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use .LAST k-keyword in min(SPC)/max(SPC) #209

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Mar 3, 2021
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use .LAST k-keyword in min(SPC)/max(SPC) #209

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94189c8
use .LAST k-keyword in min(SPC)/max(SPC) #175 #205 #207
brownag Mar 2, 2021
26dbb89
safer for user specified metadata
brownag Mar 2, 2021
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134 changes: 33 additions & 101 deletions R/SoilProfileCollection-methods.R
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Original file line number Diff line number Diff line change
Expand Up @@ -63,61 +63,27 @@ setMethod(
f = 'min',
signature(x = "SoilProfileCollection"),
definition = function(x, v = NULL) {
h <- x@horizons

# get bottom depth column name
hz_bottom_depths <- horizonDepths(x)[2]

# handle empty spc
if(length(x@horizons[[hz_bottom_depths]]) == 0)
return(NA)

htb <- horizonDepths(x)
target.names <- c(idname(x), hzidname(x), htb)

# optionally use a horizon-level property refine calculation
if (!missing(v)) {
target.names <- c(hz_bottom_depths, idname(x), v)
} else {
target.names <- c(hz_bottom_depths, idname(x))
if (!missing(v) && !is.null(v) && v %in% horizonNames(x)) {
target.names <- c(target.names, v)
}

# filter out missing data
h <- .data.frame.j(h, target.names, aqp_df_class(x))
h <- h[complete.cases(h),]

# compute max depth within each profile
if (aqp_df_class(x) == "data.table" &
requireNamespace("data.table") ) {

# base R faster for big data with no existing key
# but if the key is already set, then this is ~2x faster with 1M profiles (sorted numeric IDs)
if (idname(x) %in% data.table::key(h)) {
idn <- idname(x)
.I <- NULL
# # with no key
# user system elapsed
# 7.26 5.52 16.83
# # with pre-set key
# user system elapsed
# 2.07 0.00 2.08

# cant invoke this for something like min/max probably -- might do better on linux
# data.table::setkeyv(h, c(idn))

dep <- h[[hz_bottom_depths]]
d <- dep[h[, .I[hz_bottom_depths == max(hz_bottom_depths, na.rm = T)][1],
by = idn]$V1]

# return from here for data.table
return(min(d, na.rm = TRUE))
}

# handle empty spc
if(length(x@horizons[[htb[2]]]) == 0) {
return(NA)
}

# tapply on a data.frame
# user system elapsed
# 4.39 0.00 4.39
d <- tapply(h[[hz_bottom_depths]], h[[idname(x)]], max, na.rm = TRUE)


# filter out missing data, accounting for optional `v`
h <- x@horizons
x@horizons <- h[complete.cases(.data.frame.j(h, target.names, aqp_df_class(x))),]

# return the shallowest (of the deepest depths in each profile)
return(min(d, na.rm = TRUE))
.LAST <- NULL
return(min(x[,, .LAST][[htb[2]]], na.rm = TRUE))
}
)

Expand All @@ -137,60 +103,26 @@ setMethod(
f = 'max',
signature(x = "SoilProfileCollection"),
definition = function(x, v = NULL) {
# get bottom depth column name
h <- x@horizons
hz_bottom_depths <- horizonDepths(x)[2]

# handle empty spc
if(length(h[[hz_bottom_depths]]) == 0)
return(NA)

htb <- horizonDepths(x)
target.names <- c(idname(x), hzidname(x), htb)

# optionally use a horizon-level property refine calculation
if (!missing(v)) {
target.names <- c(hz_bottom_depths, idname(x), v)
} else {
target.names <- c(hz_bottom_depths, idname(x))
if (!missing(v) && !is.null(v) && v %in% horizonNames(x)) {
target.names <- c(target.names, v)
}

# filter out missing data
h <- .data.frame.j(h, target.names, aqp_df_class(x))
h <- h[complete.cases(h),]

# compute max depth within each profile
if (aqp_df_class(x) == "data.table" &
requireNamespace("data.table") ) {
.I <- NULL
# base R faster for big data with no existing key
# but if the key is already set, then this is ~2x faster with 1M profiles (sorted numeric IDs)
if (idname(x) %in% data.table::key(h)) {
idn <- idname(x)
# # with no key
# user system elapsed
# 7.26 5.52 16.83
# # with pre-set key
# user system elapsed
# 2.07 0.00 2.08

# cant invoke this for something like min/max probably -- might do better on linux
# data.table::setkeyv(h, c(idn))

dep <- h[[hz_bottom_depths]]
d <- dep[h[, .I[hz_bottom_depths == max(hz_bottom_depths, na.rm = T)][1],
by = idn]$V1]

# return from here for data.table
return(max(d, na.rm = TRUE))
}


# handle empty spc
if(length(x@horizons[[htb[2]]]) == 0) {
return(NA)
}

# tapply on a data.frame
# user system elapsed
# 4.39 0.00 4.39
d <- tapply(h[[hz_bottom_depths]], h[[idname(x)]], max, na.rm = TRUE)

# return the deepest depth (of the deepest depths in each profile)
return(max(d, na.rm = TRUE))
# filter out missing data, accounting for optional `v`
h <- x@horizons
x@horizons <- h[complete.cases(.data.frame.j(h, target.names, aqp_df_class(x))),]

# return the deepest (of the deepest depths in each profile)
.LAST <- NULL
return(max(x[,, .LAST][[htb[2]]], na.rm = TRUE))
}
)

Expand Down