R ggplot2 Example

Loading Packages

We are going to load the tidyverse to use a few data manipulation tools.

library(tidyverse)
library(ggplot2)
library(patchwork)

Data

The ggplot2 package comes with a small economics dataset that we will use.

economics

# A tibble: 574 × 6
   date         pce    pop psavert uempmed unemploy
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl>
 1 1967-07-01  507. 198712    12.6     4.5     2944
 2 1967-08-01  510. 198911    12.6     4.7     2945
 3 1967-09-01  516. 199113    11.9     4.6     2958
 4 1967-10-01  512. 199311    12.9     4.9     3143
 5 1967-11-01  517. 199498    12.8     4.7     3066
 6 1967-12-01  525. 199657    11.8     4.8     3018
 7 1968-01-01  531. 199808    11.7     5.1     2878
 8 1968-02-01  534. 199920    12.3     4.5     3001
 9 1968-03-01  544. 200056    11.7     4.1     2877
10 1968-04-01  544  200208    12.3     4.6     2709
# ℹ 564 more rows

Economic Variable Definitions

• pce personal consumption expenditures, in bilions of dollars
• pop total population, in thousands
• psavert personal savings rate
• empmed median duration of unemployment, weeks
• unemploy number of unemployed, thousands

Plotting Unemployment

urate_plot <- 
  economics |>
  mutate(urate = 100 * (unemploy / pop)) |>
  ggplot(aes(
    x = date,
    y = urate
  )) +
  geom_line()
urate_plot

This doesn’t look right! Wasn’t the unemployment rate ~ 10% during the 08-09 recession?

Yes. We calculated the unemployment rate out of the total U.S. population, when the official statistic is calculated from the labor force population (i.e. 18+, looking for a job, etc.).

Creating our own theme

Let’s create our own theme to use on all of our plots. It’s easiest to start with prebuilt theme that’s close to what you want, but I am going to list everything out just as an example.

my_theme <-
  # theme_bw() +  ## Normally you'd start from a default you like
  theme(
    panel.background = element_rect(fill = "cornsilk", color = "wheat"),
    panel.grid.major = element_line(color = "wheat"),
    panel.grid.minor = element_blank()
  )

Notice the three element_ calls.

Many theme arguments take a specific element depending on what they are—backgrounds are rectangles, the grids are lines, axes are also lies, text is text.

Each element_ call has further arguments controlling its colors, fills, lineweights, etc.

Also, you can always pass an element_blank() to theme argument to simply remove that visual object. We did that for the minor grid (the ticks between the labeled tick marks).

And here is how it looks:

urate_plot <- urate_plot + my_theme
urate_plot

Creating a Chartpack

Let’s say we want a chartpack of the 08-09 recession using our economics data.

We should calculate what the inflation rate is, and maybe show how it changes along with the savings rate, then print everything out to a pdf.

economics <- economics |> mutate(infrate = 100 * (pce / lag(pce, 12) - 1))

inf_plot <-
  economics |>
  ggplot(aes(
    x = date
  )) +
  geom_line(aes(y = infrate)) +
  geom_line(aes(y = psavert)) + 
  my_theme
inf_plot

We need to set a color aesthetic to be able to distinguish the two lines. While we are at it, we will go ahead and add a color scale.

inf_plot <-
  economics |>
  ggplot(aes(
    x = date
  )) +
  geom_line(aes(y = infrate, color = "Inflation")) +
  geom_line(aes(y = psavert, color = "Savings Rate")) + 
  scale_color_manual(values = c(Inflation = "steelblue", `Savings Rate` = "lightblue")) +
  my_theme
inf_plot

Looks like there is a big time trend down for both, but what’s the actual correlation between the two?

scatter_plot <- 
  economics |>
  ggplot(aes(
    x = infrate,
    y = psavert
  )) +
  geom_point() + 
  geom_smooth(method = "lm", se = FALSE, color = "firebrick") +
  my_theme
scatter_plot

It would be nice to actually label the correlation between the two series on the plot.

This is where the annotate function is very handy. It lets you draw a single instance of a geom, without needing to reference your data.

corr_val <- cor(economics$infrate, economics$psavert, use = "complete")

scatter_plot <- scatter_plot +
  annotate("text", x = -2, y = 17, label = round(corr_val, 4), color = "firebrick")
scatter_plot

Combine all the plots

comb_plot <- urate_plot + inf_plot + scatter_plot + guide_area() + plot_layout(guides = "collect")
comb_plot

Saving to a pdf

Finally, let’s save it out as full page of a pdf.

ggsave("test-ggplot-chartpack.pdf", comb_plot, width = 7.5, height = 10, units = "in")