This function dissolves or combines horizons that have a common set of grouping variables. It only combines those horizon records that are sequential (e.g. share a horizon boundary). Thus, it can be used to identify discontinuities in the grouping variables along a profile and their unique depths. It is particularly useful for determining the depth to the top or bottom of horizons with a specific category, and should be simpler than previous methods that require aggregating over profiles.
hz_dissolve(
object,
by,
idcol = "id",
depthcols = c("top", "bottom"),
collapse = FALSE,
order = FALSE
)
dissolve_hz(
object,
by,
id = "idcol",
hztop = "top",
hzbot = "bottom",
collapse = FALSE,
order = FALSE
)a data.frame
character: column names, to be used as grouping variables, within the object.
character: column name of the pedon ID within the object.
a character vector of length 2 specifying the names of the horizon depths (e.g. c("top", "bottom")).
logical: indicating whether to not combine grouping variables before dissolving.
logical: indicating whether or not to order the object by the id, hztop, and hzbot columns.
deprecated and replaced with idcol.
deprecated and replaced by depthcols.
deprecated and replaced by depthcols.
A data.frame with the original idcol, by grouping variables, and non-consecutive horizon depths.
This function assumes the profiles and horizons within the object follow the logic defined by checkHzDepthLogic (e.g. records are ordered sequentially by id, hztop, and hzbot and without gaps). If the records are not ordered, set the order = TRUE.
# example 1
data(jacobs2000)
spc <- jacobs2000
spc$dep_5 <- spc$depletion_pct >=5
spc$genhz <- generalize.hz(spc$name, c("A", "E", "B", "C"), c("A", "E", "B", "C"))
h <- horizons(spc)
test <- hz_dissolve(h, by = c("genhz", "dep_5"), idcol = "id", depthcols = c("top", "bottom"))
#> non-character grouping variables are being converted to characters
vars <- c("id", "top", "bottom", "genhz", "dep_5")
h[h$id == "92-1", vars]
#> id top bottom genhz dep_5
#> 1 92-1 0 18 A FALSE
#> 2 92-1 18 43 E FALSE
#> 3 92-1 43 79 B FALSE
#> 4 92-1 79 130 B FALSE
#> 5 92-1 130 153 B FALSE
#> 6 92-1 153 156 C FALSE
#> 7 92-1 156 213 C TRUE
test[test$id == "92-1", ]
#> id top bottom variable value dissolve_id
#> 1 92-1 0 18 genhz A 92-1_000-018_A
#> 2 92-1 18 43 genhz E 92-1_018-043_E
#> 3 92-1 43 153 genhz B 92-1_043-153_B
#> 4 92-1 153 213 genhz C 92-1_153-213_C
#> 27 92-1 0 156 dep_5 FALSE 92-1_000-156_FALSE
#> 28 92-1 156 213 dep_5 TRUE 92-1_156-213_TRUE
# example 2
df <- data.frame(
id = 1,
top = c(0, 5, 10, 15, 25, 50),
bottom = c(5, 10, 15, 25, 50, 100),
hzname = c("A1", "A2", "E/A", "2Bt1", "2Bt2", "2C"),
genhz = c("A", "A", "E", "2Bt", "2Bt", "2C"),
texcl = c("sil", "sil", "sil", "sl", "sl", "s")
)
df
#> id top bottom hzname genhz texcl
#> 1 1 0 5 A1 A sil
#> 2 1 5 10 A2 A sil
#> 3 1 10 15 E/A E sil
#> 4 1 15 25 2Bt1 2Bt sl
#> 5 1 25 50 2Bt2 2Bt sl
#> 6 1 50 100 2C 2C s
hz_dissolve(df, c("genhz", "texcl"))
#> id top bottom variable value dissolve_id
#> 1 1 0 10 genhz A 1_000-010_A
#> 2 1 10 15 genhz E 1_010-015_E
#> 3 1 15 50 genhz 2Bt 1_015-050_2Bt
#> 4 1 50 100 genhz 2C 1_050-100_2C
#> 5 1 0 15 texcl sil 1_000-015_sil
#> 6 1 15 50 texcl sl 1_015-050_sl
#> 7 1 50 100 texcl s 1_050-100_s
hz_dissolve(df, c("genhz", "texcl"), collapse = TRUE)
#> id top bottom variable value dissolve_id
#> 1 1 0 10 genhz & texcl A & sil 1_000-010_A & sil
#> 2 1 10 15 genhz & texcl E & sil 1_010-015_E & sil
#> 3 1 15 50 genhz & texcl 2Bt & sl 1_015-050_2Bt & sl
#> 4 1 50 100 genhz & texcl 2C & s 1_050-100_2C & s
test <- hz_dissolve(df, "genhz")
subset(test, value == "2Bt")
#> id top bottom variable value dissolve_id
#> 3 1 15 50 genhz 2Bt 1_015-050_2Bt