Plot Multiple Faceted Scatter with Averages

This function creates a faceted scatter plot to compare observed and predicted values, including error bars and visual grouping by variants. It is designed for visualising mixing proportions or similar data with multiple sources. It's mostly used to plot outputs of make_post_prop_long.

plot_multiple_faceted_scatter_avg(data, ...)

Arguments

data

A data frame containing the data to be plotted. It must include the following columns:

Observed

Numeric. The observed values for each source.

Predicted

Numeric. The predicted values for each source.

ymin

Numeric. The lower bound of the error bar for the predicted values.

ymax

Numeric. The upper bound of the error bar for the predicted values.

Variant:

Factor or character. The grouping variable for different variants.

source

Factor or character. The source variable used for faceting.

...

Additional arguments passed to the labs function for customizing plot labels.

Value

A ggplot object representing the faceted

Details

The function generates a scatter plot with error bars for predicted values, points representing observed vs. predicted values, and a diagonal reference line (slope = 1) for visual comparison. The plot is faceted by the source variable and grouped by the Variant: variable, with customizable shapes and colors for each variant.

The scale_fill_jco function from the ggsci package is used to apply a color palette, and the scale_shape_manual function is used to define point shapes.

See also

make_post_prop_long

Examples

library(MixMustR)
data <- data.frame(
  Observed = c(0.2, 0.4, 0.6, 0.8),
  Predicted = c(0.25, 0.35, 0.65, 0.75),
  ymin = c(0.2, 0.3, 0.6, 0.7),
  ymax = c(0.3, 0.5, 0.7, 0.9),
  `Variant:` = c("A", "B", "A", "B"),
  source = c("Source1", "Source1", "Source2", "Source2"),
  check.names = FALSE
)
plot_multiple_faceted_scatter_avg(data)