Splines in ggplot2

When visualizing trend data, standard line plots can sometimes look a bit too jagged. If you want to create a smoother, more aesthetically pleasing curve through your data points, splines are a great solution.

In this post, we’ll look at how to easily replace simple line plots with X-splines in ggplot2 using the geom_xspline() function from the ggfun package.

The Jagged Baseline

First, let’s look at the default behavior. We’ll set up a simple dataset with two groups and plot it using the standard geom_line(). Notice how the lines connect point-to-point with sharp angles.

library(ggplot2)

df <- data.frame(
  group = rep(LETTERS[1:2], each = 10),
  x = rep(1:10, 2),
  y = c(2, 5, 9, 15, 19, 19, 15, 9, 5, 2, 
        1, 0, 3, 2, 3, 4, 14, 24, 24, 25) * 0.01
)

plot <- ggplot(
  df, 
  aes(x, y, group=group, color=factor(group))
) +
  geom_point(aes(color = group)) +
  geom_text(
    aes(label=y),
    hjust=1, 
    vjust=-1,
    show.legend = FALSE
  ) +
  scale_y_continuous(
    limits = c(0, 0.35),
    labels = scales::percent
  ) +
  scale_x_continuous(
    breaks = 1:10,
    minor_breaks = NULL
  ) +
  theme_bw()

plot + geom_line()

Adding a Subtle Curve

To smooth things out, we replace geom_line() with ggfun::geom_xspline(). The most important argument here is shape.

When shape is negative, the curve is forced to pass exactly through the data points (interpolation). A value close to zero (like -0.1) gives a very subtle rounding to the corners.

plot + ggfun::geom_xspline(shape = -0.1)

A Much Smoother Curve

If we decrease the shape value further to -0.5, the spline bows out more between the points.

plot + ggfun::geom_xspline(shape = -0.5)

Approximating the Points

When shape is greater than 0, the line acts as an approximation.

plot + ggfun::geom_xspline(shape = 0.5)

Summary

• Use geom_line() for strict point-to-point connections.

• Use geom_xspline(shape = -0.5) (or similar negative values) for a smooth curve that still honors your exact data values.

• Use geom_xspline(shape = 0.5) (or positive values) when you want to show a generalized, smoothed trend that approximations the data path.