plotSizeCompsAsRidges: Plot annual size comps (or other similar) using 'ggridges'
In wStockhausen/wtsSizeComps: Various functions for dealing with size compositions

plotSizeCompsAsRidges

R Documentation

Plot annual size comps (or other similar) using ggridges

Description

Function to plot annual size comps (or other similar) using ggridges

Usage

plotSizeCompsAsRidges(
  dfr_,
  sizes_in = "size",
  values_in = "abundance",
  y_positions = "year",
  removeZeros = TRUE,
  reverseY = FALSE,
  colour = NULL,
  fill = NULL,
  group_by = y_positions,
  normalize_by = NULL,
  xlim = c(100, 200),
  ylim = c(1980, 2022),
  x_breaks = seq(5, 200, 5),
  y_breaks = seq(1900, 2100, 5),
  legend.position = c(0.98, 0.98),
  legend.justification = c(1, 1)
)

Arguments

`dfr_`	- input dataframe
`sizes_in`	- name of column with "size" information (default = "size")
`values_in`	- name of column with "abundance" information (default = "abundance")
`y_positions`	- name of column with vertical axis location information (default = "year")
`removeZeros`	- remove comps with zero abundance in all bins(default = TRUE)
`reverseY`	- reverse y scale (default = FALSE)
`colour`	- column name for colour variable (default = NULL)
`fill`	- column name for fill variable (default = NULL)
`group_by`	- string vector with column names for grouping before summarizing (default = value of y_positions)
`normalize_by`	- string vector with column names for grouping before normalizing (default = NULL)
`xlim`	- x axis limits (default = c(0,200))
`ylim`	- y axis limits (default = c(1980,2022))
`x_breaks`	- x axis breaks (default = seq(5,200,5))
`y_breaks`	- y axis breaks (default = seq(1900,2100,5))
`legend.position`	- legend position (as in [ggplot2::theme()], default = c(0.98,0.98))
`legend.justification`	- legend justification on plot (as in [ggplot2::theme()], default = c(1,1))

Details

Uses [ggridges::geom_density_ridges()] to plot the size compositions. The returned plot object can be faceted, etc., for more complex plotting situations.

Aggregation will be performed after grouping the data using [dplyr::group_by()], where the grouping structure is a combination of the variables specified in 'group_by', 'colour', and 'fill'.