Deep dive: themes + axes + annotation

---

# Announcements:

For next class: you will need an API key: https://docs.stadiamaps.com/tutorials/getting-started-in-r-with-ggmap/#set-up-api-key-authentication

---
# Agenda

* Themes
* Axes
* Walk-through of in-depth example

---

``` r
# Setup: load packages
library(tidyverse)
library(palmerpenguins)
library(ggthemes)
library(ggpomological)
library(colorspace)
library(lubridate)
#library(hrbrthemes)
library(WDI)
library(ggrepel)
library(ggtext)

# set default theme for ggplot2
ggplot2::theme_set(ggplot2::theme_gray(base_size = 16))

# set default figure parameters for knitr
knitr::opts_chunk$set(
  fig.width = 8, fig.asp = 0.618,
  fig.retina = 2, dpi = 150
)

# dplyr print min and max
options(dplyr.print_max = 6, dplyr.print_min = 6)
```

---

# Themes

---

# Should you stick with defaults?
.pull-left[
<img src="index_files/figure-html/standard-grey-1.png" width="95%" style="display: block; margin: auto;" />

Some say these are good enough. Others want more thought. 
]

<img src="https://media1.tenor.com/m/rr9KpqBthKQAAAAC/its-me-hi-taylor-swift.gif" width="85%" style="display: block; margin: auto;" />
]

---

# Or should you pursue alternatives -- like this!

``` r
library(ggthemes)

ggplot(penguins, aes(x = bill_depth_mm,
 y = bill_length_mm,
 color = species)) +
 geom_point(size = 2) +
* theme_tufte()
```
]
.pull-right[
<img src="index_files/figure-html/unnamed-chunk-2-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# And this!

``` r
library(ggthemes)

ggplot(penguins, aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     color = species)) +
  geom_point(size = 2) +
* theme_solarized()
```
]

.pull-right[
<img src="index_files/figure-html/unnamed-chunk-3-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Or this!

``` r
library(ggthemes)

ggplot(penguins, aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     color = species)) +
  geom_point(size = 2) +
* scale_color_economist() +
* theme_economist()
```
]

.pull-right[
<img src="index_files/figure-html/unnamed-chunk-4-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# And even this!

``` r
library(ggpomological)
library(showtext)

font_add_google("Homemade Apple")
showtext_auto() #load fonts

ggplot(penguins, aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     color = species)) +
  geom_point(size = 2) +
* scale_color_pomological() +
* theme_pomological_fancy()
```
]

.pull-right[
<img src="index_files/figure-html/unnamed-chunk-5-1.png" width="100%" style="display: block; margin: auto;" />
]

---

## One magic, powerful function

---

# The anatomy of a `ggplot()` theme

---

# Theme system

.footnote[Source: [`ggplot2` theme elements demonstration](https://henrywang.nl/ggplot2-theme-elements-demonstration/)]

---

# Theme elements

### Each element in the plot can be targeted

- Plot title = `plot.title`

- Grid lines = `panel.grid`

- Legend background = `legend.background`

---

# Theme functions

### Use special functions to manipulate specific elements

- Text-based things = `element_text()`

- Rectangular things (backgrounds) = `element_rect()`

- Line-based things (axis lines, grid lines) = `element_line()`

- Disable element completely = `element_blank()`

---

# How to learn `theme()`

- The `theme()` function has **94** possible arguments

- You can get hyper-specific with things like `axis.ticks.length.x.bottom`

- The only way to learn how to use `theme()` is to use it and tinker with it

---

# .hand[Live coding]

```r
# install if necessary
install.packages(c("tidyverse", "here", "colorspace", "showtext",
                   "scales", "ggrepel", "ggtext", "usethis"))

usethis::use_course("MACS40700/themes-annotations")
```

# Note: might need to install ggtext (and others)

---

# Axes

---

## Axis breaks

---

## Context matters

``` r
m_plot_imp <- m_plot + scale_y_discrete(label=abbreviate(midnights$track_name, minlength=10 ),limits=rev) +
* scale_x_continuous(breaks = seq(from = 0, to = 200, by = 10))

m_plot_imp
```

---

## Conciseness matters

``` r
ggplot(midnights, aes(x = tempo, y = track_name, fill = track_name)) +
  geom_col(show.legend = FALSE) + expand_limits(x = c(0, 1)) +
  labs(y = NULL) +  theme_minimal() +
  scale_fill_taylor_d(album = "Midnights")
```

``` r
  scale_x_continuous(breaks = seq(0, 200, 50))
```

```
## <ScaleContinuousPosition>
## Range: 
## Limits: 0 -- 1
```

---

## Precision matters

``` r
m_plot_imp +
  scale_x_continuous(breaks = seq(0, 200, 25)) +
* labs(x = "Tempo (bpm)")
```

---
# Zooming: comparison

``` r
ggplot( midnights, aes(x = tempo, color = tempo)) + coord_flip() +
  geom_boxplot(show.legend = FALSE) +   labs(x = NULL) +  theme_minimal() +
  scale_color_pomological() + theme_pomological_fancy() + scale_x_continuous(breaks = seq(0, 200, 10))
```

<img src="index_files/figure-html/unnamed-chunk-11-1.png" width="45%" style="display: block; margin: auto;" />
]
.panel[
.panel-name[Opt 1: subset]

``` r
midnights %>% filter(tempo > 125) %>%
ggplot( aes(x = tempo, color = tempo)) + 
  geom_boxplot(show.legend = FALSE) +   labs(x = NULL) +  theme_minimal() +
  scale_color_pomological() + theme_pomological_fancy() + scale_x_continuous(breaks = seq(0, 200, 10)) + coord_flip() 
```

<img src="index_files/figure-html/unnamed-chunk-12-1.png" width="45%" style="display: block; margin: auto;" />
]
.panel[.panel-name[Opt 2: limits]

``` r
ggplot( midnights, aes(x = tempo, color = tempo)) + 
  geom_boxplot(show.legend = FALSE) +   labs(x = NULL) +  theme_minimal() +
  scale_color_pomological() + theme_pomological_fancy() + xlim(125, 200) + scale_x_continuous(breaks = seq(125, 200, 10))+  coord_flip() 
```

<img src="index_files/figure-html/unnamed-chunk-13-1.png" width="45%" style="display: block; margin: auto;" />
]

]

---
class: inverse, middle

# Fretting the little things

---

# Little details matter

]

???

https://commons.wikimedia.org/wiki/File:Steve_Jobs_Headshot_2010-CROP.jpg

https://en.wikipedia.org/wiki/Apple_IIe#/media/File:Iie-system.jpg

---

``` r
ggplot(midnights, aes(x = tempo, y = reorder(track_name, tempo, sum))) +
  geom_col(show.legend = FALSE, fill = 'lightgray') +
  expand_limits(x = c(0, 1)) +
  labs(y = NULL) +
  theme_minimal()
```

---

# Human-focused design

.large[
> "This is what customers pay us for -- to sweat all these details so it's easy and pleasant for them to use our computers."
]

???

Steve Jobs in *Fortune*, as quoted in [Alice Rawsthorn, *Hello World: Where Design Meets Life*, p. 110(?)](https://www.google.com/books/edition/Hello_World/b6iMDwAAQBAJ?hl=en&gbpv=1&dq=to+sweat+all+these+details+so+it%E2%80%99s+easy+and+pleasant+for+them+to+use+our+computers&pg=PT110&printsec=frontcover)

---

# Graph details: Color coding

.pull-left[
<img src="index_files/figure-html/unnamed-chunk-17-1.png" width="100%" style="display: block; margin: auto;" />

]

.pull-right[
<img src="index_files/figure-html/unnamed-chunk-18-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Graph details: Consistent ordering

.pull-left[
<img src="index_files/figure-html/unnamed-chunk-19-1.png" width="100%" style="display: block; margin: auto;" />
]

.pull-right[
<img src="index_files/figure-html/unnamed-chunk-20-1.png" width="100%" style="display: block; margin: auto;" />
]

???

Source: Claus Wilke: https://clauswilke.com/dataviz/redundant-coding.html

---

# Details matter

---

# Text in plots

---

# Including text on a plot

## Label actual data points

`geom_text()`, `geom_label()`, `geom_text_repel()`, etc.

## Add arbitrary annotations

`annotate()`

---

# Label actual data points

``` r
library(gapminder)

gapminder_europe <- gapminder %>% 
 filter(year == 2007, 
 continent == "Europe")

ggplot(gapminder_europe, 
       aes(x = gdpPercap, y = lifeExp)) +
  geom_point() +
* geom_text(aes(label = country))
```

]

.pull-right[
<img src="index_files/figure-html/unnamed-chunk-21-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Label actual data points

``` r
ggplot(gapminder_europe, 
       aes(x = gdpPercap, y = lifeExp)) +
  geom_point() +
* geom_label(aes(label = country))
```

]

.pull-right[
<img src="index_files/figure-html/unnamed-chunk-22-1.png" width="100%" style="display: block; margin: auto;" />

]

---

# Solution 1: Repel labels

``` r
*library(ggrepel)

ggplot(gapminder_europe, 
       aes(x = gdpPercap, y = lifeExp)) +
  geom_point() +
* geom_text_repel(aes(label = country))
```
]

.pull-right[
<img src="index_files/figure-html/unnamed-chunk-23-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Solution 1: Repel labels

``` r
library(ggrepel)

ggplot(gapminder_europe, 
       aes(x = gdpPercap, y = lifeExp)) +
  geom_point() +
* geom_label_repel(aes(label = country))
```
]

.pull-right[
<img src="index_files/figure-html/unnamed-chunk-24-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Solution 2a: Don't use so many labels

``` r
gapminder_europe <- gapminder_europe %>% 
 mutate(should_be_labeled = 
 ifelse(country %in% c("Albania", 
 "Norway", 
 "Hungary"), 
 TRUE, FALSE))

ggplot(gapminder_europe, 
       aes(x = gdpPercap, y = lifeExp)) +
  geom_point() +
  geom_label_repel(
*   data = filter(gapminder_europe,
*                 should_be_labeled == TRUE),
    aes(label = country)
  )
```
]
]

.pull-right[
<img src="index_files/figure-html/unnamed-chunk-25-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Solution 2b: Use other aesthetics too

``` r
ggplot(gapminder_europe, 
       aes(x = gdpPercap, y = lifeExp)) +
  geom_point(aes(color = should_be_labeled)) +
  geom_label_repel(
    data = filter(
      gapminder_europe, 
      should_be_labeled == TRUE
    ),
    aes(label = country, 
        fill = should_be_labeled),
    color = "white"
  ) +
  scale_color_manual(values = c("grey50", 
                                "red")) +
  scale_fill_manual(values = c("red")) +
  guides(color = "none", fill = "none")
```
]

.pull-right[
<img src="index_files/figure-html/unnamed-chunk-26-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# (Highlight non-text things too!)

``` r
# Color just Oceania
gapminder_highlighted <- gapminder %>% 
 mutate(is_oceania = 
 ifelse(continent == "Oceania", 
 TRUE, FALSE))

ggplot(gapminder_highlighted, 
       aes(x = year, y = lifeExp, 
           group = country, 
           color = is_oceania, 
           size = is_oceania)) +
  geom_line() +
  scale_color_manual(values = c("grey70", 
                                "red")) +
  scale_size_manual(values = c(0.1, 0.5)) +
  guides(color = "none", size = "none") +
  theme_minimal()
```
]

.pull-right[
<img src="index_files/figure-html/unnamed-chunk-27-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Including text on a plot

## Label actual data points

`geom_text()`, `geom_label()`, `geom_text_repel()`, etc.

## Add arbitrary annotations

`annotate()`

---

# Adding arbitrary annotations

``` r
ggplot(gapminder_europe, 
       aes(x = gdpPercap, y = lifeExp)) +
  geom_point() +
* annotate(geom = "text",
*          x = 40000, y = 76,
*          label = "Some text!")
```
]

.pull-right[
<img src="index_files/figure-html/unnamed-chunk-28-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Adding arbitrary annotations

``` r
ggplot(gapminder_europe, 
       aes(x = gdpPercap, y = lifeExp)) +
  geom_point() +
* annotate(geom = "label",
           x = 40000, y = 76,
           label = "Some text!")
```
]

.pull-right[
<img src="index_files/figure-html/unnamed-chunk-29-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Any geom works

``` r
ggplot(gapminder_europe, 
       aes(x = gdpPercap, y = lifeExp)) +
  geom_point() +
* # This is evil though!!!
* # We just invented a point
* annotate(geom = "point",
*          x = 40000, y = 76,
*          color = "red3")
```
]

.pull-right[
<img src="index_files/figure-html/unnamed-chunk-30-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Any geom works

``` r
ggplot(gapminder_europe, 
       aes(x = gdpPercap, y = lifeExp)) +
  geom_point() +
* annotate(geom = "rect",
*          xmin = 30000, xmax = 50000,
*          ymin = 78, ymax = 82,
*          fill = "red", alpha = 0.2)
```
]

.pull-right[
<img src="index_files/figure-html/unnamed-chunk-31-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Use multiple annotations

``` r
ggplot(gapminder_europe, 
       aes(x = gdpPercap, y = lifeExp)) +
  geom_point() +
  annotate(geom = "rect", 
           xmin = 30000, xmax = 50000,
           ymin = 78, ymax = 82,
           fill = "red", alpha = 0.2) +
* annotate(geom = "label",
*          x = 40000, y = 76.5,
*          label = "Rich and long-living") +
* annotate(geom = "segment",
*          x = 40000, xend = 40000,
*          y = 76.8, yend = 77.8,
*          arrow = arrow(
*            length = unit(0.1, "in")))
```
]

.pull-right[
<img src="index_files/figure-html/unnamed-chunk-32-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Complex annotations

---

## World development indicators

``` r
#wdi_co2_raw <- read_csv(here("data", "wdi_co2.csv"))
indicators <- c(
 "SP.POP.TOTL", # Population
 "EN.GHG.CO2.MT.CE.AR5", # CO2 emissions
 "NY.GDP.PCAP.KD") # GDP per capita
wdi_co2_raw <- WDI::WDI(
 country = "all", indicators, extra = TRUE,
 start = 1995, end = 2015)
```

``` r
wdi_clean <- wdi_co2_raw %>%
 filter(region != "Aggregates") %>%
 select(iso2c, iso3c, country, year,
 population = SP.POP.TOTL,
 co2_emissions = EN.GHG.CO2.MT.CE.AR5,
 gdp_per_cap = NY.GDP.PCAP.KD,
 region, income )
```

---

## Clean and reshape data

``` r
co2_rankings <- wdi_clean %>%
 # Get rid of smaller countries
 filter(population > 200000) %>%
 # Only look at two years
 filter(year %in% c(1995, 2014)) %>%
 # Get rid of all the rows that have missing values in co2_emissions
 drop_na(co2_emissions) %>%
 # Look at each year individually and rank countries based on their emissions that year
 group_by(year) %>%
 mutate(ranking = rank(co2_emissions)) %>% ungroup() %>%
 # Only select required columns
 select(iso3c, country, year, region, income, ranking) %>%
 pivot_wider(names_from = year, names_prefix = "rank_", values_from = ranking) %>%
 # Find the difference in ranking between 2014 and 1995
 mutate(rank_diff = rank_2014 - rank_1995) %>%
 # Remove all rows where there's a missing value in the rank_diff column
 drop_na(rank_diff) 
```
]

---

## Clean and reshape data

``` r
co2_rankings <- co2_rankings %>%
 # Make an indicator variable that is true of the absolute value of the
 # difference in rankings is greater than 25
 mutate(big_change = ifelse(abs(rank_diff) >= 25, TRUE, FALSE)) %>%
 # Make another indicator variable that indicates if the rank improved by a
 # lot, worsened by a lot, or didn't change much.
 mutate(better_big_change = case_when(
 rank_diff <= -25 ~ "Rank improved",
 rank_diff >= 25 ~ "Rank worsened",
 TRUE ~ "Rank changed a little"
 ))
```
]

---

``` r
head(wdi_clean) 
```

```
##   iso2c iso3c     country year population co2_emissions gdp_per_cap     region
## 1    AF   AFG Afghanistan 1999   19887785        1.3633          NA South Asia
## 2    AF   AFG Afghanistan 2001   20284307        0.9402    277.1181 South Asia
## 3    AF   AFG Afghanistan 2014   32792523        7.9588    575.1462 South Asia
## 4    AF   AFG Afghanistan 1998   19159996        1.4093          NA South Asia
## 5    AF   AFG Afghanistan 1997   18452091        1.3735          NA South Asia
## 6    AF   AFG Afghanistan 2000   20130327        1.0093    308.3183 South Asia
##       income
## 1 Low income
## 2 Low income
## 3 Low income
## 4 Low income
## 5 Low income
## 6 Low income
```
]

``` r
head(co2_rankings)
```

```
## # A tibble: 6 × 9
## iso3c country region income rank_2014 rank_1995 rank_diff big_change
## <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <lgl> 
## 1 AFG Afghanistan South Asia Low i… 66 38 28 TRUE 
## 2 ALB Albania Europe & Ce… Upper… 48 45 3 FALSE 
## 3 DZA Algeria Middle East… Upper… 142 134 8 FALSE 
## 4 AGO Angola Sub-Saharan… Lower… 101 90 11 FALSE 
## 5 ARG Argentina Latin Ameri… Upper… 146 143 3 FALSE 
## 6 ARM Armenia Europe & Ce… Upper… 54 61 -7 FALSE 
## # ℹ 1 more variable: better_big_change <chr>
```
]

---

## Plot the data and annotate

---

``` r
*set.seed(13)
```
]
]
.pull-right[

]

---

``` r
set.seed(13)

*ggplot(
* data = co2_rankings,
* mapping = aes(x = rank_1995,
* y = rank_2014))
```
]
]
.pull-right[
<img src="index_files/figure-html/unnamed-chunk-35-1.png" width="100%" style="display: block; margin: auto;" />
]

---

``` r
set.seed(13)

ggplot(
 data = co2_rankings,
 mapping = aes(x = rank_1995, 
 y = rank_2014)) +
* geom_point(aes(
* color = better_big_change))
```
]
]
.pull-right[
<img src="index_files/figure-html/unnamed-chunk-36-1.png" width="100%" style="display: block; margin: auto;" />
]

---

``` r
set.seed(13)

ggplot(
 data = co2_rankings,
 mapping = aes(x = rank_1995, 
 y = rank_2014)) +
 geom_point(aes(color = better_big_change)) +
* geom_label_repel(
* data = filter(co2_rankings, big_change == TRUE),
* aes(label = country, fill = better_big_change),
* color = "white", family = "bell")
```
]
]
.pull-right[
<img src="index_files/figure-html/unnamed-chunk-37-1.png" width="100%" style="display: block; margin: auto;" />
]

---

.task[
.tiny[
Add a reference line in the background to show what it would look like if countries did not change rank order. Note `xend` and `yend` differ due to new countries forming during the 20 year period.
]
]

``` r
set.seed(13)

base <- ggplot(
 data = co2_rankings,
 mapping = aes(x = rank_1995, y = rank_2014)
) +
* annotate(geom = "segment", x = 0,
* xend = 172, y = 0, yend = 178) +
 geom_point(aes(color = better_big_change)) +
 geom_label_repel(
 data = filter(co2_rankings, 
 big_change == TRUE),
 aes(label = country, 
 fill = better_big_change),
 color = "white", family = "comic neue")

base
```
]
]
.pull-right[
<img src="index_files/figure-html/unnamed-chunk-38-1.png" width="100%" style="display: block; margin: auto;" />
]

---

``` r
set.seed(13)

base +
* annotate(
* geom = "text", x = 170, y = 6,
* label = "Outliers improving",
* family = "comic neue",
* fontface = "italic",
* hjust = 1,
* color = "grey50" )
```
]
]
.pull-right[
<img src="index_files/figure-html/unnamed-chunk-39-1.png" width="100%" style="display: block; margin: auto;" />
]

---

``` r
set.seed(13)

base2 <-base + 
 annotate(
 geom = "text", x = 170, y = 6, label = "Outliers improving",
 family = "comic neue", fontface = "italic", hjust = 1, color = "grey50"
 ) +
* annotate(
* geom = "text", x = 2, y = 170, label = "Outliers worsening",
* family = "comic neue", fontface = "italic", hjust = 0, color = "grey50" )

base2
```
]
]
.pull-right[
<img src="index_files/figure-html/unnamed-chunk-40-1.png" width="100%" style="display: block; margin: auto;" />
]

---

``` r
set.seed(13)

base2 +
* scale_color_manual(
* values = c("grey50",
* "#0074D9", "#FF4136"))
```
]
]
.pull-right[
<img src="index_files/figure-html/unnamed-chunk-41-1.png" width="100%" style="display: block; margin: auto;" />
]

---

.task[
.tiny[
Use the same colors for the labels. Only need the last two values in the original palette.
]
]

``` r
set.seed(13)

base2 + 
 scale_color_manual(
 values = c("grey50", 
 "#0074D9", "#FF4136")) +
* scale_fill_manual(
* values = c("#0074D9", "#FF4136"))
```
]
]
.pull-right[
<img src="index_files/figure-html/unnamed-chunk-42-1.png" width="100%" style="display: block; margin: auto;" />
]

---

``` r
set.seed(13)

base2 +
 scale_color_manual(values = c("grey50", "#0074D9", "#FF4136")) +
 scale_fill_manual(values = c("#0074D9", "#FF4136")) +
* scale_x_continuous(expand = c(0, 0),
* breaks =
* seq(0, 175, 25)) +
* scale_y_continuous(expand = c(0, 0),
* breaks =
* seq(0, 175, 25))
```
]
]
.pull-right[
<img src="index_files/figure-html/unnamed-chunk-43-1.png" width="100%" style="display: block; margin: auto;" />
]

---

``` r
set.seed(13)

base2 +
 scale_color_manual(values = c("grey50", "#0074D9", "#FF4136")) +
 scale_fill_manual(values = c("#0074D9", "#FF4136")) +
 scale_x_continuous(expand = c(0, 0), breaks = seq(0, 175, 25)) +
 scale_y_continuous(expand = c(0, 0), breaks = seq(0, 175, 25)) +
* labs(
* x = "Rank in 1995", y = "Rank in 2014",
* title = "Changes in CO2 emission rankings between 1995 and 2014",
* subtitle = "Countries that improved or worsened more than 25 positions in the rankings highlighted",
* caption = "Source: The World Bank.\nCountries with populations of less than 200,000 excluded."
* )
```
]
]
.pull-right[
<img src="index_files/figure-html/unnamed-chunk-44-1.png" width="100%" style="display: block; margin: auto;" />
]

---

``` r
set.seed(13)

base3 <-base2 +
 scale_color_manual(values = c("grey50", "#0074D9", "#FF4136")) +
 scale_fill_manual(values = c("#0074D9", "#FF4136")) +
 scale_x_continuous(expand = c(0, 0), breaks = seq(0, 175, 25)) +
 scale_y_continuous(expand = c(0, 0), breaks = seq(0, 175, 25)) +
 labs(
 x = "Rank in 1995", y = "Rank in 2014",
 title = "Changes in CO2 emission rankings between 1995 and 2014",
 subtitle = "Countries that improved or worsened more than 25 positions in the rankings highlighted",
 caption = "Source: The World Bank.\nCountries with populations of less than 200,000 excluded."
 ) +
* guides(color = "none", fill = "none")

base3
```
]
]
.pull-right[
<img src="index_files/figure-html/unnamed-chunk-45-1.png" width="100%" style="display: block; margin: auto;" />
]

---

``` r
set.seed(13)

base3 + 
* theme_bw(base_family = "Roboto Condensed")
```
]
]
.pull-right[
<img src="index_files/figure-html/unnamed-chunk-46-1.png" width="100%" style="display: block; margin: auto;" />
]

---

.task[
.tiny[
Use HTML and Markdown syntax to customize the visual appearance of the title and subtitle.
]
]

``` r
set.seed(13)

base4 <- base3 + 
 labs(
 x = "Rank in 1995", y = "Rank in 2014",
* title = "Changes in CO2 emission rankings between 1995 and 2014",
* subtitle = "Countries that **improved** or **worsened** more than 25 positions in the rankings highlighted",
 caption = "Source: The World Bank.\nCountries with populations of less than 200,000 excluded."
 ) +
 guides(color = "none", fill = "none") +
 theme_bw(base_family = "comic neue")
base4 
```
]
]
.pull-right[
<img src="index_files/figure-html/unnamed-chunk-47-1.png" width="100%" style="display: block; margin: auto;" />
]

---

``` r
set.seed(13)

base4 +
 labs(
 x = "Rank in 1995", y = "Rank in 2014",
 title = "Changes in CO2 emission rankings between 1995 and 2014",
 subtitle = "Countries that **improved** or **worsened** more than 25 positions in the rankings highlighted",
 caption = "Source: The World Bank.\nCountries with populations of less than 200,000 excluded."
 ) +
 guides(color = "none", fill = "none") +
 theme_bw(base_family = "comic neue") +
* theme(
* plot.title = element_markdown(face = "bold",
* size = rel(1.6)),
* plot.subtitle = element_markdown(
* size = rel(1.3)),
* plot.margin = unit(c(0.5, 1, 0.5, 0.5),
* units = "lines"))
```
]
]
.pull-right[
<img src="index_files/figure-html/unnamed-chunk-48-1.png" width="100%" style="display: block; margin: auto;" />
]

---

---

<img src="index_files/figure-html/unnamed-chunk-50-1.png" width="90%" style="display: block; margin: auto;" />
---

# Recap

* Thinking about how to customize your plots to provide clear information for readers
* Select themes that work best with your data and narrative
* Fonts can be finnicky!!