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Raw IFN data to tibble

ifn_to_tibble.Rd

Transform raw IFN plot data into tidy data for easier use

Usage

ifn_to_tibble(
  provinces,
  versions,
  filter_list = NULL,
  folder,
  clean_empty = NULL,
  as_sf = FALSE,
  ...,
  .parallel_options = furrr::furrr_options(scheduling = 1L, stdout = TRUE),
  .verbose = TRUE
)

Arguments

provinces

A character vector with the two-number codes for the provinces.

versions

A character vector with the ifn versions. Valid versions are "ifn2", "ifn3" and "ifn4".

filter_list

A list of provinces and plots to extract the data from.

folder

The path to the folder containing the IFN db files, as character.

clean_empty

Vector with column names from where to remove empty results. Can be one or more of "tree", "shrub", "herbs" and "regen". If more than one, only plots with data in all columns selected will be retained. Default to NULL, no cleaning is done.

as_sf

Logical indicating if the data must be returned as an spatial object. This always can be done later, as the data contains coordinates and crs info. Default to FALSE.

...

Not used at the moment

.parallel_options

An object of class furrr_options. See furrr_options.

.verbose

Logical controlling if progress messages are shown.

Value

A nested tibble. This tibble contains a row per plot/year combination, with the plot metadata included, as well as columns containing tibbles with tree, shrub, herbs and soil information. See vignette("inventory_data_tibble", package = "forestables")

Details

This function will take every year indicated and retrieve and transform the plot data for the provinces, versions and plots provided. For that, IFN db files must reside in the folder indicated in the folder argument.

Filter list

If no filter_list argument is provided, ifn_to_tibble will attempt to process all plots for the provinces and ifn versions provided. This will result in sometimes thousands plots to be extracted, processed and returned, which in turn will cause a big use of memory (specially when running in parallel processes) and long times of calculation. Is better to provide a list of departments with the provinces and plots to look for to narrow the process. This filter_list should have the following structure:


   list(
   "01" = c("01_0644_NN_A1_A1"),
   "08" = c("08_1256_NN_A1_xx", "08_0056_xx_A4_xx"),
   "24" = c("24_0270_xx_A4_xx")
  )

forestables package offers workflows to create this automatically, see vignette("selecting_plots", package = "forestables") for more details.

Parallel

Processing the plots from within an IFN version can be done in parallel (forestables uses internally the furrr package for this). This means that, if parallelization is active, several processes are launched to retrieve the plots data for that IFN version. This is repeated for all versions provided.

.parallel_options controls the finer details of how parallelization is performed (see furrr_options). But no parallelization can occur without setting first a plan. By default, the chosen plan is sequential, so no parellization is done. Changing the plan, i.e. to multisession will allow ifn_to_tibble to use parallelization when retrieving the data.

Examples

# \donttest{
if (FALSE) { # \dontrun{
library(forestables)
ifn_to_tibble(
  provinces = c("24"), versions = c("ifn3"),
  filter_list = list("24" = c("24_0270_xx_A4_xx")),
  folder = "path/to/ifn/data"
)
} # }
# }