My question is about extracting values from rasters at the location of points. With the function extract this is very easy, and the function gives me a dataframe with the values of all the variables in the points. I want to have in that dataframe the coordinates of each point.
How can I make that happen, perhaps by saying to R that when extracting the values from the raster also add the columns of the location point?
This is my procedure:
presencias=read.table("c:/SDM_R/presencias/P_lentiscus_pres.csv",header=TRUE,sep=";")
lista_variables <-list.files(path="Variables_modelizacion/solo_ascii",pattern='*.asc',full.names=TRUE)
variables <- stack(lista_variables)
variables_presencia<-extract(variables, presencias)
the results are something like these:
> bio1 bio12 bio18 bio2 bio4
> 90 875 165 95 4886
> 115 1085 158 83 4075
> 135 1153 153 67 3402
> 85 1026 137 99 5203
> 96 667 128 108 5823
> 98 531 109 113 6305
> 132 450 63 123 6598
> 132 569 104 106 5963
> 95 814 196 98 5571
> 146 474 39 114 6603
But I want two more columns with the coordinate data (but could be others columns from my csv extracting location table).
Best Answer
Assuming that
presenciasandvariablesshare the same projection, this should be an easy task. I recommend you to add these lines of code after yourread.table()statement in order to convertpresenciasdataframe to a SpatialPointsDataFrame object (just refine the names of the columns containing x and y coordinates if they differ from my example).To provide a reproducible example, I try to open up the scope of my answer a little more. First of all, download and unzip this ESRI shapefile with more or less important locations in Germany. These will serve as point data later on. You will also need packages
dismo,rgdalandrasterfor this short example, so make sure that these libraries (and all their dependencies) are installed on your local hard drive.Let's start with loading the required packages.
Next, you should generate a sample RasterLayer. In our case, we will make use of the
gmap()function from thedismopackage in order to obtain a physical map of Germany.You can now import your point shapefile via
readOGRfrom R'srgdalpackage. Make sure to adjust the data source name (dsn = ...). The whole projection stuff is obsolete in your particular case. However, it has to be done in our example in order to successfully overlay our point data with the Germany RasterLayer.To reduce the huge size of our point data, we will draw a random sample of ten locations in Germany. This should suffice for our purposes.
Now that the preparation stuff is finished, we could just start to extract the values of those particular pixels our ten randomly sampled points lie within.
In order to merge the point coordinates with the extracted pixel values, we just need to set up a dataframe containing the coordinates of our SpatialPointsDataFrame. That's it!