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facets

faceted plots (or small multiple plots) are a way to divide your data up by a categorical variable. Facets are “not a geom, but rather a way of organizing a series of geoms” (Kieran Healy).

facets: think about the comparison!

GDP/pc development by continent.

In ggplot, we use the facet_wrap() building block to specify the faceting variable(s).

p <- ggplot(data = gapminder, 
            mapping = aes(x = year, 
                          y = gdpPercap)) + 
  geom_line(color="gray70", 
            aes(group = country)) + # recall we need to map group to country
  facet_wrap(~ continent, # "~" 
             ncol = 5) # how many columns?

facets: think about the comparison!

print(p)

facets: all elements

library(tidyverse)
library(gapminder)

p <- ggplot(data = gapminder, 
            mapping = aes(x = year, 
                          y = gdpPercap)) + 
  geom_line(color="gray70", aes(group = country)) +
  # add smoother
  geom_smooth(size = 1.1, method = "loess", se = FALSE) +
  # log y axis (could've also wrapped y=log(gdpPercap) in aes() above)
  scale_y_log10(labels=scales::dollar) +
  # facet command
  facet_wrap(~ continent, ncol = 5) +
  # labels and appearance tweaks
  labs(x = "Year",
       y = "GDP per capita",
       title = "GDP per capita on Five Continents") +
  theme_bw() +
  theme(axis.text.x = element_text(size = 5))

facets: all elements

print(p)

facets: more applications

Recall our example: relationship between GDP per capita and population in Asia.

gdp_pop_plot <- ggplot(gapminder %>% filter(continent == "Asia"), 
                       aes(x = log(pop), 
                           y = log(gdpPercap))) +
  geom_point(alpha = 0.5, size = 2) +
  geom_smooth(method = "lm")

print(gdp_pop_plot)

class: inverse, center, middle

Really a negative relationship?

facets: more applications

Plot regression line by country (without facets)

gdp_pop_plot <- ggplot(gapminder %>% filter(continent == "Asia"), 
                       aes(x = log(pop), 
                           y = log(gdpPercap))) +
  geom_point(aes(color = country), 
             alpha = 0.5, size = 2) +
  geom_smooth(aes(fill = country, color = country),
              method = "lm") +
  theme(legend.position = "none")

facets: more applications

facets: more applications

Prior plot useful for MoMA, but not for the data analyst. How to do better?

facets!

gapminder_asia <- gapminder %>% 
  filter(continent == "Asia")

gdp_pop_plot <- ggplot(gapminder_asia, 
                       aes(x = log(pop), 
                           y = log(gdpPercap))) +
  geom_point(alpha = 0.5, size = 1) +
  geom_smooth(method = "lm", size = 0.7) + 
  facet_wrap(~ country, scales = "free") + # scales = "free" to vary axis limits +
  theme_bw() +
  theme(axis.text = element_text(size = 4), 
        strip.text = element_text(size = 6)) 

facets: more applications

class: inverse background-image: url(“Ninja-header.svg_opacity1.png”) background-size: contain

Useful tips from the dataviz ninja

1. Think hard about what you want to visualize!

2. Don’t use too many aesthetics - just use those that help you clarify your comparison!

3. Trial and error is your friend!

4. Alphabet is the least useful ways to organize information.

facets: order by summary statistic

library(forcats) # useful to reorder factors or ordered categorical variables

gapminder_asia <- gapminder %>% 
  filter(continent == "Asia") %>% 
  # do all data manipulation by country
  group_by(country) %>% 
  # extract beta coefficient from reg of GDP on pop 
  mutate(beta = coef(lm(log(gdpPercap) ~ log(pop)))[2]) %>%  
  # remove country grouping 
  ungroup() %>% 
  # sort "country" variable by beta
  mutate(country_order = fct_reorder(country, beta))

head(gapminder_asia, 5)

## # A tibble: 5 x 8
##   country     continent  year lifeExp      pop gdpPercap    beta country_~
##   <fctr>      <fctr>    <int>   <dbl>    <int>     <dbl>   <dbl> <fctr>   
## 1 Afghanistan Asia       1952    28.8  8425333       779 -0.0380 Afghanis~
## 2 Afghanistan Asia       1957    30.3  9240934       821 -0.0380 Afghanis~
## 3 Afghanistan Asia       1962    32.0 10267083       853 -0.0380 Afghanis~
## 4 Afghanistan Asia       1967    34.0 11537966       836 -0.0380 Afghanis~
## 5 Afghanistan Asia       1972    36.1 13079460       740 -0.0380 Afghanis~

facets: order by summary statistic

facets: Exercise

Plot the relationship between gdpPercap and lifeExp in the Americas, faceted by country.

Bonus: sort country by the direction + strength of the relationship between gdpPercap and lifeExp

What is surprising?

facets: Exercise Solution

gapminder_americas <- gapminder %>% 
  filter(continent == "Americas") %>% 
  group_by(country) %>% 
  mutate(beta = coef(lm(lifeExp ~ log(gdpPercap)))[2]) %>% 
  ungroup() %>% 
  mutate(country_order = fct_reorder(country, beta))

gdp_lifeexp_americas <- ggplot(gapminder_americas, 
                       aes(x = log(gdpPercap), 
                           y = lifeExp)) +
  geom_point(alpha = 0.5, size = 1) +
  geom_smooth(method = "lm", size = 0.7) + 
  facet_wrap(~ country_order,  # facet by "country_order"!
             scales = "free") + # scales = "free" to vary axis limits + 
  theme_bw() +
  theme(axis.text = element_text(size = 4), 
        strip.text = element_text(size = 6)) 

facets: Exercise Solution