SpatialGridDataFrame to data.frame

On Thu, 12 Feb 2009, Ned Horning wrote:

Roger and Robert,

Thanks for the help. Once again I am close but I can't figure out how 
to use the attribute data to control the sampling. I'd like to 
stratify the sampling by attribute value. In the Shapefile I have an 
integer attribute "covertype". There can be several polygons with the 
same covertype ID. What I want to do is get 100 sample points from 
polygons of covertype=1, 100 points from polygons of covertype=2... 
Is that possible? I looked at readShapePoly, readORG, and the suite 
of tools in spsample and but didn't see what I was looking for.

It looks like overlay() is a good way to assign the value of 
covertype to coordinate pairs.

One solution to fix all of this is to have a separate Shapefile for 
each covertype but I'd like to avoid that if possible.

If you subset the SpatialPolygonsDataFrame object by covertype, and 
then spsample() for the subset, you might get a bit nearer. Subset 
using the [ operator as for data.frames:

X_forest <- X[X$covertype="forest",]
forest_pts <- spsample(X_forest, n=100)

(untried)

Using lapply, you could probably step along the factor (categorical) 
levels of covertype if there are many and (say) n is fixed.

Roger

Ned

Roger Bivand wrote:

On Thu, 12 Feb 2009, Robert Hijmans wrote:

Hi Ned,

Good to hear that. For your other question, have a look at:

require(maptools)
?readShapePoly

Or more generally readOGR() in rgdal for another use of the 
underlying shapelib code.

require(sp)
?sample.Polygons

Most likely the spsample() method will be enough, without having to 
pick a specific method - but if necessary the Polygons objects can 
be dissolved using unionSpatialPolygons() in maptools.

Roger

Robert


On Thu, Feb 12, 2009 at 2:30 PM, Ned Horning <horning at amnh.org> wrote:

Robert,

This worked - thanks. It's always uplifting to see an actual image 
after
working on something for a while. Now I can start playing with 
parameters
and playing with different approaches. I'm just (re)starting my R 
education
and I'm pretty slow getting the hang of it but your examples help 
a lot.
They also give me more avenues to discover other functionality and 
different
ways of doing things. I hope I can keep working with R and GRASS 
until I
know it this time. My goal is to get to the point where I can be 
productive
with these packages and start training other folks.

The raster package looks very nice and I'll keep an eye on its 
development.
One step that I am currently doing in GRASS is to read a Shapefile 
with
training data (polygons with an integer attribute representing a 
land cover
type) and then randomly create 100 points within each set of polygons
representing a specific land cover type. I do this for each land 
cover type
and then concatenate the results into a text file. This file has the
coordinates that I use in xyValues to get the pixels values from 
the SPOT
image. Is there a way to do this sampling using the raster package or
another R package that you are familiar with? In GRASS I convert the
Shapefile to a raster image before doing the random sampling and 
it would be
nice if I could skip this step.

All the best,

Ned

Robert Hijmans wrote:

I see, in my example, I had a single quantitative variable but you
probably have land cover classes or something like that. If the
classes are in fact numbers stored as text then use

pred <- as.numeric(pred)

but if you have words such as 'forest', 'crops', 'water' you 
could do
something like

...
  pred <- predict(randfor, rowvals)
  pred[pred=='forest'] <- 1
  pred[pred=='crops'] <- 2
  pred[pred=='water'] <- 3
  pred <- as.numeric(pred)
  predrast <- setValues(predrast, pred, r)
...

not pretty, you could also fit RF with classes that can be 
interpreted
as numbers..
Make sure you do not get:

Warning message:
NAs introduced by coercion


which would suggest that some character values could not be
transformed to numbers.

On Thu, Feb 12, 2009 at 1:56 AM, Ned Horning <horning at amnh.org> 
wrote:

Robert,

Using predrast <- setValues(predrast, as.vector(pred), r) I got 
another
error: values must be numeric, integer or logical.

class(pred) = "factor"
dim(pred) = NULL
class(v) = "character"
length(v) == ncol(spot) = TRUE





Ned

Robert Hijmans wrote:

Strange. You could try
    predrast <- setValues(predrast, as.vector(pred), r)

But it would be good to know what pred is.

Can you do this:

class(pred)
dim(pred)
v <- as.vector(pred)
class(v)
length(v) == ncol(spot)


Robert




On Wed, Feb 11, 2009 at 11:16 PM, Ned Horning 
<horning at amnh.org> wrote:

Robert and Roger,

Thanks for the information and pointers. The raster package 
looks quite
interesting and I'll try to get up to speed on some of its
capabilities.
Are
the man pages the best way to do that or is that a single 
document
available?

I made some progress but still have some questions. I followed 
the
steps
laid out by Robert and everything went fine except I ran into 
an error
with
"predrast <- setValues(predrast, pred, r)" in the for loop 
when I tried
processing one line at a time and "r <- setValues(r, pred)" 
when I ran
the
full image in one go. The error was: "values must be a 
vector." Any
idea
what I'm doing wrong?

I tried to read the GRASS files directly but got a message 
saying it is
not
a supported file format. Can you confirm that is the case or 
am I doing
something wrong? I was able to read a tiff version of the 
image. I am
able
to run gdalinfo on GRASS files just fine from a terminal window.

Thanks again for the help.

Ned


Robert Hijmans wrote:

Ned,

This is an example of running a RandomForest prediction with the
raster package (for the simple case that there are no NA 
values in the
raster data; if there are, you have to into account that 
"predict'
does not return any values (not even NA) for those cells).

Robert

#install.packages("raster", 
repos="http://R-Forge.R-project.org")
require(raster)
require(randomForest)

# for single band files
spot <- stack('b1.tif', 'b2.tif', 'b3.tif')
# for multiple band files
# spot <- stackFromFiles(c('bands.tif', 'bands.tif', 
'bands.tif'),
c(1,2,3) )

# simulate random points and values to model with
xy <- xyFromCell(spot, round(runif(100) * ncell(spot)))
response <- runif(100) * 100
# read values of raster layers at points, and bind to respinse
trainvals <- cbind(response, xyValues(spot, xy))

# run RandomForest
randfor <- randomForest(response ~ b1 + b2 + b3, data=trainvals)

# apply the prediction, row by row
predrast <- setRaster(spot)
filename(predrast) <- 'RF_pred.grd'
for (r in 1:nrow(spot)) {
     spot <- readRow(spot, r)
     rowvals <- values(spot, names=TRUE)
# this next line should not be necessary, but it is
# I'll fix that
     colnames(rowvals) <- c('b1', 'b2', 'b3')
     pred <- predict(randfor, rowvals)
     predrast <- setValues(predrast, pred, r)
     predrast <- writeRaster(predrast, overwrite=TRUE)
}

plot(predrast)




On Wed, Feb 11, 2009 at 5:09 PM, Roger Bivand 
<Roger.Bivand at nhh.no>
wrote:

Ned:


The three bands are most likely treated as 4-byte integers, 
so the
object
will be 2732 by 3058 by 3 by 4 plus a little bit. That's 
100MB. They
may
get copied too. There are no single byte user-level 
containers for
you
(there is a raw data type, but you can't calculate with it). 
Possibly
saying spot_frame <- slot(spot, "data") will save one copying
operation,
but its hard to tell - your choice of method first adds inn 
all the
coordinates, which are 8-byte numbers, so more than doubles 
its size
and
makes more copies (to 233MB for each copy). Running gc() 
several
times
manually between steps often helps by making the garbage 
collector
more
aggressive.

I would watch the developments in the R-Forge package 
"raster", which
builds on some of these things, and try to see how that 
works. If you
have
the GDAL-GRASS plugin for rasters, you can use readGDAL to 
read from
GRASS
- which would work with raster package functions now. Look 
at the
code
of
recent readRAST6 to see which incantations are needed. If 
you are
going
to
use randomForest for prediction, you can use smaller tiles 
until you
find
an alternative solution. Note that feeding a data frame of 
integers
to
a
model fitting or prediction function will result in coercion 
to a
matrix of doubles, so your subsequent workflow should take 
that into
account.
 Getting more memory is another option, and may be very cost 
and
especially
time effective - at the moment your machine is swapping. Buying
memory
may
save you time programming around too little memory.

Hope this helps,

Roger


---
Roger Bivand, NHH, Helleveien 30, N-5045 Bergen,
Roger.Bivand at nhh.no



-----Original Message-----
From: r-sig-geo-bounces at stat.math.ethz.ch on behalf of Ned 
Horning
Sent: Wed 11.02.2009 07:40
To: r-sig-geo at stat.math.ethz.ch
Subject: [R-sig-Geo] SpatialGridDataFrame to data.frame

Greetings,

I am trying to read an image from GRASS using the spgrass6 
command
readRAST6 and then convert it into a data.frame object so I 
can use
it
with randomForest. The byte image I'm reading is 2732 rows x 
3058
columns x 3 bands. It's a small subset of a larger image I 
would like
to
use eventually. I have no problem reading the image using 
readRAST6
but
when I try to convert it to a data.frame object my linux system
resources (1BG RAM, 3GB swap) nearly get maxed out and it 
runs for a
couple hours before I kill the process. The image is less 
than 25MB
so
I'm surprised it requires this level of memory. Can someone 
let me
know
why this is. Should I use something other than the GRASS 
interface
for
this? These are the commands I'm using:

spot <- readRAST6(c("subset.red", "subset.green", 
"subset.blue"))
spot_frame <- as(spot, "data.frame")

Any help would be appreciated.

All the best,

Ned