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

(optional) helpful functions and some options setup

#prevent printing of scientific notation
options(scipen = 10E6)

#prevent storing strings as factors
options(stringsAsFactors = F)

#helper for writing paths to clipboard 
writeClipboard2 = function(x) writeClipboard(charToRaw(paste0(x, ' ')))

#generic function to slash direction from clipboard
reslash<- function(x=NA, slash=c("back","forward")){
  if(is.na(x)) x = readClipboard()
  if(slash[1]=="back") path <- shQuote(gsub("/","\\\\",gsub("\\", "\\\\", x, fixed = TRUE), fixed = TRUE))
  if(slash[1]=="forward") path <- shQuote(gsub("\\", "/", x, fixed = TRUE))
  writeClipboard2(path)
  return(path)
}

#backslash a path on clipboard
bs=function(x=NA){reslash(x,slash="back")}

#forwardslash a path on clipboard
fs=function(x=NA){reslash(x,slash="forward")}

#get the names of a data.frame as a quoted string vector
nmsVec=function(x){x=paste("c('",paste(names(x),collapse="','"),"')",sep=""); writeClipboard2(x);return(x)}

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, it is always a good idea to start with a small dataset
  # Note that in the FVS ready data, plots are treated as stands with 4 plots

    conSQL = DBI::dbConnect( RSQLite::SQLite() , dbname= dir_fvs_in )
      dbListTables(conSQL)
      fvs_stands = dbGetQuery( conSQL , "select * from FVS_STANDINIT_PLOT where INV_YEAR > 2010")
      fvs_trees = dbGetQuery( conSQL , "select * from FVS_TREEINIT_PLOT where STAND_limit 500")
    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_cn"] = fvs_stands$STAND_CN
    df_params[,"std_id"] = fvs_stands$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

    #here we override the defualt to used std_id and use std_cn instead
    key_proto = fvs_prototype_keyfile(
                             notriple="NoTriple"
                            , std_id = "@std_cn@"
                            , std_cn = "@std_cn@"
                            , where_std = "WHERE Stand_CN = '%Stand_CN%'"
                            )

  #convert prototype key file into series of key files associated with each stand_id
    df_keys = fvs_make_keyfiles(df_params[2:10,], key_proto = key_proto, cluster = clus1 , id="std_cn" , clear_db = T, clear_keys = T)

  #make sure to look at a few keyword files to see that they are  (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 = NA, merge_dbs=T, db_merge="c:/temp/RForInvt/fvs/fVS_out_RI.db" , append=F)

  #release resources assigned to cluster  
    parallel::stopCluster(clus1);rm(clus1)

read in some FVS grown data

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