Updating functions

R/get_range.R

@@ -4,11 +4,10 @@
 #' Create a species range map based on a get_gbif() output
 #' 
 #' Estimates species ranges based on occurrence data (GBIF or not) and ecoregions
-#' (external, but we provide some ecoregions, see function 'make_ecoregion'). 
+#' (related functions 'make_ecoregion'). 
 #' It first deletes outliers from the observation dataset and then creates a polygon 
-#' (convex hull) with a user
-#' specified buffer around all the observations of one ecoregion. If there is only
-#' one observation in an ecoregion, a buffer around this point will be created. If
+#' (convex hull) with a user specified buffer around all the observations of one ecoregion.
+#' If there is only one observation in an ecoregion, a buffer around this point will be created. If
 #' all points in an ecoregion are on a line, the function will also create a buffer
 #' around these points, however, the buffer size increases with the number of points
 #' in the line. Finally, also note that in case of too many records, get_range can be
@@ -21,16 +20,15 @@
 #' @param bioreg  'SpatialPolygonsDataFrame', 'SpatVector' or 'sf' object containg different
 #' ecoregions (convex hulls will be classified on a bioreg basis) and of CRS WGS84. Note
 #' that this parameter may be fed with an external, generated (function make_ecoregion) or
-#' in-house ecoregion shapefile. Three in-house shapefiles are already included in the library:
-#' 'eco.earh' (for terrestrial species; Nature conservancy version adapted from Olson & al. 2001),
-#' 'eco.marine' (for marine species; Spalding & al. 2007, 2012) and 'eco.fresh' (for freshwater
-#' species; Abell & al. 2008). For marine species, eco.earth may also be used if the user wants
-#' to represent the terrestrial range of species that also partially settle on mainland. For
-#' fresh water species, same may be done if the user considers that terrestrial ecoregions
-#' should be more representtaive of the species ecology.
+#' in-house ecoregion shapefile. Four shapefiles can be downloaded with the library (function get_bioreg
+#' and others): eco_terra' (for terrestrial species; Nature conservancy version adapted from Olson & al. 2001),
+#' 'eco_marine' and 'eco_hd_marine' (for marine species; Spalding & al. 2007, 2012) and 'eco_fresh' (for freshwater
+#' species; Abell & al. 2008). For marine species, 'eco_terra' may also be used if the user wants to represent
+#' the terrestrial range of species that also partially settle on mainland. For fresh water species, same may be
+#' done if the user considers that terrestrial ecoregions should be more representative of the species ecology.
 #' @param bioreg_name Character. How is the shapefile attribute containing the ecoregion names called?
-#' Default is the very detailed level of 'eco.earth' (aka 'ECO_NAME'). Note that 'EcoRegion'
-#' must always be used when using a make_ecoregion() output. See details.
+#' E.g., very detailed level of 'eco_terra' is 'ECO_NAME'. Note that 'EcoRegion' (default) must always be supplied when
+#' using a make_ecoregion() output. See details.
 #' @param degrees_outlier Numeric. Distance threshold (degrees) for outlier classification.
 #' If the nearest minimal distance to the next point is larger than this threshold, it will be
 #' considered as an outlier.
@@ -58,17 +56,10 @@
 #' Each ecoregion shapefile has one or more categories, which describe more or less precisely the
 #' ecoregion world distribution (from the more to the less detailed):
 #' 
-#' - 'eco.earth' has three different levels: 'ECO_NAME', 'WWF_MHTNAM' and 'WWF_REALM2'.
-#' 
-#' - 'eco.fresh' has only one: 'FEOW_ID'.
-#' 
-#' - 'eco.marine' contains a mix of two types of marine ecoregions. Either common ('PROVINC' and 'REALM')
-#' or distinct levels:
-#' 
-#' ---> For PPOW (Pelagic provinces of the world): 'BIOME'.
-#' 
-#' ---> For MEOW (Marine ecoregions of the world): 'ECOREGION'.
-#' 
+#' - eco_terra has three different levels: 'ECO_NAME', 'WWF_MHTNAM' and 'WWF_REALM2'.
+#' - eco_fresh has only one: 'ECOREGION'.
+#' - eco_marine and eco_hd_marine (very coastal-precise version) contains three distinct levels:
+#' 'ECOREGION', 'PROVINCE' and 'REALM'.
 #' 
 #' @return A 'SpatVector' or 'SpatRaster'.
 #' @references
@@ -131,8 +122,8 @@
 #' @importFrom ClusterR KMeans_rcpp
 get_range <- function (sp_name = NULL, 
                        occ_coord = NULL, 
-                       bioreg = eco.earth, 
-                       bioreg_name = "ECO_NAME", 
+                       bioreg = NULL, 
+                       bioreg_name = "EcoRegion", 
                        degrees_outlier = 3,
                        clustered_points_outlier = 2,
                        buffer_width_point = 4, 
@@ -146,6 +137,12 @@ get_range <- function (sp_name = NULL,
   ### Object conditions + remove duplicates
   ### =========================================================================
 
+  # bioreg
+  if (methods::is(bioreg_name,"NULL")) {
+    stop("An ecoregion polygon should be supplied; if unavailable see command 'bioreg_list' and
+      internal functions get_bioreg(), read_bioreg() and check_and_get_bioreg()")
+  } 
+
   # occ_coord
   if (!methods::is(occ_coord,"data.frame")) {
     stop("'occ_coord' is not a data.frame...")

inst/examples/get_doi_help.R

@@ -1,6 +1,7 @@
 # Download worldwide the observations of Panthera tigris and Ailuropoda melanoleuca
 obspt <- get_gbif("Panthera tigris")
 obsam <- get_gbif("Ailuropoda melanoleuca")
+
 \dontrun{
 # Retrieve DOI for only one get_gbif() output
 get_doi(obspt,title="GBIF_test1",description="A small example 1",

inst/examples/get_gbif_help.R

@@ -1,12 +1,10 @@
 # Downloading worldwide the observations of Panthera tigris
-obs.pt <- get_gbif("Panthera tigris",
-basis=c("OBSERVATION","HUMAN_OBSERVATION","MACHINE_OBSERVATION"))
+obs.pt <- get_gbif("Panthera tigris",basis=c("OBSERVATION","HUMAN_OBSERVATION","MACHINE_OBSERVATION"))
 countries <- terra::vect(rnaturalearth::ne_countries(type = "countries",returnclass = "sf"))
 terra::plot(countries,col="#bcbddc")
 points(obs.pt[,c("decimalLongitude","decimalLatitude")],pch=20,col="#238b4550",cex=4)
 
 \dontrun{
-
 # Downloading worldwide the observations of Ailuropoda melanoleuca (with a 100km grain, after 1990
 # and by keeping duplicates and by adding the name of the person who collected the panda records)
 obs.am <- get_gbif("Ailuropoda melanoleuca", grain = 100000 , duplicates = TRUE,

inst/examples/get_range_help.R

@@ -1,22 +1,20 @@
 \dontrun{
 # Load available ecoregions
-data(ecoregions)
+check_and_get_bioreg(bioreg_name = "eco_terra", save_dir = NULL)
+eco_terra = read_bioreg(bioreg_name = "eco_terra", save_dir = NULL)
 
-# First download the worldwide observations of Panthera tigris and convert to SpatialPoints
-# get occurance points from GBIF
+# First download the worldwide observations of Panthera tigris from GBIF
 occ <- get_gbif("Panthera tigris",
 				  basis=c("OBSERVATION","HUMAN_OBSERVATION","MACHINE_OBSERVATION"))
-# make range from occurance points
-range <- get_range("Panthera tigris", occ ,eco.earth ,"ECO_NAME") #TODO change eco.earth to eco.land
-## TODO download=TRUE by default, check presence of file.
+
+# Make range from occurance points
+range <- get_range("Panthera tigris", occ ,eco_terra ,"ECO_NAME") #TODO change eco.earth to eco.land
+
 # Plot
 terra::plot(range, axes = FALSE, box = FALSE, legend=FALSE, col="chartreuse4")
-# plot political world boundaries
+
+# Plot political world boundaries
 terra::plot(rnaturalearth::ne_countries(returnclass = "sf")[1], add=T, col=NA)
 graphics::points(occ[,c("decimalLongitude","decimalLatitude")],pch=20,col="#99340470",cex=1.5)
 
-# plot(countries,col="#bcbddc")
-# plot(ne_countries(returnclass = "sf")[4] , col="antiquewhite")
-# TODO FIX THIS
-# points(occ[,c("decimalLongitude","decimalLatitude")],pch=20,col="#99340470",cex=1.5)
 }

inst/examples/obs_filter_help.R

@@ -14,8 +14,6 @@ graphics::points(obs.saxi[,c("decimalLongitude","decimalLatitude")],pch=20,col="
 # rbind both datasets
 both.sp <- rbind(obs.arcto,obs.saxi)
 
-
-# # TODO FIX THIS
 # Run function
 obs.filt <- obs_filter(both.sp,rst)