README.md

In jstrunk001/RForInvt: Forest Inventory Data Compilation and FVS Growth Projections

RForInvt

A collection of forest inventory related tools

This functionality is being split off of RSForInvt because of the dependency nightmare resulting from packing too much into a single package.

You can either download this repo as a zip and install from local .zip file in R, or use the devtools or remotes packages to install from github

Install

Install the latest version of RForInvt from github with devtools.

You may have to manually install a few packages that aren't brought in by the package for some reason.

You will need the remotes (or devtools) package to install from github.

Install with remotes

install.packages("remotes")
remotes::install_github("jstrunk001/RForInvt")

OR Install with devtools - my preference as I often use devtools

install.packages("devtools")
devtools::install_github("jstrunk001/RForInvt")

Manual way to install this package:

Download this git repository to a local zip file, then rename the downloaded zip archive from "RForinvt-master.zip" -> "RForInvt.zip", and then use the R remotes::install_local to install from .zip package file or just use the r or RStudio dropdown to inall a local zip.

install.packages("remotes)
#your path will vary here!
remotes::install_local("c:\\temp\\RForInvt.zip")

Package usage is also fairly simple

RForInvt FVS example

  #load required packages

    require(RForInvt)
    require(parallel)
    require(RSQLite)

  #create cluster with 4 nodes  

    clus1=makeCluster(4)

  #in this case supply the path to your sqlite fvs database with tables FVS_StandInit, and FVS_TreeInit

    dir_fvs_in = system.file("extdata", "FIADB_RI.db", package = "RForInvt")

  #load FVS data - but limit to the first 50 plots
  # Note that in the FVS ready data, plots are treated as stands

    conSQL = DBI::dbConnect( RSQLite::SQLite() , dbname= dir_fvs_in )
      dbListTables(conSQL)
      fvs_stands = dbGetQuery( conSQL , "select * from FVS_STANDINIT_PLOT limit 50")
    dbDisconnect( conSQL )

  #assume a typical inventory dataset and prepare fvs parameters
  #the fvs_make_keyfiles will take this list of parameters and make a
  #separate key file for each record in the FVS_StandInit table

    df_params = fvs_protype_params()
    df_params[1:nrow(fvs_stands),]=NA
    df_params[,"std_id"] = fvs_stands$STAND_ID
    df_params[,"invyr"] = fvs_stands$INV_YEAR
    df_params[,"timeint"] = 10
    df_params[,"numcycle"] = 1
    df_params[,"input_db"] = dir_fvs_in
    df_params[,"fvs_path"] = "C:/FVSbin/FVSne.exe"
    df_params[,"tree_table"] = "FVS_TREEINIT_PLOT"
    df_params[,"stand_table"] = "FVS_STANDINIT_PLOT"
    df_params

  #prepare prototype key file - the fvs_prototype_keyfile function just puts together a 
  #series of text strings. Some common keywords are suggested (e.g. notriple, nodgl, and dgstdev). 
  #If the parameter is NULL, then the keyword is not included, and other keywords can be supplied with "other_keywords" parameter

    #By default, notriple = NULL, so these two are equivalent
    key_proto = fvs_prototype_keyfile(notriple="NoTriple")
    key_proto = fvs_prototype_keyfile(other_keywords="NoTriple")

  #convert prototype key file into series of key files associated with each stand_id

    df_keys = fvs_make_keyfiles(df_params, key_proto = key_proto, cluster = clus1 , id="std_id")

  #make sure to look at a few keyword files to see that they are formulated correctly  (especially the db paths)

    df_keys

  #lastly, actually run fvs - if processing in parallel this function creates multiple output dbs, by default these are merged and temp files deleted

    fvs_run(df_keys, cluster = clus1, merge_dbs=T, db_merge="c:/temp/RForInvt/fVS_db_out_RI.db")

  #release resources assigned to cluster  

    parallel::stopCluster(clus1);rm(clus1)

RForInvt NVEL example

        require(RForInvt)

        #grab list of species
        if(!"dfSpp" %in% ls()){
          library(RSQLite)
          db0 = dbConnect(RSQLite::SQLite(), system.file("misc/NBEL/BiomassEqns.db", package="RForInvt"))
            dfSpp = dbGetQuery(db0, paste("select * from tblspp"))
            dfCoeff = dbGetQuery(db0, paste("select * from BM_EQCoefs"))
          dbDisconnect(db0)
        }

        #build a fake tree list
        if(!"df_fake" %in% ls()){
          set.seed=111
          nfake=length(unique(dfCoeff$species_code))

          df_fake = data.frame(
            trid=1:(nfake)
            ,region = 6
            ,forest = "01"
            ,district = "01"
            ,dbh=10*abs(rnorm(nfake))
            ,ht=100*abs(rnorm(nfake))
            ,spcd = unique(dfCoeff$species_code)#'     sample(c("a","b","c","d") , nfake , T)
          )

        }

        #get volumes
        NVEL_volume( dfTL = df_fake )

jstrunk001/RForInvt documentation built on April 17, 2025, 5:02 p.m.