Scatter plot — plot_scatter • thisplot

Plot two- or three-column data as a scatter plot with theme_this and thisplot palettes. Optional smoothing, correlation annotation, point density, and marginal plots are available.

Usage

plot_scatter(
  data,
  smoothing_method = c("lm", "loess"),
  palette = "Chinese",
  palcolor = NULL,
  title = NULL,
  xlab = NULL,
  ylab = NULL,
  legend.title = NULL,
  legend.position = "right",
  theme_use = "theme_this",
  theme_args = list(),
  margins = c("both", "x", "y"),
  marginal_type = NULL,
  margins_size = 10,
  compute_correlation = TRUE,
  compute_correlation_method = c("pearson", "spearman"),
  keep_aspect_ratio = TRUE,
  facet = FALSE,
  se = FALSE,
  pointdensity = TRUE
)

Arguments

data: A data frame with 2 columns (x, y) or 3 columns (x, y, and a grouping column). Extra columns are ignored.
smoothing_method: Smoothing method passed to ggplot2::geom_smooth. Can be "lm" or "loess".
palette: Palette name used for groups or point-density coloring. See show_palettes.
palcolor: Custom colors used to create a color palette.
title, xlab, ylab: Plot titles.
legend.title: Legend title.
legend.position: Legend position.
theme_use: Theme function applied to the plot.
theme_args: A named list of arguments passed to theme_use.
margins, marginal_type, margins_size: Marginal plot controls used when ggExtra is installed. marginal_type can be "density", "histogram", "boxplot", "violin", or "densigram".
compute_correlation: Whether to annotate Pearson or Spearman correlation.
compute_correlation_method: Correlation method: "pearson" or "spearman".
keep_aspect_ratio: Whether to use a 1:1 aspect ratio.
facet: Whether to facet by the grouping column when data has 3 columns.
se: Whether to show smoothing uncertainty.
pointdensity: Whether to color ungrouped points by local density when ggpointdensity is installed.

Value

A ggplot object, or a ggExtra grob when marginal plots are added.

Examples

set.seed(1)
plot_scatter(data.frame(x = rnorm(80), y = rnorm(80)))

plot_scatter(
  data.frame(
    x = rnorm(80),
    y = rnorm(80),
    cluster = rep(c("A", "B"), each = 40)
  )
)