Visualization Functions — build_display

Functions for creating maps and visualizations of NUTS-level data. build_display_sf creates an sf object selecting the best available NUTS level for each country-year combination.

Usage

build_display_sf(out_nuts2, geopolys, var, years = NULL, 
  skip_nuts0 = TRUE, scale = c("per_year", "global"))
lc_build_display_sf(out_nuts2, geopolys, var, years = NULL, 
  skip_nuts0 = TRUE, scale = c("per_year", "global"), keep = NULL)
plot_best_by_country_level(out_nuts2, geopolys, var, years = NULL, 
  skip_nuts0 = TRUE, scale = c("per_year", "global"), title = NULL, 
  pdf_file = paste0("Map_", var, "_country_level_scaled.pdf"), 
  bb_x = c(2400000, 7800000), bb_y = c(1320000, 5650000), 
  col_var = NULL, n_breaks = 7, breaks = NULL)
build_multi_var_sf(out_nuts2, geopolys, vars, years = 2010:2024, 
  var_labels = NULL, pillar_mapping = NULL)
level_col_for(var, special_cases = NULL)
level_cols_for(vars)

Arguments

out_nuts2: Dataframe with cascaded NUTS2 data
geopolys: sf object with NUTS geometries
var: Character string of variable to display
vars: Character vector of variables
years: Integer vector of years to include
skip_nuts0: Logical, whether to skip NUTS0 level display
scale: Character, "per_year" or "global" scaling
keep: Character vector of additional columns to preserve
title: Optional custom title
pdf_file: Optional PDF filename for output
bb_x: Numeric vector of x bounding box limits
bb_y: Numeric vector of y bounding box limits
col_var: Column to use for coloring
n_breaks: Number of legend breaks
breaks: Optional custom breaks vector
var_labels: Named character vector mapping vars to display labels
pillar_mapping: Named character vector mapping vars to pillars
special_cases: Optional named character vector of special variable-to-column mappings. If NULL, uses default health indicator mappings.

Value

sf object for mapping or printed tmap plots

Examples

if (FALSE) { # \dontrun{
sf_data <- build_display_sf(cascaded, geopolys, var = "beds", years = 2020:2024)
plot_best_by_country_level(cascaded, geopolys, var = "beds", years = 2020:2024)
} # }