*tidyr and magrittr mess up extract function
library(foreign)
#library(dplyr)
#library(magrittr)
#library(tidyr)
library(gridExtra)
library(ggplot2)
#spatial
library(raster)
## Loading required package: sp
library(rasterVis)
## Warning: package 'rasterVis' was built under R version 3.3.2
## Loading required package: lattice
## Loading required package: latticeExtra
## Loading required package: RColorBrewer
##
## Attaching package: 'latticeExtra'
## The following object is masked from 'package:ggplot2':
##
## layer
library(rgdal)
## rgdal: version: 1.1-10, (SVN revision 622)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 1.11.4, released 2016/01/25
## Path to GDAL shared files: /Library/Frameworks/R.framework/Versions/3.3/Resources/library/rgdal/gdal
## Loaded PROJ.4 runtime: Rel. 4.9.1, 04 March 2015, [PJ_VERSION: 491]
## Path to PROJ.4 shared files: /Library/Frameworks/R.framework/Versions/3.3/Resources/library/rgdal/proj
## Linking to sp version: 1.2-3
library(dismo)
#load image of land use into R
x<-raster("GLOBCOVER_L4_200901_200912_V2.3.tif")
#plot it to see if we did it right
plot(x,axes=T,xlim=c(-150,-30),ylim=c(-50,50))
##reading in Triatoma nitida
#Tnit<-read.csv("Globcover2009_V2.3_Global_/Triatoma_nitida.csv",header=TRUE)
#points(Tnit$Lon,Tnit$Lat,cex=1,pch=16,col="red") run this code with previous line to put points onto the map
#no clue waht this does, but following the code
#coordinates(Tnit)<-Tnit[,c("Lon","Lat")]
#it looks like it converted lon lat into spaace
#plot(Tnit)
#important code: projecting points from lat lon into space
crs.go<-CRS("+proj=longlat +ellps=WGS84 +datum=WGS84")
#proj4string(Tnit)<-crs.go
#summary(Tnit)
#extract values of lat lon from the raster file
#Tnit$worked<-extract(x,cbind(Tnit$Lon,Tnit$Lat))
#Tnit$worked
whole<-read.csv("20160328_species_distribution.csv",skip=0)#"metadata on first 2 lines"
coordinates(whole)<-whole[,c("Lon","Lat")]
plot(whole)
proj4string(whole)<-crs.go
summary(whole)
## Object of class SpatialPointsDataFrame
## Coordinates:
## min max
## Lon -121.9043 -35.58767
## Lat -45.9382 50.84757
## Is projected: FALSE
## proj4string :
## [+proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0]
## Number of points: 889
## Data attributes:
## Species Locality Lat Lon
## T_dimidiata :122 Ponto 12 : 18 Min. :-45.938 Min. :-121.90
## T_infestans : 67 Ponto 13 : 17 1st Qu.:-20.772 1st Qu.: -84.81
## T_sordida : 58 Ponto 14 : 17 Median : -4.653 Median : -66.18
## T_maculata : 42 Ponto 10 : 15 Mean : -1.736 Mean : -70.68
## T_guasayana : 37 Ponto 11 : 15 3rd Qu.: 14.690 3rd Qu.: -57.17
## T_patagonica: 35 Ponto 8 : 15 Max. : 50.848 Max. : -35.59
## (Other) :528 (Other) :792
## Elevation.feet. Binary
## Min. : 0.1 Min. :0.0000
## 1st Qu.: 335.6 1st Qu.:0.0000
## Median : 903.4 Median :1.0000
## Mean : 1651.8 Mean :0.6063
## 3rd Qu.: 2085.7 3rd Qu.:1.0000
## Max. :17835.2 Max. :1.0000
## NA's :48
#plotting out points
plot(x,axes=T,main="Species distributions",xlim=c(-130,-30),ylim=c(-60,60))
points(whole$Lon,whole$Lat,cex=.5,pch=16,col="red")
#grabbing land use
whole$land.use<-extract(x,cbind(whole$Lon,whole$Lat))
whole$land.use
## [1] 50 50 190 130 130 130 140 40 40 20 40 30 50 50 130 20 30
## [18] 30 14 20 40 20 14 130 20 20 130 20 20 14 130 40 30 50
## [35] 40 180 40 40 20 40 20 130 130 40 20 14 14 20 30 30 20
## [52] 30 130 110 40 110 140 180 190 190 40 40 40 40 40 40 40 130
## [69] 40 40 130 30 30 20 30 130 30 30 30 40 180 14 14 30 30
## [86] 30 30 14 40 20 40 40 40 40 40 40 20 30 130 30 14 40
## [103] 40 40 40 40 40 40 40 40 40 40 14 120 130 14 120 190 120
## [120] 190 190 30 110 110 190 40 50 30 130 40 40 40 40 20 30 130
## [137] 130 40 40 40 40 40 40 210 140 30 140 20 140 40 30 140 14
## [154] 30 40 210 40 30 40 30 130 150 30 110 40 110 40 40 40 40
## [171] 140 140 140 30 40 110 140 40 40 20 30 20 20 30 30 30 40
## [188] 20 20 40 140 30 110 130 140 140 50 190 50 130 140 70 140 190
## [205] 40 40 140 190 40 30 130 140 140 120 140 110 30 20 100 30 40
## [222] 110 30 30 140 140 30 30 40 130 130 30 140 40 210 40 40 190
## [239] 40 120 40 130 120 140 50 100 70 50 140 70 30 40 40 40 40
## [256] 40 40 40 40 40 40 40 40 130 40 140 40 30 40 40 40 40
## [273] 40 40 40 40 140 40 120 130 190 120 30 130 120 130 190 200 120
## [290] 190 40 14 30 40 14 40 40 30 20 20 190 50 110 190 190 210
## [307] 50 130 120 120 200 130 120 110 30 120 160 40 40 40 40 40 40
## [324] 40 40 40 130 120 130 110 130 190 130 130 140 30 130 20 140 140
## [341] 140 50 40 20 40 40 30 14 120 130 14 20 30 110 190 120 130
## [358] 190 200 120 130 190 210 190 40 120 190 40 30 50 20 50 30 40
## [375] 40 40 40 40 40 40 40 40 40 40 190 110 120 30 130 14 30
## [392] 110 110 190 120 130 130 120 130 190 200 200 110 190 130 130 14 14
## [409] 210 20 14 14 30 110 200 120 20 190 50 40 200 190 200 200 130
## [426] 130 20 14 40 20 40 40 40 20 20 50 30 120 130 130 130 40
## [443] 14 160 20 30 30 140 140 110 40 20 14 140 160 40 40 40 40
## [460] 40 130 20 130 30 40 140 110 30 210 30 120 140 210 40 20 140
## [477] 140 30 30 210 30 140 40 30 110 20 30 140 40 30 160 40 40
## [494] 40 30 30 20 50 20 40 130 20 30 40 20 40 130 130 20 130
## [511] 30 40 40 40 40 40 40 130 20 30 40 20 130 20 20 14 40
## [528] 40 40 40 40 50 40 20 40 40 130 70 120 140 130 30 130 50
## [545] 130 50 30 140 40 140 40 40 40 30 30 210 140 110 20 140 30
## [562] 160 40 40 190 40 130 40 130 14 40 40 40 40 180 40 40 50
## [579] 40 140 30 50 70 100 70 30 140 14 140 130 30 110 110 130 120
## [596] 120 130 190 14 120 14 130 130 200 120 190 120 190 130 110 14 30
## [613] 14 30 180 40 40 40 40 40 40 40 40 40 130 20 30 130 160
## [630] 40 40 40 40 40 130 130 40 30 120 130 70 70 140 130 30 130
## [647] 50 130 30 190 130 130 130 130 110 40 40 20 140 30 14 50 50
## [664] 50 30 130 20 190 30 14 20 130 20 20 20 40 14 130 130 40
## [681] 130 40 40 40 40 40 40 40 20 30 130 70 50 50 130 130 50
## [698] 70 50 50 30 120 20 40 30 40 130 70 120 140 130 130 130 50
## [715] 50 30 120 140 140 30 70 50 50 140 40 20 20 70 130 14 30
## [732] 130 40 30 30 20 20 30 14 40 40 40 40 40 30 130 40 40
## [749] 20 50 70 30 30 70 50 70 70 14 20 50 20 190 170 140 70
## [766] 50 70 140 140 50 30 70 120 50 130 130 70 130 130 30 70 140
## [783] 140 70 50 130 30 50 50 20 130 70 140 110 140 50 130 14 180
## [800] 30 130 210 120 20 50 50 14 40 110 40 130 30 40 40 14 20
## [817] 50 20 14 40 130 14 40 20 120 190 40 20 20 130 40 190 130
## [834] 130 20 40 30 50 50 14 20 20 20 30 210 40 40 40 160 40
## [851] 180 40 40 40 130 20 40 20 40 20 190 40 190 14 190 130 40
## [868] 40 40 40 40 40 40 40 40 40 40 40 110 40 40 40 40 40
## [885] 30 40 40 140 40
#write.csv(whole,"20160328_species_distribution_with_LANDUSE.csv")
none<-read.csv("20160328_species_distribution.csv")
str(none)
none<-na.omit(none);dim(none)
coordinates(none)<-none[,c("Lon","Lat")]
plot(none)
proj4string(none)<-crs.go
summary(none)
#plotting out points
plot(x,axes=T,main="Species distributions")
points(none$Lon,none$Lat,cex=.5,pch=16,col="red")
#grabbing land use
none$land.use<-extract(x,cbind(none$Lon,none$Lat))
none$land.use
write.csv(none,"20160227_absence_species_with_land_use.csv")
sessionInfo()
## R version 3.3.1 (2016-06-21)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.12.1 (Sierra)
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] dismo_1.1-1 rgdal_1.1-10 rasterVis_0.41
## [4] latticeExtra_0.6-28 RColorBrewer_1.1-2 lattice_0.20-33
## [7] raster_2.5-8 sp_1.2-3 ggplot2_2.1.0
## [10] gridExtra_2.2.1 foreign_0.8-66
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.7 knitr_1.14 magrittr_1.5
## [4] munsell_0.4.3 viridisLite_0.1.3 colorspace_1.2-6
## [7] stringr_1.1.0 plyr_1.8.4 tools_3.3.1
## [10] parallel_3.3.1 grid_3.3.1 gtable_0.2.0
## [13] htmltools_0.3.5 yaml_2.1.13 digest_0.6.10
## [16] formatR_1.4 evaluate_0.9 rmarkdown_1.0
## [19] stringi_1.1.2 scales_0.4.0 hexbin_1.27.1
## [22] zoo_1.7-13