Loads FIA Datatables into R from .csv files stored in a local directory. If you have not previously downloaded FIA Data from the FIA Datamart, use getFIA to download data for your region of interest and load it into R. Capable of merging multiple state downloads of the FIA database for regional analysis. Simply store each set of state data, as downloaded from the FIA Datamart, in the same directory and hand to readFIA.
readFIA(dir, common = TRUE, tables = NULL, nCores = 1, ...)directory where FIA Datatables are stored.
logical; if TRUE, only import most commonly used tables, including all required for rFIA functions (see Details for list of tables).
character vector; names of specific tables to be imported (e.g. 'PLOT', 'TREE', 'COND', 'TREE_GRM_COMPONENT').
numeric; number of cores to use for parallel implementation. Check available cores using detectCores. Default = 1, serial processing.
other arguments to pass to fread.
List object containing FIA Datatables. List elements represent individual FIA Datatables stored as data.frame objects. Names of list elements reflect names of files from which they were read into R environment (File names should not be changed after download from FIA Datamart).
If multiple subsets of the FIA database are held in the same directory (e.g. Michigan and Indiana state downloads), corresponding tables will be merged (e.g. PLOT table returned contains plots in both Michigan and Indiana).
Download subsets of the FIA Database using getFIA (recommended), or manually from the FIA Datamart: https://apps.fs.usda.gov/fia/datamart/datamart.html. Once downloaded, unzip the directory (if downloaded manually), and read into R using readFIA.
If common = TRUE, the following tables will be imported: COND, COND_DWM_CALC, INVASIVE_SUBPLOT_SPP, PLOT, POP_ESTN_UNIT, POP_EVAL, POP_EVAL_GRP, POP_EVAL_TYP, POP_PLOT_STRATUM_ASSGN, POP_STRATUM, SUBPLOT, TREE, TREE_GRM_COMPONENT. These tables currently support all functionality with rFIA, and it is recommended that only these tables be imported to conserve RAM and reduce processing time.
If you wish to merge multiple state downloads of FIA data (e.g. Michigan and Indiana state downloads), simply place both sets of datatables in the same directory (done for you when using getFIA) and import with readFIA. Upon import, corresponding tables (e.g. MI_PLOT and IN_PLOT) will be merged, and analysis can be completed for the entire region or within spatial units which transcend state boundaries (e.g. Ecoregion subsections).
Easy, efficient parallelization is implemented with the parallel package. Users must only specify the nCores argument with a value greater than 1 in order to implement parallel processing on their machines. Parallel implementation is achieved using a snow type cluster on any Windows OS, and with multicore forking on any Unix OS (Linux, Mac). Implementing parallel processing may substantially decrease decrease free memory during processing, particularly on Windows OS. Thus, users should be cautious when running in parallel, and consider implementing serial processing for this task if computational resources are limited (nCores = 1).
FIA DataMart: https://apps.fs.usda.gov/fia/datamart/datamart.html
FIA Database User Guide: https://www.fia.fs.fed.us/library/database-documentation/
# NOT RUN {
## First we write the 'fiaRI' object to a temporary directory
## Replace temp_dir with the path to your directory (where data will be saved)
temp_dir = tempdir()
writeFIA(fiaRI, dir = temp_dir)
## Then read it back in with readFIA
db <- readFIA(dir = temp_dir)
# }
Run the code above in your browser using DataLab