FIA Midwest Climate Data

This markdown file documents how data from three different publicly available databases (FIA DataMart, Climatology Lab, and USFS Insect & Disease Detection Survey) can be combined into one easily understandable dataframe. The final dataframe produced will match every tree recorded by the USFS in the northeastern portion of the United States with the climatology data recorded by the closest weather station to each individual tree. This will result in 14 different climatology variables per tree. Using ArcGIS 10.8.1 to extract the necessary Insect & Disease Detection Survey shapefiles provided by the USFS, each tree can then be related to the different types of insect and disease damage found nearby.

The following code was executed using R 4.2.1.

Downloading Necessary Files

Section 1: Downloading FIA Data

This section of code downloads the “FIA_PLOT” and “FIA_TREE” data for every state in FIA region 9. These files can be found on the U.S. Forest Service Webpage.

pacman::p_load(pacman, caret, lars, tidyverse, rio)

states<-c("CT","DE","IA","IL","IN","MA","MD","ME","MI","MN","MO","NH","NJ","NY","OH","PA","RI","VT","WI","WV")
for(i in 1:length(states)){
  download.file(paste("https://apps.fs.usda.gov/fia/datamart/CSV/",states[i],"_PLOT.csv",sep=""),paste("FIA_",states[i],"PLOT.csv"),sep="")
}

for(i in 1:length(states)){
  download.file(paste("https://apps.fs.usda.gov/fia/datamart/CSV/",states[i],"_TREE.csv",sep=""),paste("FIA_",states[i],"TREE.csv"),sep="")
}

Section 2: Downloading Climate Data

This section of code downloads every climate variable reported in 2016-2018 from the Climatology Lab.

TERRA_Clim_Vars<-c("aet","def","PDSI","pet","ppt","q","soil","srad","swe","tmax","tmin","vap","vpd","ws")
for(i in 1:length(TERRA_Clim_Vars)){
  download.file(paste("https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_",TERRA_Clim_Vars[i],"_2016. nc",sep=""),paste("TERACLIM_",TERRA_Clim_Vars[i],"2016. nc"),sep="",mode = "wb")
}

for(i in 1:length(TERRA_Clim_Vars)){
  download.file(paste("https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_",TERRA_Clim_Vars[i],"_2017. nc",sep=""),paste("TERACLIM_",TERRA_Clim_Vars[i],"2017. nc"),sep="",mode = "wb")
}

for(i in 1:length(TERRA_Clim_Vars)){
  download.file(paste("https://climate.northwestknowledge.net/TERRACLIMATE-DATA/TerraClimate_",TERRA_Clim_Vars[i],"_2018. nc",sep=""),paste("TERACLIM_",TERRA_Clim_Vars[i],"2018. nc"),sep="",mode = "wb")
}

Modifying the Appearance of Climate Data

Section 3: Modifying Climatology Data

The files downloaded from the Climatology Lab in Section 2 are nc files that need to be opened, reduced in size, and converted into csv files. In order to accomplish this, the following steps are taken:

The file names (files downloaded in Section 2) are entered and ncvar_open() is used to create a connection to the file without actually opening it (var).
print (var) is used to view the types of data contained in each file. Within each file you can see the number of variables (1) and the dimensions of those variables (“lat”, “lon”, and “time”). For example, in the file containing aet data from 2016 the size of “lat” is 4320, “lon” is 8640, and “time” is 12. Because this file is pretty big, we need to break them down into smaller sections.
ncvar_get () is used to request where/when the “lat”, “lon”, and “time” data was recorded.
Because “time” is recorded as days since 1900-01-01, it needs to be calibrated into a more easily understandable unit. To do this, as.date () is used to specify the origin and time zone.
To reduce the size of “lat” and “lon”, the latitudinal and longitudinal coordinates of each state are specified in the file “split_state_coords.csv”. In this file, I further split each state into smaller sections to speed up the amount of time required to analyze the data. Note: if you get the error:“attempt to set ‘colnames’ on an object with less than two dimension”, the divisions you specified lat_rng or lon_rng are likely too small.
ncvar_get () is used again to create a time series for each state per month.
The row and column names of the data frame are then changed to match the variables “lat” and “lon”.
melt() is used to change the shape of the data frame and display “lat”, “lon”, and each corresponding climate variable in a more understandable way.
A column specifying which month each variable was pulled from is also added (“var_month”).

This process is then repeated for every climate variable per month in each state section per year. The final csv files created each consist of 4 columns: latitude, longitude, a climate variable, and the month the data was collected.

years<-c("2016", "2017", "2018")
state_coord<-import("C:/Users/Swenson/Documents/R/Denise/Midwest_states_data/split_state_coords.csv")
month<-c(1:12)

library(reshape2)
library(ncdf4)
for(i in 1:length(years)){
  for(j in 1:nrow(state_coord)){
    for(k in 1:length(TERRA_Clim_Vars)){
      for(y in 1:length(month)){
        filename<-paste("C:/Users/Swenson/Documents/R/Denise/TERRA_Clim/TERRA_Clim_",years[i],"/TERACLIM_ ",TERRA_Clim_Vars[k]," ",years[i],".nc",sep="")
        var<-nc_open(filename) 
        print(var)
        lat<-ncvar_get(var, "lat")
        lon<-ncvar_get(var, "lon")
        time<-ncvar_get(var, "time")
        time2<-as.Date(time, origin="1900-01-01", tz="UTC")
        lat_rng<-c(state_coord[j,3],state_coord[j,4])
        lon_rng<-c(state_coord[j,1],state_coord[j,2])
        lat_ind<-which(lat>=lat_rng[1]&lat<=lat_rng[2])
        lon_ind<-which(lon>=lon_rng[1]&lon<=lon_rng[2])
        lat[lat_ind]
        lon[lon_ind]
        var_tm<-ncvar_get(var,paste("",TERRA_Clim_Vars[k],"",sep=""),start = c(lon_ind[1],lat_ind[1],month[y]),count = c(length(lon_ind),length(lat_ind),1))
        dim(var_tm)
        var_tm2<-var_tm
        row.names(var_tm2)<-lon[lon_ind]
        colnames(var_tm2)<-lat[lat_ind]
        var_tmmelt<-melt(var_tm2,id.vars=lat[lat_ind])
        colnames(var_tmmelt)<-c("lon","lat",paste("",TERRA_Clim_Vars[k],"",sep=""))
        var_month<-matrix(paste("",month[y],"",sep=""),ncol=1,nrow=nrow(var_tmmelt))
        var_tmmelt<-cbind(var_tmmelt,var_month)
        setwd(paste("C:/Users/Swenson/Documents/R/Denise/Uni_FIA_Clim_var/Clim_year/Split_var_original/",month[y],"/",TERRA_Clim_Vars[k],"/",years[i],"",sep=""))
        write.csv(var_tmmelt, paste("",TERRA_Clim_Vars[k],"_",state_coord[j,5],"lat_lon_",years[i],".csv",sep=""))
      }
    }
  }
}

Section 4: Creating Climate Station Identifier

This section of code adds a column (“station_ID”) to each modified climate data file (files created in Section 3) to identify the station the data was recorded. This is repeated for every climate variable per month in each state section per year.

Clim_states<-c("CT","DE","RI","MA","MD_1","MD_2","IA_1","IA_2","IA_3","IA_4","IA_5","IA_6","IA_7","IA_8","IA_9","IA_10","IA_11","IA_12","IA_13","IA_14","IA_15","IA_16","IN_1","IN_2","IN_3","IN_4","IN_5","IN_6","IN_7","IN_8","IN_9","IN_10","IN_11","IN_12","IN_13","IN_14","IN_15","IN_16","IL_1","IL_2","IL_3","IL_4","IL_5","IL_6","IL_7","IL_8","IL_9","IL_10","IL_11","IL_12","IL_13","IL_14","IL_15","IL_16","ME_1","ME_2","ME_3","ME_4","ME_5","ME_6","ME_7","ME_8","ME_9","ME_10","ME_11","ME_12","ME_13","ME_14","ME_15","ME_16","ME_17","ME_18","ME_19","ME_20","ME_21","ME_22","ME_23","ME_24","ME_25","ME_26","ME_27","ME_28","ME_29","ME_30","ME_31","ME_32","NH_1","NH_2","NH_3","NH_4","NH_5","NH_6","NH_7","NH_8","NH_9","NH_10","NH_11","NH_12","NH_13","NH_14","NH_15","NH_16","NJ_1","NJ_2","NJ_3","NJ_4","NJ_5","NJ_6","NJ_7","NJ_8","NJ_9","NJ_10","NJ_11","NJ_12","NJ_13","NJ_14","NJ_15","NJ_16","OH_1","OH_2","OH_3","OH_4","OH_5","OH_6","OH_7","OH_8","OH_9","OH_10","OH_11","OH_12","OH_13","OH_14","OH_15","OH_16","PA_1","PA_2","PA_3","PA_4","PA_5","PA_6","PA_7","PA_8","PA_9","PA_10","PA_11","PA_12","PA_13","PA_14","PA_15","PA_16","VT_1","VT_2","VT_3","VT_4","VT_5","VT_6","VT_7","VT_8","VT_9","VT_10","VT_11","VT_12","VT_13","VT_14","VT_15","VT_16","WV_1","WV_2","WV_3","WV_4","WV_5","WV_6","WV_7","WV_8","WV_9","WV_10","WV_11","WV_12","WV_13","WV_14","WV_15","WV_16","NY_1","NY_2","NY_3","NY_4","NY_5","NY_6","NY_7","NY_8","NY_9","NY_10","NY_11","NY_12","NY_13","NY_14","NY_15","NY_16","MO_1","MO_2","MO_3","MO_4","MO_5","MO_6","MO_7","MO_8","MO_9","MO_10","MO_11","MO_12","MO_13","MO_14","MO_15","MO_16","MI_1","MI_2","MI_3","MI_4","MI_5","MI_6","MI_7","MI_8","MI_9","MI_10","MI_11","MI_12","MI_13","MI_14","MI_15","MI_16","MI_17","MI_18","MI_19","MI_20","MI_21","MI_22","MI_23","MI_24","MI_25","MI_26","MI_27","MI_28","MI_29","MI_30","MI_31","MI_32","WI_1","WI_2","WI_3","WI_4","WI_5","WI_6","WI_7","WI_8","WI_9","WI_10","WI_11","WI_12","WI_13","WI_14","WI_15","WI_16","WI_17","WI_18","WI_19","WI_20","WI_21","WI_22","WI_23","WI_24","WI_25","WI_26","WI_27","WI_28","WI_29","WI_30","WI_31","WI_32","WI_33","WI_34","WI_35","WI_36","WI_37","WI_38","WI_39","WI_40","WI_41","WI_42","WI_43","WI_44","WI_45","WI_46","WI_47","WI_48","WI_49","WI_50","MN_1","MN_2","MN_3","MN_4","MN_5","MN_6","MN_7","MN_8","MN_9","MN_10","MN_11","MN_12","MN_13","MN_14","MN_15","MN_16","MN_17","MN_18","MN_19","MN_20","MN_21","MN_22","MN_23","MN_24","MN_25","MN_26","MN_27","MN_28","MN_29","MN_30","MN_31","MN_32","MN_33","MN_34","MN_35","MN_36","MN_37","MN_38","MN_39","MN_40","MN_41","MN_42","MN_43","MN_44","MN_45","MN_46","MN_47","MN_48","MN_49","MN_50")

for(i in 1:length(Clim_states)){
  for(j in 1:length(years)){
    for(y in 1:length(month)){
      for(k in 1:length(TERRA_Clim_Vars)){
        var_year<-import(paste("C:/Users/Swenson/Documents/R/Denise/Uni_FIA_Clim_var/Clim_year/Split_var_original/",month[y],"/",TERRA_Clim_Vars[k],"/",years[j],"/",TERRA_Clim_Vars[k],"_",Clim_states[i],"lat_lon_",years[j],".csv",sep=""))
        colnames(var_year)<-c("V1","LON","LAT",paste("",TERRA_Clim_Vars[k],"",sep=""),"var_month")
        as.matrix(var_year)
        Clim_year<-as.matrix(var_year[,c(2:5)])
        Clim_year<-as.data.frame(Clim_year)
        station<-paste(rep("station",nrow(Clim_year)),c(1:nrow(Clim_year)),sep="_")
        Clim_year<-cbind(Clim_year,station)
        Clim_year<-na.omit(Clim_year)
        as.character(Clim_year$LON)
        as.character(Clim_year$LAT)
        as.character(Clim_year$station)
        setwd(paste("C:/Users/Swenson/Documents/R/Denise/Uni_FIA_Clim_var/Clim_year/Split_var_reduced/",month[y],"/All_var/",years[j],"",sep=""))
        write.csv(Clim_year,paste("",TERRA_Clim_Vars[k],"_",Clim_states[i],"_",years[j],".csv",sep=""))
      }
    }
  }
}

Modifying the Appearance of FIA Data

Section 5: Determining FIA Tree Coordinates

This section of code combines the “FIA_PLOT” and “FIA_TREE” data obtained from the U.S. Forest Service Webpage (files downloaded in Section 1). From the “FIA_PLOT” data, we are interested in the variables “CN”, “PLOT”, “MEASYEAR”, “MEASMON”, “MEASDAY”, “LAT”, “LON”, “ELEV”. From the “FIA_TREE” data, we are interested in the variables “PLTCN”, “STATECD”, “PLOT”, “STATUSCD”, “SPCD”, “SPGRPCD”, “DIA”, “HT”. In order to combine these files, the following steps are taken:

“FIA_TREE” and “FIA_PLOT” data is imported for each state per year (files downloaded in Section 1).
The variables we are interested in are pulled out of each document.
Because the variables “CN” and “PLTCN” display the same data, these variables are renamed and used to merge the two documents.
All trees except ones sampled from 2016-2018 are removed from the data frame.

This process is repeated for each state per year creating csv files with “FIA_PLOT” and “FIA_TREE” variables.

A key for each column name can be found here. A key for each species code (“SPCD”) can be found here.

for (i in 1:length(states)) {
  for (j in 1:length(years)){
    FIA_Tree<- import(paste("C:/Users/Swenson/Documents/R/Denise/FIA_Project/FIA_Tree/FIA_ ",states[i]," TREE.csv",sep=""))
    FIA_Plot<- import(paste("C:/Users/Swenson/Documents/R/Denise/FIA_Project/FIA_PLOT/FIA_ ",states[i]," PLOT.csv",sep=""))
    
    FIA_Plot2<-FIA_Plot[,c(1,9,12,13,14,20,21,22)]
    FIA_Tree2<-FIA_Tree[,c(2,5,8,15,16,17,18,20)]
    colnames(FIA_Plot2)[1]="CN/PLTCN match"
    colnames(FIA_Tree2)[1]="CN/PLTCN match"
    FIA_Tree_Plot<-merge(FIA_Tree2,FIA_Plot2)
    FIA_Tree_Plot2<-FIA_Tree_Plot[FIA_Tree_Plot$MEASYEAR==years[j],]
    setwd(paste("C:/Users/Swenson/Documents/R/Denise/FIA_Tree_Coords/FIA_Trees_",years[j],"_Coord",sep=""))
    write.csv(FIA_Tree_Plot2,paste("FIA_",states[i],"trees_",years[j],".csv",sep=""))
  }
}

Section 6: Determining Unique FIA Coordinates

The coordinates for each “FIA_TREE” are recorded based on what “PLOT” they are located in. As a result, multiple trees have the same coordinates. This section of code looks at the every merged “FIA_PLOT” and “FIA_TREE” csv file (files created in Section 5) and pulls out every unique “LAT”/“LON” coordinate value. This process is repeated for every state per year.

for(i in 1:length(states)){
  for(j in 1:length(years)){
    
    FIA_Tree_Plot2<-import(paste("C:/Users/Swenson/Documents/R/Denise/FIA_Tree_Coords/FIA_Trees_",years[j],"_Coord/FIA_",states[i],"trees_",years[j],".csv",sep=""))
    as.matrix(FIA_Tree_Plot2)
    
    FIA_year<-as.matrix(FIA_Tree_Plot2[,c(14,13)])
    FIA_year<-as.data.frame(FIA_year)
    Uni_FIA_year<-unique(FIA_year[,c("LON","LAT")],)
    as.character(Uni_FIA_year$LAT)
    as.character(Uni_FIA_year$LON)
    
    setwd(paste("C:/Users/Swenson/Documents/R/Denise/Uni_FIA_Clim_var/Uni_FIA_year/Uni_FIA_",years[j],"",sep=""))
    write.csv(Uni_FIA_year,paste("Uni_FIA_",states[i],"_",years[j],".csv",sep=""))
    
  }
}

Relating Climate and FIA Data

Section 7: Determining the Closest Climate Station to each FIA Plot

For this section of code I chose to focus on the variable “aet” in the month of May because the stations are the same for each variable per year; however, any month or variable would suffice. In order to accomplish this, the following steps are taken:

The reduced climate data files for each split state section per year in the month of May (a portion of the files created in Section 4) along with the unique FIA state files (created in Section 6) are imported.
The variables “V1” and “var_month” are removed from each imported climate file.
The variable “V1” is removed from each imported FIA file.
A “unique ID” for each plot location is added to each FIA file (data frame labeled UNI_FIA_year).
The distance between the coordinates of each unique FIA tree and the coordinates of each state’s climate station is placed into a matrix (var 1) where each row is labeled by the respective “station ID”.
The minimum distance between the coordinates of each unique FIA tree and the coordinates of each state’s climate station is recorded in a matrix (var 2) with the respective “station ID”.
The rows of the matrix var 2 are changed to reflect each “unique ID” they relate to, and the columns of var 2 are renamed.
var 2 and the modified unique FIA state files (UNI_FIA_year) are combined together to show the closest climate station to each unique FIA tree coordinate, along with the distance to that station.
All the variables in the reduced climate data files for each split state section per year in the month of May (Clim_year) are removed except for “aet” (climate variable) and the “station_ID” where data was recorded.
The climate variable “aet” (or whatever variable you chose to complete this portion of code; data frame labeled Clim_year_var) is added to the data frame Uni_FIA_year_var based on the “station_ID”.

The process is repeated for each split state section per year.

for(i in 1:length(Clim_states)){
  for(j in 1:length(years)){
    Clim_year<-read.csv(paste("C:/Users/Swenson/Documents/R/Denise/Uni_FIA_Clim_var/Clim_year/Split_var_reduced/5/All_var/",years[j],"/aet_",Clim_states[i],"_",years[j],".csv",sep=""))
    Clim_year<-Clim_year[,c(2,3,4,6)]
    
    Uni_FIA_year<-read.csv(paste("C:/Users/Swenson/Documents/R/Denise/Uni_FIA_Clim_var/Uni_FIA_year/Uni_FIA_",years[j],"/Uni_FIA_",gsub("_.*","",Clim_states[i]),"_",years[j],".csv",sep=""))
    Uni_FIA_year<-Uni_FIA_year[,c(2,3)]
    Unique_ID<-paste(rep("Unique ID",nrow(Uni_FIA_year)),c(1:nrow(Uni_FIA_year)),sep="_")
    Uni_FIA_year<-cbind(Uni_FIA_year,Unique_ID)
    as.character(Uni_FIA_year$Unique_ID)
    
    var1<-matrix(NA,ncol=1,nrow=nrow(Clim_year))
    var2<-matrix(NA,ncol=2,nrow=nrow(Uni_FIA_year))
    
    for(k in 1:nrow(Uni_FIA_year)){
      for(l in 1:nrow(Clim_year)){
        
        var1[l,] = dist(rbind(Uni_FIA_year[k,1:2], Clim_year[l,1:2]))
        rownames(var1)=Clim_year[,4]
        var2[k,1] = min(var1)
        var2[k,2] = rownames(var1)[rank(var1)==1]
        rownames(var2) = Uni_FIA_year[,3]
        colnames(var2)=c("Dist(km)", "station")
        Uni_FIA_year_var<-cbind(Uni_FIA_year,var2)
        Clim_year_var<-Clim_year[,c(3,4)]
        Uni_FIA_year_var<-merge(Uni_FIA_year_var,Clim_year_var,by="station")
        setwd(paste("C:/Users/Swenson/Documents/R/Denise/Uni_FIA_Year_Var/Uni_FIA_all_states_aet_v2/",years[j],"",sep=""))
        write.csv(Uni_FIA_year_var,paste("Uni_FIA_",Clim_states[i],"_aet_",years[j],"_v2. csv",sep=""))
      }
    }
  }
}

Section 8: Adding Climate Station Coordinates

For this section of code the coordinates of each closest climate station was added to each FIA file created in Section 7 . In order to accomplish this, the following steps are taken:

Files created in Section 7 (data frame labeled Uni_FIA_Clim) and each May aet file created in Section 4 (data frame labeled aet) are imported. Note that I choose the variable aet in the month of May because they were used to do the analysis in Section 7.
All the columns of each May aet file were renamed to match Uni_FIA_Clim and the two data frames were merged together (var).
The variable “aet”, “V1.x”, “V1.y”, and “var_month” were all removed from the data frame var. I chose to remove “aet” and “var_month” because they will be added later; however, if you would like to keep these variables at this point in the analysis the code can easily be modified to do so.

This process was repeated for every state section per year.

for(i in 1:length(years)){
  for(j in 1:length(Clim_states)){
    Uni_FIA_Clim<-import(paste("C:/Users/Swenson/Documents/R/Denise/Uni_FIA_Year_Var/Uni_FIA_all_states_aet_v2/",years[i],"/Uni_FIA_",Clim_states[j],"_aet_",years[i],"_v2. csv",sep=""))
    aet<-import(paste("C:/Users/Swenson/Documents/R/Denise/Uni_FIA_Clim_var/Clim_year/Split_var_reduced/5/All_var/",years[i],"/aet_",Clim_states[j],"_",years[i],".csv",sep=""))
    colnames(aet)<-c("V1","station_LON","station_LAT","aet","var_month","station")
    var<-merge(Uni_FIA_Clim,aet, by=c("station","aet"),na.omit=T)
    var<-var[,c(1,4:7,9:10)]
    setwd(paste("C:/Users/Swenson/Documents/R/Denise/Uni_FIA_Year_Var/Matched_station/Per_state_sections/aet_only/",years[i],"",sep=""))
    write.csv(var, paste("Matched_aet",Clim_states[j],"_",years[i],".csv",sep=""))
  }
}

Section 9: Adding All variables

This section of code adds every variable to each state section per month per year.To accomplish this, the following steps are taken:

Files created in Section 4 are listed by state section in var_list (14 docs per each list).
Each of the files in var_list are opened, and put the data into datalist0.
All of the data in datalist0 is combined together into one data frame (All_vars). Now, All_vars contains every variable in each state section per month per year.
The “station_ID” is then removed from the data frame and the station “LAT”/“LON” columns are renamed (data frame labeled All_vars_2).
The files created in Section 8 are imported (data frame labeled var).
The variable “V1” is removed from the data frame var.
The data frames var and All_vars_2 are combined together by the climate station coordinates. This adds the “unique_ID”, FIA tree/plot coordinates (“LAT”/“LON”), and distance from each plot to the closest station (“Dist”) to each file.

This process is repeated for each state section per month per year.

for(i in 1:length(years)){
  for(j in 1:length(Clim_states)){
    for(y in 1:length(month)){
      var_list<-list.files(path=paste("C:/Users/Swenson/Documents/R/Denise/Uni_FIA_Clim_var/Clim_year/Split_var_reduced/",month[y],"/All_var/",years[i],"",sep=""),
                           pattern=paste("_",Clim_states[j],"_",sep=""))
      datalist0<-list()
      for(k in 1:length(var_list)){
        var0<-import(paste("C:/Users/Swenson/Documents/R/Denise/Uni_FIA_Clim_var/Clim_year/Split_var_reduced/",month[y],"/All_var/",years[i],"/",var_list[k],"",sep=""))
        datalist0[[k]]<-var0
      }
      All_vars<-Reduce(function(x, y) merge(x, y), datalist0)
      All_vars2<-All_vars[,c(2:4,6:19)]
      colnames(All_vars2)[1]<-"station_LON"
      colnames(All_vars2)[2]<-"station_LAT"
      var<-import(paste("C:/Users/Swenson/Documents/R/Denise/Uni_FIA_Year_Var/Matched_station/Per_state_sections/aet_only/",years[i],"/Matched_aet",Clim_states[j],"_",years[i],".csv",sep=""))
      var<-var[,c(2:8)]
      All_vars3<-merge(var,All_vars2,by=c("station_LON","station_LAT"))
      setwd(paste("C:/Users/Swenson/Documents/R/Denise/Uni_FIA_Year_Var/Matched_station/Per_state_sections/All_vars/",month[y],"/",years[i],"",sep=""))
      write.csv(All_vars3,paste("All_var_matched_",Clim_states[j],"_",years[i],".csv",sep=""))
    }
  }
}

Combining All Data per Year

Section 10: Combining All State Sections per Year

This section of code takes the files created in Section 9 and combines them into data frames per state per month per year. This is accomplished by the following steps:

Files created in Section 9 (contains each variable per state section per month per year) are listed in var1 by state, imported, placed into datalist, and bound together into the data frame var 3.
Only the closest station to each unique tree plot is kept and placed into the data frame min_val.This data is then placed into datalist2 and bound together in var 4.
Files created in Section 5 (FIA_tree_coord_Year) that contain all FIA trees regardless of “LAT”/“LON” coordinates are uploaded and merged with var 4 by the variables “LAT” and “LON” (data frame labeled FIA_Full).
FIA_Full is then reduced in size to contain every variable except “V1x” and “V1y” (var 5).

This process is then repeated for each state section per month per year.

for(i in 1:length(years)){
  for(j in 1:length(states)){
    for(y in 1:length(month)){
      var1<-list.files(path=paste("C:/Users/Swenson/Documents/R/Denise/Uni_FIA_Year_Var/Matched_station/Per_state_sections/All_vars/",month[y],"/",years[i],"",sep=""),
                       pattern=paste("All_var_matched_",states[j],"",sep=""))
      datalist<-list()
      for(k in 1:length(var1)){
        var2<-import(paste("C:/Users/Swenson/Documents/R/Denise/Uni_FIA_Year_Var/Matched_station/Per_state_sections/All_vars/",month[y],"/",years[i],"/",var1[k],"",sep=""))
        datalist[[k]]<-var2
      }
      var3<-do.call(bind_rows,datalist)
      num<-1:nrow(var2)
      datalist2<-list()
      for(l in 1:length(num)){
        min_val<-min(var3[var3$Unique_ID==paste("Unique ID_",num[l],sep=""),]$Dist)
        datalist2[[l]]<-var3[grep(min_val,var3$Dist),]
      }
      var4<-do.call(bind_rows,datalist2)
      FIA_Tree_Coord_Year<-import(paste("C:/Users/Swenson/Documents/R/Denise/FIA_Tree_Coords/FIA_Trees_",years[i],"_Coord/FIA_",states[j],"trees_",years[i],".csv",sep=""))
      FIA_full<-merge(FIA_Tree_Coord_Year,var4,by=c("LAT","LON"))
      var5<-FIA_full[,c(1:2,4:15,17:36)]
      setwd(paste("C:/Users/Swenson/Documents/R/Denise/Uni_FIA_Year_Var/Matched_station/Per_state/",month[y],"",sep=""))
      write.csv(var5,paste("All_var_matched_",states[j],"_",years[i],".csv",sep=""))
    }
  }
}

Section 11: Combining All States per Year

This section of code takes the files created in Section 10 and combines them into data frames per month per year. This is accomplished by the following steps:

Files created in Section 10 are listed in var6 by state and imported (var7).
The variable “V1” is removed from var7 and the new var7 is placed into datalist3.
All of the files in datalist3 and bound together (var8).
A file containing all of the common and scientific names of each tree species (“species_code.csv”) is imported and merged with var8.

This process is repeated for each state per month per year.

for(i in 1:length(years)){
  for(y in 1:length(month)){
    var6<-list.files(path=paste("C:/Users/Swenson/Documents/R/Denise/Uni_FIA_Year_Var/Matched_station/Per_state/",month[y],"",sep=""),
                     pattern=paste("_",years[i],".csv",sep=""))
    datalist3<-list()
    for(k in 1:length(var6)){
      var7<-import(paste("C:/Users/Swenson/Documents/R/Denise/Uni_FIA_Year_Var/Matched_station/Per_state/",month[y],"/",var6[k],"",sep=""))
      var7<-var7[,c(2:35)]
      datalist3[[k]]<-var7
    }
    var8<-do.call(bind_rows,datalist3)
    state_codes<-import("C:/Users/Swenson/Documents/R/Denise/Midwest_states_data/species_codes.csv")
    var8<-merge(var8,state_codes)
    setwd(paste("C:/Users/Swenson/Documents/R/Denise/Uni_FIA_Year_Var/Matched_station/Per_year/",years[i],"",sep=""))
    write.csv(var8,paste("All_var_matched_",month[y],"_",years[i],".csv",sep=""))
  }
}

Section 12: Combining All Variables per Month per Year

This section of code takes the files created in Section 11 and combines them into data frames per year. This is accomplished by the following steps:

The files created in Section 11 are listed in var9 by year, imported (var10), and placed into datalist4.
All of the data in datalist4 is bound together in var11.
The variable “V1” is removed from var11.

This process is repeated for each year and creates three files (1 file per year).

Note: that this includes trees of all “STATUSCD” (0:no status,1:live trees,2:dead trees,3:removed trees).

for(i in 1:length(years)){
  var9<-list.files(path=paste("C:/Users/Swenson/Documents/R/Denise/Uni_FIA_Year_Var/Matched_station/Per_year/",years[i],"",sep=""),
                   pattern=".csv")
  datalist4<-list()
  for(k in 1:length(var9)){
    var10<-import(paste("C:/Users/Swenson/Documents/R/Denise/Uni_FIA_Year_Var/Matched_station/Per_year/",years[i],"/",var9[k],"",sep=""))
    datalist4[[k]]<-var10
  }
  var11<-do.call(bind_rows,datalist4)
  var11<-var11[,c(2:35)]
  setwd("C:/Users/Swenson/Documents/R/Denise/Uni_FIA_Year_Var/Matched_station/Per_year_all_month")
  write.csv(var11,paste("All_var_matched_",years[i],".csv",sep=""))
}

Creating a Community Data Matrix (CDM)

Section 13: Creating a CDM per Month per Year

This section of code takes the files created in Section 11, removes some variables, and creates a community data matrix (CDM) per month per year. This is accomplished by the following steps:

Files created in Section 11 are imported into a data frame labeled var12 (need files that do not include all months because it complicates the CDM).
The columns “STATECD” AND “PLOT” in var12 are combined together to distinguish between plots labeled with the same number in different states (This will be necessary when creating the CDM).
The name of the column “PLOT” in var12 is changed to “STATECD_PLOT”.
The data frame is then reduced to only include trees with a “STATUSCD”=1 (status 1:live trees).
table () is used to determine how many times each tree species (specified by “Scientific Name”) is found in each “STATECD_PLOT” (table_1).
table_1 is converted into a data frame and the names of the columns in table_1 are changed to match var12.
The columns in table_1 are ordered to match the structure required to run matrify(), where the first column is the tree species, the second column is the state and plot where the tree was sampled, and the third column is the number of times the tree species in column 1 was recorded in the state and plot in column 2.
matrify() is used to create a CDM from the data frame table_1.
In order to relate var13 to the variables in var12, all the unique “STATECD_PLOT” variables from var12 (or table_1 for that matter) are put into there own data frame (new_col), labeled “STATECD_PLOT”, and combined with data frame var13 (var14).
The variables “V1”, “SPCD”, “SPGRPCD”, “DIA”, “HT”, “common name”, and “scientific name” are removed because these variables do not apply to every tree within a specific “STATECD_PLOT”.
Only unique rows in var12 are kept. Notice that there is now only 1 row per “STATECD_PLOT”.
var14 and var12 are combined into one data frame to create one CDM.

This process is then repeated for each month per year.

library(labdsv)
for(i in 1:length(years)){
  for(y in 1:length(month)){
    var12<-import(paste("C:/Users/Swenson/Documents/R/Denise/Uni_FIA_Year_Var/Matched_station/Per_year/",years[i],"/All_var_matched_",month[y],"_",years[i],".csv",sep=""))
    var12$PLOT<-paste(var12$STATECD,var12$PLOT,sep="_")
    colnames(var12)[colnames(var12)=="PLOT"]<-"STATECD_PLOT"
    var12<-var12[var12$STATUSCD==1,]
    table1<-table(var12$"Scientific Name",var12$"STATECD_PLOT")
    table1<-as.data.frame(table1)
    colnames(table1)<-c("Scientific Name","STATECD_PLOT","Freq")
    table1<-table1[,c(2,1,3)]
    var13<-matrify(table1)
    new_col<-as.data.frame(unique(var12$STATECD_PLOT))
    colnames(new_col)<-"STATECD_PLOT"
    var14<-cbind(new_col,var13)
    var12<-var12[,c(3:8,12:35)]
    var12<-unique(var12)
    var15<-merge(var14,var12)
    setwd(paste("C:/Users/Swenson/Documents/R/Denise/CDM/Per_month/",years[i],"",sep=""))
    write.csv(var15,paste("CDM_",month[y],"_",years[i],".csv",sep=""))
  }
}

Section 14: Creating a CDM per Year

This section of code takes the files created in Section 13 and creates a CDM per year. This is accomplished by the following steps:

The files created in Section 13 are listed in var16 by year, imported (var17), and placed into datalist5.
All of the data in datalist5 is bound together in var18.

This process is repeated for each year and creates three CDM files (1 file per year).

years<-c("2016","2017","2018")
for(i in 1:length(years)){
  var16<-list.files(path=paste("C:/Users/Swenson/Documents/R/Denise/CDM/Per_month/",years[i],"",sep=""),
                    pattern=".csv")
  datalist5<-list()
  for(k in 1:length(var16)){
    var17<-import(paste("C:/Users/Swenson/Documents/R/Denise/CDM/Per_month/",years[i],"/",var16[k],"",sep=""))
    datalist5[[k]]<-var17
  }
  var18<-do.call(bind_rows,datalist5)
  setwd("C:/Users/Swenson/Documents/R/Denise/CDM/Per_year")
  write.csv(var18,paste("CDM_",years[i],".csv",sep=""))
}

Section 15: Reducing and Reorganizing each CDM File

This section of code takes the files created in Section 14, ensures each of these files has the same number of columns, reorganizes the data, and removes any unnecessary columns. This is accomplished by the following steps:

The 2016 file created in Section 14 is imported (CDM_2016).
The “V1” columns are removed and the column “Quercus nigra” is moved from the last column to the 51st column of the data frame. By moving the column “Quercus nigra” all of the tree species are now in alphabetical order.
A for loop is created to modify the 2017 and 2018 files created in Section 14 and the 2017 and 2018 files are imported (CDM).
A “Albizia_julibrissin” matrix is created with 1 column and the same number of rows as CDM, where every value in this matrix is NA.
The column name of the matrix “Albizia_julibrissin” is then changed to match the “Albizia julibrissin” column in CDM_2016.
The matrix “Albizia_julibrissin” is then combined with CDM.
The columns of CDM are then reorganized and reduced in size to match CDM_2016.

This process creates three modified CDM files (1 file per year).

CDM_2016<-import("C:/Users/Swenson/Documents/R/Denise/CDM/Per_year/CDM_2016.csv")
CDM_2016<-CDM_2016[,c(3:52,95,53:94)]
setwd("C:/Users/Swenson/Documents/R/Denise/CDM/Per_year/Reorganized_data")
write.csv(CDM_2016,"CDM_2016_V2.csv")

years_17_18<-c("2017","2018")
for(i in 1:length(years_17_18)){
  CDM<-import(paste("C:/Users/Swenson/Documents/R/Denise/CDM/Per_year/CDM_",years_17_18[i],".csv",sep=""))
  Albizia_julibrissin<-matrix(NA,ncol=1,nrow=nrow(CDM))
  colnames(Albizia_julibrissin)<-"Albizia julibrissin"
  CDM<-cbind(CDM,Albizia_julibrissin)
  CDM<-CDM[,c(3:9,95,10:94)]
  setwd("C:/Users/Swenson/Documents/R/Denise/CDM/Per_year/Reorganized_data")
  write.csv(CDM,paste("CDM_",years_17_18[i],"_V2.csv",sep=""))
}

Reducing the Size of Files Created in Acrmap

Section 16: CDM and USFS Surveyed Area

This section of code reduces the number of columns in the CDM and USFS Disease Data surveyed area files created in Arcmap. In order to accomplish this, the following steps are taken:

The CDM and USFS Disease Data survey area text files are imported (CDM_SA).
Every column expect “TARGET_FID”, “JOIN_FID”, columns originating from the CDM files created in Section 15, along with the “REGION_ID”, “SURVEY_ID”, “legacy” data, and “source” data from the USFS Disease Data file are removed from CDM_SA.

This process is repeated for each CDM and USFS surveyed area file per year.

for(i in 1:length(years)){
  CDM_SA<-import(paste("C:/Users/Swenson/Documents/R/Denise/GIS/Feature_Class_Tables/CDM_",years[i],"_Proj_Clip_SA_Join_Table.txt",sep=""))
  CDM_SA<-CDM_SA[,c(3,4,6:98,100,101,107:111,116,117)]
  setwd("C:/Users/Swenson/Documents/R/Denise/GIS/Reduced_Join_Tables/CDM/Per_year")
  write.csv(CDM_SA,paste("CDM_",years[i],"_Proj_Clip_SA_Join_Table_Reduced.csv",sep=""))
}

Section 17: CDM and USFS Damaged Area

This section of code reduces the number of columns in the CDM and USFS Disease Data damaged area files created in Arcmap. In order to accomplish this, the following steps are taken:

The CDM and USFS Disease Data damaged area text files are imported (CDM_DA).
Every column expect “TARGET_FID”, “JOIN_FID”, columns originating from the CDM files created in Section 15, along with the “REGION_ID”, “LABEL”, “host” columns, “DCA” columns, “damage” columns, columns that quantify damage, “NOTES”, “OBSERVATION_COUNT”, “ACRES”, “SURVEY_ID”, “legacy” data, and “source” data from the USFS Disease Data file are removed from CDM_DA.

This process is repeated for each CDM and USFS damaged area file per year.

for(i in 1:length(years)){
  CDM_DA<-import(paste("C:/Users/Swenson/Documents/R/Denise/GIS/Feature_Class_Tables/CDM_",years[i],"_Proj_Clip_DA_Join_Table.txt",sep=""))
  CDM_DA<-CDM_DA[,c(3,4,6:98,104:117,119,120,125,127:135,138,139)]
  setwd("C:/Users/Swenson/Documents/R/Denise/GIS/Reduced_Join_Tables/CDM/Per_year")
  write.csv(CDM_DA,paste("CDM_",years[i],"_Proj_Clip_DA_Join_Table_Reduced.csv",sep=""))
}

Section 18: USFS Damaged Points and USFS Surveyed/Damaged Area

This section of code reduces the number of columns in the USFS Disease Data damaged points and USFS surveyed/damaged area created in Arcmap. In order to accomplish this, the following steps are taken:

The USFS Disease Data damaged points and USFS surveyed/damaged area text file are imported (The Dpts).
Only the columns originating from the USFS Disease Data damaged points file, downloaded from the USFS Webpage, are preserved in Dpts.

This process is repeated for each USFS surveyed/damaged area (“DA” and “SA”) per year.

file_type<-c("SA","DA")
for(i in 1:length(years)){
  for(j in 1:length(file_type)){
    Dpts<-import(paste("C:/Users/Swenson/Documents/R/Denise/GIS/Feature_Class_Tables/Dpts_",years[i],"_Clip_",file_type[j],"_Join_Table.txt",sep=""))
    Dpts<-Dpts[,c(3,4,10,12:23,26:29,31,34:35)]
    setwd("C:/Users/Swenson/Documents/R/Denise/GIS/Reduced_Join_Tables/Dpts/Per_year")
    write.csv(Dpts,paste("Dpts_",years[i],"_Clip_",file_type[j],"_Join_Table_Reduced.csv",sep=""))
  }
}

Section 19: USFS Damaged Points and USFS Surveyed/Damaged Area

This section of code combines the files created in Section 16 and Section 17 into one csv file per USFS Disease Data survey/damage area. This is accomplished by the following steps:

The files created in Section 16 and Section 17 are listed in var19 by USFS Disease Data type, imported (var20), and placed into datalist6.
All of the data in datalist6 is bound together in var21.

This process is repeated for each USFS Disease Data survey/damage area.

for(i in 1:length(file_type)){
  var19<-list.files(path=paste("C:/Users/Swenson/Documents/R/Denise/GIS/Reduced_Join_Tables/CDM/Per_year"),
                   pattern=paste("",file_type[i],"_Join_Table_Reduced.csv",sep=""))
  datalist6<-list()
  for(j in 1:length(var19)){
    var20<-import(paste("C:/Users/Swenson/Documents/R/Denise/GIS/Reduced_Join_Tables/CDM/Per_year/",var19[j],"",sep=""))
    datalist6[[j]]<-var20
  }
  var21<-do.call(bind_rows,datalist6)
  setwd("C:/Users/Swenson/Documents/R/Denise/GIS/Reduced_Join_Tables/CDM/All_years")
  write.csv(var21,paste("CDM_Proj_Clip_",file_type[i],"_Join_Table_Reduced.csv",sep=""))
}

Section 20: USFS Damaged Points and USFS Surveyed/Damaged Area

This section of code combines the files created in Section 18 into one csv file per USFS Disease Data survey/damage area. This is accomplished by the following steps:

The files created in Section 18 are listed in var22 by USFS Disease Data type, imported (var23), and placed into datalist7.
All of the data in datalist7 is bound together in var24.