aqp (version 0.85)

soil.slot: Soil Profile Slotting

Description

Align a single soil property to a user-defined basis.

Usage

soil.slot(data, seg_size = NA, seg_vect = NA, 
return.raw = FALSE, use.wts = FALSE, compute.depth.prob = FALSE)

Arguments

data

A dataframe representing a 'stack' of soil profiles and having the following format: [object Object],[object Object],[object Object],[object Object]

seg_size

User-degined segment size, default is 1.

seg_vect

User-degined segment structure (needs work)

return.raw

If TRUE, un-aggregated data will be returned by depth-slice. Useful for debugging, or for the generation of box and whisker plots.

use.wts

If TRUE, then a column called 'wt' should be present in the source dataframe, and must make sense within the context of the data. Weighted means and standard deviations will be returned by depth-slice.

compute.depth.prob

If TRUE, depth probability is computed and returned. Setting a dummy 'prop' column to 1 in the source dataframe is suggested. See examples below.

Value

• A dataframe with slice-wise aggregation, weighted aggregation, class probabilities, or depth probabilities.

Details

The conditional.sd function is a wrapper to sd, that replicates the pre-R 2.8 behavior of sd. See the sample dataset 'sp1' documentation for further examples on how to used soil.slot. Basic error checking is performed to make sure that bottom and top horizon boundaries make sense. Note that the horizons should be sorted according to depth before using this function.

References

http://casoilresource.lawr.ucdavis.edu/

See Also

sp1, unroll, soil.slot.multiple

Examples

# setup environment
library(reshape)
library(lattice)

# load example data
data(sp1)


# test that mean of 1cm slotted property is equal to the 
# hz-thickness weighted mean value of that property
sp1.sub <- subset(sp1, sub=id == 'P009')
hz.wt.mean <- with(sp1.sub, 
sum((bottom - top) * prop) / sum(bottom - top) 
)
a <- soil.slot(sp1.sub)
all.equal(mean(a$p.mean), hz.wt.mean)

#
# 1 standard usage, and plotting example
#

# slot at two different segment sizes
a <- soil.slot(sp1)
b <- soil.slot(sp1, seg_size=5)

# stack into long format
ab <- make.groups(a, b)
ab$which <- factor(ab$which, levels=c('a','b'), 
labels=c('1-cm Interval', '5-cm Interval'))

# manually add mean +/- SD
ab$upper <- with(ab, p.mean+p.sd)
ab$lower <- with(ab, p.mean-p.sd)

# use mean +/- 1 SD
# custom plotting function for uncertainty viz.
xyplot(top ~ p.mean | which, data=ab, ylab='Depth',
xlab='mean bounded by +/- 1 SD',
lower=ab$lower, upper=ab$upper, ylim=c(250,-5), alpha=0.5, 
panel=panel.depth_function, 
prepanel=prepanel.depth_function,
layout=c(2,1), scales=list(x=list(alternating=1))
)

# use median and IQR
# custom plotting function for uncertainty viz.
xyplot(top ~ p.q50 | which, data=ab, ylab='Depth',
xlab='median bounded by 25th and 75th percentiles',
lower=ab$p.q25, upper=ab$p.q75, ylim=c(250,-5), alpha=0.5, 
panel=panel.depth_function, 
prepanel=prepanel.depth_function,
layout=c(2,1), scales=list(x=list(alternating=1))
)



#
# 1.1 try slotting categorical variables
#

# normalize horizon names:
sp1$name[grep('O', sp1$name)] <- 'O'
sp1$name[grep('A[0-9]', sp1$name)] <- 'A'
sp1$name[grep('AB', sp1$name, ignore.case=TRUE)] <- 'A'
sp1$name[grep('BA', sp1$name)] <- 'B'
sp1$name[grep('Bt', sp1$name)] <- 'B'
sp1$name[grep('Bw', sp1$name)] <- 'B'
sp1$name[grep('C', sp1$name)] <- 'C'
sp1$name[grep('R', sp1$name)] <- 'R'

# generate new data for testing soil.slot()
y <- with(sp1, data.frame(id=id, top=top, bottom=bottom, prop=name))
# convert name back to a character
y$prop <- as.character(y$prop)
# fix factor levels
y$id <- factor(y$id)

# default slotting-- 1cm intervals
a <- soil.slot(y)

# reshape into long format for plotting
a.long <- melt(a, id.var=c('top','bottom'))

a.long$variable <- factor(a.long$variable, levels=c('O','A','B','C','R'))

# plot horizon type proportions
xyplot(top ~ value | variable, data=a.long, subset=value > 0,
ylim=c(150, -5), type=c('S','g'), horizontal=TRUE, layout=c(4,1), col=1 )


#
# 2. depth probability
#
y <- with(sp1, data.frame(id=id, top=top, bottom=bottom, prop=1))
a <- soil.slot(y, compute.depth.prob=TRUE)
xyplot(top ~ p.prop, data=a, ylim=c(250, -5), type='S', horizontal=TRUE, asp=4)


#
# 3.1 standard aggregation
#
a <- soil.slot(sp1)

# manually add mean +/- SD
a$upper <- with(a, p.mean+p.sd)
a$lower <- with(a, p.mean-p.sd)

# use custom plotting function for uncertainty viz.
xyplot(top ~ p.mean, data=a, 
lower=a$lower, upper=a$upper, ylim=c(250,-5), alpha=0.5, 
panel=panel.depth_function, 
prepanel=prepanel.depth_function
)


#
# 3.2 standard aggregation, by grouping variable
#
require(plyr)

# try str(sp1) for hints
sp1$group <- with(sp1, factor(group))
a <- ddply(sp1, .(group), .fun=soil.slot)

# manually add mean +/- SD
a$upper <- with(a, p.mean+p.sd)
a$lower <- with(a, p.mean-p.sd)



# use custom plotting function for uncertainty viz.
xyplot(
top ~ p.mean, data=a, groups=group, subscripts=TRUE, 
lower=a$lower, upper=a$upper, ylim=c(250,-5), alpha=0.5,
panel=panel.depth_function, 
prepanel=prepanel.depth_function
)