Make Some Graphs

Modern Plain Text Social Science: Week 11

Kieran Healy

Duke University

November 12, 2024

Make Some Graphs

Load our libraries

library(here)      # manage file paths
library(socviz)    # data and some useful functions
library(tidyverse) # your friend and mine

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(gapminder) # some data

A Plot’s Components

What we need our code to make

• Data represented by visual elements;
• like position, length, color, and size;
• Each measured on some scale;
• Each scale with a labeled guide;
• With the plot itself also titled and labeled.

How does
ggplot
do this?

ggplot’s flow of action

Here’s the whole thing, start to finish

Flow of action

We’ll go through it step by step

Flow of action

ggplot’s flow of action

What we start with

ggplot’s flow of action

Where we’re going

ggplot’s flow of action

Core steps

ggplot’s flow of action

Optional steps

ggplot’s flow of action: required

Tidy data

ggplot’s flow of action: required

Aesthetic mappings

ggplot’s flow of action: required

Geom

Let’s go piece by piece

Start with the data

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

dim(gapminder)

[1] 1704    6

Create a plot object

Data is the gapminder tibble.

p <- ggplot(data = gapminder)

Map variables to aesthetics

Tell ggplot the variables you want represented by visual elements on the plot

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

Map variables to aesthetics

The mapping = aes(...) call links variables to things you will see on the plot.

x and y represent the quantities determining position on the x and y axes.

Other aesthetic mappings can include, e.g., color, shape, size, and fill.

Mappings do not directly specify the particular, e.g., colors, shapes, or line styles that will appear on the plot. Rather, they establish which variables in the data will be represented by which visible elements on the plot.

p has data and mappings but no geom

p

This empty plot has no geoms.

Add a geom

p + geom_point() 

A scatterplot of Life Expectancy vs GDP

Try a different geom

p + geom_smooth() 

A scatterplot of Life Expectancy vs GDP

Build your plots layer by layer

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

Life Expectancy vs GDP, using a smoother.

This process is additive

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

This process is additive

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

This process is additive

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

Every geom is a function

Functions take arguments

p <- ggplot(data = gapminder,
            mapping = aes(x = gdpPercap,
                          y = lifeExp))
p + geom_point() + 
  geom_smooth(method = "lm") 

Keep Layering

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

Keep Layering

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

Keep Layering

p <- ggplot(data = gapminder,
             mapping = aes(x = gdpPercap,
                           y=lifeExp))
p + geom_point() +
    geom_smooth(method = "lm")

Keep Layering

p <- ggplot(data = gapminder,
             mapping = aes(x = gdpPercap,
                           y=lifeExp))
p + geom_point() +
    geom_smooth(method = "lm") +
    scale_x_log10()

Fix the labels

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

Fix the labels

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

Fix the labels

p <- ggplot(data = gapminder,
            mapping = aes(x = gdpPercap,
                          y=lifeExp))
p + geom_point() +
    geom_smooth(method = "lm")

Fix the labels

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

Add labels, title, and caption

p <- ggplot(data = gapminder, 
            mapping = aes(x = gdpPercap, 
                          y = lifeExp))
p + geom_point() + 
  geom_smooth(method = "lm") +
    scale_x_log10(labels = scales::label_dollar()) +
    labs(x = "GDP Per Capita", 
         y = "Life Expectancy in Years",
         title = "Economic Growth and Life Expectancy",
         subtitle = "Data points are country-years",
         caption = "Source: Gapminder.")

Mapping vs Setting
your plot’s aesthetics

“Can I change the color of the points?”

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

## Put in an object for convenience
p_out <- p + geom_point() +
    geom_smooth(method = "loess") +
    scale_x_log10()

What has gone wrong here?

p_out

Try again

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

## Put in an object for convenience
p_out <- p + geom_point(color = "purple") +
    geom_smooth(method = "loess") +
    scale_x_log10()

Try again

p_out

Geoms can take many arguments

• Here we set color, size, and alpha. Meanwhile x and y are mapped.
• We also give non-default values to some other arguments

p <- ggplot(data = gapminder,
            mapping = aes(x = gdpPercap,
                          y = lifeExp)) 
p_out <- p + geom_point(alpha = 0.3) +
    geom_smooth(color = "orange", 
                se = FALSE, 
                size = 8, 
                method = "lm") +
    scale_x_log10()

Geoms can take many arguments

p_out

alpha for overplotting

p <- ggplot(data = gapminder, 
            mapping = aes(x = gdpPercap, 
                          y = lifeExp))
p + geom_point(alpha = 0.3) + 
  geom_smooth(method = "lm") +
    scale_x_log10(labels = scales::label_dollar()) +
    labs(x = "GDP Per Capita", 
         y = "Life Expectancy in Years",
         title = "Economic Growth and Life Expectancy",
         subtitle = "Data points are country-years",
         caption = "Source: Gapminder.")

Map or Set values
per geom

Geoms can take their own mappings

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

Geoms can take their own mappings

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

Geoms can take their own mappings

p <- ggplot(data = gapminder,
            mapping = aes(x = gdpPercap,
                          y = lifeExp,
                          color = continent,
                          fill = continent))
p + geom_point() +
    geom_smooth(method = "loess")

Geoms can take their own mappings

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

Geoms can take their own mappings

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

Geoms can take their own mappings

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

Geoms can take their own mappings

p <- ggplot(data = gapminder,
            mapping = aes(x = gdpPercap,
                          y = lifeExp))
p + geom_point(mapping = aes(color = continent)) +
    geom_smooth(method = "loess")

Geoms can take their own mappings

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

Geoms can take their own mappings

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

Pay attention to which scales and guides are drawn, and why

Guides and scales reflect aes() mappings

• mapping = aes(color = continent, fill = continent)

Guides and scales reflect aes() mappings

• mapping = aes(color = continent, fill = continent)

• mapping = aes(color = continent)

Remember: Every mapped variable has a scale

Saving your work

Use ggsave()

## Save the most recent plot
ggsave(filename = "figures/my_figure.png")

## Use here() for more robust file paths
ggsave(filename = here("figures", "my_figure.png"))

## A plot object
p_out <- p + geom_point(mapping = aes(color = log(pop))) +
    scale_x_log10()

ggsave(filename = here("figures", "lifexp_vs_gdp_gradient.pdf"), 
       plot = p_out)

ggsave(here("figures", "lifexp_vs_gdp_gradient.png"), 
       plot = p_out, 
       width = 8, 
       height = 5)

In code chunks

Set options in any chunk:

RMarkdown Style

{r, fig.height=8, fig.width=5, fig.show = "hold", fig.cap="A caption"}

Quarto Style

#| fig.height=8 
#| fig.width=5
#| fig.show: "hold" 
#| fig.cap="A caption"

Or for the whole document:

knitr::opts_chunk$set(warning = TRUE,
                        message = TRUE,
                        fig.retina = 3,
                        fig.align = "center",
                        fig.asp = 0.7,
                        dev = c("png", "pdf"))