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Plot Circular Barplot for Feature Representation

Source: R/splnr_featureRep.R
splnr_plot_circBplot.Rd

splnr_plot_circBplot() creates a circular bar plot to visualize feature representation, categorized by groups. It's particularly useful for displaying how different categories of features meet certain targets in a radial layout.

Usage

splnr_plot_circBplot(
  df,
  legend_color,
  legend_list,
  indicateTargets = TRUE,
  impTarget = NA,
  repTarget = NA,
  colTarget = "red"
)

Arguments

df

A data.frame or tibble that must contain the following columns:

  • feature: character or factor unique identifier for each individual bar (e.g., species names).

  • value: numeric the value to be plotted on the y-axis (bar height, typically percentage representation).

  • group: character or factor for grouping factors (e.g., "important", "representative").

legend_color

A named vector of colors. Names must correspond to the unique values in the group column of df, and values are the corresponding colors. For example: c("group_name1" = "red", "group_name2" = "blue").

legend_list

A character vector of labels for the legend. This should match the names used in legend_color or the levels of group.

indicateTargets

A logical value. If TRUE, horizontal lines indicating impTarget and repTarget will be drawn on the plot.

impTarget

A numeric value representing the target percentage for 'important' features. Required if indicateTargets is TRUE.

repTarget

A numeric value representing the target percentage for 'representative' features. Required if indicateTargets is TRUE.

colTarget

A character string specifying the color for the target indicator lines.

Value

A ggplot2::ggplot object of the circular bar plot.

Examples

# DISCLAIMER: THIS SOLUTION IS NOT ACTUALLY RUN WITH THESE TARGETS YET

if (FALSE) { # \dontrun{

dat_problem <- prioritizr::problem(dat_species_bin %>% dplyr::mutate(Cost = runif(n = dim(.)[[1]])),
  features = c("Spp1", "Spp2", "Spp3", "Spp4", "Spp5"),
  cost_column = "Cost"
) %>%
  prioritizr::add_min_set_objective() %>%
  prioritizr::add_relative_targets(0.3) %>%
  prioritizr::add_binary_decisions() %>%
  prioritizr::add_default_solver(verbose = FALSE)

dat_soln <- dat_problem %>%
  prioritizr::solve.ConservationProblem()

s1 <- dat_soln %>%
  tibble::as_tibble()

p1 <- dat_problem

# Assuming eval_feature_representation_summary is from prioritizr
df_rep_imp <- prioritizr::eval_feature_representation_summary(
  p1,
  s1[, "solution_1"]
) %>%
  dplyr::select(feature, relative_held) %>%
  dplyr::mutate(relative_held = relative_held * 100)

imp_layers <- c("Spp1", "Spp3")

target <- data.frame(feature = c("Spp1", "Spp2", "Spp3", "Spp4", "Spp5")) %>%
  dplyr::mutate(class = dplyr::if_else(.data$feature %in% imp_layers,
    "important", "representative"
  )) %>%
  dplyr::mutate(target = dplyr::if_else(class == "important",
    50 / 100, 30 / 100
  ))

df <- merge(df_rep_imp, target) %>%
  dplyr::select(-target) %>%
  stats::na.omit() %>% # Use stats::na.omit
  dplyr::rename(value = relative_held) %>%
  dplyr::rename(group = class)

colors <- c(
  "important" = "darkgreen",
  "representative" = "darkred"
)
legends <- c("Important", "Representative")

(splnr_plot_circBplot(df,
  legend_list = legends,
  legend_color = colors,
  impTarget = 50, repTarget = 30
))
} # }