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Raw FIA data to tibble

fia_to_tibble.Rd

Transform raw FIA plot data into tidy data for easier use

Usage

fia_to_tibble(
  states,
  years,
  filter_list = NULL,
  folder,
  clean_empty = NULL,
  as_sf = FALSE,
  ...,
  .parallel_options = furrr::furrr_options(scheduling = 1L, stdout = TRUE),
  .verbose = TRUE
)

Arguments

states

A character vector with the two letters code for the states to extract the data from.

years

A numeric vector with the years to extract de data from.

filter_list

A nested list of states, counties and plots to extract the data from. If left NULL all plots for the state for all years will be extracted, which can use a big amount of memory. See details.

folder

The path to the folder containing the FIA csv 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 specified and will retrieve and transform the plot data for the states and plots provided. For that, csv files from FIA must reside in the folder indicated in the folder argument.

Filter list

If no filter_list argument is provided, fia_to_tibble will attempt to process all plots for the states and years provided. This will result in sometimes hundred of 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 states with the counties and plots to look for to narrow the process. This filter_list should have the following structure:


  list(
    "MN" = list("137" = c(29396, 25064), "71" = c(20210)),
    "OR" = list("59" = c(76413)),
    "CA" = list("105" = c(70128, 83043))
  )

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

Parallel

Processing the plots from within a year 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 year. This is repeated for all years 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 fia_to_tibble to use parallelization when retrieving the data.

Examples

# \donttest{
if (FALSE) { # \dontrun{
library(forestables)
fia_to_tibble(
  years = 2014, states = c("OR"),
  filter_list = list("OR" = list("59" = c(76413))),
  folder = "path/to/fia/data"
)
} # }
# }