ggnormalviolin

Suppose there are 4 hypothetically normal distributions with specific means and standard deviations. They can be plotted like so:

library(ggplot2)
library(ggnormalviolin)

# Make data
d <- data.frame(
  dist = c("A", "B", "C", "D"),
  dist_mean = c(80, 90, 110, 130),
  dist_sd = c(15, 10, 20, 5)
)

# Make base plot
p <- ggplot(data = d,
            aes(x = dist,
                mu = dist_mean,
                sigma = dist_sd,
                fill = dist)) +
  theme_light() +
  theme(legend.position = "none") +
  labs(x = NULL, y = NULL)

# Add normal violins
p + geom_normalviolin()
Plot of 4 normal violin shapes of different fill colors, means, and standard deviations
Figure 1: Example of a how to use ggnormalviolin

Tail Highlighting

Suppose you want to highlight the two tails of the distributions. Set the p_tails to specify the total area of the tails. Thus, if p_tail = 0.05, each tail will represent the outermost 2.5% of the distributions (i.e, 0.05 = 2 &mult; 0.025).

p + geom_normalviolin(p_tail = 0.05)
Plot of 4 normal violin shapes of different fill colors, means, and standard deviations, with 2.5 percent of each tail shaded.
Figure 2: How to shade both violin tails

Suppose you want to highly only the upper tails. Set p_upper_tail to the proportion desired.

p + geom_normalviolin(p_upper_tail = 0.05)
Plot of 4 normal violin shapes of different fill colors, means, and standard deviations, with 5 percent of the upper tails shaded.
Figure 3: How to shade the upper tails only

Analogously, you can highlight only the lower tails by setting the p_lower_tail parameter.

p + geom_normalviolin(p_lower_tail = 0.05)
Plot of 4 normal violin shapes of different fill colors, means, and standard deviations, with 5 percent of the lower tails shaded.
Figure 4: How to shade the lower tails only

The defaults for highlighting is accomplished by selecting a subset of the whole distribution, setting tail_fill to black, and then making the black fill transparent by setting tail_alpha = 0.4. Setting these values to other colors and levels of transparency can dramatically change the look of the plot.

p +
  geom_normalviolin(
    p_tail = 0.05,
    tail_fill = "white",
    tail_alpha = 0.8,
    color = "gray20",
    linewidth = 0.1
  )
Plot of 4 normal violin shapes of different fill colors, means, and standard deviations, with white tails, and gray outlines.
Figure 5: How to style normal violins

Direction of Violin

If you want to omit the left or right side of the violins, you can set the face_left or face_right parameters to FALSE.

p + geom_normalviolin(face_right = FALSE, p_tail = 0.05)
Plot of 4 normal half-violin shapes of different fill colors, means, and standard deviations, with 5 percent of the upper tails shaded.
Figure 6: How make half-violins

Violin Width

You can set the width of the violin to any size desired.

p + geom_normalviolin(width = 1)
Plot of 4 normal violin shapes of different fill colors, means, and standard deviations, with width set to 1.
Figure 7: How to set violin width to a constant value

If you want the shape of the distribution to remain constant, map the width parameter to a multiple of the standard deviation.

p + geom_normalviolin(aes(width = dist_sd * 0.05))
Plot of 4 normal violin shapes of different fill colors, means, and standard deviations, with width set to be proportional to the standard deviation
Figure 8: How to set violin width to be proportional to the standard deviation

Setting Limits

By default, the normal violins extend 4 standard deviations in both directions. Use the nsigma parameter to set a different value.

p + geom_normalviolin(nsigma = 1.5)
Plot of 4 normal violin shapes of different fill colors, means, and standard deviations, with height set to plus or minus 1.5 standard deviations.
Figure 9: How to set violin height

If you set limits on the y scale, it is possible that some of the violins will be distorted or cut in pieces.

p +
  geom_normalviolin() +
  ylim(50, 140)
Plot of 4 normal violin shapes of different fill colors, means, and standard deviations, with limits set too narrowly, which distors the violin shapes.
Figure 10: A distorted plot due to limits set too narrowly

This occurs because data outside the limits is discarded, breaking up the polygons that compose the violins into smaller pieces. To prevent such behavior, set the upper_limit and lower_limit parameters equal to the same limits you have specified for the y scale (or any other values you wish).

p +
  geom_normalviolin(lower_limit = 50, upper_limit = 140) +
  ylim(50, 140)
Plot of 4 normal violin shapes of different fill colors, means, and standard deviations, with limits set appropriately wide using the ylim function.
Figure 11: How to set plot limits to prevent image distortion

Alternately, you can set the limits in ggplot2::coord_cartesian (or any of the coord_* functions), which will zoom the plot instead of discarding the data.

p +
  geom_normalviolin() +
  coord_cartesian(ylim = c(50, 140))
Plot of 4 normal violin shapes of different fill colors, means, and standard deviations, with limits set appropriately wide using the coord_cartesian function's ylim parameter.
Figure 12: How to set zooming limits using a coord_* function