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It is important to note that ff_prep will take the last previous date for every month for every feature, as illustrated below.
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Exercises
Basic Usage:
Create a simple call to ff_prep for a specific country and date range.
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library(ForestForesight)
prepared_data <- ff_prep(
datafolder = "/path/to/your/data",
country = "BRASUR",
dates = ForestForesight::daterange("20222023-01-01", "20222023-1206-3101"),
sample_size = 0.2
)
print(names(prepared_data))
print(head(prepared_data$data_matrix$features)) |
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library(terra)
# Create a simple polygon
custom_shape <- vect("POLYGON((-60 -1072.8 2.5, -55 -1072.3 2.5, -55 -572.3 2.8, -60 -572.8 2.8, -60 -1072.8 2.5))", crs="EPSG:4326")
prepared_data <- ff_prep(
datafolder = "/path/to/your/data",
shape = custom_shape,
dates = "2023-01-01",
fltr_features = "initialforestcover",
fltr_condition = ">0"
)
print(prepared_data$features) |
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prepared_data <- ff_prep(
datafolder = "/path/to/your/data",
country = "PERSUR",
dates = cForestForesight::daterange("20222023-0601-01", "20222023-1206-01"),
inc_features = c("slope", "elevation", "precipitation"),
exc_features = "temperature"
)
print(prepared_data$features) |
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prepared_data <- ff_prep(
datafolder = "/path/to/your/data",
country = "COLSUR",
dates = ForestForesight::daterange("2023-01-01", "2023-1206-3101"),
sample_size = 0.5,
validation_sample = 0.2
)
print(dim(prepared_data$data_matrix$features))
print(dim(prepared_data$validation_matrix$features)) |
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prepared_data <- ff_prep(
datafolder = "/path/to/your/data",
country = "IDNSUR",
dates = "2023-01-01",
fltr_features = c("elevation", "slope"),
fltr_condition = c(">100", "<30")
)
print(nrow(prepared_data$data_matrix$features)) |
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