Lec9

# 36-315: Statistical Graphics and Visualization
## Lecture 9

Meghan Hall Department of Statistics & Data Science Carnegie Mellon University June 11, 2021

---
layout: true
<div class="my-footer">cmu-36315.netlify.app</div>
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# From last time

---

# Note on homework/labs

.large[.Rmd assignment files might not knit]
 
.medium[can add `eval = FALSE` to the function in the setup chunk]

---

# Today

---

# Today's agenda

1. **colors**

2. fonts

3. annotations

4. themes

]

---

# Types of color scales

.large[Qualitative]
 
.medium[distinguishing discrete items that don't have an order (nominal categorical)]
 
.medium[colors should be distinct and equal with none standing out]
 
 
--
 
.large[Sequential]
 
.medium[when data values are mapped to one shade]
 
.medium[e.g., in a choropleth, for an ordered categorical variable]
 
 
--
 
.large[Divergent]
 
.medium[think of it as two sequential scales with a natural midpoint]
 
.medium[midpoint could represent 0 (assuming +/- values)]
 
.medium[or 50% if your data spans the full scale]

---

# What's *not* appropriate

.large[Using a sequential scale on an unordered variable]
 
.medium[viewers will assume there's a relationship]
 
 
--
 
.large[Using a divergent scale on a data without a natural midpoint]
 
 
--
 
.large[Using a discrete scale on a continuous variable]
 
.medium[will disguise the trend]

---

# Options for finding palettes

.large[Internal within `ggplot2`]
 
.medium[ColorBrewer]
 
 
--
.center[![chart](figs/Lec9/colorbrewer.png)]

---

# ColorBrewer

---

```r
penguins %>% 
  filter(species != "Gentoo") %>% 
  mutate(label = case_when(flipper_length_mm == 192 & 
                             body_mass_g == 2700 ~ "Chinstrap",
                           flipper_length_mm == 184 & 
                             body_mass_g == 4650 ~ "Adelie")) %>% 
  ggplot(aes(x = flipper_length_mm, y = body_mass_g, 
             size = bill_length_mm, color = species)) +
  geom_point(alpha = 0.5) +
  scale_size(range = c(0.1, 7), breaks = c(35, 40, 45, 50, 55), 
             name = "Bill Length (mm)") +
  geom_label_repel(aes(x = flipper_length_mm, y = body_mass_g,
                       color = species, label = label),
                   inherit.aes = FALSE) +
* scale_color_brewer(palette = "Set2", guide = "none") +
  labs(x = "Flipper Length (mm)",
       y = "Body Mass (g)",
       title = "Chinstrap penguins tend to have longer flippers and longer bills",
       caption = "Data from the palmerpenguins package") +
  theme(legend.position = "top",
        panel.background = element_blank(),
        panel.grid.major = element_line(color = "grey90", size = 0.2),
        axis.ticks = element_line(color = "grey90", size = 0.2),
        legend.key = element_rect(fill = "transparent"))
```
]

---

# ColorBrewer

---

# ColorBrewer

---

```r
tx_map %>% 
  mutate(perc_bin = case_when(hispanic_perc >= 0.8 ~ "80-100%",
                              hispanic_perc >= 0.6 ~ "60-80%",
                              hispanic_perc >= 0.4 ~ "40-60%",
                              hispanic_perc >= 0.2 ~ "20-40%",
                              TRUE ~ "0-20%")) %>% 
  ggplot(aes(x = long, y = lat, group = group, fill = perc_bin)) +
  geom_polygon(color = "white") +
* scale_fill_brewer(palette = "Oranges", name = NULL) +
  labs(title = "Percentage of Hispanic people by county in Texas",
       subtitle = "2019 population estimate") +
  theme_void() +
  theme(legend.position = c(0.17, 0.8))
```
]

---

# ColorBrewer

---

```r
tx_map %>% 
  mutate(perc_bin = case_when(hispanic_perc >= 0.8 ~ "80-100%",
                              hispanic_perc >= 0.6 ~ "60-80%",
                              hispanic_perc >= 0.4 ~ "40-60%",
                              hispanic_perc >= 0.2 ~ "20-40%",
                              TRUE ~ "0-20%")) %>% 
  ggplot(aes(x = long, y = lat, group = group, fill = perc_bin)) +
  geom_polygon(color = "white") +
* scale_fill_brewer(palette = "BrBG", name = NULL) +
  labs(title = "Percentage of Hispanic people by county in Texas",
       subtitle = "2019 population estimate") +
  theme_void() +
  theme(legend.position = c(0.17, 0.8))
```
]

---

# ColorBrewer

---

# Options for finding palettes

.large[Internal within `ggplot2`]
 
.medium[ColorBrewer]
 
.medium[viridis: five different options; available for discrete & continuous data]

---

```r
tx_map %>% 
  ggplot(aes(x = long, y = lat, group = group, fill = hispanic_perc)) +
  geom_polygon(color = "white") +
* scale_fill_viridis_c(name = NULL, labels = percent,
*                      direction = -1, option = "D") +
  labs(title = "Percentage of Hispanic people by county in Texas", 
       subtitle = "2019 population estimate") +
  theme_void() +
  theme(legend.position = c(0.2, 0.8))
```
]

---

# viridis

---

# Options for finding palettes

.large[Internal within `ggplot2`]
 
.medium[ColorBrewer]
 
.medium[viridis: five different options; available for discrete & continuous data]
 
 
.large[External options]
 
.medium[[`ggsci`](https://cran.r-project.org/web/packages/ggsci/vignettes/ggsci.html), [`paletteer`](https://github.com/EmilHvitfeldt/paletteer), [`scico`](https://github.com/thomasp85/scico), [coolors.co](coolors.co)]
 
.medium[colorblind safe: `scico` and `viridis`]

---

# `scico`

`scico_palette_show()`

---

```r
lincoln_weather %>% 
  select(CST, temp = `Max Temperature [F]`) %>% 
  mutate(date = ymd(CST), 
         month = month(date, label = TRUE),
         day = day(date)) %>% 
  ggplot(aes(x = month, y = day, fill = temp)) +
  geom_tile(color = "white") +
  scale_y_continuous(trans = "reverse", 
                     breaks = seq(1, 31, 5)) + 
  labs(title = "Maximum temperature by day in Lincoln, NE in 2016") +
* scale_fill_scico(palette = "imola",
*                  breaks = seq(20, 90, 10),
*                  name = " °F") +
  guides(fill = guide_colorsteps()) +
  theme(axis.ticks = element_blank(),
        panel.background = element_blank(),
        axis.title = element_blank(),
        axis.text = element_text(face = 2)) +
  coord_cartesian(expand = FALSE)
```
]

---

# `scico`

---

# Options for finding palettes

---

# Today's agenda

1. colors

2. **fonts**

3. annotations

4. themes

]

---

# Fonts

.large[Be careful about readability]
 
.medium[outlandish fonts should be used sparingly, if at all]
 
.medium[know your audience]
 
 
--
 
.large[Can use fonts external to your computer]
 
.medium[using the `showtext` package]
 
.medium[easiest to use [Google fonts](https://fonts.google.com/)]

---

# Fonts

```r
# name: name of the font on Google Fonts
# family: the name you'll use to reference the font in your code

font_add_google(name = "Permanent Marker",
                family = "perm_marker")

# "turns on" showtext

showtext_auto() 
```

---

```r
txhousing %>% 
  filter(year >= 2010) %>% 
  ggplot(aes(x = as.character(year), y = median)) +
  geom_violin(draw_quantiles = c(0.25, 0.5, 0.75)) +
  geom_jitter(alpha = .25, width = .3, size = 0.5, color = "#bb0000") +
  scale_y_continuous(labels = dollar, breaks = seq(100000, 300000, 50000)) +
  labs(title = "The distribution of median home prices by city in Texas") +
  theme(axis.title = element_blank(),
        panel.background = element_blank(),
        panel.grid.major.y = element_line(color = "grey90", size = 0.2),
        panel.border = element_rect(color = "black", fill = NA, size = 0.5),
        axis.ticks = element_blank(),
        axis.text = element_text(size = 10, face = 2),
        plot.title.position = "plot",
*       plot.title = element_text(family = "perm_marker", size = 18))
```
]

---

# Fonts

---

# Today's agenda

1. colors

2. fonts

3. **annotations**

4. themes

]

---

# Annotations

.large[Useful for adding extra information to plots]
 
.medium[besides labeling specific points]
 
 
--
 
.large[Options:]
 
.medium[add text with `annotate("text")`]

---

# Annotations

---

---

# Annotations

---

# Annotations

---

```r
friends_info %>% 
  ggplot(aes(x = us_views_millions, y = imdb_rating, color = season)) + 
  geom_jitter(size = 2) +
  scale_colour_gradient(low = "#fafafa",high = "#191970",breaks = seq(1, 10, 1),
                        name = "Season") +
  scale_x_continuous(labels = label_number(suffix = "M")) +
  scale_y_continuous(breaks = seq(7, 10, 0.5)) +
  labs(title = "Two Friends episodes were viewed far more than the rest",
       subtitle = "Both highlighted episodes were two-part episodes",
       x = "US views",
       y = "IMDB rating") +
  annotate("text", x = 49, y = 8.7, label = "The One After the Super\nBowl aired in 1996", 
           size = 4.5, hjust = 0.85) + 
  annotate("text", x = 49, y = 9.7, label = "The Last One aired in 2004", 
           size = 4.5, hjust = 0.85) + 
* geom_curve(aes(x = 45, y = 8.9, xend = 52, yend = 8.9),
*            size = 1, color = "dark grey", curvature = -0.7,
*            arrow = arrow(length = unit(0.07, "npc"))) +
* geom_curve(aes(x = 45, y = 9.6, xend = 52, yend = 9.6),
*            size = 1, color = "dark grey", curvature = 0.7,
*            arrow = arrow(length = unit(0.07, "npc"))) +
  theme(legend.position = c(0.75, 0.08),
        legend.direction = "horizontal",
        legend.background = element_blank(),
        legend.title = element_text(color = "#353839", size = 11, face = "bold", vjust = 0.75),
        axis.ticks = element_blank(),
        panel.background = element_blank(),
        panel.border = element_rect(color = "black", fill = NA, size = 0.5),
        panel.grid.major = element_line(color = "grey90", size = 0.3))
```
]

---

# Annotations

---

# Annotations

---

# Annotations

---

```r
msleep %>% 
 filter(vore %in% c("carni","herbi")) %>% 
 mutate(name = fct_reorder(name, sleep_total),
 name = fct_reorder(name, vore),
 vore = fct_recode(vore, "herbivore" = "herbi", "carnivore" = "carni")) %>%
 group_by(vore) %>% 
 mutate(mean = mean(sleep_total)) %>% 
 ggplot(aes(x = sleep_total, y = name, color = vore)) +
 geom_point(size = 2) +
 scale_color_manual(values = c("#bb0000","#098641")) +
 scale_x_continuous(name = NULL, labels = number_format(suffix = " hrs", accuracy = 1)) +
 labs(y = NULL, x = "Total sleep per day (hrs)",
 title = "**Carnivores** sleep slightly more per day than **herbivores**",
 subtitle = "Among these species, carnivores sleep one hour more per day on average") +
* geom_linerange(aes(x = 10.4, ymin = 1, ymax = 19),
* color = "#bb0000", size = 1.5, linetype = "dotted") +
* geom_linerange(aes(x = 9.5, ymin = 20, ymax = 51),
* color = "#098641", size = 1.5, linetype = "dotted") +
 theme(plot.title = element_markdown(),
 plot.subtitle = element_text(margin = margin(-5, 0, 10, 0)),
 plot.title.position = "plot",
 legend.position = "none",
 panel.background = element_blank(),
 plot.background = element_rect(fill = "transparent", color = NA),
 panel.grid.major = element_line(color = "black", size = 0.1),
 axis.ticks = element_blank(),
 axis.text.y = element_text(size = 6.75))
```
]

---

# Annotations

---

# Annotations

.large[Useful for adding extra information to plots]
 
.medium[besides labeling specific points]
 
 
 
.large[Options:]
 
.medium[add text with `annotate("text")`]
 
.medium[add arrows with `geom_curve`]
 
.medium[add multiple lines with `geom_linerange`]
 
.medium[add shaded areas with `geom_rect` or `annotate("rect")`]

---

# Annotations

---

```r
economics %>% 
 ggplot() + 
* geom_rect(aes(xmin = start, xmax = end, fill = status),
* ymin = -Inf, ymax = Inf, alpha = 0.5, data = recession_list) +
 geom_line(aes(x = date, y = unemploy), size = 1) +
 scale_fill_manual(values = c("white","#bb0000")) +
 labs(y = "Unemployed (in thousands)",
 x = NULL,
 title = "The number of unemployed people tends to increase during a **recession**") +
 theme(panel.background = element_blank(),
 panel.grid.major = element_line(color = "grey90", size = 0.3),
 panel.border = element_rect(color = "black", fill = NA, size = 0.5),
 axis.ticks = element_blank(),
 plot.title = element_markdown(),
 plot.title.position = "plot",
 legend.position = "none")
```
]

---

# Annotations

---

```r
friends_info %>% 
  ggplot(aes(x = us_views_millions, y = imdb_rating, color = season)) + 
  geom_jitter(size = 2) +
  scale_colour_gradient(low = "#fafafa",high = "#191970",breaks = seq(1, 10, 1),
                        name = "Season") +
  scale_x_continuous(labels = label_number(suffix = "M")) +
  scale_y_continuous(breaks = seq(7, 10, 0.5)) +
  labs(title = "Two Friends episodes were viewed far more than the rest",
       subtitle = "Both highlighted episodes were two-part episodes",
       x = "US views",
       y = "IMDB rating") +
  annotate("text", x = 49, y = 8.7, label = "The One After the Super\nBowl aired in 1996", 
           size = 4.5, hjust = 0.85) + 
  annotate("text", x = 49, y = 9.7, label = "The Last One aired in 2004", 
           size = 4.5, hjust = 0.85) + 
  geom_curve(aes(x = 45, y = 8.9, xend = 52, yend = 8.9), 
             size = 1, color = "dark grey", curvature = -0.7, 
             arrow = arrow(length = unit(0.07, "npc"))) + 
  geom_curve(aes(x = 45, y = 9.6, xend = 52, yend = 9.6), 
             size = 1, color = "dark grey", curvature = 0.7, 
             arrow = arrow(length = unit(0.07, "npc"))) + 
* annotate("rect", xmin = 38, xmax = 52, ymin = 8.57, ymax = 8.82,
*          alpha = 0.2, color = "grey", fill = "black") +
* annotate("rect", xmin = 37, xmax = 51.5, ymin = 9.6, ymax = 9.8,
*          alpha = 0.2, color = "grey", fill = "black") +
  theme(legend.position = c(0.75, 0.08),
        legend.direction = "horizontal",
        legend.background = element_blank(),
        legend.title = element_text(color = "#353839", size = 11, face = "bold", vjust = 0.75),
        axis.ticks = element_blank(),
        panel.background = element_blank(),
        panel.border = element_rect(color = "black", fill = NA, size = 0.5),
        panel.grid.major = element_line(color = "grey90", size = 0.3))
```
]

---

# Annotations

---

# Annotations

---

# Annotations

---

```r
mpg %>% 
  filter(year == 2008 & class == "suv") %>% 
  group_by(manufacturer) %>% 
  summarize(median = median(cty),
            n = n()) %>% 
  mutate(manufacturer = str_to_title(manufacturer),
         label = paste0(manufacturer, "\n", n,
                        ifelse(n > 1, " models", " model"))) %>%
  ggplot(aes(x = reorder(label, -median), y = median)) +
  geom_bar(stat = "identity") +
  scale_y_continuous(expand = expansion(mult = c(0, .1))) +
* geom_hline(yintercept = 13.6,
*            color = "#bb0000", size = 2) +
  labs(title = "Subaru SUVs have highest median city mpg",
       subtitle = "Among manufacturers in 2008; mean value shown in red",
       x = NULL,
       y = "Miles per gallon") +
  theme(axis.ticks = element_blank(),
        panel.background = element_blank(),
        panel.grid.major.y = element_line(color = "grey90", size = 0.2),
        panel.border = element_rect(color = "black", fill = NA, size = 0.5))
```
]

---

# Annotations

---

# Today's agenda

1. colors

2. fonts

3. annotations

4. **themes**

]

---

# Themes

.large[Why use a theme?]
 
.medium[adding this, for example, to every.single.plot is annoying and a waste of time]

```r
theme(panel.background = element_blank(),
      panel.grid.major = element_line(color = "grey90", size = 0.3),
      axis.ticks = element_line(color = "grey90", size = 0.3),
      legend.key = element_rect(fill = "transparent"),
      axis.text = element_text(face = 2))
```

---

# Themes

---

.pull-left[
`theme_gray`
<img src="figs/Lec9/theme-1-1.png" width="504" style="display: block; margin: auto auto auto 0;" />
]
--
.pull-left[
`theme_bw`
<img src="figs/Lec9/theme-2-1.png" width="504" style="display: block; margin: auto auto auto 0;" />
]

---

.pull-left[
`theme_linedraw`
<img src="figs/Lec9/theme-3-1.png" width="504" style="display: block; margin: auto auto auto 0;" />
]
--
.pull-left[
`theme_light`
<img src="figs/Lec9/theme-4-1.png" width="504" style="display: block; margin: auto auto auto 0;" />
]

---

.pull-left[
`theme_dark`
<img src="figs/Lec9/theme-5-1.png" width="504" style="display: block; margin: auto auto auto 0;" />
]
--
.pull-left[
`theme_minimal`
<img src="figs/Lec9/theme-6-1.png" width="504" style="display: block; margin: auto auto auto 0;" />
]

---

.pull-left[
`theme_classic`
<img src="figs/Lec9/theme-7-1.png" width="504" style="display: block; margin: auto auto auto 0;" />
]
--
.pull-left[
`theme_void`
<img src="figs/Lec9/theme-8-1.png" width="504" style="display: block; margin: auto auto auto 0;" />
]

---

# Themes

.large[Built-in themes]
 
.medium[8 options within `ggplot2`]
 
 
 
.large[Themes available in other packages]
 
.medium[[`ggthemes`](https://github.com/jrnold/ggthemes), [`ggdark`](https://github.com/nsgrantham/ggdark), [`hrbrthemes`](https://github.com/hrbrmstr/hrbrthemes)]

---

.pull-left[
`theme_economist`
<img src="figs/Lec9/theme-9-1.png" width="504" style="display: block; margin: auto auto auto 0;" />
]
--
.pull-left[
`theme_fivethirtyeight`
<img src="figs/Lec9/theme-10-1.png" width="504" style="display: block; margin: auto auto auto 0;" />
]

---

.pull-left[
`dark_theme_minimal`
<img src="figs/Lec9/theme-11-1.png" width="504" style="display: block; margin: auto auto auto 0;" />
]
--
.pull-left[
`theme_ipsum`
<img src="figs/Lec9/theme-12-1.png" width="504" style="display: block; margin: auto auto auto 0;" />
]

---

# Themes

---

# Themes

---

# Themes

```r
data %>% 
  ggplot(aes(x = x, y = y)) + 
  geom_point(color = "#bb0000") +
  labs(title = "Sometimes visualizing data can reveal important details",
       subtitle = "From Alberto Cairo's Datasaurus") +
* cmu_theme() +
  scale_x_continuous(limits = c(0, 100)) +
* theme(panel.grid.major = element_blank(),
*       axis.title = element_blank())
```

---

# Themes

```r
cmu_theme <- function () { 
* theme_linedraw(base_size=11, base_family="source_serif_pro") %+replace%
 theme(
 panel.background = element_blank(),
 plot.background = element_rect(fill = "transparent", color = NA), 
 legend.background = element_rect(fill = "transparent", color = NA),
 legend.key = element_rect(fill = "transparent", color = NA),
 axis.ticks = element_blank(),
 panel.grid.major = element_line(color = "grey90", size = 0.3), 
 panel.grid.minor = element_blank(),
 plot.title = element_text(size = 18, hjust = 0, vjust = 0.5, face = "bold", margin = margin(b = 0.2, unit = "cm")),
 plot.subtitle = element_text(size = 12, hjust = 0, vjust = 0.5, margin = margin(b = 0.2, unit = "cm")),
 plot.caption = element_text(size = 7, hjust = 1, face = "italic", margin = margin(t = 0.1, unit = "cm")),
 axis.text.x = element_text(size = 13),
 axis.text.y = element_text(size = 13)
 )
}
```

---

# Upcoming