profileApply-methods: Apply a function to soil profiles within a SoilProfileCollection object.

Description

Apply a function to soil profiles within a SoilProfileCollection object, each iteration has access to a SoilProfileCollection object.

Usage

# method for SoilProfileCollection objects
profileApply(object, FUN, simplify=TRUE, ...)

Arguments

object

a SoilProfileCollection

FUN

a function to be applied to each profile within the collection

simplify

logical, should the result be simplified to a vector? see examples

...

further arguments passsed to FUN

Value

nrow(object) (horizon data) or of length length(object) (site data). When simplify is FALSE, a list is returned.

Methods

signature(object = "SoilProfileCollection")

Examples

Run this code

data(sp1)
depths(sp1) <- id ~ top + bottom

# estimate soil depth using horizon designations
profileApply(sp1, estimateSoilDepth, name='name', top='top', bottom='bottom')

# scale properties within each profile
# scaled = (x - mean(x)) / sd(x)
sp1$d <- profileApply(sp1, FUN=function(x) round(scale(x$prop), 2))
plot(sp1, name='d')


# compute depth-wise differencing by profile
# note that our function expects that the column 'prop' exists
f <- function(x) { c(x$prop[1], diff(x$prop)) }
sp1$d <- profileApply(sp1, FUN=f)
plot(sp1, name='d')

# compute depth-wise cumulative sum by profile
# note the use of an anonymous function
sp1$d <- profileApply(sp1, FUN=function(x) cumsum(x$prop))
plot(sp1, name='d')


# compute profile-means, and save to @site
# there must be some data in @site for this to work
site(sp1) <- ~ group
sp1$mean_prop <- profileApply(sp1, FUN=function(x) mean(x$prop, na.rm=TRUE))

# re-plot using ranks defined by computed summaries (in @site)
plot(sp1, plot.order=rank(sp1$mean_prop))


## iterate over profiles, subsetting horizon data

# example data
data(sp1)

# promote to SoilProfileCollection
depths(sp1) <- id ~ top + bottom
site(sp1) <- ~ group

# make some fake site data related to a depth of some importance
sp1$dep <- profileApply(sp1, function(i) {round(rnorm(n=1, mean=mean(i$top)))})

# custom function for subsetting horizon data, by profile
# keep horizons with lower boundary < site-level attribute 'dep'
fun <- function(i) {
  # extract horizons
  h <- horizons(i)
  # make an expression to subset horizons
  exp <- paste('bottom < ', i$dep, sep='')
  # subset horizons, and write-back into current SPC
  horizons(i) <- subset(h, subset=eval(parse(text=exp)))
  # return modified SPC
  return(i)
}

# list of modified SoilProfileCollection objects
l <- profileApply(sp1, fun, simplify=FALSE)

# re-combine list of SoilProfileCollection objects into a single SoilProfileCollection
sp1.sub <- do.call(rbind, l)

# graphically check
par(mfrow=c(2,1), mar=c(0,0,1,0))
plot(sp1)
points(1:length(sp1), sp1$dep, col='red', pch=7)
plot(sp1.sub)


## Not run: 
# ##
# ## helper functions: these must be modified to suit your own data
# ##
# 
# # compute the weighted-mean of some property within a given diagnostic horizon
# # note that this requires conditional eval of data that may contain NA
# # see ?slab for details on the syntax
# # note that function expects certain columns within 'x'
# f.diag.wt.prop <- function(x, d.hz, prop) {
#   # extract diagnostic horizon data
#   d <- diagnostic_hz(x)
#   # subset to the requested diagnostic hz
#   d <- d[d$diag_kind == d.hz, ]
#   # if missing return NA
#   if(nrow(d) == 0)
#     return(NA)
#   
#   # extract depths and check for missing
#   sv <- c(d$featdept, d$featdepb)
#   if(any(is.na(sv)))
#     return(NA)
#   
#   # create formula from named property
#   fm <- as.formula(paste('~', prop))
#   # return just the (weighted) mean, accessed from @horizons
#   s <- slab(x, fm, slab.structure=sv, slab.fun=mean)$value
#   return(s)
# }
# 
# 
# # conditional eval of thickness of some diagnostic feature or horizon
# # will return a vector of length(x), you can save to @site
# f.diag.thickness <- function(x, d.hz) {
#   # extract diagnostic horizon data
#   d <- diagnostic_hz(x)
#   # subset to the requested diagnostic hz
#   d <- d[d$diag_kind == d.hz, ]
#   # if missing return NA
#   if(nrow(d) == 0)
#     return(NA)
#   
#   # compute thickness
#   thick <- d$featdepb - d$featdept
#   return(thick)
# }
# 
# 
# # conditional eval of property within particle size control section
# # makes assumptions about the SPC that is passed-in
# f.psc.prop <- function(x, prop) {
#   # these are accessed from @site
#   sv <- c(x$psctopdepth, x$pscbotdepth)
#   # test for missing PCS data
#   if(any(is.na(sv)))
#     return(NA) 
#   
#   # this should never happen... unless someone made a mistake
#   # check to make sure that the lower PSC boundary is shallower than the depth
#   if(sv[2] > max(x))
#     return(NA)
#   
#   # create formula from named property
#   fm <- as.formula(paste('~', prop))
#   # return just the (weighted) mean, accessed from @horizons
#   s <- slab(x, fm, slab.structure=sv, slab.fun=mean)$value
#   return(s)
# }
# 
# # try with some sample data
# data(loafercreek, package='soilDB')
# 
# profileApply(loafercreek, f.diag.wt.prop, d.hz='argillic horizon', prop='clay')
# profileApply(loafercreek, f.diag.thickness, d.hz='argillic horizon')
# profileApply(loafercreek, f.psc.prop, prop='clay')
# 
# ## End(Not run)