R/loadCleanData.R

In etiennelalechere/maturitymodelling: Forest maturity modelling

#' @title Load and clean data used in Fuhr et al. (in prep.).
#' @description Function to load and clean data: select maturity, LiDAR and topographic variables and normalize intensity metrics.
#' @usage loadCleanData()
#' @param deleteGTGBna logical.
#' @return Return LiDAR metric and terrain releve data frames
#' @export
#' @importFrom BBmisc normalize
#' @examples terrain=loadCleanData(deleteGTGBna = FALSE)[[1]]
#' metric=loadCleanData(deleteGTGBna = FALSE)[[2]]
loadCleanData<-function(deleteGTGBna){

  ### Load files
  terrain=read.delim("./../data/Modeles_maturite/Points_mat_metrics_prealps_IFN_20200214_final.csv",sep=";",h=T)
  terrain=terrain[order(as.character(terrain$Placette)),]
  metric=read.delim("./../data/Modeles_maturite/Points_mat_metrics_prealps_IFN_20200214_final.csv",sep=";",h=T)
  mnt=read.table("./../data/Modeles_maturite/mntPts_complet.csv",sep=";")
  slope=read.table("./../data/Modeles_maturite/slopePts_complet.csv",sep=";")
  # parcelles=read.table("./data/placette_jmm_parcellaire.csv",sep=";",h=T)


  ### Clean data keeping 'Source' column

  # Order rows
  terrain=terrain[order(as.character(terrain$Placette)),]
  metric=metric[order(as.character(metric$Placette)),]
  # parcelles=parcelles[order(as.character(parcelles$placette)),]

  # deleted duplicated rows
  doublons=c(
    which(as.character(terrain$Placette)=="Protest_146")[2],
    which(as.character(terrain$Placette)=="LPO_7")[2],
    which(as.character(terrain$Placette)=="RBI_V_350")[2],
    which(as.character(terrain$Placette)=="Protest_161")[2],
    which(as.character(terrain$Placette)=="Protest_91")[2]
  )
  terrain=terrain[-doublons,]
  metric=metric[-doublons,]

  # Add area  (or a random column)
  # metric=cbind(metric,parcelles$area)
  # colnames(metric)[length(colnames(metric))]="Area.parc"
  # colnames(metric)
  metric=cbind(metric,metric[,1])

  # Select metrics
  # colnames(metric)
  # dim(metric)
  metric=metric[24:dim(metric)[2]]
  # colnames(metric)
  metric=metric[,-c(7:36,43:56,67:69)]
  # manque mCF, sdCH, "p.1st.hmin" , "p.hmin"
  # Tree.mea_1, Tree.mea_3, Tree.mea_3 = "Tree.meanCrownV"       "Tree.meanCanopyH"      "Tree.meanCrownH" ???
  dim(metric)
  # colnames(metric)
  metric=metric[,-c(4,6:8,11:12,17:18)]
  # Suppression de "zskew"      "zentropy"   "itot"       "imax"       "iskew"      "ikurt"      "TreeInf10." "TreeSup10."
  # dim(metric)
  # colnames(metric)
  metric=metric[,-c(11)]
  # Delete "Type_PEUP" et "Source"
  dim(metric)
  colnames(metric)
  metric=metric[,-c(14:15)]

  # Add distance to nearest road
  # nn=read.delim("./data/Proche_ancien.csv",sep=";")
  # dim(nn)
  # doublons=c(
  #   which(as.character(nn$trrn_Pl)=="Protest_146")[2],
  #   which(as.character(nn$trrn_Pl)=="LPO_7")[2],
  #   which(as.character(nn$trrn_Pl)=="RBI_V_350")[2],
  #   which(as.character(nn$trrn_Pl)=="Protest_161")[2],
  #   which(as.character(nn$trrn_Pl)=="Protest_91")[2]
  # )
  # doublons
  # nn=nn[order(as.character(nn$trrn_Pl)),]
  # nn=nn[-doublons,]
  # metric=cbind(metric,nn[,c(3)])
  # colnames(metric)[length(colnames(metric))]="near_dist"
  # colnames(metric)

  # Add terrain variables
  mnt=mnt[order(mnt[,1]),]
  slope=slope[order(slope[,1]),]
  metric=cbind(metric,mnt[,2],slope[,2])
  colnames(metric)[(dim(metric)[2]-1):dim(metric)[2]]=c("mnt","slope")
  colnames(metric)

  # Normalize imean
  # table(terrain$Source)
  metric$imean[which(terrain$Source=="ain")]=BBmisc::normalize(metric$imean[which(terrain$Source=="ain")],method="standardize")
  metric$imean[which(terrain$Source=="bauges73")]=BBmisc::normalize(metric$imean[which(terrain$Source=="bauges73")],method="standardize")
  metric$imean[which(terrain$Source=="bauges74")]=BBmisc::normalize(metric$imean[which(terrain$Source=="bauges74")],method="standardize")
  metric$imean[which(terrain$Source=="chartreuse")]=BBmisc::normalize(metric$imean[which(terrain$Source=="chartreuse")],method="standardize")
  metric$imean[which(terrain$Source=="vercors4M")]=BBmisc::normalize(metric$imean[which(terrain$Source=="vercors4M")],method="standardize")
  metric$imean[which(terrain$Source=="vercorsRBI")]=BBmisc::normalize(metric$imean[which(terrain$Source=="vercorsRBI")],method="standardize")
  # boxplot(metric$imean)

  metric$isd[which(terrain$Source=="ain")]=BBmisc::normalize(metric$isd[which(terrain$Source=="ain")],method="standardize")
  metric$isd[which(terrain$Source=="bauges73")]=BBmisc::normalize(metric$isd[which(terrain$Source=="bauges73")],method="standardize")
  metric$isd[which(terrain$Source=="bauges74")]=BBmisc::normalize(metric$isd[which(terrain$Source=="bauges74")],method="standardize")
  metric$isd[which(terrain$Source=="chartreuse")]=BBmisc::normalize(metric$isd[which(terrain$Source=="chartreuse")],method="standardize")
  metric$isd[which(terrain$Source=="vercors4M")]=BBmisc::normalize(metric$isd[which(terrain$Source=="vercors4M")],method="standardize")
  metric$isd[which(terrain$Source=="vercorsRBI")]=BBmisc::normalize(metric$isd[which(terrain$Source=="vercorsRBI")],method="standardize")
  # boxplot(metric$isd)

  # Delete area variable (or random variable)
  colnames(metric)
  metric=metric[,-which(colnames(metric)=="metric[, 1]")]

  # Select terrain columns
  colnames(terrain)
  terrain=terrain[,c(1:22)]

  # Remove treemeanC correlated with tree.densi (-0.92)
  dim(metric) # 15
  colnames(metric)
  metric=metric[,-c(which(colnames(metric)=="Tree.meanC"))] # 0.85 threshold
  colnames(metric)

  # Define colnames
  xlabs=  c( "Zmax","Zmean","Zsd","Zkurt","Imean","Isd","Tree.meanH","Tree.sdH","Tree.giniH","Tree.density","TreeSup30.density","Tree.Canopy_cover","Elevation","Slope"  )
  colnames(metric)=xlabs

  # Delete NA value
  if(deleteGTGBna==T){
    delete.rows=which(is.na(terrain$GTGB)==T)
    terrain=terrain[-delete.rows,]
    metric=metric[-delete.rows,]
  }

  return(list(terrain,metric))
}