R/generate_bn_count_map.R

In usgs-bis/bad-neighbor-invasives: Bad Neighbor Analysis Package

#' generate_bn_count_map
#' 
#' A function to generate a chloropleth map of results.
#'
#' @param df a tibble.  Typically the results from the process_bad_neighbor_groups function
#' @param taxon a string.  The [often informal] name of the taxon group to process (e.g. "Tree/Shrub")
#' @param relative a boolean. Optionally add a title indicating the data are relative to the non-native
#'     species already in the state. The data processing is done outside the plot function.
#'
#' @return a ggplot object with a chloropleth map of the bad neighbor counts for states.
#'
#' @export
#'
#' @examples
#' Load a results file from process_bad_neighbor_groups() function
#' comb_all_results_forbs <- readr::read_csv("result_csv/forbs_comb_result.csv")
#' 
#' comb_all_results_trees %>% 
#'    group_by(state_name) %>% 
#'    summarize(species_count = sum(bad_neighbor_count)) %>% 
#'    # pass to the plot function
#'    generate_bn_count_map(taxon = "Tree/Shrub")
#'    
#' Show the results relative to the non-natives already present in the states
#' comb_all_results_trees %>% 
#'    group_by(state_name) %>% 
#'    # Normalize the data
#'    mutate(relative = bad_neighbor_count / state_nn_species_count) %>% 
#'    summarise(species_count = sum(relative)) %>% 
#'    generate_bn_count_map(taxon = "Tree/Shrub", relative = TRUE)
generate_bn_count_map <- function(df, taxon, relative = FALSE) {
    # open the spatial data for US states
    us_states <- sf::st_as_sf(maps::map("state", plot = FALSE, fill = TRUE))
    
    # project to albers equal area for the U.S.
    # EPSG:102003 USA_Contiguous_Albers_Equal_Area_Conic
    # +proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=37.5 +lon_0=-96 +x_0=0 +y_0=0 +datum=NAD83 +units=m +no_defs
    us_states_alb <- sf::st_transform(us_states, crs = 102003)
    
    # mutate the state_name to match the map data
    df <- dplyr::mutate(df, state_name = stringr::str_to_lower(stringr::str_replace_all(df$state_name, "_", " ")))
    
    # join the data
    us_states_alb <- dplyr::left_join(us_states_alb, df, by = c("ID" = "state_name"))
    
    # build a title, replacing _ as needed
    t <- stringr::str_c("Threat Assessment of Invasion by",
                        stringr::str_replace_all(taxon, "_", " "), "Bad Neighbors",
                        sep = " ") 
    
    # Add an option to normalize the data
    if (relative) {
        t <- stringr::str_c(t, "\nRelative to Existing State Non-Native", sep = "")
    }
        
    # create a plot with states
    p <- ggplot2::ggplot() +
        ggplot2::geom_sf(data = us_states_alb, ggplot2::aes( fill = species_count)) +
        ggplot2::ggtitle(t) +
        # ggplot2::scale_fill_gradient(name = "Bad Neighbors", low = "#fff7fb", high = "#014636") +
        # ggplot2::scale_fill_gradient(name = "Bad Neighbors", low = "#fee8c8", high = "#e34a33") +
        ggplot2::scale_fill_distiller(name = "Bad Neighbors", palette = "RdYlGn") +
        # ggthemes::theme_map()+
        ggplot2::scale_y_continuous(breaks = c(25, 35, 45)) +
        ggplot2::scale_x_continuous(breaks = c(-120, -100, -70)) +
        ggplot2::theme(plot.title = ggplot2::element_text(size = ggplot2::rel(1.5)))
    
    # return the plot object
    return(p)
}