Create a scatter plot visualisation from a <summarised_result> object

Create a scatter plot visualisation from a <summarised_result> object

Usage

scatterPlot(
  result,
  x,
  y,
  line,
  point,
  ribbon,
  ymin = NULL,
  ymax = NULL,
  facet = NULL,
  colour = NULL,
  style = NULL,
  type = NULL,
  group = colour,
  label = character()
)

Arguments

result: A <summarised_result> object.
x: Column or estimate name that is used as x variable.
y: Column or estimate name that is used as y variable.
line: Whether to plot a line using geom_line.
point: Whether to plot points using geom_point.
ribbon: Whether to plot a ribbon using geom_ribbon.
ymin: Lower limit of error bars, if provided is plot using geom_errorbar.
ymax: Upper limit of error bars, if provided is plot using geom_errorbar.
facet: Variables to facet by, a formula can be provided to specify which variables should be used as rows and which ones as columns.
colour: Columns to use to determine the colours.
style: Visual theme to apply. Character, or NULL. If a character, this may be either the name of a built-in style (see plotStyle()), or a path to a .yml file that defines a custom style. If NULL, the function will use the explicit default style, unless a global style option is set (see setGlobalPlotOptions()), or a _brand.yml file is present (in that order). Refer to the package vignette on styles to learn more.
type: Character string indicating the output plot format. See plotType() for the list of supported plot types. If type = NULL, the function will use the global setting defined via setGlobalPlotOptions() (if available); otherwise, a standard ggplot2 plot is produced by default.
group: Columns to use to determine the group.
label: Character vector with the columns to display interactively in plotly.

Value

A plot object.

Examples

result <- mockSummarisedResult() |>
  dplyr::filter(variable_name == "age")

scatterPlot(
  result = result,
  x = "cohort_name",
  y = "mean",
  line = TRUE,
  point = TRUE,
  ribbon = FALSE,
  facet = age_group ~ sex)
#> `geom_line()`: Each group consists of only one observation.
#> ℹ Do you need to adjust the group aesthetic?
#> `geom_line()`: Each group consists of only one observation.
#> ℹ Do you need to adjust the group aesthetic?
#> `geom_line()`: Each group consists of only one observation.
#> ℹ Do you need to adjust the group aesthetic?
#> `geom_line()`: Each group consists of only one observation.
#> ℹ Do you need to adjust the group aesthetic?
#> `geom_line()`: Each group consists of only one observation.
#> ℹ Do you need to adjust the group aesthetic?
#> `geom_line()`: Each group consists of only one observation.
#> ℹ Do you need to adjust the group aesthetic?
#> `geom_line()`: Each group consists of only one observation.
#> ℹ Do you need to adjust the group aesthetic?
#> `geom_line()`: Each group consists of only one observation.
#> ℹ Do you need to adjust the group aesthetic?
#> `geom_line()`: Each group consists of only one observation.
#> ℹ Do you need to adjust the group aesthetic?