discretize_depth: Interpolate over depth to layered profile

In valentingar/ConFluxPro: A toolkit for soil gas analysis

Interpolate over depth to layered profile

Description

Interpolate and discretize data into a layered structure. The output is a data.frame where each profile is separated into layers that intersect at depths defined in the function call. See cfp_layered_profile().

There are different interpolation methods implemented, which might be more practical for different parameters or tasks.

• A 'linear' interpolation for continuous parameters, (e.g. soil temperature).

• The 'boundary' interpolation is only suitable for data that is already layered. It selects the value from the old layer that in which the new layer will lay in.

• A 'linspline' interpolation fits a linear spline model to the data with knots defined in knots

• 'nearest' finds the closest value to the new layer. You can define whether the closest value should be nearest to the top 1, or bottom 0 of the layer using int_depth

• 'harmonic' is similar to a linear interpolation but it uses the harmonic mean harm() using the distance in depth to each value as weights.

Multiple variables can be discretized at the same time by supplying multiple column names in param. It is also possible to use different method and controlling parameters int_depth and knots for each param. Just provide a list of settings the same length as param. If only one value is given, but multiple param the settings are reused for each parameter.

Usage

discretize_depth(
  df,
  param,
  method,
  depth_target,
  boundary_nearest = FALSE,
  boundary_average = "none",
  int_depth = 0.5,
  knots = NULL,
  ...
)

## S3 method for class 'cfp_profile'
discretize_depth(
  df,
  param,
  method,
  depth_target,
  boundary_nearest = FALSE,
  boundary_average = "none",
  int_depth = 0.5,
  knots = NULL,
  ...
)

## S3 method for class 'data.frame'
discretize_depth(
  df,
  param,
  method,
  depth_target,
  boundary_nearest = FALSE,
  boundary_average = "none",
  int_depth = 0.5,
  knots = NULL,
  id_cols = NULL,
  ...
)

Arguments

df	(dataframe) The dataframe containing the parameters to be interpolated, as well as the columns "depth", "upper" and "lower".
param	(character vector) The column names name of the parameters to be interpolated.
method	(character vector) a character (-vector) specifying the methods to be used for interpolation. Must be in the same order as param. One of linear boundary linspline nearest harmonic
depth_target	(numeric vector or data frame) specifying the new layers. Must include n+1 depths for n target layers. If the target layers are different for id_cols, enter a data.frame instead. This data frame must have a "depth" column, as well as well as all id_cols needed that must be at least a subset of the id_cols of the original data.
boundary_nearest	(logical) = TRUE/FALSE if it is TRUE then for target depth steps (partially) outside of the parameter boundaries, the nearest neighbor is returned, else returns NA. Default is FALSE.
boundary_average	("character) Defines what happens if the new layer contains multiple old layers. one of none = the default the new layer is set to NA arith the new layer is calculated as the arithmetic mean of the old harm the new layer is calculated as the harmonic mean of the old
int_depth	(numeric vector) = value between 0 and 1 for 1 = interpolation takes the top of each depth step, 0.5 = middle and 0= bottom. Default = 0.5
knots	(numeric vector) = the depths at which knots for the 'linspline' method are to be placed. If this differs for the parameters, a list of numeric vectors with the same length as "param" can be provided. Cannot differ between id_cols.
...	Arguments passed on to cfp_profile
id_cols	Column names in data.frame that uniquely identify each profile.

Value

A cfp_layered_profile() data.frame with the variables upper and lower defining the layers derived from depth_target. The column depth is the middle of each layer. And all variables from param

See Also

Other soilphys: check_soilphys(), complete_soilphys(), soilphys_layered()

Examples

{

data("soiltemp")
library(dplyr)

dt <- soiltemp %>%
  select(site,depth) %>%
  distinct() %>%
  group_by(site) %>%
  slice_max(depth) %>%
  reframe(depth = c(depth,seq(0,-100,-10)))

discretize_depth(df = soiltemp,
                 param = "t",
                 method = "linear",
                 depth_target = dt,
                 id_cols = c(
                   "site","Date"))
}

valentingar/ConFluxPro documentation built on Dec. 1, 2024, 9:35 p.m.