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Map a cube simulation function over multiple rows of a dataframe

Source: R/map_simulation_functions.R
map_simulation_functions.Rd

This function executes a cube simulation function (simulate_occurrences(), sample_observations(), filter_observations(), add_coordinate_uncertainty(), or grid_designation()) over multiple rows of a dataframe with potentially different function arguments over multiple columns.

Usage

map_simulation_functions(f, df, nested = TRUE, progress = FALSE)

Arguments

f

One of five cube simulation functions: simulate_occurrences(), sample_observations(), filter_observations(), add_coordinate_uncertainty(), or grid_designation().

df

A dataframe containing multiple rows, each representing a different species. The columns are function arguments with values used for mapping f for each species. Columns not used by this function will be retained in the output.

nested

Logical. If TRUE (default), retains list-column containing dataframes calculated by f. Otherwise, expands this list-column into rows and columns.

progress

Logical. Whether to show a progress bar. Set to TRUE to display a progress bar, FALSE (default) to suppress it.

Value

In case of nested = TRUE, a dataframe identical to df, with an extra list-column called mapped_col containing an sf object for each row computed by the function specified in f. In case of nested = FALSE, this list-column is expanded into additional rows and columns.

Examples

if (FALSE) { # \dontrun{
# Load packages
library(sf)
library(dplyr)

# Create polygon
plgn <- st_polygon(list(cbind(c(5, 10, 8, 2, 3, 5), c(2, 1, 7, 9, 5, 2))))

## Example with simple column names
# Specify dataframe for 3 species with custom function arguments
species_dataset_df <- tibble(
  taxonID = c("species1", "species2", "species3"),
  species_range = rep(list(plgn), 3),
  initial_average_occurrences = c(50, 100, 500),
  n_time_points = rep(6, 3),
  temporal_function = c(simulate_random_walk, simulate_random_walk, NA),
  sd_step = c(1, 1, NA),
  spatial_pattern = "random",
  seed = 123)

# Simulate occurrences
sim_occ_raw <- map_simulation_functions(
  f = simulate_occurrences,
  df = species_dataset_df)
sim_occ_raw

# Unnest output and create sf object
sim_occ_raw_unnested <- map_simulation_functions(
  f = simulate_occurrences,
  df = species_dataset_df,
  nested = FALSE)

sim_occ_raw_unnested %>%
   st_sf()
} # }