This package provides geoms to rearrange scatter-plot coordinates on
regular grids while strictly avoiding over-plotting. The applications
are similar to geom_jitter.
You can install the development version from github with the following command (in your R console):
if(!require('remotes')) install.packages('remotes')
remotes::install_github("nevrome/ggpointgrid")
library(ggplot2)
set.seed(5)geom_pointgrid aims to optimize the arrangement of observations on a
regular grid. This works well for figures with continuously scaled x-
and y-axes, so for scatter-plots or even map plots. Just as in
geom_jitter the rearrangement of the points reduces accuracy and
precision of positional information on x and y in favour of making
every observation visible.
The grid properties are controlled with the parameters grid_x and
grid_y, which allow to precisely specify the desired graticules.
df <- tibble::tibble(
x = rep(c(1,1,2,3,3), times = 10),
y = rep(c(1,3,2,1,3), times = 10),
var = sample(c("A", "B", "C"), size = 50, replace = T)
)
coord <- coord_fixed(xlim = c(0.5,3.5), ylim = c(0.5,3.5))
p1 <- ggplot(df) +
geom_point(aes(x, y, color = var)) +
coord + ggtitle("geom_point")
p2 <- ggplot(df) +
geom_jitter(aes(x, y, color = var), width = 0.3, height = 0.3) +
coord + ggtitle("geom_jitter")
p3 <- ggplot(df) +
ggpointgrid::geom_pointgrid(aes(x, y, color = var), grid_x = 15, grid_y = 15) +
coord + ggtitle("geom_pointgrid")
p4 <- ggplot(df) +
ggpointgrid::geom_pointgrid(
aes(x, y, color = var),
grid_x = seq(min(df$x) - 0.3, max(df$x) + 0.3, length.out = 18),
grid_y = seq(min(df$y) - 0.3, max(df$y) + 0.3, length.out = 18)
) +
coord + ggtitle("geom_pointgrid, grid specified")
cowplot::plot_grid(p1, p2, p3, p4)
geom_textgrid performs the same arrangement operation on text data. It
is to geom_text what geom_pointgrid is to geom_point.
geom_pointrect was designed for a slightly different use-case than
geom_pointgrid. Here all observations that share the x- and
y-coordinate are spread out into a rectangular grid, representing only
this one position. This is especially useful, when the x- and y- axis
are ordinally scaled.
The order within each rectangle can be set by the order of the input
data.frame and the arguments scale_x and scale_y control the size of
the per-position box. The arguments round_x and round_y allow to
specify how data on continuously scaled x- and y-axes should be
aggregated.
df <- tibble::tibble(
x = rep(letters[c(1,1,2,3,3)], times = 10),
y = rep(letters[c(1,3,2,1,3)], times = 10),
var = sample(c("A", "B", "C"), size = 50, replace = T)
) |> dplyr::arrange(var)
coord <- coord_fixed()
p4 <- ggplot(df) +
geom_point(aes(x, y, color = var)) +
coord + ggtitle("geom_point")
p5 <- ggplot(df) +
geom_jitter(aes(x, y, color = var), width = 0.3, height = 0.3) +
coord + ggtitle("geom_jitter")
p6 <- ggplot(df) +
ggpointgrid::geom_pointrect(aes(x, y, color = var)) +
coord + ggtitle("geom_pointrect")
p7 <- ggplot(df) +
ggpointgrid::geom_pointrect(
aes(x, y, color = var),
scale_x = 0.2,
scale_y = 0.2
) +
coord + ggtitle("geom_pointrect, scaling set")
cowplot::plot_grid(p4, p5, p6, p7)