I also found issues with the raster::extract function, for some reason, I
got crazy values when I compared the resulting value with the one from
QGIS(Zonal Statistics Plugin) and ArcGIS（zonal statistics), while the value
from qgis and arcgis were close but the raster::extract has a lot of
Chris,
Actually I take that back, using extract() with `weights=TRUE` returned
`0` instead of NA values for these problematic coastal admin units.
Think I might have to impute them manually.
--Mel.
On 10/18/2016 2:33 PM, Bacou, Melanie wrote:
>
> Chris,
> Thanks, using `weights=TRUE` to
Chris,
Thanks, using `weights=TRUE` to compute the means fixed the problem.
Perfect, --Mel.
On 10/18/2016 11:10 AM, chris english wrote:
>
> Mel,
>
> Looking at detail in cjg.png the northmost missing data island shows
> approx 20-25 in Southern three quarters and 0-5 the small North at tip
>
Mel,
Sorry, your pseudo-code:
raster::extract(udel, admin, fun=mean, na.rm=T, small=T, weights = TRUE)
Chris
On Tue, Oct 18, 2016 at 6:10 PM, chris english <
englishchristoph...@gmail.com> wrote:
> Mel,
>
> Looking at detail in cjg.png the northmost missing data island shows
> approx 20-25 in
Mel,
Looking at detail in cjg.png the northmost missing data island shows approx
20-25 in Southern three quarters and 0-5 the small North at tip of island.
Directly above this 0-5 another coastal 0-5.
If a substantial east-west scarp bisected the island it might explain. I
would otherwise expect
Hi,
I'm summarizing biophysical rasters (UDEL precipitation and temperature)
across administrative units for countries in Africa using (pseudo code):
raster::extract(udel, admin, fun=mean, na.rm=T, small=T)
Out of the 756 units I need data for, extract() fails to return means
for a few
