# Generate a Heatmap Overview of Individual Risk Scores, Aggregated Risk Scores, and Overall Ecosystem Risk The function `plot_heatmap()` creates for each effect type an aggregated plot with a heatmap of the risk scores of each state indicator - pressure combination. The aggregated multi pressure and multi state indicator scores are shown to the left and below the heatmap. In the bottom left corner the ecosystem risk is displayed. Uncertainty can be plotted as frame of the heatmap tiles on a gray scale. ```r plot_heatmap( risk_scores, aggregated_scores, order_ind = NULL, order_press = NULL, pathway = "combined", uncertainty = TRUE, output_2_pathway_indicators = NULL, title = NULL, risk_scale_steps = 1, text_size_axis_text = NULL, text_size_axis_title = NULL ) ``` ## Arguments - `risk_scores`: output from the `risk` function. - `aggregated_scores`: output from the `aggregate_risk` function. - `order_ind`: character value defining the order of state indicators shown on the y-axis from top to bottom. If `NULL` (default), order is alphabetic. - `order_press`: character value defining the order of pressures shown on the x-axis from left to right. If `NULL` (default), order is alphabetic. - `pathway`: a character string specifying the pathway which should be used for the multi pressure and multi indicator scores. Default is "combined". - `uncertainty`: logical, determines whether uncertainty should be plotted or not, if uncertainty scores are provided by the risk scores. Default is `TRUE`. - `output_2_pathway_indicators`: Optionally. An integer value specifying whether for those state indicators that have been assessed with both pathways two plots should generated, one for each pathway (`2`), or only one plot, where the risk scores are averaged from both pathways (`1`). The default is `NULL`. - `title`: a string specifying the title of the heatmap. If `NULL` (default), the type of the output data frame from the `risk` function is displayed. - `risk_scale_steps`: integer value representing the step size for the risk scale in the legend. Can only take the value 1 (default), 2 and 5. - `text_size_axis_text`: integer value specifying text size of axis text. If `NULL` (default), ggplot2 default settings are used. - `text_size_axis_title`: integer value specifying text size of axis title. If `NULL` (default), ggplot2 default settings are used. ## Returns a list of ggplot objects, one for each type of effect. ## Examples ```r ### Demo with output data from the risk() and aggregate_risk() functions # based on expert scores. # Using default settings for the overall risk scores and associated uncertainty # scores (i.e. in this case, combined across both types) p_heat <- plot_heatmap( risk_scores = ex_output_risk_expert, aggregated_scores = ex_output_aggregate_risk_expert ) # For each type in both input datasets, a heatmap is generated p_heat[[1]] # display direct effects p_heat[[2]] # display direct/indirect effects # The following examples can run for a longer time, thus they are in dontrun{}. # Hide uncertainty results and order indicators and pressures manually ## Not run: p_heat_mod <- plot_heatmap( risk_scores = ex_output_risk_expert, aggregated_scores = ex_output_aggregate_risk_expert, order_ind = c("phytoplankton", "herring", "cod", "seabirds"), order_press = c("temperature", "salinity", "oxygen", "nutrient", "fishing"), uncertainty = FALSE ) p_heat_mod[[1]] ## End(Not run) ### Demo with combined expert-based and model-based pathways ## Not run: combined_risk <- rbind(ex_output_risk_expert, ex_output_risk_model) aggr_risk <- aggregate_risk(risk_results = combined_risk) # Default settings (combined type and pathway) p_heat_comb <- plot_heatmap( risk_scores = combined_risk, aggregated_scores = aggr_risk ) p_heat_comb[[1]] ## End(Not run) ### Demo with two indicators assessed with both pathways ## Not run: risk_model <- ex_output_risk_model[c(1, 3, 5, 7, 8, 9, 12, 14:16), ] risk_model$pressure <- c( "nutrient", "temperature", "salinity", "oxygen", "fishing", # for zooplankton "nutrient", "temperature", "salinity", "oxygen", "fishing") # for cod dummy_model <- risk_model |> dplyr::mutate(indicator = dplyr::case_when( indicator == "zooplankton_mean_size" ~ "phytoplankton", .default = "cod" )) ## End(Not run) ## Not run: risk_comb <- rbind(ex_output_risk_expert, dummy_model) aggr_risk_comb <- aggregate_risk(risk_results = risk_comb) # show results from both types and pathways individually and order the state # indicators manually p_heat_2_paths <- plot_heatmap(risk_scores = risk_comb, aggregated_scores = aggr_risk_comb, output_2_pathway_indicators = 2, order_ind = c("phytoplankton", "herring", "cod", "seabirds")) p_heat_2_paths # show one plot per type and average across the pathways p_heat_mean_path <- plot_heatmap(risk_scores = risk_comb, aggregated_scores = aggr_risk_comb, output_2_pathway_indicators = 1, order_ind = c("phytoplankton", "herring", "cod", "seabirds")) p_heat_mean_path ## End(Not run) ``` ## See Also `risk`, `aggregate_risk` to generate result tables/output that serve here as input.

plot_heatmap function