Data visualization

Overview

  1. The ggplot function

  2. Mapping data to aesthetics

  3. Different geoms

  4. Scales

  1. Facets

  2. Coordinates

  3. Themes

The ggplot function

tidyverse

ggplot

The ggplot() function

ggplot() from the ggplot2 package is what we’re gonna use for all our plots

It takes the following core arguments:

ggplot(data, aes()) + geometry + other_stuff
  • Data: the values to plot
  • Mapping (aes, for aesthetics): the structure of the plot
  • Geometry: the type of plot

You can also use a pipe

data |> 
ggplot(aes()) + geometry + other_stuff

The ggplot() function

Take for instance the gapminder data you’ve previously installed.

library(gapminder)

# The data() function in R is used to list, load, 
# and access built-in or package-provided datasets. 
data(gapminder)

Let’s get a quick overview of the data again.

head(gapminder)
# A tibble: 6 × 6
  country     continent  year lifeExp      pop gdpPercap
  <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
1 Afghanistan Asia       1952    28.8  8425333      779.
2 Afghanistan Asia       1957    30.3  9240934      821.
3 Afghanistan Asia       1962    32.0 10267083      853.
4 Afghanistan Asia       1967    34.0 11537966      836.
5 Afghanistan Asia       1972    36.1 13079460      740.
6 Afghanistan Asia       1977    38.4 14880372      786.

What’s the relationship between life expectancy and GDP per capita?



… we expect of course that higher GDP per capita leads to greater life expactancy.

What’s the relationship between life expectancy and GDP per capita?

  • We first assign the gapminder data to ggplot()

  • The result is just an empty plot

ggplot(data = gapminder)

What’s the relationship between life expectancy and GDP per capita?

  • Next, we map out the plot by adding the x and y axes
ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp))

What’s the relationship between life expectancy and GDP per capita?

  • We then define how we want to plot our data
  • In this case, let’s go for the raw data points
ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp)) +
  geom_point()

Mapping data to aesthetics

So far, only two variables appear in our plot (mapped onto the x and the y axis)


But we can add more variables to the plot, by assigning them to certain asthetics

Mapping data to aesthetics

  • For example, we can display the variable continent as colors
ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp)) +
  geom_point()

Mapping data to aesthetics

  • For example, we can display the variable continent as colors

  • Note that a legend gets added automatically to the plot

ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp, color = continent)) +
  geom_point()

Mapping data to aesthetics

  • We could further display population size by mapping it to the size aesthetic
ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp, color = continent, 
                     size = pop)) +
  geom_point()

Grammatical Layers

So far we know about data, aesthetics, and geometries


Think of these components as layers


We add them to foundational ggplot() with +

Possible aesthetics

color (discrete)

color (continuous)

size

fill

shape

alpha

Possible geoms

Example geom What it makes
geom_col() Bar charts
geom_text() Text
geom_point() Points
geom_boxplot()  Boxplots
geom_sf() Maps

Possible geoms

There are dozens of possible geoms and
each class session will cover different ones.


See the {ggplot2} documentation for complete examples of all the different geom layers

Additional Layers

There are many of other grammatical layers we can use to describe graphs!

We sequentially add layers onto the foundational ggplot() plot to create complex figures

Scales

Scales change how variables are mapped

Example layer What it does
scale_x_continuous() Make the x-axis continuous
scale_x_continuous(breaks = 1:5)  Manually specify axis ticks
scale_x_log10() Log the x-axis
scale_color_gradient() Use a gradient
scale_fill_viridis_d() Fill with discrete viridis colors

Scales

ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp, color = continent, 
                     size = pop)) +
  geom_point()

Scales

ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp, color = continent, 
                     size = pop)) +
  geom_point() +
  scale_x_log10()

Scales

ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp, color = continent, 
                     size = pop)) +
  geom_point() +
  scale_x_log10() +
  scale_color_viridis_d()

Facets

Facets show subplots for different subsets of data

Example layer What it does
facet_wrap(vars(continent)) Plot for each continent
facet_wrap(vars(continent, year))  Plot for each continent/year
facet_wrap(…, ncol = 1) Put all facets in one column
facet_wrap(…, nrow = 1) Put all facets in one row

Facets

ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp, color = continent, 
                     size = pop)) +
  geom_point() +
  scale_x_log10() +
  scale_color_viridis_d()

Facets

ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp, color = continent, 
                     size = pop)) +
  geom_point() +
  scale_x_log10() +
  scale_color_viridis_d() +
  facet_wrap(vars(continent))

Facets

ggplot(data = gapminder |> 
         filter(year %in% c(2002, 2007)), 
       mapping = aes(x = gdpPercap, y = lifeExp, color = continent, 
                     size = pop)) +
  geom_point() +
  scale_x_log10() +
  scale_color_viridis_d() +
  facet_wrap(vars(continent, 
                  year), nrow = 2)

Coordinates

Change the coordinate system

Example layer What it does
coord_cartesian() Plot for each continent
coord_cartesian(ylim = c(1, 10))  Zoom in where y is 1–10
coord_flip() Switch x and y
coord_polar() Use circular polar system

Coordinates

ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp, color = continent, 
                     size = pop)) +
  geom_point() +
  scale_x_log10() +
  scale_color_viridis_d()

Coordinates

ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp, color = continent, 
                     size = pop)) +
  geom_point() +
  scale_x_log10() +
  scale_color_viridis_d() +
  coord_cartesian(ylim = c(70, 80), 
                  xlim = c(10000, 30000))

Coordinates

ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp, color = continent, 
                     size = pop)) +
  geom_point() +
  scale_x_log10() +
  scale_color_viridis_d() + 
  coord_flip()

Labels

Add labels to the plot with a single labs() layer

Example layer What it does
labs(title = “Neat title”) Title
labs(caption = “Something”) Caption
labs(y = “Something”) y-axis
labs(size = “Population”) Title of size legend 
ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp, color = continent, 
                     size = pop)) +
  geom_point() +
  scale_x_log10() +
  scale_color_viridis_d() 

ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp, color = continent, 
                     size = pop)) +
  geom_point() +
  scale_x_log10() +
  scale_color_viridis_d() +
  labs(title = "Health and wealth grow together")

ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp, color = continent, 
                     size = pop)) +
  geom_point() +
  scale_x_log10() +
  scale_color_viridis_d() +
  labs(title = "Health and wealth grow together",
       subtitle = "Data from the world")

ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp, color = continent, 
                     size = pop)) +
  geom_point() +
  scale_x_log10() +
  scale_color_viridis_d() +
  labs(title = "Health and wealth grow together",
       subtitle = "Data from the world",
       x = "Wealth (GDP per capita)")

ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp, color = continent, 
                     size = pop)) +
  geom_point() +
  scale_x_log10() +
  scale_color_viridis_d() +
  labs(title = "Health and wealth grow together",
       subtitle = "Data from the world",
       x = "Wealth (GDP per capita)",
       y = "Health (life expectancy)")

ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp, color = continent, 
                     size = pop)) +
  geom_point() +
  scale_x_log10() +
  scale_color_viridis_d() +
  labs(title = "Health and wealth grow together",
       subtitle = "Data from the world",
       x = "Wealth (GDP per capita)",
       y = "Health (life expectancy)",
       color = "Continent")

ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp, color = continent, 
                     size = pop)) +
  geom_point() +
  scale_x_log10() +
  scale_color_viridis_d() +
  labs(title = "Health and wealth grow together",
       subtitle = "Data from the world",
       x = "Wealth (GDP per capita)",
       y = "Health (life expectancy)",
       color = "Continent",
       size = "Population")

ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp, color = continent, 
                     size = pop)) +
  geom_point() +
  scale_x_log10() +
  scale_color_viridis_d() +
  labs(title = "Health and wealth grow together",
       subtitle = "Data from the world",
       x = "Wealth (GDP per capita)",
       y = "Health (life expectancy)",
       color = "Continent",
       size = "Population",
       caption = "Source: The Gapminder Project")

Theme

Change the appearance of anything in the plot

There are many built-in themes

Example layer What it does
theme_grey() Default grey background
theme_bw() Black and white
theme_dark() Dark
theme_minimal() Minimal 
ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp, color = continent, 
                     size = pop)) +
  geom_point() +
  scale_x_log10() +
  scale_color_viridis_d() +
  labs(title = "Health and wealth grow together",
       subtitle = "Data from the world",
       x = "Wealth (GDP per capita)",
       y = "Health (life expectancy)",
       color = "Continent",
       size = "Population",
       caption = "Source: The Gapminder Project")

ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp, color = continent, 
                     size = pop)) +
  geom_point() +
  scale_x_log10() +
  scale_color_viridis_d() +
  labs(title = "Health and wealth grow together",
       subtitle = "Data from the world",
       x = "Wealth (GDP per capita)",
       y = "Health (life expectancy)",
       color = "Continent",
       size = "Population",
       caption = "Source: The Gapminder Project") +
  theme_dark()

ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp, color = continent, 
                     size = pop)) +
  geom_point() +
  scale_x_log10() +
  scale_color_viridis_d() +
  labs(title = "Health and wealth grow together",
       subtitle = "Data from the world",
       x = "Wealth (GDP per capita)",
       y = "Health (life expectancy)",
       color = "Continent",
       size = "Population",
       caption = "Source: The Gapminder Project") +
  theme_minimal()

Theme

There are collections of pre-built themes online,
like the {ggthemes} package

Theme

Organizations often make their own custom themes, like the BBC

Theme options

Make theme adjustments with theme()

There are a billion options here!

ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp, color = continent, 
                     size = pop)) +
  geom_point() +
  scale_x_log10() +
  scale_color_viridis_d() +
  labs(title = "Health and wealth grow together",
       subtitle = "Data from the world",
       x = "Wealth (GDP per capita)",
       y = "Health (life expectancy)",
       color = "Continent",
       size = "Population",
       caption = "Source: The Gapminder Project") +
  theme_minimal()

Theme options

Make theme adjustments with theme()

There are a billion options here!

ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp, color = continent, 
                     size = pop)) +
  geom_point() +
  scale_x_log10() +
  scale_color_viridis_d() +
  labs(title = "Health and wealth grow together",
       subtitle = "Data from the world",
       x = "Wealth (GDP per capita)",
       y = "Health (life expectancy)",
       color = "Continent",
       size = "Population",
       caption = "Source: The Gapminder Project") +
  theme_minimal() +
  theme(legend.position = "top",
        plot.title = element_text(face = "bold"),
        axis.title.y = element_text(face = "italic"))

There are many, many more options

See the {ggplot2} documentation for complete examples of everything you can do

Your turn #1: untidy temperatures

Take this tibble (very similar to a data.frame) of temperature recordings at three stations on three dates:

temp_data_untidy <- tribble(
  ~date, ~station1, ~station2,  ~station3,
  "2023-10-01", 30.1, 29.8,  31.2,
  "2023-11-01", 28.6, 29.1,  33.4,
  "2023-12-01", 29.9, 28.5,  32.3
)

Imagine our goal is to track temperature across time.

Your turn #1: untidy temperatures

  1. What makes this data untidy? Describe.

  2. Make a new data frame called temp_data_tidy. Use pivot_longer() to tidy the data and create a new temperature and station variable.

  3. Make a plot that tracks the temperature changes over time for station1 only. Use filter() to select the station and use mutate() in combination with the as_date() function to convert the date variable from character to a date format. into a date. Use geom_line for the plot.

  4. Now use the the non-filtered data frame with all stations. Add another aesthetic layer to your previous plot, so that your new plot allows to differentiate temperature changes between the different stations. Tip: Use color

06:00

Your turn #1: untidy temperatures

1.  What makes this data untidy? Describe.

  1. Variables are columns
  2. Observations are rows
  3. Values are cells
date station1 station2 station3
2023-10-01 30.1 29.8 31.2
2023-11-01 28.6 29.1 33.4
2023-12-01 29.9 28.5 32.3

Multiple observations (temperature recordings) per row

Your turn #1: untidy temperatures

  1. Make a new data frame called temp_data_tidy. Use pivot_longer() to tidy the data and create a new temperature and station variable.
temp_data_tidy <- temp_data_untidy |> 
  pivot_longer(cols = starts_with("station"),
               names_to = "station",
               values_to = "temperature")
date station temperature
2023-10-01 station1 30.1
2023-10-01 station2 29.8
2023-10-01 station3 31.2
2023-11-01 station1 28.6
2023-11-01 station2 29.1
2023-11-01 station3 33.4
2023-12-01 station1 29.9
2023-12-01 station2 28.5
2023-12-01 station3 32.3

Your turn #1: untidy temperatures

  1. Make a plot that tracks the temperature changes over time for station1 only. Use filter() to select the station and use mutate() in combination with the as_date() function to convert the date variable from character to a date format. into a date. Use geom_line for the plot.
temp_data_tidy |> 
  filter(station == "station1") |> 
  mutate(date = as_date(date)) |> 
  ggplot(aes(x = date, y = temperature)) +
  geom_line()

Your turn #1: untidy temperatures

  1. Now use the the non-filtered data frame with all stations. Add another aesthetic layer to your previous plot, so that your new plot allows to differentiate temperature changes between the different stations. Tip: Use color
temp_data_tidy |> 
  mutate(date = as_date(date)) |> 
  ggplot(aes(x = date, y = temperature, color = station)) +
  geom_line()

That’s it for today :)