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ggplot2

The “gg” in ggplot2 stands for the “Grammar of Graphics.” The grammar of graphics is a philosophy of data visualization which forces you to think about what you want to visualize how. Hadley Wickham followed this philosophy to implement the ggplot2 package.

The anatomy of a ggplot2 plot

The grammar of graphics specifies building blocks out of which an analyst builds a plot. These include, in the order of application:

Data (what do you want to plot?)
Aesthetic mapping (what comes on the x and y axes? )
Geometric object (geoms) (How do we want to see our data? Points, lines, bars, …)
Add more geoms (e.g. add regression lines to a scatterplot)
Polish labels, scales, legends, and appearance.

(see this link for more details)

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

Useful tips from the dataviz ninja

Think hard about what you want to visualize!

“Think of graphs as comparison” - Andrew Gelman

ggplot2 building blocks

Let’s look at the ggplot2 building blocks in practice:

library(gapminder) # loads the gapminder data
library(tidyverse) # loads ggplot2 and other packages

example_plot <- ggplot(data = gapminder, # specify which dataset to use
                       aes(x = year, # what goes on the x axis?
                           y = lifeExp )) + # what's on the y axis?
  geom_point() # with which geometric object should the data be displayed?

Note the + that ties the building blocks together.

ggplot2 building blocks

print(example_plot)

Aesthetics - Size

library(gapminder) 
library(tidyverse) 

example_plot <- ggplot(data = gapminder, 
                       aes(x = year,  # the aes() function defines aesthetics
                           y = lifeExp, 
                           size = gdpPercap)) + # map the aesthetic 'size' to gdp/pc
  geom_point() 

# print(example_plot)

Aesthetics - Size

print(example_plot)

Aesthetics II - Color

library(gapminder) 
library(tidyverse) 

example_plot <- ggplot(data = gapminder, 
                       # the aes() function defines aesthetics
                       aes(x = year,  # x axis
                           y = lifeExp, # y axis
                           color = continent, # map color to continent
                           size = gdpPercap)) + # map the aesthetic 'size' to gdp/pc
  geom_point()

Aesthetics II - Color

print(example_plot)

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

Useful tips from the dataviz ninja

Think hard about what you want to visualize!
Don’t use too many aesthetics - just use those that help you clarify your comparison! > “When ggplot successfully makes a plot but the result looks insane, the reason is almost always that something has gone wrong in the mapping between the data and aesthetics for the geom being used” - Kieran Healy

geoms

library(gapminder) 
library(tidyverse) 

example_plot <- ggplot(data = gapminder, 
                       aes(x = year, 
                           y = lifeExp)) +
  geom_line() # lines instead of points

geoms

Whoops! What happened here?

print(example_plot)

geoms

library(gapminder) 
library(tidyverse) 

example_plot <- ggplot(data = gapminder, 
                       aes(x = year, 
                           y = lifeExp, 
                           group = country)) +  # tell ggplot2 which 
                                                # observations belong together
  geom_line()

geoms

print(example_plot)

Combining geoms

library(gapminder) 
library(tidyverse) 

example_plot <- ggplot(data = gapminder, 
                       aes(x = year, 
                           y = lifeExp)) +  
  geom_point() +
  geom_smooth(method = "lm") # add regression line

Combining geoms

print(example_plot)

Combining geoms II

library(gapminder) 
library(tidyverse) 

example_plot <- ggplot(data = gapminder, 
                       aes(x = year, 
                           y = lifeExp)) +  
  geom_point() +
  geom_smooth(method = "lm") +
  geom_smooth(method = "loess", 
              color = "firebrick") # fix smoother color

Combining geoms II

print(example_plot)

Short exercise

In the prior example we fix the color, i.e. we map it to a fixed value (firebrick which is red). What happens if we use the aes()function within the the geom_smooth() geom to map color to a variable in the gapminder dataset, such as continent?

Try out yourself!

Manipulate and Preprocess Data

Suppose we want to compare GDP/capita between North and South America visually.

We need to filter our data. Filtering helps to reduce complexity & get at the comparison that we want. To do that, we use the dplyr package which is part of the tidyverse.

To filter data, we use the filter() function which comes with the dplyr package.

library(tidyverse) # loads dplyr package, among others
library(gapminder)

gapminder_americas <- gapminder %>% # the %>% `chains` together functions
  filter(continent == "Americas") # that's two "=" 

head(gapminder_americas, 5)

## # A tibble: 5 x 6
##   country   continent  year lifeExp      pop gdpPercap
##   <fctr>    <fctr>    <int>   <dbl>    <int>     <dbl>
## 1 Argentina Americas   1952    62.5 17876956      5911
## 2 Argentina Americas   1957    64.4 19610538      6857
## 3 Argentina Americas   1962    65.1 21283783      7133
## 4 Argentina Americas   1967    65.6 22934225      8053
## 5 Argentina Americas   1972    67.1 24779799      9443

Manipulate and Preprocess Data

Modify/add variables to existing data frame. We modify data with the mutate() function and chain them together using the pipe operator %>%.

library(tidyverse) # loads dplyr package, among others
library(gapminder)

gapminder_americas <- gapminder %>% 
  filter(continent == "Americas") %>% 
  # create a character/categorical variable
  # to distinguish between North/South America
  mutate(north_america = ifelse(country == "United States" |
                                  country == "Canada", 
                                "north_america", 
                                "south_america"))
head(gapminder_americas,3)

## # A tibble: 3 x 7
##   country   continent  year lifeExp      pop gdpPercap north_america
##   <fctr>    <fctr>    <int>   <dbl>    <int>     <dbl> <chr>        
## 1 Argentina Americas   1952    62.5 17876956      5911 south_america
## 2 Argentina Americas   1957    64.4 19610538      6857 south_america
## 3 Argentina Americas   1962    65.1 21283783      7133 south_america

Manipulate and Preprocess Data

Use filtered and preprocessed data to highlight comparisons in ggplot:

ggplot(gapminder_americas, # only use data for Americas
       aes(x = year, 
           y = gdpPercap, 
           color = north_america)) + # map "north_america" category to color
  geom_point()

Exercise

Plot the development of population size (pop variable in the gapminder data) over time (year variable in the gapminder data) in Asia (hint: continent == "Asia"). Add a trend line and/or smooth line.

Bonus exercise: Plot the relationship between population size pop and gdpPercap! (hint: might make sense to wrap pop and gdpPercap in log()).

Solution

library(tidyverse)
library(gapminder)

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

asia_pop <- ggplot(gapminder_asia, 
                   aes(x = year, y = pop)) +
  geom_point() +
  geom_smooth(method = "lm")

print(asia_pop)

Walkthrough Exercise

Goal:

What do we want to visualize?

Think about the data! What is the comparison?

Genocide vs. non-genocide countries => Rwanda vs. rest of Africa

library(gapminder)
library(tidyverse)

gapminder_africa <- gapminder %>% 
  # filter only African countries
  filter(continent == "Africa") %>% 
  # create a categorical variable that distinguishes
  # between Rwanda and other African countries
  mutate(color_plot = ifelse(country != "Rwanda", # != = "!" + "="
                             "Other African Countries", 
                             "Rwanda"))

Add geom_line() + map color/alpha

rwanda_plot <- ggplot(gapminder_africa, 
                      aes(x = year, 
                          y = lifeExp, 
                          group = country, 
                          color = color_plot)) + 
  geom_line(aes(alpha = color_plot)) # map alpha to "color_plot" variable     
                                     # ggplot chooses alpha level automatically
  
  

print(rwanda_plot)

Add color/alpha scales

rwanda_plot <- ggplot(gapminder_africa, 
                      aes(x = year, 
                          y = lifeExp, 
                          group = country, 
                          color = color_plot)) + 
  geom_line(aes(alpha = color_plot)) + 
  
  # we assign colors/alpha values/other "aes" through "scale" functions
  scale_alpha_discrete("", range = c(0.5, 1)) +  
  scale_color_manual("", values = c("lightgrey", "black")) 


print(rwanda_plot)

Manipulate appearance: add theme

rwanda_plot <- ggplot(gapminder_africa, 
                      aes(x = year, 
                          y = lifeExp, 
                          group = country, 
                          color = color_plot)) + 
  geom_line(aes(alpha = color_plot)) + 
  scale_alpha_discrete("", range = c(0.5, 1)) +  
  scale_color_manual("", values = c("lightgrey", "black")) + 
  
  # add theme
  theme_bw() + # black and white theme
  theme(legend.position = "bottom", # legend position
        panel.grid = element_blank()) # remove grid lines

Manipulate appearance: add theme

print(rwanda_plot)

Manipulate appearance: change labels

rwanda_plot <- ggplot(gapminder_africa, 
                      aes(x = year, 
                          y = lifeExp, 
                          group = country, 
                          color = color_plot)) + 
  geom_line(aes(alpha = color_plot)) + 
  scale_alpha_discrete("", range = c(0.5, 1)) +  
  scale_color_manual("", values = c("lightgrey", "black")) + 
  theme_bw() + 
  theme(legend.position = "bottom", 
        panel.grid = element_blank()) +
  
  # labels, captions, and title/subtitle
  labs(x = "", y = "Life Expectancy in Years", 
       title = "The Impact of Genocide on Life Expectancy", 
       subtitle = "Life expectancy for newborns extrapolated from mortality rate in a given year.", 
       caption = " Data source: gapminder.org")

Manipulate appearance: change labels

print(rwanda_plot)

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

Useful tips from the dataviz ninja

Think hard about what you want to visualize!
Don’t use too many aesthetics - just use those that help you clarify your comparison!
Trial and error is your friend!
> “If you are unsure of what each piece of code does, take advantage of ggplot’s additive character. Working backwards from the bottom up, remove each + some_function(…) statement one at a time to see how the plot changes.” - Kieran Healy

Session 2: ggplot2

Slides

ggplot2

The anatomy of a ggplot2 plot

Useful tips from the dataviz ninja

ggplot2 building blocks

ggplot2 building blocks

Aesthetics - Size

Aesthetics - Size

Aesthetics II - Color

Aesthetics II - Color

Useful tips from the dataviz ninja

geoms

geoms

geoms

geoms

Combining geoms

Combining geoms

Combining geoms II

Combining geoms II

Short exercise

Manipulate and Preprocess Data

Manipulate and Preprocess Data

Manipulate and Preprocess Data

Exercise

Solution

Walkthrough Exercise

What do we want to visualize?

Add geom_line() + map color/alpha

Add color/alpha scales

Manipulate appearance: add theme

Manipulate appearance: add theme

Manipulate appearance: change labels

Manipulate appearance: change labels

Useful tips from the dataviz ninja