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.

dissolve_hz(
  object,
  by,
  id = "peiid",
  hztop = "hzdept",
  hzbot = "hzdepb",
  collapse = FALSE,
  order = FALSE
)

Arguments

object

a data.frame

by

character: column names, to be used as grouping variables, within the object.

id

character: column name of the pedon ID within the object.

hztop

character: column name of the horizon top depth within the object.

hzbot

character: column name of the horizon bottom depth in the object.

collapse

logical: indicating whether to not combine grouping variables before dissolving.

order

logical: indicating whether or not to order the object by the id, hztop, and hzbot columns. #'

Value

A data.frame with the original id, by grouping variables, and non-consecutive horizon depths.

Details

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.

Author

Stephen Roecker

Examples


# 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 <- dissolve_hz(h, by = c("genhz", "dep_5"), id = "id", hztop = "top", hzbot = "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
#> 1  92-1   0     18    genhz     A
#> 2  92-1  18     43    genhz     E
#> 3  92-1  43    153    genhz     B
#> 4  92-1 153    213    genhz     C
#> 27 92-1   0    156    dep_5 FALSE
#> 28 92-1 156    213    dep_5  TRUE


# example 2
df <- data.frame(
    peiid = 1,
    hzdept = c(0, 5,  10, 15, 25, 50), 
    hzdepb = 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
#>   peiid hzdept hzdepb 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

dissolve_hz(df, c("genhz", "texcl"))
#>   peiid hzdept hzdepb variable value
#> 1     1      0     10    genhz     A
#> 2     1     10     15    genhz     E
#> 3     1     15     50    genhz   2Bt
#> 4     1     50    100    genhz    2C
#> 5     1      0     15    texcl   sil
#> 6     1     15     50    texcl    sl
#> 7     1     50    100    texcl     s
dissolve_hz(df, c("genhz", "texcl"), collapse = TRUE)
#>   peiid hzdept hzdepb      variable    value
#> 1     1      0     10 genhz & texcl  A & sil
#> 2     1     10     15 genhz & texcl  E & sil
#> 3     1     15     50 genhz & texcl 2Bt & sl
#> 4     1     50    100 genhz & texcl   2C & s

test <- dissolve_hz(df, "genhz")
subset(test, value == "2Bt")
#>   peiid hzdept hzdepb variable value
#> 3     1     15     50    genhz   2Bt