Redefine classes

DESCRIPTION

@@ -26,7 +26,7 @@ Depends:
     R (>= 3.5),
     dimensio (>= 0.8.0)
 Imports: 
-    arkhe (>= 1.6.0),
+    arkhe (>= 1.7.0),
     graphics,
     grDevices,
     isopleuros (>= 1.2.0),

NAMESPACE

@@ -26,12 +26,8 @@ exportMethods("%power%")
 exportMethods("[")
 exportMethods("[<-")
 exportMethods("[[<-")
-exportMethods("set_groups<-")
-exportMethods("set_samples<-")
 exportMethods("set_totals<-")
 exportMethods(aggregate)
-exportMethods(any_assigned)
-exportMethods(any_replicated)
 exportMethods(as_amounts)
 exportMethods(as_composition)
 exportMethods(as_features)
@@ -43,11 +39,8 @@ exportMethods(covariance)
 exportMethods(describe)
 exportMethods(dist)
 exportMethods(get_groups)
-exportMethods(get_samples)
 exportMethods(get_totals)
 exportMethods(hist)
-exportMethods(is_assigned)
-exportMethods(is_replicated)
 exportMethods(mahalanobis)
 exportMethods(margin)
 exportMethods(mean)

R/AllClasses.R

@@ -46,9 +46,7 @@ setClassUnion("index", members = c("logical", "numeric", "character"))
 #' An S4 class to represent compositional data.
 #' @slot totals A [`numeric`] vector to store the absolute row sums (before
 #'  the closure of the compositions).
-#' @slot samples A [`character`] vector to store the sample identifiers
-#'  (allows duplicates in case of repeated measurements).
-#' @slot groups A [`character`] vector to store the group names.
+#' @slot extra A [`list`] of extra variables.
 #' @section Coerce:
 #'  In the code snippets below, `x` is a `CompositionMatrix` object.
 #'  \describe{
@@ -72,8 +70,7 @@ setClassUnion("index", members = c("logical", "numeric", "character"))
   Class = "CompositionMatrix",
   slots = c(
     totals = "numeric",
-    samples = "character",
-    groups = "character"
+    extra = "list"
   ),
   contains = c("NumericMatrix")
 )
@@ -84,9 +81,7 @@ setClassUnion("index", members = c("logical", "numeric", "character"))
 #' S4 classes to represent log-ratio data transformations.
 #' @slot totals A [`numeric`] vector to store the absolute row sums (before
 #'  the closure of the compositions).
-#' @slot samples A [`character`] vector to store the sample identifiers
-#'  (allows duplicates in case of repeated measurements).
-#' @slot groups A [`character`] vector to store the group names.
+#' @slot extra A [`list`] of extra variables.
 #' @slot parts A [`character`] vector to store the part names.
 #' @slot ratio A [`character`] vector to store the ratio names.
 #' @slot order An [`integer`] vector to store the original ordering of the
@@ -112,8 +107,7 @@ setClassUnion("index", members = c("logical", "numeric", "character"))
   Class = "LogRatio",
   slots = c(
     totals = "numeric",
-    samples = "character",
-    groups = "character",
+    extra = "list",
 
     parts = "character",
     ratio = "character",

R/AllGenerics.R

@@ -18,31 +18,16 @@ NULL
 #'
 #' Coerces an object to a `CompositionMatrix` object.
 #' @param from A [`matrix`] or [`data.frame`] to be coerced.
-#' @param samples An [`integer`] giving the index of the column to be used for
-#'  sample identification: allows duplicates to identify replicated measurements.
-#'  If `NULL` (the default), row names will be used as sample IDs.
-#' @param groups An [`integer`] giving the index of the column to be used to
-#'  group the samples. If `NULL` (the default), no grouping is stored.
-#' @param auto A [`logical`] scalar: try to automatically detect `codes`,
-#'  `samples` and `groups` columns?
+#' @param parts A `vector` giving the index of the column to be used a
+#'  compositional parts. If `NULL` (the default), all [`double`] columns will be
+#'  used.
 #' @param verbose A [`logical`] scalar: should \R report extra information
 #'  on progress?
 #' @param ... Currently not used.
 #' @details
-#'  The [`CompositionMatrix-class`] class has special slots:
-#'
-#'  * `samples` for [repeated measurements/observation][samples],
-#'  * `groups` to [group data by site/area][group].
-#'
-#'  When coercing a `data.frame` to a [`CompositionMatrix-class`] object, an
-#'  attempt is made to automatically assign values to these slots by mapping
-#'  column names (case insensitive, plural insensitive). This behavior can be
-#'  disabled by setting `options(nexus.autodetect = FALSE)` or overridden by
-#'  explicitly specifying the columns to be used.
-#'
 #'  See `vignette("nexus")`.
 #' @note
-#'  All non-numeric variable will be removed.
+#'  All non-numeric variable will be internally stored.
 #' @return A [`CompositionMatrix-class`] object.
 #' @example inst/examples/ex-coerce.R
 #' @author N. Frerebeau
@@ -112,7 +97,7 @@ NULL
 #'  \describe{
 #'   \item{`%perturbe%`}{[Perturbation operation][perturbation()].}
 #'   \item{`%power%`}{[Powering operation][powering()].}
-#' }
+#'  }
 #' @return
 #'  A [`CompositionMatrix-class`] object or a [`numeric`] vector (same as `x`).
 #' @example inst/examples/ex-arith.R
@@ -267,62 +252,6 @@ setGeneric(
   def = function(x, value) standardGeneric("set_groups<-")
 )
 
-#' Working With Samples
-#'
-#' Retrieves or defines the sample names.
-#' @param x An object from which to get or set `samples`.
-#' @param value A possible value for the `samples` of `x`.
-#' @details
-#'  In some situations, measurements may have been repeated (e.g. multiple
-#'  chemical analyses on the same sample). The presence of repeated
-#'  measurements can be specified by giving several observations the same
-#'  sample name.
-#'
-#'  See `vignette("nexus")`.
-#' @return
-#'  * `set_samples()` returns an object of the same sort as `x` with the new
-#'    sample names assigned.
-#'  * `get_samples()` returns a [`character`] vector giving the sample names of `x`.
-#'  * `any_replicated()` returns a [`logical`] scalar specifying whether or not
-#'    `x` has replicated observations.
-#'  * `is_replicated()` returns a [`logical`] vector specifying whether or not
-#'    an observation is a replicate.
-#' @example inst/examples/ex-mutators.R
-#' @author N. Frerebeau
-#' @docType methods
-#' @family mutators
-#' @name samples
-#' @rdname samples
-NULL
-
-#' @rdname samples
-#' @aliases any_replicated-method
-setGeneric(
-  name = "any_replicated",
-  def = function(x) standardGeneric("any_replicated")
-)
-
-#' @rdname samples
-#' @aliases is_replicated-method
-setGeneric(
-  name = "is_replicated",
-  def = function(x) standardGeneric("is_replicated")
-)
-
-#' @rdname samples
-#' @aliases get_samples-method
-setGeneric(
-  name = "get_samples",
-  def = function(x) standardGeneric("get_samples")
-)
-
-#' @rdname samples
-#' @aliases set_samples-method
-setGeneric(
-  name = "set_samples<-",
-  def = function(x, value) standardGeneric("set_samples<-")
-)
-
 #' Row Sums
 #'
 #' Retrieves or defines the row sums (before closure).
@@ -662,7 +591,8 @@ setGeneric(
 #' returns the result.
 #' @param x A [`CompositionMatrix-class`] object.
 #' @param by A `vector` or a list of grouping elements, each as long as the
-#'  variables in `x`. The elements are coerced to factors before use.
+#'  variables in `x`. The elements are coerced to factors before use
+#'  (in the sense that [`as.factor(by)`][as.factor()] defines the grouping).
 #' @param FUN A [`function`] to compute the summary statistics.
 #' @param simplify A [`logical`] scalar: should the results be simplified to a
 #'  matrix if possible?
@@ -721,7 +651,10 @@ NULL
 #'
 #' Splits the data into subsets and computes compositional mean for each.
 #' @param x A [`CompositionMatrix-class`] object.
-#' @param by A `vector` of grouping elements, as long as the variables in `x`.
+#' @param by A `vector` of grouping elements, as long as the variables in `x`
+#'  (in the sense that [`as.factor(by)`][as.factor()] defines the grouping).
+#'  If a single `character` string is passed, it must be the name of a
+#'  categorical variable from the original dataset.
 #' @param ... Further arguments to be passed to [mean()].
 #' @return A [`CompositionMatrix-class`] object.
 #' @seealso [mean()], [aggregate()]
@@ -979,12 +912,14 @@ NULL
 #'
 #' Displays a compositional bar chart.
 #' @param height A [`CompositionMatrix-class`] object.
+#' @param groups A `vector` of grouping elements, as long as the variables in
+#'  `height`. If a single `character` string is passed, it must be the name of a
+#'  categorical variable from the original dataset.
+#'  If set, a matrix of panels defined by `groups` will be drawn.
 #' @param order An [`integer`] vector giving the index of the column to be used
 #'  for the ordering of the data.
 #' @param decreasing A [`logical`] scalar: should the sort order be increasing
 #'  or decreasing?
-#' @param groups A `vector` of grouping elements, as long as the variables in
-#'  `height`. If set, a matrix of panels defined by `groups` will be drawn.
 #' @param horiz A [`logical`] scalar. If `FALSE`, the bars are drawn vertically
 #'  with the first bar to the left. If `TRUE` (the default), the bars are drawn
 #'  horizontally with the first at the bottom.
@@ -1051,7 +986,9 @@ NULL
 #'
 #' Displays a matrix of ternary plots.
 #' @param x A [`CompositionMatrix-class`] object.
-#' @param groups A `vector` of grouping elements, as long as the variables in `x`.
+#' @param groups A `vector` of grouping elements, as long as the variables in
+#'  `x`. If a single `character` string is passed, it must be the name of a
+#'  categorical variable from the original dataset.
 #' @inheritParams isopleuros::ternary_pairs
 #' @return
 #'  `plot()` is called for its side-effects: is results in a graphic being
@@ -1070,7 +1007,9 @@ NULL
 #'
 #' Displays a density plot.
 #' @param x A [`LogRatio-class`] object.
-#' @param groups A `vector` of grouping elements, as long as the variables in `x`.
+#' @param groups A `vector` of grouping elements, as long as the variables in
+#'  `x`. If a single `character` string is passed, it must be the name of a
+#'  categorical variable from the original dataset.
 #'  If set, a matrix of panels defined by `groups` will be drawn.
 #' @param rug A [`logical`] scalar: should a *rug* representation (1-d plot) of
 #'  the data be added to the plot?
@@ -1245,7 +1184,9 @@ NULL
 #'  `quantile` is used as a cut-off value for outlier detection: observations
 #'  with larger (squared) Mahalanobis distance are considered as potential
 #'  outliers.
-#' @param groups A `vector` of grouping elements, as long as the variables in `object`.
+#' @param groups A `vector` of grouping elements, as long as the variables in
+#'  `object`. If a single `character` string is passed, it must be the name of a
+#'  categorical variable from the original dataset.
 #' @details
 #'  An outlier can be defined as having a very large Mahalanobis distance from
 #'  all observations. In this way, a certain proportion of the observations can

R/barplot.R

@@ -5,9 +5,9 @@ NULL
 # CompositionMatrix ============================================================
 #' @export
 #' @method barplot CompositionMatrix
-barplot.CompositionMatrix <- function(height, ...,
+barplot.CompositionMatrix <- function(height, ..., groups = NULL,
                                       order = NULL, decreasing = FALSE,
-                                      groups = get_groups(height), horiz = TRUE,
+                                      horiz = TRUE,
                                       xlab = NULL, ylab = NULL,
                                       main = NULL, sub = NULL,
                                       ann = graphics::par("ann"), axes = TRUE,
@@ -36,8 +36,8 @@ barplot.CompositionMatrix <- function(height, ...,
   y_side <- if (horiz) 2 else 1
 
   ## Grouping
-  n <- 0
-  if (!all(is.na(groups))) {
+  groups <- get_variable(height, which = groups)
+  if (!is.null(groups) && !all(is.na(groups))) {
     arkhe::assert_length(groups, nrow(z))
     groups <- groups[ordering]