Plotting basics

Data Visualization and Exploration

Ozan Kahramanoğulları

GGplot2

GGplot2 is a tidyverse library for plotting.
It builds on top of a “grammar of graphics”.
Makes building plots modular.

The ggplot workflow

Building plots incrementally

renv::install("gapminder")

library(gapminder)
gapminder

# A tibble: 1,704 × 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.
 7 Afghanistan Asia       1982    39.9 12881816      978.
 8 Afghanistan Asia       1987    40.8 13867957      852.
 9 Afghanistan Asia       1992    41.7 16317921      649.
10 Afghanistan Asia       1997    41.8 22227415      635.
# ℹ 1,694 more rows

Building plots incrementally

p <- ggplot(data = gapminder, 
            mapping = aes(
              x = gdpPercap, 
              y = lifeExp))
p

Building plots incrementally

p <- ggplot(data = gapminder, 
            mapping = aes(
              x = gdpPercap, 
              y = lifeExp))
p

Building plots incrementally

p <- ggplot(data = gapminder, 
            mapping = aes(
              x = gdpPercap, 
              y = lifeExp))
p

gapminder %>%
  select(gdpPercap, lifeExp) %>%
  summarise(
    minGdp = min(gdpPercap),
    maxGdp = max(gdpPercap),
    minLifeExp = min(lifeExp),
    maxLifeExp = max(lifeExp)
  )

# A tibble: 1 × 4
  minGdp  maxGdp minLifeExp maxLifeExp
   <dbl>   <dbl>      <dbl>      <dbl>
1   241. 113523.       23.6       82.6

Building plots incrementally

p + geom_point()

Building plots incrementally

p + geom_point()

Building plots incrementally

p + geom_smooth()

Building plots incrementally

p + geom_smooth()

Stacking geoms

p + geom_point() + geom_smooth()

Stacking geoms

p + geom_point() + geom_smooth()

Stacking geoms

What happens if we swap the order of two geoms?

p + geom_smooth() + geom_point()

Stacking geoms

What happens if we swap the order of two geoms?

p + geom_smooth() + geom_point()

Beware of line breaks!

This works.

p + geom_smooth() + geom_point()

Also, this.

p + geom_smooth() + 
  geom_point()

This doesn’t.

p + geom_point()  
  + geom_smooth()

Building plots incrementally

p + geom_point() + geom_smooth(method = "lm")

Building plots incrementally

p + geom_point() + geom_smooth(method = "lm")

Playing with scales

p + geom_point() + 
    geom_smooth(method = "gam")

Playing with scales

p + geom_point() + 
    geom_smooth(method = "gam")

Playing with scales

p + geom_point() + 
    geom_smooth(method = "gam") + 
    scale_x_log10()

Playing with scales

p + geom_point() + 
    geom_smooth(method = "gam") + 
    scale_x_log10()

Playing with scales

p + geom_point() + 
    geom_smooth(method = "gam") + 
    scale_x_log10()

ggplot applies the scale transformations before fitting the model line.

Changing labels

p + geom_point() + 
    geom_smooth(method = "gam") + 
    scale_x_log10(labels = scales::dollar)

Changing labels

p + geom_point() + 
    geom_smooth(method = "gam") + 
    scale_x_log10(labels = scales::dollar)

Setting labels

p <- ggplot(data = gapminder, 
            mapping = aes(x = gdpPercap, 
                          y = lifeExp))
p + geom_point() + 
    geom_smooth(method = "gam") + 
    scale_x_log10(labels = scales::dollar) +
    labs(
      x = "GDP per capita",
      y = "Life Expectancy in Years",
      title = "Economic growth and life expectancy",
      caption = "Source: Gapminder."
    )

Setting labels

p <- ggplot(data = gapminder, 
            mapping = aes(x = gdpPercap, 
                          y = lifeExp))
p + geom_point() + 
    geom_smooth(method = "gam") + 
    scale_x_log10(labels = scales::dollar) +
    labs(
      x = "GDP per capita",
      y = "Life Expectancy in Years",
      title = "Economic growth and life expectancy",
      caption = "Source: Gapminder."
    )

Finishing up?

p <- ggplot(data = gapminder,
            mapping = aes(x = gdpPercap, 
                          y = lifeExp))
p + geom_point() + 
    geom_smooth(method = "gam") + 
    scale_x_log10(labels = scales::dollar) +
    labs(
      x = "GDP per capita",
      y = "Life Expectancy in Years",
      title = "Economic growth and life expectancy",
      caption = "Source: Gapminder."
    ) +
    theme_bw()

A finished plot?

Look again at this picture: can we do better?

Looking at the dataset, which information are we ignoring?

What about colors?

p <- ggplot(data = gapminder, 
            mapping = aes(x = gdpPercap, 
                          y = lifeExp, 
                          color=continent))
p + geom_point() + 
    geom_smooth(method = "gam") + 
    scale_x_log10(labels = scales::dollar)

What about colors?

p <- ggplot(data = gapminder, 
            mapping = aes(x = gdpPercap, 
                          y = lifeExp, 
                          color=continent))
p + geom_point() + 
    geom_smooth(method = "gam") + 
    scale_x_log10(labels = scales::dollar)

What about colors?

p <- ggplot(data = gapminder, 
            mapping = aes(x = gdpPercap, 
                          y = lifeExp, 
                          color=continent))
p + geom_point() + 
    geom_smooth(method = "gam") + 
    scale_x_log10(labels = scales::dollar)

That’s quite a mess!

Changing aesthetics for single geoms

p <- ggplot(data = gapminder, 
            mapping = aes(x = gdpPercap, 
                          y = lifeExp, 
                          color=continent))
p + geom_point(size = .01) + 
    geom_smooth(method = "gam") + 
    scale_x_log10(labels = scales::dollar)

Changing aesthetics for single geoms

p <- ggplot(data = gapminder, 
            mapping = aes(x = gdpPercap, 
                          y = lifeExp))
p + geom_point(size = .01, color="gray") + 
    geom_smooth(mapping = aes(color = continent),
                method = "gam") + 
    scale_x_log10(labels = scales::dollar)

Changing aesthetics for single geoms

p <- ggplot(data = gapminder, 
            mapping = aes(x = gdpPercap, 
                          y = lifeExp))
p + geom_point(mapping = aes(color = "gray"), size = .01) + 
    geom_smooth(mapping = aes(color = continent),
                method = "gam") + 
    scale_x_log10(labels = scales::dollar)

What is hapenning here?

Changing aesthetics for different geoms

p <- ggplot(data = gapminder, 
            mapping = aes(x = gdpPercap, 
                          y = lifeExp))
p + geom_point(mapping = aes(color = continent), size = .01) +
    geom_smooth(method = "gam", color = "black") + 
    scale_x_log10(labels = scales::dollar)

Maybe in this case it’s better to have a global smoothing line.

Combining with dplyr

filtered_gapminder <- gapminder %>% filter(year == 2007)
p <- ggplot(data = filtered_gapminder, 
            mapping = aes(x = gdpPercap, 
                          y = lifeExp,
                          color = continent))
p + geom_point() + 
    scale_x_log10(labels = scales::dollar)

Combining with dplyr

filtered_gapminder <- gapminder %>% filter(year == 2007)
p <- ggplot(data = filtered_gapminder, 
            mapping = aes(x = gdpPercap, 
                          y = lifeExp,
                          color = continent))
p + geom_point() + 
    scale_x_log10(labels = scales::dollar,
                  breaks = c(300, 3000, 30000))

Faceting

filtered_gapminder <- gapminder %>% 
  filter(year == 2007)
p <- ggplot(data = filtered_gapminder, 
            mapping = aes(x = gdpPercap, 
                          y = lifeExp,
                          color = continent))
p + geom_point() + 
    scale_x_log10(labels = scales::dollar,
                  breaks = c(300,3000,30000)) +
    facet_wrap(vars(continent))

Faceting

Wrap up

What happens if you map year to color?

p <- ggplot(gapminder, 
            aes(x=gdpPercap, 
                y=lifeExp,
                color=year))
p + geom_point() +
    scale_x_log10()

p <- ggplot(gapminder, 
            aes(x=gdpPercap, 
                y=lifeExp,
                color=factor(year)))
p + geom_point() +
    scale_x_log10()

Wrap up

p <- ggplot(data = gapminder, 
            mapping = aes(x = gdpPercap, 
                          y = lifeExp,
                          color = continent,
                          fill = continent))
p + geom_point(color="gray") + 
    geom_smooth(method = "gam") + 
    scale_x_log10(labels = scales::dollar) +
    labs(
      x = "GDP per capita",
      y = "Life Expectancy in Years",
      title = "Economic growth and life expectancy",
      caption = "Source: Gapminder."
    ) +
    theme_bw()

Wrap up

Look closely at the legend. How is it related to the geoms you use?

Saving your plots

You can save your plots within Quarto documents.
You can export them to an external file.

Including in Quarto documents

You can use the execution options to set some parameters.

See https://quarto.org/docs/computations/execution-options.html

globally:

or locally on the R chunks

Saving to file

Save to png file the last plot you rendered.

ggsave(filename = "myplot.png")

save to pdf file a specific plot object.

ggsave(filename = "myplot.pdf", plot = p)