updated package renamed a few functions and added get_biome_names

.Rbuildignore

+^.*\.Rproj$
+^\.Rproj\.user$

.gitignore

+.Rproj.user
+.Rhistory
+.RData
+.Ruserdata

DESCRIPTION

 Package: biospheremetrics
 Type: Package
 Title: Biosphere integrity metrics for LPJmL
-Version: 0.1.1
+Version: 0.1.2
 Author: Fabian Stenzel, Johanna Braun, Jannes Breier
 Maintainer: Fabian Stenzel <stenzel@pik-potsdam.de>
 Description: Functions to compute Biosphere integrity metrics M-COL and M-ECO based on output from LPJmL
 License: GPL-3
 Encoding: UTF-8
 LazyData: true
-RoxygenNote: 7.2.2
+RoxygenNote: 7.2.3
 Depends:
     lpjmliotools

NAMESPACE

 # Generated by roxygen2: do not edit by hand
 
+export(average_nyear_window)
 export(calcDeltaV)
-export(calcGamma)
 export(calcMCOL)
+export(calcMECO)
 export(classify_biomes)
-export(disaggregateGammaIntoBiomes)
-export(evaluateGamma)
-export(gammaCrossTable)
+export(disaggregateMECOintoBiomes)
+export(get_biome_names)
 export(list_needed_outputs)
+export(mecoCrossTable)
+export(plotBiomeAverages)
+export(plotBiomeAveragesToScreen)
 export(plotBiomes)
-export(plotBiomesAverage)
-export(plotBiomesAveragesToScreen)
 export(plotBiomesToScreen)
-export(plotGammaCrossTable)
-export(plotGammaCrossTableToScreen)
-export(plotGammaMap)
-export(plotGammaMapToScreen)
-export(plotGammaRadial)
-export(plotGammaRadial4)
-export(plotGammaRadialToScreen)
 export(plotMCOL)
 export(plotMCOLmap)
 export(plotMCOLovertime)
+export(plotMECOcrossTable)
+export(plotMECOcrossTableToScreen)
+export(plotMECOmap)
+export(plotMECOmapToScreen)
+export(plotMECOradial)
+export(plotMECOradialPanel)
+export(plotMECOradialToScreen)
 export(plot_biomes)
-export(readGammaData)
 export(readMCOLdata)
+export(readMECOData)
+export(replaceRefDataWithAverageRefBiomeCell)
+importFrom(magrittr,"%>%")

R/MCOL.R

@@ -78,9 +78,9 @@ plotMCOLmap <- function(data, file, title, legendtitle, zeroThreshold = 0.1, eps
 #' }
 #' @export
 plotMCOLovertime <- function(mcolData, file, firstyr, plotyrs, highlightyrs = 2000, minVal = 0,
-                            maxVal = 100, legendpos = "topleft", ext = FALSE, eps = FALSE){
+                            maxVal = 100, legendpos = "topleft", ext = FALSE, eps = FALSE, ref = "pi"){
   lastyr = firstyr + length(mcolData$npp_act_overtime) - 1
-  colz = c("slateblue","gold","green3","red3","darkorange","black")
+  colz = c("slateblue","gold","green3","darkorange","black","red3")
   if (eps) {
     file = strsplit(file,".",fixed=TRUE)[[1]]
     file = paste(c(file[1:(length(file) - 1)],"eps"),collapse=".")
@@ -90,29 +90,33 @@ plotMCOLovertime <- function(mcolData, file, firstyr, plotyrs, highlightyrs = 20
     png(file, width=3.5,  height = 3, units = "in", res = 300, pointsize = 6,type="cairo")
   }
   par(bty="o",oma=c(0,0,0,0),mar=c(4,5,1,3))
-  plot(x=seq(firstyr,lastyr,1),y=mcolData$npp_pot_overtime,ylab="GtC/yr",xlab="Year",xlim=plotyrs,
-       ylim=c(minVal, maxVal),type = "l",col=colz[1],xaxs="i",yaxs="i")
+  plot(NA,ylab="GtC/yr",xlab="Year",xlim=plotyrs,
+       ylim=c(minVal, maxVal),xaxs="i",yaxs="i")
+  grid()
+  lines(x=seq(firstyr,lastyr,1),y=mcolData$npp_pot_overtime,type = "l",col=colz[1])
   lines(x=seq(firstyr,lastyr,1),y=mcolData$npp_act_overtime,type = "l",col=colz[2])
   lines(x=seq(firstyr,lastyr,1),y=mcolData$npp_eco_overtime,type = "l",col=colz[3])
-
+  lines(x=seq(firstyr,lastyr,1),y=mcolData$npp_luc_overtime,type = "l",col=colz[4])
+  lines(x=seq(firstyr,lastyr,1),y=mcolData$mcol_overtime,type = "l",col=colz[5])
+  
   par(bty="n",oma=c(0,0,0,0),mar=c(4,5,1,3), new = T)
   if (ref == "pi") {
     plot(x=seq(firstyr,lastyr,1),y=mcolData$mcol_overtime_perc_piref,ylab="",xlab="",xlim=plotyrs,
-         ylim=c(10, 30),type = "l",col=colz[4],xaxs="i", yaxs="i", axes = F)
+         ylim=c(10, 30),type = "l",col=colz[6],xaxs="i", yaxs="i", axes = F)
   } else if (ref == "act") {
     plot(x=seq(firstyr,lastyr,1),y=mcolData$mcol_overtime,ylab="",xlab="",xlim=plotyrs,
-         ylim=c(10, 30),type = "l",col=colz[4],xaxs="i", yaxs="i", axes = F)
+         ylim=c(10, 30),type = "l",col=colz[6],xaxs="i", yaxs="i", axes = F)
   }else stop(paste0("Unknown value for parameter ref: ",ref," - Aborting."))
 
-  axis(side = 4, col = colz[4],col.axis = colz[4])
-  mtext(text = "%", col=colz[4], side = 4,line = 2)
+  axis(side = 4, col = colz[6],col.axis = colz[6])
+  mtext(text = "%", col=colz[6], side = 4,line = 2)
 
   if (!is.null(highlightyrs)){
     for (y in highlightyrs){
       lines(x=c(y,y),y=c(minVal,maxVal),col="grey40")
     }
   }
-  legend(legendpos,legend = c("NPPpot (PNV)","NPPact (landuse)","NPPeco","M-COL [% NPPpi]"),col=colz[1:4] ,lty=1,cex=1)
+  legend(legendpos,legend = c("NPPpot (PNV)","NPPact (landuse)","NPPeco","NPPluc","M-COLabs","M-COL [% NPPpi]"),col=colz ,lty=1,cex=1)
   dev.off()
 }
 #' Calculate the ecosystem change metric gamma between 2 simulations/timesteps
@@ -510,13 +514,15 @@ calcMCOL <- function(inFol_lu, inFol_pnv, startyr, stopyr, gridbased = T, npp_th
   timber_harvest_overtime <- colSums(timber*cellarea)/10^15 # from gC/m2 to GtC
   fire_overtime <- colSums(fire*cellarea)/10^15 # from gC/m2 to GtC
 
-
   mcol_overtime <- harvest_cft_overtime + rharvest_cft_overtime +
                     harvest_grasslands_overtime + harvest_bioenergy_overtime +
                     timber_harvest_overtime + fire_overtime + npp_luc_overtime
   mcol_overtime_perc_piref <- mcol_overtime/mean(npp_pot_overtime[1:10])*100
-  mcol <- harvest_cft + rharvest_cft + harvest_grasslands + harvest_bioenergy + timber + fire + ynpp_potential - ynpp
-  mcol[ynpp_potential<npp_threshold] <- 0 # set to 0 below lower threshold of NPP
+  mcol_luc <- ynpp_potential - ynpp
+  mcol_luc_piref <- rep(rowMeans(ynpp_potential[,1:10]),times = length(ynpp[1,])) - ynpp # always compare to pi_ref
+  mcol_harvest <- harvest_cft + rharvest_cft + harvest_grasslands + harvest_bioenergy + timber + fire
+  mcol <- mcol_harvest + mcol_luc
+  mcol[abs(ynpp_potential)<npp_threshold] <- 0 # set to 0 below lower threshold of NPP
   mcol_perc <- mcol/ynpp_potential*100 #actual NPPpot as ref
   mcol_perc_piref <- mcol/rowMeans(ynpp_potential[,1:10])*100 # NPPpi as ref
 
@@ -529,7 +535,8 @@ calcMCOL <- function(inFol_lu, inFol_pnv, startyr, stopyr, gridbased = T, npp_th
                 harvest_cft_overtime = harvest_cft_overtime, npp_luc_overtime = npp_luc_overtime,
                 rharvest_cft_overtime = rharvest_cft_overtime, fire_overtime = fire_overtime,
                 timber_harvest_overtime = timber_harvest_overtime, harvest_cft = harvest_cft,
-                grassland_scaling_factor_cellwise = grassland_scaling_factor_cellwise))
+                grassland_scaling_factor_cellwise = grassland_scaling_factor_cellwise,
+                mcol_harvest = mcol_harvest, mcol_luc = mcol_luc, mcol_luc_piref = mcol_luc_piref))
   }else{
     return(list(mcol_overtime = mcol_overtime, mcol = mcol, mcol_perc = mcol_perc,
                 mcol_overtime_perc_piref = mcol_overtime_perc_piref,
@@ -538,9 +545,9 @@ calcMCOL <- function(inFol_lu, inFol_pnv, startyr, stopyr, gridbased = T, npp_th
                 npp_eco_overtime = npp_eco_overtime, #npp_bioenergy_overtime = npp_bioenergy_overtime,
                 harvest_cft_overtime = harvest_cft_overtime, npp_luc_overtime = npp_luc_overtime,
                 rharvest_cft_overtime = rharvest_cft_overtime, fire_overtime = fire_overtime,
-                timber_harvest_overtime = timber_harvest_overtime, harvest_cft = harvest_cft))
+                timber_harvest_overtime = timber_harvest_overtime, harvest_cft = harvest_cft,
+                mcol_harvest = mcol_harvest, mcol_luc = mcol_luc, mcol_luc_piref = mcol_luc_piref))
   }
-
 }
 
 #' Plot absolute MCOL, overtime, maps, and npp into given folder
@@ -575,19 +582,34 @@ plotMCOL <- function(mcolData, outFol, plotyears, minVal, maxVal, legendpos,
   print(paste0("Plotting MCOL figures"))
   dir.create(file.path(outFol),showWarnings = F)
   lpjmliotools::plotGlobal(data = rowMeans(mcolData$mcol[,(mapindex-mapyear_buffer):(mapindex+mapyear_buffer)]),
-             file = paste0(outFol,"MCOL",mapyear,"_absolute.png"),
-             title = paste0("MCOL in ",mapyear), min = 0, max = 1000,
-             legendtitle = "GtC", legYes = T, onlyPos = F, eps = eps, type = "lin")
+             file = paste0(outFol,"MCOL",mapyear,"_absolute.png"), type = "exp",
+             title = paste0("MCOL_abs in ",mapyear), pow2min = 0, pow2max = 12,
+             legendtitle = "GtC", legYes = T, onlyPos = F, eps = eps)
+  lpjmliotools::plotGlobal(data = rowMeans(mcolData$mcol_luc[,(mapindex-mapyear_buffer):(mapindex+mapyear_buffer)]),
+                           file = paste0(outFol,"MCOL",mapyear,"_luc.png"), type = "exp",
+                           title = paste0("MCOL_luc in ",mapyear), pow2min = 0, pow2max = 12,
+                           legendtitle = "GtC", legYes = T, onlyPos = F, eps = eps)
+  lpjmliotools::plotGlobal(data = rowMeans(mcolData$mcol_luc_piref[,(mapindex-mapyear_buffer):(mapindex+mapyear_buffer)]),
+                           file = paste0(outFol,"MCOL",mapyear,"_luc_piref.png"), type = "exp",
+                           title = paste0("MCOL_luc piref in ",mapyear), pow2min = 0, pow2max = 12,
+                           legendtitle = "GtC", legYes = T, onlyPos = F, eps = eps)
+  lpjmliotools::plotGlobal(data = rowMeans(mcolData$mcol_harvest[,(mapindex-mapyear_buffer):(mapindex+mapyear_buffer)]),
+                           file = paste0(outFol,"MCOL",mapyear,"_harv.png"), type = "exp",
+                           title = paste0("MCOL_harv in ",mapyear), pow2min = 0, pow2max = 12,
+                           legendtitle = "GtC", legYes = T, onlyPos = F, eps = eps)
   plotMCOLovertime(mcolData = mcolData, file = paste0(outFol,"MCOL_overtime_LPJmL_",plotyears[1],"-",plotyears[2],".png"),
                   firstyr = startyr, plotyrs = plotyears, minVal = minVal, ref = "pi",
                   legendpos = legendpos, maxVal = maxVal, eps = eps, highlightyrs = highlightyear)
   plotMCOLmap(data = rowMeans(mcolData$mcol_perc[,(mapindex-mapyear_buffer):(mapindex+mapyear_buffer)]),
                file = paste0(outFol,"MCOL",mapyear,"_LPJmL.png"),legendtitle = "% of NPPpot", eps = eps,
-               title = paste0(mapindex-mapyear_buffer, " - ",mapindex+mapyear_buffer) )
+               title = paste0("MCOL_perc ",mapyear-mapyear_buffer, " - ",mapyear+mapyear_buffer) )
   plotMCOLmap(data = rowMeans(mcolData$mcol_perc_piref[,(mapindex-mapyear_buffer):(mapindex+mapyear_buffer)]),
                file = paste0(outFol,"MCOL_piref_",mapyear,"_LPJmL.png"),
-               title = paste0(mapindex-mapyear_buffer, " - ",mapindex+mapyear_buffer),legendtitle = "% of NPPpi", eps = eps)
-  lpjmliotools::plotGlobalMan(data = rowMeans(mcolData$ynpp[,(mapindex-mapyear_buffer):(mapindex+mapyear_buffer)]),file = paste0(outFol,"NPP",mapyear,"_LPJmL.png"), brks = seq(0,1000,100),
+               title = paste0("MCOL_perc ",mapyear-mapyear_buffer, " - ",mapyear+mapyear_buffer),legendtitle = "% of NPPpi", eps = eps)
+  lpjmliotools::plotGlobalMan(data = rowMeans(mcolData$ynpp[,(mapindex-mapyear_buffer):(mapindex+mapyear_buffer)]),
+                file = paste0(outFol,"NPP",mapyear,"_LPJmL.png"), brks = seq(0,1000,100),
                 palette = c("orangered4","orangered","orange","gold","greenyellow","limegreen","green4","darkcyan","darkslategrey","navy"),
-                title = paste0("NPP average ",mapindex-mapyear_buffer, "-",mapindex+mapyear_buffer),legendtitle = "gC/m2",legYes = T)
+                title = paste0("NPP average ",mapyear-mapyear_buffer, "-",mapyear+mapyear_buffer),
+                legendtitle = "gC/m2",legYes = T)
+  
 } # end of plotMCOL

R/average_nyear_window.R

+#' Calculate averages (mean) for defined window sizes
+#'
+#' Define window sizes (nyear_window) to be used to calculate averages (mean)
+#' for each window (`dim(x)[3] / nyear_window`). Instead of discrete windows,
+#' also moving averages can be computed as well as years inbetween interpolated.
+#'
+#' @param x LPJmL output array with `dim(x)=c(cell, month, year)`
+#'
+#' @param nyear_window integer, if supplied it defines the years for each window
+#' to be averaged over in `dim(x)[3]`. If `nyear_window == 1` values are used
+#' directly (instead of calculating an average). nyear_window has to be smaller
+#' than `dim(x)[3]` and `dim(x)[3]` is ideally a multipe of nyear_window.
+#' Defaults to `NULL`
+#'
+#' @param moving_average logical. If `TRUE` moving average is computed. start
+#' and end are interpolated using spline interpolation.
+#'
+#' @param interpolate logical. If `TRUE` and nyear_window is defined (with
+#' `moving_average == FALSE` years are interpolated (spline) to return array
+#' with same dimensions as `x` (mainly`dim(x)[3]` -> year).
+#'
+#' @param nyear_reference integer, if supplied (default NULL), it defines a
+#' time_span for ideally reference runs to be used as a baseline. E.g.
+#' `nyear_reference = 30` to be used for preindustrial climate reference.
+#'
+#' @return array with same amount of cells and months as x. 3rd dimension is
+#' defined by nyear_window, basically `dim(x)[3]/nyear_window` or equal to
+#' dim(x)[3] if `moving_average == TRUE` or `interpolate == TRUE`
+#'
+#' @md
+#' @importFrom magrittr %>%
+#' @export
+average_nyear_window <- function(x,
+                                 nyear_window = NULL,
+                                 moving_average = FALSE,
+                                 interpolate = FALSE,
+                                 nyear_reference = NULL) {
+
+  third_dim <- names(dim(x))[
+    !names(dim(x)) %in% c("cell", "year")
+  ] %>% {
+    if (rlang::is_empty(.)) NULL else .
+  }
+
+  if (length(third_dim) > 1) {
+    stop(paste0("x has to have dimensions \"cell\", \"year\" and can have ",
+                "one third dimension (e.g. \"month\", \"year\""))
+  }
+  # check validity of x dimensions
+  if (!all(names(dim(x)) %in% c("cell", third_dim, "year"))) {
+    stop(paste0("x has to have dimensions \"cell\"",
+                ifelse(is.null(third_dim),
+                       "",
+                       paste0(", \"", third_dim, "\"")),
+                " and \"year\"."))
+  }
+
+  # moving average function - spline interpolation to fill NAs at start/end
+  moving_average_fun <- function(x, n) {
+    stats::filter(x, rep(1 / n, n), sides = 2) %>%
+      zoo::na.spline()
+  }
+
+  # utility function to interpolate inbetween averaging windows via spline
+  interpolate_spline <- function(x, y, nyear_window) {
+    rep(NA, dim(y)["year"]) %>%
+      `[<-`(seq(round(nyear_window / 2), dim(y)["year"], nyear_window),
+                value = x) %>%
+      zoo::na.spline()
+  }
+
+  # if nyear_window is supplied not all years are used for averaging
+  if (!is.null(nyear_window)) {
+    if (!is.null(nyear_reference)) {
+      orig_x <- x
+      x <- lpjmlKit::subset_array(x, list(year = 1:nyear_reference))
+    }
+    # only valid for nyear_window <  years of x (dim(x)[3])
+    if (nyear_window > 1 & nyear_window <= dim(x)["year"]) {
+      # check if multiple (can also be left out)
+      # if (dim(x)[3] %% nyear_window == 0) {
+      if (moving_average) {
+        y <- aperm(apply(x,
+                         c("cell", third_dim),
+                         moving_average_fun,
+                         nyear_window),
+                   c("cell", third_dim, ""))
+      } else {
+        # calculate mean for discret windows/bins with size of nyear_window
+        y <- array(x,
+                   # set correct dimensions (with names)
+                   dim = c(dim(x)[c("cell", third_dim)],
+                           year = nyear_window,
+                           window = dim(x)[["year"]] / nyear_window),
+                   # set correct dimensions names with nyear and windows
+                   dimnames = append(dimnames(x)[c("cell", third_dim)],
+                                     list(year = seq_len(nyear_window),
+                                          window = dimnames(x)[["year"]][
+                                            seq(round(nyear_window / 2),
+                                                dim(x)[["year"]],
+                                                nyear_window)
+                                          ]))) %>%
+          apply(c("cell", third_dim, "window"),  mean)
+        if (interpolate) {
+          y <- aperm(apply(y,
+                           c("cell", third_dim),
+                           interpolate_spline,
+                           x,
+                           nyear_window),
+                       c("cell", third_dim, ""))
+        }
+      }
+      # }
+    } else if (nyear_window == 1) {
+      y <- x
+    } else {
+      stop(paste0("Amount of nyear_window (", nyear_window, ") not supported."))
+    }
+    # recycle nyear_reference subset for original x (years)
+    if (!is.null(nyear_reference)) {
+      # multiple factor
+      nmultiple <- round(dim(orig_x)[["year"]] / nyear_reference)
+      replace_multiple_id <- nmultiple * dim(y)[[3]]
+      # if average window is returned as year dimension
+      if (!moving_average & !interpolate) {
+        z <- array(NA,
+                   dim = c(dim(y)[c("cell", third_dim)],
+                           window = replace_multiple_id),
+                   dimnames = append(dimnames(y)[c("cell", third_dim)],
+                                     list(window = rep(dimnames(y)[[3]],
+                                                        nmultiple))))
+      # return as original year dimension
+      } else {
+        # years vector also for non multiples (subset only partly recycled)
+        years <- rep(NA, dim(orig_x)[["year"]]) %>%
+          `[<-`(, value = dimnames(x)[["year"]]) %>%
+          suppressWarnings()
+        z <- array(NA,
+                   dim = dim(orig_x),
+                   dimnames = append(dimnames(y)[c("cell", third_dim)],
+                                     list(year = years)))
+      }
+      # recycle subset y for rest of original (x) years in z
+      z[, , seq_len(replace_multiple_id)] <- y
+      # check if not multiple - then only partly recylce array
+      if ((dim(z)[3] - replace_multiple_id) > 0) {
+        z[, , (replace_multiple_id + 1):dim(z)[3]] <- (
+          y[, , seq_len(dim(z)[3] - replace_multiple_id)]
+        )
+      }
+      return(z)
+    } else {
+      # rename dimnames of array
+      if (all(dim(y) == dim(x))) {
+        dim(y) <- dim(x)
+        dimnames(y) <- dimnames(x)
+      }
+    }
+  } else {
+    y <- apply(x, c("cell", third_dim), mean)
+    if (is.null(dim(y))) {
+      y <- array(
+        y,
+        dim = c(cell = dim(x)[["cell"]], 1),
+        dimnames = list(cell = dimnames(x)[["cell"]], 1)
+      )
+    }
+  }
+  return(y)
+}

R/plot_biomes.R

@@ -30,12 +30,12 @@ plot_biomes <- function(biome_data,
   lpjml_extent <- c(-180, 180, -60, 85)
 
   bounding_box <- system.file("extdata", "ne_110m_wgs84_bounding_box.shp",
-                              package = "pbfunctions") %>%
+                              package = "biospheremetrics") %>%
       rgdal::readOGR(layer = "ne_110m_wgs84_bounding_box", verbose = FALSE) %>%
       { if(to_robinson) sp::spTransform(., sp::CRS("+proj=robin")) else . } # nolint
 
   countries <- system.file("extdata", "ne_110m_admin_0_countries.shp",
-                              package = "pbfunctions") %>%
+                              package = "biospheremetrics") %>%
       rgdal::readOGR(layer = "ne_110m_admin_0_countries", verbose = FALSE) %>%
       crop(., lpjml_extent) %>%
       { if(to_robinson) sp::spTransform(., CRS("+proj=robin")) else . } # nolint
@@ -47,7 +47,7 @@ plot_biomes <- function(biome_data,
                    "#FFFFD4", "white", "#dad4d4")
 
   biome_mapping <- system.file("extdata", "biomes.csv",
-                              package = "pbfunctions") %>%
+                              package = "biospheremetrics") %>%
                    readr::read_csv2()
   names(biome_cols) <- biome_mapping$short_name
 

inst/extdata/biomes.csv

+id;name;short_name;abbreviation
+1;Tropical Rainforest;Tropical Rain.;TrRF
+2;Tropical Seasonal & Deciduous Forest;Tropical Decid. Forest;TrDF
+3;Temperate Broadleaved Evergreen Forest;Temp. Broad. Ever. Forest;TeBE
+4;Temperate Broadleaved Deciduous Forest;Temp. Broad. Decid. Forest;TeBD
+5;Mixed Forest;Mixed Forest;MF
+6;Temperate Coniferous Forest;Temp. Conif. Forest;TeCF
+7;Boreal Evergreen Forest;Bor. Ever. Forest;BoEF
+8;Boreal Deciduous Forest;Bor. Decid. Forest;BoDF
+9;Warm Woody Savanna, Woodland & Shrubland;Warm Woodland;WaWo
+10;Warm Savanna & Open Shrubland;Warm Savanna;WaSa
+11;Warm Grassland;Warm Grassland;WaGr
+12;Temperate Woody Savanna, Woodland & Shrubland;Temp. Woodland;TeWo
+13;Temperate Savanna & Open Shrubland;Temp. Savanna;TeSa
+14;Temperate Grassland;Temp. Grassland;TeGr
+15;Montane Grassland;Montane Grassland;MoGr
+16;Arctic Tundra;Arctic Tundra;ArTu
+17;Desert;Desert;Des
+18;Rocks and Ice;Rocks and Ice;RoIc
+19;Water;Water;Wat

inst/extdata/ne_110m_admin_0_countries.cpg

+UTF-8
\ No newline at end of file

inst/extdata/ne_110m_admin_0_countries.prj

+GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.017453292519943295]]
\ No newline at end of file

inst/extdata/ne_110m_wgs84_bounding_box.prj

+GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.017453292519943295]]
\ No newline at end of file