vayr
In vayr: Extensions for 'ggplot2' to Visualize as You Randomize

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  out.width = "100%"
)

The goal of vayr is to provide ggplot2 extensions that foster "visualize as you randomize" principles. These principles are outlined in detail in "Visualize as You Randomize: Design-Based Statistical Graphs for Randomized Experiments." That paper can be accessed as a PDF. The package includes position adjustments to avoid over-plotting, facilitating the the organization of "data-space" to better contextualize statistical models.

Installation

The release version of vayr can be installed from CRAN, and the development version can be installed from GitHub using a package like remotes, devtools, or pak. It is important to note that vayr relies on ggplot2, packcircles, and withr, so these must be installed as well.

# From CRAN
install.packages("vayr")

# From GitHub
# install.packages("pak")
pak::pak("acoppock/vayr")

vayr contains a handful of ggplot2 functions that apply as position adjustments to "point-like" geoms such as geom_point or geom_text:

position_jitter_ellipse() and position_jitterdodge_ellipse()
position_sunflower() and position_sunflowerdodge()
position_circlepack() and position_circlepackdodge()

These functions avoid over-plotting, so they are especially useful when plotting discrete rather than continuous data. As a matter of demonstration, they are used below to visualize synthetic data, over-plotted at the origin.

suppressMessages(library(dplyr))

library(dplyr)
library(estimatr)
library(ggplot2)
library(patchwork)
library(vayr)

dat <- data.frame(
  x = c(rep(0, 200)),
  y = c(rep(0, 200)),
  group = (rep(c("A", "B", "B", "B"), 50)),
  size = runif(200, 0, 1)
)

If position is the product of discrete variables alone, then over-plotting is of particular concern. position_jitter() can mitigate this concern. It introduces variation by randomly sampling points on a rectangle. This approach is effective but can be unattractive. The position adjustments in vayr are attempts to do better.

# perfectly over-plotted points
over_plot <- ggplot(dat, aes(x = x, y = y)) +
  geom_point() +
  coord_equal(xlim = c(-1.1, 1.1), 
              ylim = c(-1.1, 1.1)) +
  theme_bw() +
  theme(axis.title = element_blank(),
        plot.title = element_text(hjust = 0.5, face = "bold")) +
  ggtitle('"perfect over-plotting"')

# position_jitter()
jitter_plot <- ggplot(dat, aes(x = x, y = y)) + 
  geom_point(position = position_jitter(width = 0.5, 
                                        height = 0.5)) +
  coord_equal(xlim = c(-1.1, 1.1), 
              ylim = c(-1.1, 1.1)) +
  theme_bw() +
  theme(axis.title = element_blank(),
        plot.title = element_text(hjust = 0.5, face = "bold")) +
  ggtitle("position_jitter()")

over_plot + jitter_plot

Position Jitter Ellipse

This position adjustment adds elliptical random noise to perfectly over-plotted points, offering a pleasing way to visualize many points that represent the same position. The benefit of sampling on an ellipse of a given height and width rather than on a rectangle is that the resulting dispersion retains the impression of a single point. The size of the ellipses stays constant, while their density varies depending on the amount of data.

# position_jitter_ellipse()
jitter_ellipse_plot <- ggplot(dat, aes(x = x, y = y)) +
  geom_point(position = position_jitter_ellipse(width = 0.5, 
                                                height = 0.5)) +
  coord_equal(xlim = c(-1.1, 1.1), 
              ylim = c(-1.1, 1.1)) +
  theme_bw() +
  theme(axis.title = element_blank(),
        plot.title = element_text(hjust = 0.5, face = "bold")) +
  ggtitle("position_jitter_ellipse()")

# position_jitterdodge_ellipse()
jitterdodge_ellipse_plot <- ggplot(dat, aes(x = x, y = y, color = group)) +
  geom_point(position = position_jitterdodge_ellipse(dodge.width = 2, 
                                                     jitter.width = 0.5, 
                                                     jitter.height = 0.5)) +
  coord_equal(xlim = c(-1.1, 1.1), 
              ylim = c(-1.1, 1.1)) +
  theme_bw() +
  theme(legend.position = "none",
        axis.title = element_blank(),
        plot.title = element_text(hjust = 0.5, face = "bold")) +
  ggtitle("position_jitterdodge_ellipse()")

jitter_ellipse_plot + jitterdodge_ellipse_plot

Position Sunflower

This position adjustment arranges perfectly over-plotted points using a sunflower algorithm, which produces a pattern that resembles the seeds of a sunflower, working from the inside out in the order of the data. The parameters for this position adjustment are density and aspect_ratio. The size of the flowers varies depending on the amount over over-plotting, but the density of the pattern remains constant. It is generally recommended that the position adjustment be used along with coord_equal(), in which case the default aspect ratio of 1 yields perfectly circular flowers, but the aspect ratio of the flowers can be adjusted if need be.

# position_sunflower()
sunflower_plot <- ggplot(dat, aes(x = x, y = y)) +
  geom_point(position = position_sunflower(density = 1, 
                                           aspect_ratio = 1)) +
  coord_equal(xlim = c(-2.1, 2.1), 
              ylim = c(-2.1, 2.1)) +
  theme_bw() +
  theme(axis.title = element_blank(),
        plot.title = element_text(hjust = 0.5, face = "bold")) +
  ggtitle("position_sunflower()")

# position_sunflowerdodge()
sunflowerdodge_plot <- ggplot(dat, aes(x = x, y = y, color = group)) +
  geom_point(position = position_sunflowerdodge(width = 4, 
                                                density = 1, 
                                                aspect_ratio = 1)) +
  coord_equal(xlim = c(-2.1, 2.1), 
              ylim = c(-2.1, 2.1)) +
  theme_bw() + 
  theme(legend.position = "none",
        axis.title = element_blank(),
        plot.title = element_text(hjust = 0.5, face = "bold")) +
  ggtitle("position_sunflowerdodge()")

sunflower_plot + sunflowerdodge_plot

The density parameter controls the density of the pattern. A density of 1 is normalized to 100 points in a unit circle; a density of 2, 200 points; and a density of 0.5, 50 points. Because density is normalized relative to Cartesian units, its visual effect depends on the ranges of the axes and the dimensions of the saved image. Smaller ranges or larger dimensions require a greater density to produce the same visual effect. The size of points should also be taken into account.

``` {r contents_2B, dpi = 96, fig.width = 12, fig.height = 12, fig.alt = 'density', echo = FALSE} densities <- rep(c(0.5, 1, 2), each = 3) ns <- rep(c(50, 100, 200), 3)

density_plots <- list()

for (i in 1:9) { density_dat <- data.frame(x = c(rep(0, ns[i])), y = c(rep(0, ns[i])))

density_plots[[i]] <- ggplot(density_dat, aes(x, y)) + geom_point(position = position_sunflower(density = densities[i])) + coord_equal(xlim = c(-2, 2), ylim = c(-2, 2)) + theme_bw() + labs(title = paste0("n = ", ns[i], ", density = ", densities[i])) + theme(axis.title = element_blank(), panel.grid.minor = element_blank(), plot.title = element_text(face = "bold")) }

wrap_plots(density_plots, ncol = 3)

The `aspect_ratio` parameter changes the aspect ratio of the flowers, which is
their width divided by their height. This is useful when using the position 
adjustment without `coord_equal()`. The flowers can be made wider or taller to 
compensate for the aspect ratio of the axes or the image. The aspect ratio of the 
flowers should be set to the opposite of the aspect ratio for which it must compensate.
For instance, consider a plot with an x axis that ranges from 0 to 1, and a y axis 
that ranges from 0 to 2. Saving this plot as a square image would squish the y 
axis, resulting in wider flowers. Setting the aspect ratio of the flowers to 0.5 
would offset this distortion. Note that while the aspect ratio is parameterized as width to height,
the `ratio` parameter for coord_fixed() is height to width. So in this case, setting 
`aspect_ratio` equal to `ratio` results in non-distorted distributions.

``` {r contents_2C, dpi = 96, fig.width = 12, fig.height = 12, fig.alt = 'aspect_ratio', echo = FALSE}
flower_ratios <- rep(c(0.5, 1, 2), each = 3)
axis_ratios <- rep(c(2, 1, 0.5), 3)

aspect_ratio_dat <- data.frame(x = c(rep(0, 100)), y = c(rep(0, 100)))

aspect_ratio_plots <- list()

for (i in 1:9) {

  aspect_ratio_plots[[i]] <- ggplot(dat, aes(x, y)) +
    geom_point(position = position_sunflower(aspect_ratio = flower_ratios[i])) +
    coord_fixed(xlim = c(-2, 2),
                ylim = c(-2, 2),
                ratio = axis_ratios[i]) +
    theme_bw() +
    labs(title = paste0("aspect_ratio = ", flower_ratios[i], "\ncoord_fixed(ratio = ", axis_ratios[i], ")")) +
    theme(axis.title = element_blank(),
          panel.grid.minor = element_blank(),
          plot.title = element_text(face = "bold", size = 10))
}

wrap_plots(aspect_ratio_plots, ncol = 3)

Position Circle Pack

This position adjustment uses circle packing algorithms from packcircles to arrange perfectly over-plotted points of varying sizes into an elliptical area. It also takes density and aspect_ratio as parameters. This position adjustment should not be confused with geom_circlepack() from ggcirclepack which can be found on GitHub.

# position_circlepack()
circlepack_plot <- ggplot(dat, aes(x = x, y = y, size = size)) +
  geom_point(alpha = 0.25,
             position = position_circlepack(density = 0.25, 
                                            aspect_ratio = 1)) +
  coord_equal(xlim = c(-1, 1), 
              ylim = c(-1.1, 1.1)) +
  theme_bw() +
  theme(legend.position = "none",
        axis.title = element_blank(),
        plot.title = element_text(hjust = 0.5, face = "bold")) +
  ggtitle("position_circlepack()")

# position_circlepackdodge()
circlepackdodge_plot <- ggplot(dat, aes(x = x, y = y, color = group, size = size)) +
  geom_point(alpha = 0.25,
             position = position_circlepackdodge(width = 2, 
                                                 density = 0.25, 
                                                 aspect_ratio = 1)) +
  coord_equal(xlim = c(-1, 1), 
              ylim = c(-1.1, 1.1)) +
  theme_bw() + 
  theme(legend.position = "none",
        axis.title = element_blank(),
        plot.title = element_text(hjust = 0.5, face = "bold")) +
  ggtitle("position_circlepackdodge()")

circlepack_plot + circlepackdodge_plot

Like position_sunflower() position_circlepack() works from the inside out in the order of the data. So arranging the data by size organizes the points accordingly.

# random size, base plot
random <- ggplot(dat, aes(x = x, y = y, size = size)) +
  geom_point(alpha = 0.25,
             position = position_circlepack(density = 0.075, 
                                            aspect_ratio = 1)) +
  coord_equal(xlim = c(-1, 1), 
              ylim = c(-1.1, 1.1)) +
  theme_bw() +
  theme(legend.position = "none",
        axis.title = element_blank(),
        plot.title = element_text(hjust = 0.5, face = "bold")) +
  ggtitle("random")

# ascending size
ascending <- random %+% 
  arrange(dat, size) + 
  ggtitle("ascending")

# descending size
descending <- random %+% 
  arrange(dat, desc(size)) + 
  ggtitle("descending")

random + ascending + descending

Example

vayr also includes data from the Patriot Act experiment described in Persuasion in Parallel. The Patriot Act was an anti-terrorism law, and the patriot_act dataset contains data relating to an experiment that measured support for this law after randomly exposing participants to statements that cast the legislation in either a negative or positive light. The experiment was conducted in 2009 with a nationwide sample, and it was replicated in 2015 with a sample of MTurkers. In both instances, the treatments had a similar effect on Democrats and Republicans. There are four variables in the data:

sample_label, the study to which the participant belonged
pid_3, the partisanship of the participant
T1_content, the statements to which the participant was exposed
PA_support, the participant's post-treatment support for the Patriot Act

The data is visualized below using position_sunflowerdodge() from vayr. Note that both density and aspect_ratio are adjusted. A high density is needed because of a small point size, and a tall aspect_ratio accounts for a wide plot.

# A df for statistical models
summary_df <- patriot_act |>
  group_by(T1_content, pid_3, sample_label) |>
  reframe(tidy(lm_robust(PA_support ~ 1)))

# A df for direct labels
label_df <- summary_df |>
  filter(sample_label == "Original Study", T1_content == "Control") |>
  mutate(
    PA_support = case_when(
      pid_3 == "Democrat" ~ conf.low - 0.15,
      pid_3 == "Republican" ~ conf.high + 0.15
    )
  )

ggplot(patriot_act, aes(T1_content, PA_support, color = pid_3, group = pid_3)) +
  # the data
  geom_point(position = position_sunflowerdodge(width = 0.5, 
                                                density = 50,
                                                aspect_ratio = 0.5),
             size = 0.1, alpha = 0.5) +
  # the statistical model
  geom_line(data = summary_df, aes(x = T1_content, y = estimate),  
            position = position_dodge(width = 0.5), linewidth = 0.5) +  
  geom_point(data = summary_df, aes(x = T1_content, y = estimate),  
             position = position_dodge(width = 0.5), size = 3) +
  geom_linerange(data = summary_df, aes(x = T1_content, y = estimate,
                                        ymin = conf.low, ymax = conf.high),
                 position = position_dodge(width = 0.5)) +
  # the direct labels
  geom_text(data = label_df, aes(label = pid_3)) +
  # the rest
  scale_color_manual(values = c("blue4", "red3")) +
  scale_y_continuous(breaks = 1:7) +
  coord_fixed(ratio = 0.5) + # ratio for coord_fixed is y/x rather than x/y
  facet_wrap(~sample_label) +
  theme_bw() +
  theme(legend.position = "none",
        strip.background = element_blank(),
        panel.grid.minor = element_blank()) +
  labs(y = "Do you oppose or support the Patriot Act?
            [1: Oppose very strongly to 7: Support very strongly]",
       x = "Randomly assigned information")