get_benchmarking_data: Download data for benchmarking
In boyanangelov/sdmbench: Benchmark Species Distribution Models

Description Usage Arguments Value Examples

Download data for benchmarking

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get_benchmarking_data(scientific_name, limit = 1000,
  climate_type = "default", climate_resolution = 10,
  projected_model = "BC", rcp = 45, year = 50)

`scientific_name`	A character string indicating scientific species name.
`limit`	A numeric value indicating maximum number of occurrence records requested. The value has to be positive and has a maximum of 200000 (observations you can download with a single call).
`climate_type`	A character string indicating type of climate variables, either 'default' for current climate or 'future' for CMIP5 projections.
`climate_resolution`	A numeric value indicating the resolution of the raster environmental variables. For a climate type of 'default' possible resolutions are: 0.5, 2.5, 5, and 10 (minutes of a degree). For a 'future' climate: 2.5, 5, and 10.
`projected_model`	A character string indicates the type of future climate projection. Possible values are: "AC", "BC", "CC", "CE", "CN", "GF", "GD", "GS", "HD", "HG", "HE", "IN", "IP", "MI", "MR", "MC", "MP", "MG", or "NO".
`rcp`	A numeric value indicating representative concentration pathways. Possible values: 26, 45, 60, or 85.
`year`	A numeric value indicating the number of years into the future for projection. Can be 50 or 70.

A list containing the downloaded datasets.

## Not run: 
# get data using the default parameters (the only required one is the species name)
result_data <- get_benchmarking_data("Lynx lynx")

# get a custom number of observations at a higher climate resolution
# note that downloading higher resolution data takes longer
result_data <- get_benchmarking_data("Lynx lynx",
                                     limit = 1500,
                                     climate_resolution = 5)

# get environmental data for a future climate projection (CMIP5)
result_data <- get_benchmarking_data("Lynx lynx",
                                     limit = 1500,
                                     climate_resolution = 5,
                                     climate_type = "future")

# specify projection model
# the default is BC (Beijing Climate Center Climate System Model)
# note that not all combinations of climate values are possible
result_data <- get_benchmarking_data("Lynx lynx",
                                     limit = 1500,
                                     climate_resolution = 10,
                                     climate_type = "future",
                                     projected_model = "AC")

# specify number of years into the future
# if you are interested in the longer term effects of climate change
result_data <- get_benchmarking_data("Lynx lynx",
                                     limit = 1500,
                                     climate_resolution = 5,
                                     climate_type = "future",
                                     year = 70)

# specify RCP (representative concentration pathway)
# this value represents one of four greenhouse gas concentration trajectories
result_data <- get_benchmarking_data("Lynx lynx",
                                     limit = 1500,
                                     climate_resolution = 5,
                                     climate_type = "future",
                                     year = 70,
                                     rcp = 26)

# after obtaining the data you can inspect its different components
# raw data
head(result_data$df_data)

# check class balance (presence / absence)
table(result_data$df_data$label)

# the result object also contains the data in raster format
result_data$raster_data

## End(Not run)