[R-sig-Geo] Convert rasters to data frame with time stamp
Robert J. Hijmans
r.hijmans at gmail.com
Sun Oct 18 19:28:01 CEST 2015
Thiago,
This is really a qmap question but it would seem that you need to
provide two Raster objects with the same number of layers (matching
time steps). For example do mod <- mod[[1:408]]
Robert
On Sat, Oct 17, 2015 at 3:22 PM, Thiago V. dos Santos
<thi_veloso at yahoo.com.br> wrote:
> Thank you Dominik, Vijay and Robers for all the valuable inputs.
>
> Robert, the function you devised is really convenient. However, it fails when the observed and modelled raster objects have different number of layers.
>
> Please notice that the obs and model objects on the example from the qmap packages have different lengths:
>
> library(qmap)
> data(obsprecip)
> data(modprecip)
>
> dim(obsprecip);dim(modprecip) # notice the difference in the lengths
>
> #Fit a quantile mapping function to observed and modeled data
> qm.fit <- fitQmap(obsprecip,modprecip,
> method="QUANT",qstep=0.01)
>
> #Perform bias correction on modeled data
> qm <- doQmap(modprecip, qm.fit, type="tricub")
>
>
>
> Now, an error is displayed when trying to apply your function on rasters with different "lengths" (number of layers):
>
> library(raster)
> library(qmap)
>
> #Create a rasterStack similar to my data - same dimensions and layer names
> r <- raster(ncol=60, nrow=60)
> obs <- stack(lapply(1:408, function(x) setValues(r, runif(ncell(r)))))
> mod <- stack(lapply(1:800, function(x) setValues(r, runif(ncell(r)))))
>
> #Define bias-correction function
> f <- function(obs, mod, ...) {
> obs <- t(obs)
> qm.fit <- fitQmap(obs, t(mod), method="QUANT",qstep=1/15)
> t( doQmap(mod, qm.fit, type="tricub") )
> }
>
> # Test function on rasters with different # of layers
> x <- f(head(obs), head(mod))
>
>
> Error in t(doQmap(mod, qm.fit, type = "tricub")) :
> error in evaluating the argument 'x' in selecting a method for function 't': Error in doQmapQUANT.matrix(x, fobj, ...) :
> 'ncol(x)' and 'nrow(fobj$par$modq)' should be eaqual
>
>
> Any way to circumvent this?
>
> Greetings,
> -- Thiago V. dos Santos
>
> PhD student
> Land and Atmospheric Science
> University of Minnesota
>
>
>
> On Saturday, October 17, 2015 1:59 AM, Robert J. Hijmans <r.hijmans at gmail.com> wrote:
> Thiago,
>
>> Therefore, I need to load data as rasters and iterate through all individual gridcells to create a data frame containing:
>> date1, cell1, cell2, cell3, ..., cell3600
>> date2, cell1, cell2, cell3, ..., cell3600
>> date3, cell1, cell2, cell3, ..., cell3600...
>> date408, cell1, cell2, cell3, ..., cell3600
>> then apply the fit function and finally convert the data back to a raster.
>
> That would be easy enough by using, e.g., as.data.frame, as Dominik
> and Vijay suggested, but I do not think that you _need_ to do that.
> The manual says that you can use a matrix (no need for data.frame).
> The dates are row.names, but I do not think they are used by the
> algorithm, so you do not need to supply them (it would be possible).
> In most cases like this, you can wrap the functions you need into a
> short new function that can be passed to a raster function (calc or
> overlay). In this case perhaps something like:
>
> f <- function(obs, mod, ...) {
> obs <- t(obs)
> qm.fit <- fitQmap(obs, t(mod), method="QUANT",qstep=0.01)
> t( doQmap(obs, qm.fit, type="tricub") )
> }
>
> try it
>
> x <- f(head(s), head(s * 10))
> x[, 1:5]
>
> model <- s * 10
> x <- overlay(s, model, fun=f)
>
>
> Robert
>
>
> On Fri, Oct 16, 2015 at 1:39 PM, Thiago V. dos Santos
> <thi_veloso at yahoo.com.br> wrote:
>> Dear list,
>>
>> Generally speaking, I love R. However, one of the things I like least in R is the need to interchange between the various data formats required by different packages.
>>
>> I am trying to apply a bias-correction function on some gridded climate data. The qmap package has functions to perform bias correction on climate data, but the problem I am grasping with is that it requires data to be organized as data.frames:
>>
>>
>> library(qmap)
>> data(obsprecip)
>> data(modprecip)
>>
>> #Fit a quantile mapping function to observed and modeled data
>> qm.fit <- fitQmap(obsprecip,modprecip,
>> method="QUANT",qstep=0.01)
>>
>> #Perform bias correction on modeled data
>> qm <- doQmap(modprecip, qm.fit, type="tricub")
>>
>>
>> And that's all. But notice that both observed and modeled data in this example are data frames for different locations (Moss, Geiranger and Barkestad):
>>
>>> head(obsprecip)
>> MOSS GEIRANGER BARKESTAD
>> 1-1-1961 0.1 0 0
>> 2-1-1961 0.2 0 0
>> 3-1-1961 0.9 0 0
>> 4-1-1961 10.6 0 0
>> 5-1-1961 1.5 0 0
>> 6-1-1961 1.2 0 2
>>
>>> head(modprecip)
>> MOSS GEIRANGER BARKESTAD
>> 2-1-1961 2.283 0.0000 3.177000
>> 3-1-1961 2.443 10.8600 1.719000
>> 4-1-1961 3.099 12.7300 6.636000
>> 5-1-1961 0.000 9.7720 9.676000
>> 6-1-1961 0.140 0.6448 7.110000
>> 7-1-1961 13.470 3.3570 0.001107
>>
>>
>>
>> Now, let's back to my problem. I have monthly precip data to which I want to apply the same function above, but my data is gridded:
>>
>> library(raster)
>>
>> #Create a rasterStack similar to my data - same dimensions and layer names
>> r <- raster(ncol=60, nrow=60)
>> s <- stack(lapply(1:408, function(x) setValues(r, runif(ncell(r)))))
>> names(s) <- paste0('X', seq(as.Date("1980/1/1"), by = "month", length.out = 408))
>> s
>>
>>
>> Therefore, I need to load data as rasters and iterate through all individual gridcells to create a data frame containing:
>>
>>
>> date1, cell1, cell2, cell3, ..., cell3600
>> date2, cell1, cell2, cell3, ..., cell3600
>> date3, cell1, cell2, cell3, ..., cell3600...
>> date408, cell1, cell2, cell3, ..., cell3600
>>
>> then apply the fit function and finally convert the data back to a raster.
>>
>>
>> Any ideas on how to efficiently convert rasters to data frames containing their time stamp and then back to a raster again??
>>
>> Any hint is much appreciated.
>> Greetings,
>> -- Thiago V. dos Santos
>>
>> PhD student
>> Land and Atmospheric Science
>> University of Minnesota
>>
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