fix color scale

Paul, this nicely illustrates the power of ggplot2.

In the resulting plot, however, it seems to me that the

+ opts(aspect.ratio = 1)

does not result in the desired effect that 1 m in the x direction equals
1 m in the y direction. Standard plot (asp = 1), and lattice plots (asp
= "iso") have this; what does ggplot2 need?

On 10/31/2010 11:31 AM, Paul Hiemstra wrote:

Hi Peter,

When creating such a large amount of illustrations with the same
colorscale, I automatically think of lattice graphics. Under the hood
spplot also uses lattice graphics. Take a look at the levelplot()
function from lattice which produces the grid plots for spplot (if I'm
correct). Alternatively, I've been using ggplot now for quite a while to
make plots of a lot of grids. A small example says more than a thousand
words:

library(ggplot2)
library(sp)

data(meuse.grid)
summary(meuse.grid)

# Note that I do not transform meuse.grid to SpatialPixelsDataFrame
# Let's make a simple grid plot
dum = meuse.grid[c("x","y","dist")]
ggplot(aes(x = x, y = y, fill = dist), data = dum) + geom_tile()

# Let's make a few more attributes to the grid
# could be measurements on other dates for example
new_atts = do.call("cbind", lapply(1:100, function(num) dum$dist +
runif(dum$dist)))
summary(new_atts)
dum = data.frame(cbind(dum, new_atts))

# Important step now is to
# restructure the data
dum_ggplot = melt(dum, id.vars = c("x","y"))

# Now make a plot using dum_ggplot
# of 'x' and 'y' using value as a 'fill'
# with a plot per 'variable', can take a minute to plot
ggplot(aes(x = x, y = y, fill = value), data = dum_ggplot) + geom_tile()
+ facet_wrap(~variable) +
? ? ? scale_x_continuous('', labels = NA, breaks = NA) +
? ? ? scale_y_continuous('', labels = NA, breaks = NA) +
opts(aspect.ratio = 1)
# These last two lines get rid of the labels on the axes and set aspect
ratio to 1

Now you have a plot with 101 maps with the same colorscale, with ggplot
doing all the hard work. It takes some time to get the hang of ggplot,
but I think it is worth the investment, also for spatial plots.

cheers and hope this helps,
Paul

On 10/28/2010 09:12 PM, Peter Larson wrote:

Hello!

I have a problem.

I am using IDW to interpolate a daily series of geospatial
observations. Thus, I want to produce a large number of sequential
maps.

I want them to all represent the same color scale. Is there any way to
fix the color scale so that it is the same for all the plots?

Thanks,

Pete

--
Edzer Pebesma
Institute for Geoinformatics (ifgi), University of M?nster
Weseler Stra?e 253, 48151 M?nster, Germany. Phone: +49 251
8333081, Fax: +49 251 8339763 ?http://ifgi.uni-muenster.de
http://www.52north.org/geostatistics ? ? ?e.pebesma at wwu.de

Paul, this nicely illustrates the power of ggplot2.

In the resulting plot, however, it seems to me that the

+ opts(aspect.ratio = 1)

does not result in the desired effect that 1 m in the x direction equals
1 m in the y direction. Standard plot (asp = 1), and lattice plots (asp
= "iso") have this; what does ggplot2 need?

On 10/31/2010 11:31 AM, Paul Hiemstra wrote:

Hi Peter,

When creating such a large amount of illustrations with the same
colorscale, I automatically think of lattice graphics. Under the hood
spplot also uses lattice graphics. Take a look at the levelplot()
function from lattice which produces the grid plots for spplot (if I'm
correct). Alternatively, I've been using ggplot now for quite a while to
make plots of a lot of grids. A small example says more than a thousand
words:

library(ggplot2)
library(sp)

data(meuse.grid)
summary(meuse.grid)

# Note that I do not transform meuse.grid to SpatialPixelsDataFrame
# Let's make a simple grid plot
dum = meuse.grid[c("x","y","dist")]
ggplot(aes(x = x, y = y, fill = dist), data = dum) + geom_tile()

# Let's make a few more attributes to the grid
# could be measurements on other dates for example
new_atts = do.call("cbind", lapply(1:100, function(num) dum$dist +
runif(dum$dist)))
summary(new_atts)
dum = data.frame(cbind(dum, new_atts))

# Important step now is to
# restructure the data
dum_ggplot = melt(dum, id.vars = c("x","y"))

# Now make a plot using dum_ggplot
# of 'x' and 'y' using value as a 'fill'
# with a plot per 'variable', can take a minute to plot
ggplot(aes(x = x, y = y, fill = value), data = dum_ggplot) + geom_tile()
+ facet_wrap(~variable) +
? ? ? scale_x_continuous('', labels = NA, breaks = NA) +
? ? ? scale_y_continuous('', labels = NA, breaks = NA) +
opts(aspect.ratio = 1)
# These last two lines get rid of the labels on the axes and set aspect
ratio to 1

Now you have a plot with 101 maps with the same colorscale, with ggplot
doing all the hard work. It takes some time to get the hang of ggplot,
but I think it is worth the investment, also for spatial plots.

cheers and hope this helps,
Paul

On 10/28/2010 09:12 PM, Peter Larson wrote:

Hello!

I have a problem.

I am using IDW to interpolate a daily series of geospatial
observations. Thus, I want to produce a large number of sequential
maps.

I want them to all represent the same color scale. Is there any way to
fix the color scale so that it is the same for all the plots?

Thanks,

Pete

--
Edzer Pebesma
Institute for Geoinformatics (ifgi), University of M?nster
Weseler Stra?e 253, 48151 M?nster, Germany. Phone: +49 251
8333081, Fax: +49 251 8339763 ?http://ifgi.uni-muenster.de
http://www.52north.org/geostatistics ? ? ?e.pebesma at wwu.de

Interesting demonstration. However, it seems like sp objects and
spplot are still more efficient at storing / plotting large grids. The
example listed below required over 2 minutes to complete on a 2.4 Ghz
Intel processor. Also, storing the coordinates + data in long format
could easily fill available memory with large rasters. I have found
that storing multiple raster attributes as columns in the @data slot
of a SpatialGridDataFrame object give me flexibility to plot images
with a consistent color scheme, without needing to store the data in
long format. Also with lattice, the plots are generated in a couple of
seconds (500x700 px grid). I would be very interested in knowing how
to make ggplot() any faster.

Cheers!
Dylan

On Sun, Nov 7, 2010 at 11:55 AM, Edzer Pebesma
<edzer.pebesma at uni-muenster.de> wrote:

Paul, this nicely illustrates the power of ggplot2.

In the resulting plot, however, it seems to me that the

+ opts(aspect.ratio = 1)

does not result in the desired effect that 1 m in the x direction equals
1 m in the y direction. Standard plot (asp = 1), and lattice plots (asp
= "iso") have this; what does ggplot2 need?

On 10/31/2010 11:31 AM, Paul Hiemstra wrote:

Hi Peter,

When creating such a large amount of illustrations with the same
colorscale, I automatically think of lattice graphics. Under the hood
spplot also uses lattice graphics. Take a look at the levelplot()
function from lattice which produces the grid plots for spplot (if I'm
correct). Alternatively, I've been using ggplot now for quite a while to
make plots of a lot of grids. A small example says more than a thousand
words:

library(ggplot2)
library(sp)

data(meuse.grid)
summary(meuse.grid)

# Note that I do not transform meuse.grid to SpatialPixelsDataFrame
# Let's make a simple grid plot
dum = meuse.grid[c("x","y","dist")]
ggplot(aes(x = x, y = y, fill = dist), data = dum) + geom_tile()

# Let's make a few more attributes to the grid
# could be measurements on other dates for example
new_atts = do.call("cbind", lapply(1:100, function(num) dum$dist +
runif(dum$dist)))
summary(new_atts)
dum = data.frame(cbind(dum, new_atts))

# Important step now is to
# restructure the data
dum_ggplot = melt(dum, id.vars = c("x","y"))

# Now make a plot using dum_ggplot
# of 'x' and 'y' using value as a 'fill'
# with a plot per 'variable', can take a minute to plot
ggplot(aes(x = x, y = y, fill = value), data = dum_ggplot) + geom_tile()
+ facet_wrap(~variable) +
      scale_x_continuous('', labels = NA, breaks = NA) +
      scale_y_continuous('', labels = NA, breaks = NA) +
opts(aspect.ratio = 1)
# These last two lines get rid of the labels on the axes and set aspect
ratio to 1

Now you have a plot with 101 maps with the same colorscale, with ggplot
doing all the hard work. It takes some time to get the hang of ggplot,
but I think it is worth the investment, also for spatial plots.

cheers and hope this helps,
Paul

On 10/28/2010 09:12 PM, Peter Larson wrote:

Hello!

I have a problem.

I am using IDW to interpolate a daily series of geospatial
observations. Thus, I want to produce a large number of sequential
maps.

I want them to all represent the same color scale. Is there any way to
fix the color scale so that it is the same for all the plots?

Thanks,

Pete

--
Edzer Pebesma
Institute for Geoinformatics (ifgi), University of M?nster
Weseler Stra?e 253, 48151 M?nster, Germany. Phone: +49 251
8333081, Fax: +49 251 8339763  http://ifgi.uni-muenster.de
http://www.52north.org/geostatistics      e.pebesma at wwu.de

Dylan, I believe that lattice (grid) recently improved the plotting of
rasters quite a bit, by using panel.levelplot.raster instead of
panel.levelplot - some function in grid now renders rasters faster on
devices for which this is optimed.

spplot also has to transform the data to long format, by calling
sp:::spmap.to.lev, which in turn uses a stack method for
SpatialPointsDataFrame objects; this in turn uses stack.data.frame

Bests,


On 11/08/2010 05:00 AM, Dylan Beaudette wrote:

Interesting demonstration. However, it seems like sp objects and
spplot are still more efficient at storing / plotting large grids. The
example listed below required over 2 minutes to complete on a 2.4 Ghz
Intel processor. Also, storing the coordinates + data in long format
could easily fill available memory with large rasters. I have found
that storing multiple raster attributes as columns in the @data slot
of a SpatialGridDataFrame object give me flexibility to plot images
with a consistent color scheme, without needing to store the data in
long format. Also with lattice, the plots are generated in a couple of
seconds (500x700 px grid). I would be very interested in knowing how
to make ggplot() any faster.

Cheers!
Dylan

On Sun, Nov 7, 2010 at 11:55 AM, Edzer Pebesma
<edzer.pebesma at uni-muenster.de> wrote:

Paul, this nicely illustrates the power of ggplot2.

In the resulting plot, however, it seems to me that the

+ opts(aspect.ratio = 1)

does not result in the desired effect that 1 m in the x direction equals
1 m in the y direction. Standard plot (asp = 1), and lattice plots (asp
= "iso") have this; what does ggplot2 need?

On 10/31/2010 11:31 AM, Paul Hiemstra wrote:

Hi Peter,

When creating such a large amount of illustrations with the same
colorscale, I automatically think of lattice graphics. Under the hood
spplot also uses lattice graphics. Take a look at the levelplot()
function from lattice which produces the grid plots for spplot (if I'm
correct). Alternatively, I've been using ggplot now for quite a while to
make plots of a lot of grids. A small example says more than a thousand
words:

library(ggplot2)
library(sp)

data(meuse.grid)
summary(meuse.grid)

# Note that I do not transform meuse.grid to SpatialPixelsDataFrame
# Let's make a simple grid plot
dum = meuse.grid[c("x","y","dist")]
ggplot(aes(x = x, y = y, fill = dist), data = dum) + geom_tile()

# Let's make a few more attributes to the grid
# could be measurements on other dates for example
new_atts = do.call("cbind", lapply(1:100, function(num) dum$dist +
runif(dum$dist)))
summary(new_atts)
dum = data.frame(cbind(dum, new_atts))

# Important step now is to
# restructure the data
dum_ggplot = melt(dum, id.vars = c("x","y"))

# Now make a plot using dum_ggplot
# of 'x' and 'y' using value as a 'fill'
# with a plot per 'variable', can take a minute to plot
ggplot(aes(x = x, y = y, fill = value), data = dum_ggplot) + geom_tile()
+ facet_wrap(~variable) +
? ? ? scale_x_continuous('', labels = NA, breaks = NA) +
? ? ? scale_y_continuous('', labels = NA, breaks = NA) +
opts(aspect.ratio = 1)
# These last two lines get rid of the labels on the axes and set aspect
ratio to 1

Now you have a plot with 101 maps with the same colorscale, with ggplot
doing all the hard work. It takes some time to get the hang of ggplot,
but I think it is worth the investment, also for spatial plots.

cheers and hope this helps,
Paul

On 10/28/2010 09:12 PM, Peter Larson wrote:

Hello!

I have a problem.

I am using IDW to interpolate a daily series of geospatial
observations. Thus, I want to produce a large number of sequential
maps.

I want them to all represent the same color scale. Is there any way to
fix the color scale so that it is the same for all the plots?

Thanks,

Pete

--
Edzer Pebesma
Institute for Geoinformatics (ifgi), University of M?nster
Weseler Stra?e 253, 48151 M?nster, Germany. Phone: +49 251
8333081, Fax: +49 251 8339763 ?http://ifgi.uni-muenster.de
http://www.52north.org/geostatistics ? ? ?e.pebesma at wwu.de

--
Edzer Pebesma
Institute for Geoinformatics (ifgi), University of M?nster
Weseler Stra?e 253, 48151 M?nster, Germany. Phone: +49 251
8333081, Fax: +49 251 8339763 ?http://ifgi.uni-muenster.de
http://www.52north.org/geostatistics ? ? ?e.pebesma at wwu.de

Dylan, I believe that lattice (grid) recently improved the plotting of
rasters quite a bit, by using panel.levelplot.raster instead of
panel.levelplot - some function in grid now renders rasters faster on
devices for which this is optimed.

spplot also has to transform the data to long format, by calling
sp:::spmap.to.lev, which in turn uses a stack method for
SpatialPointsDataFrame objects; this in turn uses stack.data.frame

Bests,


On 11/08/2010 05:00 AM, Dylan Beaudette wrote:

Interesting demonstration. However, it seems like sp objects and
spplot are still more efficient at storing / plotting large grids. The
example listed below required over 2 minutes to complete on a 2.4 Ghz
Intel processor. Also, storing the coordinates + data in long format
could easily fill available memory with large rasters. I have found
that storing multiple raster attributes as columns in the @data slot
of a SpatialGridDataFrame object give me flexibility to plot images
with a consistent color scheme, without needing to store the data in
long format. Also with lattice, the plots are generated in a couple of
seconds (500x700 px grid). I would be very interested in knowing how
to make ggplot() any faster.

Cheers!
Dylan

On Sun, Nov 7, 2010 at 11:55 AM, Edzer Pebesma
<edzer.pebesma at uni-muenster.de> wrote:

Paul, this nicely illustrates the power of ggplot2.

In the resulting plot, however, it seems to me that the

+ opts(aspect.ratio = 1)

does not result in the desired effect that 1 m in the x direction equals
1 m in the y direction. Standard plot (asp = 1), and lattice plots (asp
= "iso") have this; what does ggplot2 need?

On 10/31/2010 11:31 AM, Paul Hiemstra wrote:

Hi Peter,

When creating such a large amount of illustrations with the same
colorscale, I automatically think of lattice graphics. Under the hood
spplot also uses lattice graphics. Take a look at the levelplot()
function from lattice which produces the grid plots for spplot (if I'm
correct). Alternatively, I've been using ggplot now for quite a while to
make plots of a lot of grids. A small example says more than a thousand
words:

library(ggplot2)
library(sp)

data(meuse.grid)
summary(meuse.grid)

# Note that I do not transform meuse.grid to SpatialPixelsDataFrame
# Let's make a simple grid plot
dum = meuse.grid[c("x","y","dist")]
ggplot(aes(x = x, y = y, fill = dist), data = dum) + geom_tile()

# Let's make a few more attributes to the grid
# could be measurements on other dates for example
new_atts = do.call("cbind", lapply(1:100, function(num) dum$dist +
runif(dum$dist)))
summary(new_atts)
dum = data.frame(cbind(dum, new_atts))

# Important step now is to
# restructure the data
dum_ggplot = melt(dum, id.vars = c("x","y"))

# Now make a plot using dum_ggplot
# of 'x' and 'y' using value as a 'fill'
# with a plot per 'variable', can take a minute to plot
ggplot(aes(x = x, y = y, fill = value), data = dum_ggplot) + geom_tile()
+ facet_wrap(~variable) +
      scale_x_continuous('', labels = NA, breaks = NA) +
      scale_y_continuous('', labels = NA, breaks = NA) +
opts(aspect.ratio = 1)
# These last two lines get rid of the labels on the axes and set aspect
ratio to 1

Now you have a plot with 101 maps with the same colorscale, with ggplot
doing all the hard work. It takes some time to get the hang of ggplot,
but I think it is worth the investment, also for spatial plots.

cheers and hope this helps,
Paul

On 10/28/2010 09:12 PM, Peter Larson wrote:

Hello!

I have a problem.

I am using IDW to interpolate a daily series of geospatial
observations. Thus, I want to produce a large number of sequential
maps.

I want them to all represent the same color scale. Is there any way to
fix the color scale so that it is the same for all the plots?

Thanks,

Pete

--
Edzer Pebesma
Institute for Geoinformatics (ifgi), University of M?nster
Weseler Stra?e 253, 48151 M?nster, Germany. Phone: +49 251
8333081, Fax: +49 251 8339763  http://ifgi.uni-muenster.de
http://www.52north.org/geostatistics      e.pebesma at wwu.de

-- 
Edzer Pebesma
Institute for Geoinformatics (ifgi), University of M?nster
Weseler Stra?e 253, 48151 M?nster, Germany. Phone: +49 251
8333081, Fax: +49 251 8339763  http://ifgi.uni-muenster.de
http://www.52north.org/geostatistics      e.pebesma at wwu.de