final tweaks for publication

Author: tiemvanderdeure

correlative_model/01_spatial_thin.R

@@ -1,49 +1,49 @@
-##############################
-###### Spatial thinning ######
-##############################
-
-
-# Load libraries
-library(spThin)
-library(ggplot2)
-library(rnaturalearth)
-
-# Set your paths
-source("paths.R")
-
-#read in the data
-data <- read.csv(path2allpresences, header=T)
-
-#plot the species presence points
-plot(data$x, data$y)
-data$Species<-"target_group"
-#thin the data
-thinned_data_full <- thin(loc.data=data, long.col = "x", lat.col="y", spec.col="Species", 
-                          thin.par=5, reps=100, # thin.par specifies the minimum distance between points in kilometers
-                          locs.thinned.list.return = TRUE,
-                          write.files=FALSE,write.log.file=FALSE)
-
-#All datasets seem to retain the same number of occurences. Plot one of them above the previous plot to show which points are retained.
-thinned<-thinned_data_full[[1]]
-points(thinned$Longitude, thinned$Latitude, col="red")# plots the retained points over the plot previously made 
-
-names(thinned) <- c("x", "y")
-
-# now retrieve all the columns from the data dataframe and merge them with the columns from the thinned dataframe
-spt<-merge(thinned,data,by= c("row.names", "x", "y"))
-
-#Plot the datapoints on a map:
-sf_world <- ne_countries(returnclass = "sf")
-tibble::glimpse(sf_world)
-ggplot(sf_world) +
-  geom_sf()+
-  geom_point(data=thinned, aes(x=x, y=y))
-
-#export the dataframe with the selected points
-write.csv(thinned,file=path2presence,row.names=FALSE)
-
-#Transform this data into a shapefile for easy visualisation in gis software
-# WGScoor <- spt
-# coordinates(WGScoor)=~x+y
-# proj4string(WGScoor)<-CRS("+proj=longlat +datum=WGS84")
+##############################
+###### Spatial thinning ######
+##############################
+
+
+# Load libraries
+library(spThin)
+library(ggplot2)
+library(rnaturalearth)
+
+# Set your paths
+source("paths.R")
+
+#read in the data
+data <- read.csv(path2allpresences, header=T)
+
+#plot the species presence points
+plot(data$x, data$y)
+data$Species<-"target_group"
+#thin the data
+thinned_data_full <- thin(loc.data=data, long.col = "x", lat.col="y", spec.col="Species", 
+                          thin.par=5, reps=100, # thin.par specifies the minimum distance between points in kilometers
+                          locs.thinned.list.return = TRUE,
+                          write.files=FALSE,write.log.file=FALSE)
+
+#All datasets seem to retain the same number of occurences. Plot one of them above the previous plot to show which points are retained.
+thinned<-thinned_data_full[[1]]
+points(thinned$Longitude, thinned$Latitude, col="red")# plots the retained points over the plot previously made 
+
+names(thinned) <- c("x", "y")
+
+# now retrieve all the columns from the data dataframe and merge them with the columns from the thinned dataframe
+spt<-merge(thinned,data,by= c("row.names", "x", "y"))
+
+#Plot the datapoints on a map:
+sf_world <- ne_countries(returnclass = "sf")
+tibble::glimpse(sf_world)
+ggplot(sf_world) +
+  geom_sf()+
+  geom_point(data=thinned, aes(x=x, y=y))
+
+#export the dataframe with the selected points
+write.csv(thinned,file=path2presence,row.names=FALSE)
+
+#Transform this data into a shapefile for easy visualisation in gis software
+# WGScoor <- spt
+# coordinates(WGScoor)=~x+y
+# proj4string(WGScoor)<-CRS("+proj=longlat +datum=WGS84")
 # raster::shapefile(WGScoor,"thinned_data.shp",overwrite=TRUE)