Extracting climate data from WorldClim.org gridded climate data files (geoTIFFs) for specific locations, and converting the climate information into a useful data table. See http://wp.me/p1Ye5e-aD for more info. To beautify resulting dataframe, see https://gist.github.com/kgturner/6644150.

Extract.climate.data.R

#Extract climate data from WorldClim.org tiles for several locations and make data table
#Kathryn Turner Sept 16, 2013
 
#download and unzip all relevant WorldClim geoTIFF files into a single directory.
#I used the highest resolution (~1km2), but should work for other resolutions too.

#load packages: raster, rgdal, foreach
library(rgdal)
library(raster)
library(foreach)
 
#Read names of all files in directory into a list
#from http://stackoverflow.com/questions/5319839/read-multiple-csv-files-into-separate-data-frames
filenames <- list.files(path="~/data_directory/")
 
#Load all geoTIFF files
for(i in filenames){
filepath <- file.path("~/data_directory/",i)
assign(i, raster(filepath))
}
 
#check that all files loaded properly by raster
#from http://stackoverflow.com/questions/15387727/use-object-names-as-list-names-in-r
list <- mget(filenames, envir=globalenv())
 
for(i in list){
if (hasValues(i)==FALSE){
print(i,"hasValues error")
}
if (inMemory(i)==TRUE){
print(i, "inMemory error")
}
else{
print("All checked out!")
}
}
 
#load table of latitude and longitude for locations of interest
pop <- read.table("pop.txt", header=T, sep="\t")
row.names(pop) <- pop$Pop
pop$Pop <- as.factor(pop$Pop)
> head(pop)
      Latitude  Longitude   Pop
CA001 49.01794 -123.08185 CA001
CA008 49.01208 -118.64571 CA008
CA009 49.29610 -118.47383 CA009
CA010 49.32018 -118.37044 CA010
US014 40.12227 -101.28028 US014
BG001 43.38943   28.45741 BG001

####NOTE: WorldClim data, even at highest res, is averaged over 1 km2.
#If your location is too close to a coast (i.e. less than 1 km2),
#there will not be any information (nothing but NAs) in this data set.
#Therefore, it may be necessary to approximate some locations by moving
#them inland. I did this using Google Earth.
 
#load location coordinates as SpatialPoints
for(i in pop$Pop){
assign(i,SpatialPoints(as.matrix(t(c(pop[i,2], pop[i,1])))))
}
 
#check that SpatialPoints load correctly from geoTIFFs
poplist <- mget(levels(pop$Pop), envir=globalenv())
 
tiffvector <- unlist(list)

#Optional quality check step. For smaller datasets, will tell you which population locations should be adjusted,
#in other words, which rows are all NA. See Note above, line 51. Or check after extracting data, see line 
foreach(p=poplist, .combine='rbind') %:%
foreach(t=tiffvector, .combine='cbind') %do%{
is.na(extract(t,p))
} #may take a while
 
#make climate data table
climate <- foreach(p=poplist, .combine='rbind') %:%
foreach(t=tiffvector, .combine='cbind') %do%{
myValue<-extract(t, p)
} #may take a while
 
#tidy table
popnames <- sort(as.character(pop$Pop))
clim <- as.data.frame(climate, row.names=popnames)
colnames(clim) <- filenames

#To check for populations that need to be adjusted/rows that are all NAs (See note, line 51)
#find rows that are all NAs, these are likely populations too close to large bodies of water
movepops <- clim[rowSums(is.na(clim)) == ncol(clim),]
#adjust population coordinates in pop df and re-run from line 63

 > head(clim)
      alt_07.tif alt_11.tif alt_12.tif alt_15.tif alt_16.tif alt_17.tif bio1_07.tif bio1_11.tif bio1_12.tif bio1_15.tif bio1_16.tif bio1_17.tif bio10_07.tif
BG001         NA         NA         NA         NA         61         NA          NA          NA          NA          NA         121          NA           NA
CA001         NA         24         NA         NA         NA         NA          NA         100          NA          NA          NA          NA           NA
CA008         NA         NA       1308         NA         NA         NA          NA          NA          39          NA          NA          NA           NA
CA009         NA         NA        599         NA         NA         NA          NA          NA          73          NA          NA          NA           NA
CA010         NA         NA        935         NA         NA         NA          NA          NA          57          NA          NA          NA           NA
GR001         NA         NA         NA         NA          1         NA          NA          NA          NA          NA         159          NA           NA

#write table
write.table(clim, file="bioclimdata.txt")
 
#load table
clim <- read.table("bioclimdata.txt", header=TRUE)

#now you have table of values and also a lot of NAs. So see "squishr" gist at https://gist.github.com/kgturner/6644150!

There appears to be a bug in this code. Specifically, at least one location (CA008) is somehow being associated with data from the wrong tile. Working on it...

Bug fixed (rows were named incorrectly in the final data frame).

pop data frame row names must be renamed to match population names in order for following for loop to work. Fixed.

Hi,
I am trying to extract some data from Worldclim.org.
I downloaded and unzip all relevant WorldClim geoTIFF files into a single directory and installed all packages.
However, when I try load all geoTIFF files, I get this error message:

Error in .local(.Object, ...) :
The selected file is an ESRI BIL header file, but to
open ESRI BIL datasets, the data file should be selected
instead of the .hdr file. Please try again selecting
the data file (often with the extension .bil) corresponding
to the header file: ~\prec1.hdr

Any ideas of what could be happening?
Thanks in advance.

Hi, Sorry for the delay, just saw this comment. This was originally written to work with Worldclim version 1, geoTIFF files, downloaded by tile (for example, the geoTIFF bioclim link here: http://www.worldclim.org/tiles.php?Zone=12). I'm not sure it will work with generic format (BIL) or ESRI files, or with Worldclim version 2. If I figure how to make it work with those, I will update.