#to make table of climate data from WorldClim.org tiles #load packages: raster, rgdal, sp #load files for one tile tile12bioclim1 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_12\\bio1_12.tif") hasValues(tile12bioclim1) #[1] TRUE inMemory(tile12bioclim1) #[1] FALSE #repeat for all other tiffs #tile12 tile12bioclim2 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_12\\bio2_12.tif") hasValues(tile12bioclim2) inMemory(tile12bioclim2) tile12bioclim3 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_12\\bio3_12.tif") hasValues(tile12bioclim3) inMemory(tile12bioclim3) tile12bioclim4 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_12\\bio4_12.tif") hasValues(tile12bioclim4) inMemory(tile12bioclim4) tile12bioclim5 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_12\\bio5_12.tif") hasValues(tile12bioclim5) inMemory(tile12bioclim5) tile12bioclim6 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_12\\bio6_12.tif") hasValues(tile12bioclim6) inMemory(tile12bioclim6) tile12bioclim7 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_12\\bio7_12.tif") hasValues(tile12bioclim7) inMemory(tile12bioclim7) tile12bioclim8 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_12\\bio8_12.tif") hasValues(tile12bioclim8) inMemory(tile12bioclim8) tile12bioclim9 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_12\\bio9_12.tif") hasValues(tile12bioclim9) inMemory(tile12bioclim9) tile12bioclim10 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_12\\bio10_12.tif") hasValues(tile12bioclim10) inMemory(tile12bioclim10) tile12bioclim11 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_12\\bio11_12.tif") hasValues(tile12bioclim11) inMemory(tile12bioclim11) tile12bioclim12 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_12\\bio12_12.tif") hasValues(tile12bioclim12) inMemory(tile12bioclim12) tile12bioclim13 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_12\\bio13_12.tif") hasValues(tile12bioclim13) inMemory(tile12bioclim13) tile12bioclim14 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_12\\bio14_12.tif") hasValues(tile12bioclim14) inMemory(tile12bioclim14) tile12bioclim15 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_12\\bio15_12.tif") hasValues(tile12bioclim15) inMemory(tile12bioclim15) tile12bioclim16 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_12\\bio16_12.tif") hasValues(tile12bioclim16) inMemory(tile12bioclim16) tile12bioclim17 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_12\\bio17_12.tif") hasValues(tile12bioclim17) inMemory(tile12bioclim17) tile12bioclim18 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_12\\bio18_12.tif") hasValues(tile12bioclim18) inMemory(tile12bioclim18) tile12bioclim19 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_12\\bio19_12.tif") hasValues(tile12bioclim19) inMemory(tile12bioclim19) #tile13 tile13bioclim1 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_13\\bio1_13.tif") hasValues(tile13bioclim1) inMemory(tile13bioclim1) tile13bioclim2 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_13\\bio2_13.tif") hasValues(tile13bioclim2) inMemory(tile13bioclim2) tile13bioclim3 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_13\\bio3_13.tif") hasValues(tile13bioclim3) inMemory(tile13bioclim3) tile13bioclim4 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_13\\bio4_13.tif") hasValues(tile13bioclim4) inMemory(tile13bioclim4) tile13bioclim5 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_13\\bio5_13.tif") hasValues(tile13bioclim5) inMemory(tile13bioclim5) tile13bioclim6 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_13\\bio6_13.tif") hasValues(tile13bioclim6) inMemory(tile13bioclim6) tile13bioclim7 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_13\\bio7_13.tif") hasValues(tile13bioclim7) inMemory(tile13bioclim7) tile13bioclim8 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_13\\bio8_13.tif") hasValues(tile13bioclim8) inMemory(tile13bioclim8) tile13bioclim9 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_13\\bio9_13.tif") hasValues(tile13bioclim9) inMemory(tile13bioclim9) tile13bioclim10 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_13\\bio10_13.tif") hasValues(tile13bioclim10) inMemory(tile13bioclim10) tile13bioclim11 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_13\\bio11_13.tif") hasValues(tile13bioclim11) inMemory(tile13bioclim11) tile13bioclim12 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_13\\bio12_13.tif") hasValues(tile13bioclim12) inMemory(tile13bioclim12) tile13bioclim13 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_13\\bio13_13.tif") hasValues(tile13bioclim13) inMemory(tile13bioclim13) tile13bioclim14 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_13\\bio14_13.tif") hasValues(tile13bioclim14) inMemory(tile13bioclim14) tile13bioclim15 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_13\\bio15_13.tif") hasValues(tile13bioclim15) inMemory(tile13bioclim15) tile13bioclim16 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_13\\bio16_13.tif") hasValues(tile13bioclim16) inMemory(tile13bioclim16) tile13bioclim17 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_13\\bio17_13.tif") hasValues(tile13bioclim17) inMemory(tile13bioclim17) tile13bioclim18 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_13\\bio18_13.tif") hasValues(tile13bioclim18) inMemory(tile13bioclim18) tile13bioclim19 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_13\\bio19_13.tif") hasValues(tile13bioclim19) inMemory(tile13bioclim19) #tile16 tile16bioclim1 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_16\\bio1_16.tif") hasValues(tile16bioclim1) inMemory(tile16bioclim1) tile16bioclim2 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_16\\bio2_16.tif") hasValues(tile16bioclim2) inMemory(tile16bioclim2) tile16bioclim3 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_16\\bio3_16.tif") hasValues(tile16bioclim3) inMemory(tile16bioclim3) tile16bioclim4 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_16\\bio4_16.tif") hasValues(tile16bioclim4) inMemory(tile16bioclim4) tile16bioclim5 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_16\\bio5_16.tif") hasValues(tile16bioclim5) inMemory(tile16bioclim5) tile16bioclim6 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_16\\bio6_16.tif") hasValues(tile16bioclim6) inMemory(tile16bioclim6) tile16bioclim7 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_16\\bio7_16.tif") hasValues(tile16bioclim7) inMemory(tile16bioclim7) tile16bioclim8 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_16\\bio8_16.tif") hasValues(tile16bioclim8) inMemory(tile16bioclim8) tile16bioclim9 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_16\\bio9_16.tif") hasValues(tile16bioclim9) inMemory(tile16bioclim9) tile16bioclim10 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_16\\bio10_16.tif") hasValues(tile16bioclim10) inMemory(tile16bioclim10) tile16bioclim11 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_16\\bio11_16.tif") hasValues(tile16bioclim11) inMemory(tile16bioclim11) tile16bioclim12 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_16\\bio12_16.tif") hasValues(tile16bioclim12) inMemory(tile16bioclim12) tile16bioclim13 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_16\\bio13_16.tif") hasValues(tile16bioclim13) inMemory(tile16bioclim13) tile16bioclim14 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_16\\bio14_16.tif") hasValues(tile16bioclim14) inMemory(tile16bioclim14) tile16bioclim15 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_16\\bio15_16.tif") hasValues(tile16bioclim15) inMemory(tile16bioclim15) tile16bioclim16 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_16\\bio16_16.tif") hasValues(tile16bioclim16) inMemory(tile16bioclim16) tile16bioclim17 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_16\\bio17_16.tif") hasValues(tile16bioclim17) inMemory(tile16bioclim17) tile16bioclim18 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_16\\bio18_16.tif") hasValues(tile16bioclim18) inMemory(tile16bioclim18) tile16bioclim19 <- raster("~\\grad work\\Courses\\KH_microarray\\bio_16\\bio19_16.tif") hasValues(tile16bioclim19) inMemory(tile16bioclim19) #make a vector for each tile tile12tiffvector<-c(tile12bioclim1, tile12bioclim2, tile12bioclim3, tile12bioclim4,tile12bioclim5,tile12bioclim6,tile12bioclim7,tile12bioclim8,tile12bioclim9,tile12bioclim10,tile12bioclim11,tile12bioclim12,tile12bioclim13,tile12bioclim14,tile12bioclim15,tile12bioclim16,tile12bioclim17,tile12bioclim18,tile12bioclim19) tile13tiffvector<-c(tile13bioclim1, tile13bioclim2, tile13bioclim3, tile13bioclim4,tile13bioclim5,tile13bioclim6,tile13bioclim7,tile13bioclim8,tile13bioclim9,tile13bioclim10,tile13bioclim11,tile13bioclim12,tile13bioclim13,tile13bioclim14,tile13bioclim15,tile13bioclim16,tile13bioclim17,tile13bioclim18,tile13bioclim19) tile16tiffvector<-c(tile16bioclim1, tile16bioclim2, tile16bioclim3, tile16bioclim4,tile16bioclim5,tile16bioclim6,tile16bioclim7,tile16bioclim8,tile16bioclim9,tile16bioclim10,tile16bioclim11,tile16bioclim12,tile16bioclim13,tile16bioclim14,tile16bioclim15,tile16bioclim16,tile16bioclim17,tile16bioclim18,tile16bioclim19) #make empty vector for values valuevectorPop1<-NULL valuevectorPop2<-NULL valuevectorPop3<-NULL valuevectorPop4<-NULL valuevectorPop5<-NULL valuevectorPop6<-NULL valuevectorPop7<-NULL valuevectorPop8<-NULL valuevectorPop9<-NULL valuevectorPop10<-NULL valuevectorPop11<-NULL valuevectorPop12<-NULL #load each collection site as SpatialPoints popdata<-read.table(file.choose(), header=T, sep="") #collection location information #longitude, latitude!! Pop1<-SpatialPoints(as.matrix(t(c(-95.958333,44.32536)))) Pop1<-SpatialPoints(as.matrix(t(c(popdata[1,4], popdata[1,3])))) #rest of the pops Pop2<-SpatialPoints(as.matrix(t(c(popdata[2,4], popdata[2,3])))) Pop3<-SpatialPoints(as.matrix(t(c(popdata[3,4], popdata[3,3])))) Pop4<-SpatialPoints(as.matrix(t(c(popdata[4,4], popdata[4,3])))) Pop5<-SpatialPoints(as.matrix(t(c(popdata[5,4], popdata[5,3])))) Pop6<-SpatialPoints(as.matrix(t(c(popdata[6,4], popdata[6,3])))) Pop7<-SpatialPoints(as.matrix(t(c(popdata[7,4], popdata[7,3])))) Pop8<-SpatialPoints(as.matrix(t(c(popdata[8,4], popdata[8,3])))) Pop9<-SpatialPoints(as.matrix(t(c(popdata[9,4], popdata[9,3])))) Pop10<-SpatialPoints(as.matrix(t(c(popdata[10,4], popdata[10,3])))) Pop11<-SpatialPoints(as.matrix(t(c(popdata[11,4], popdata[11,3])))) Pop12<-SpatialPoints(as.matrix(t(c(popdata[12,4], popdata[12,3])))) #which tile is the population on? check pops 5 and 6 for tile13 and pop 8 for tile15 extract(tile16bioclim1, Pop8) #for loop for one collection site, creating value vector for(map in tile12tiffvector){ myValue<-extract(map, Pop1) valuevectorPop1<-c(valuevectorPop1, myValue) } #repeat for all pops, tile12 for(map in tile12tiffvector){ myValue<-extract(map, Pop2) valuevectorPop2<-c(valuevectorPop2, myValue) } for(map in tile12tiffvector){ myValue<-extract(map, Pop3) valuevectorPop3<-c(valuevectorPop3, myValue) } for(map in tile12tiffvector){ myValue<-extract(map, Pop4) valuevectorPop4<-c(valuevectorPop4, myValue) } #for loops, tile13 for(map in tile13tiffvector){ myValue<-extract(map, Pop5) valuevectorPop5<-c(valuevectorPop5, myValue) } for(map in tile13tiffvector){ myValue<-extract(map, Pop6) valuevectorPop6<-c(valuevectorPop6, myValue) } #for loops, tile16 for(map in tile16tiffvector){ myValue<-extract(map, Pop7) valuevectorPop7<-c(valuevectorPop7, myValue) } for(map in tile16tiffvector){ myValue<-extract(map, Pop8) valuevectorPop8<-c(valuevectorPop8, myValue) } for(map in tile16tiffvector){ myValue<-extract(map, Pop9) valuevectorPop9<-c(valuevectorPop9, myValue) } for(map in tile16tiffvector){ myValue<-extract(map, Pop10) valuevectorPop10<-c(valuevectorPop10, myValue) } for(map in tile16tiffvector){ myValue<-extract(map, Pop11) valuevectorPop11<-c(valuevectorPop11, myValue) } for(map in tile16tiffvector){ myValue<-extract(map, Pop12) valuevectorPop12<-c(valuevectorPop12, myValue) } #check length of all vectors length(valuevectorPop4) #join value vectors into table KHbioclim<-rbind(valuevectorPop1,valuevectorPop2,valuevectorPop3,valuevectorPop4,valuevectorPop5,valuevectorPop6,valuevectorPop7,valuevectorPop8,valuevectorPop9,valuevectorPop10,valuevectorPop11,valuevectorPop12) KHbioclim<-as.data.frame(KHbioclim) #change col names colnames(KHbioclim)<-c("BIO1", "BIO2", "BIO3", "BIO4","BIO5","BIO6","BIO7","BIO8","BIO9","BIO10","BIO11","BIO12","BIO13","BIO14","BIO15","BIO16","BIO17","BIO18","BIO19") #add pop names KHbioclim<-data.frame(KHbioclim, popdata$Population) names(KHbioclim)[20]<-"Population" #change rownames rownames(KHbioclim)<-NULL #write table write.csv(KHbioclim, file="KHbioclimdata.csv")