Merge branch 'main' into radiate + correct le roux name

NAMESPACE

@@ -353,6 +353,7 @@ export(stat_spantree)
 export(stat_star)
 export(svd_ord)
 export(theme_biplot)
+export(theme_scaffold)
 export(tidy)
 export(transmute_cols)
 export(transmute_rows)

NEWS.md

@@ -34,6 +34,10 @@ The 'projection' referential stat prepares segment endpoints between `x,y` posit
 
 A new 'rule' statistical transformation computes additional position aesthetics that the 'axis' geom uses to limit and offset axes. The stat is referential and expects a set of functions that compute limits `lower` and `upper` along the axes and `yintercept` and `xintercept` associated with offset axes. The 'axis' geom preprocesses these aesthetics to rule endpoints `xmin,ymin,xmax,ymax` and offset vectors `xend,yend` to force the plotting window to contain the limited axis segments or, if the axes remain lines, the offsets where they are centered.
 
+## scaffold theme
+
+The 'biplot' theme has been renamed 'scaffold', with an alias for backward compatibility.
+
 # ordr 0.1.1
 
 ## `linewidth` aesthetic (breaking change)

R/stat-rule.r

@@ -58,7 +58,7 @@
 #' @example inst/examples/ex-stat-rule-glass.r
 #' @export
 stat_rule <- function(
-    mapping = NULL, data = NULL, geom = "axis", position = "identity",
+    mapping = NULL, data = NULL, geom = "rule", position = "identity",
     fun.lower = "minpp", fun.upper = "maxpp",
     fun.offset = "minabspp",
     fun.args = list(),

R/themes.r

@@ -1,21 +1,30 @@
-#' @title Biplot theme
+#' @title Scaffolding theme
 #'
-#' @description Omit coordinate visual aids from biplots.
+#' @description Omit cartesian coordinate visual aids.
 #'
-#' @details
+#' @details Geometric data analysis concerns the intrinsic geometry of data.
+#'   Analyses often use artificial or arbitrary coordinate systems that carry no
+#'   useful interpretation but instead serve as scaffolding, especially for
+#'   graphical elements like [axes][geom_axis] that represent other variables
+#'   (Gardner, 2001). In such cases, the visual aids (tick marks and labels,
+#'   grid lines) used to recover the coordinates of the row and column markers
+#'   would add unnecessary clutter and should be omitted. This partial theme
+#'   updates the current theme by removing these elements. The biplot theme is
+#'   an alias included for convenience and backward compatibility.
 #'
-#' Because the artificial axes often go uninterpreted, biplots may omit the
-#' visual aids (tick marks and labels, grid lines) used to recover the
-#' artificial coordinates of the row and column markers The biplot (partial)
-#' theme removes these elements from the current theme. This can be especially
-#' helpful when plotting [axes][geom_axis()] or [isolines][geom_isoline()].
-#' 
+
+#' @template ref-gardner2001
+
 #' @return A ggplot [theme][ggplot2::theme].
 #' @export
-theme_biplot <- function() {
+theme_scaffold <- function() {
   theme(
     axis.ticks = element_blank(),
     axis.text = element_blank(),
     panel.grid = element_blank()
   )
 }
+
+#' @rdname theme_scaffold
+#' @export
+theme_biplot <- theme_scaffold

R/zzz-biplot-stats.r

@@ -546,7 +546,7 @@ StatRowsRule <- ggproto(
 stat_rows_rule <- function(
   mapping = NULL,
   data = NULL,
-  geom = "axis",
+  geom = "rule",
   position = "identity",
   fun.lower = "minpp",
   fun.upper = "maxpp",
@@ -596,7 +596,7 @@ StatColsRule <- ggproto(
 stat_cols_rule <- function(
   mapping = NULL,
   data = NULL,
-  geom = "axis",
+  geom = "rule",
   position = "identity",
   fun.lower = "minpp",
   fun.upper = "maxpp",

README.md

@@ -244,7 +244,7 @@ origin:
 
 ``` r
 ggbiplot(iris_pca, axis.type = "predictive", axis.percents = FALSE) +
-  theme_biplot() +
+  theme_scaffold() +
   geom_rows_point(aes(color = Species, shape = Species)) +
   stat_rows_center(
     aes(color = Species, shape = Species),
@@ -312,7 +312,7 @@ monograph
 [*Biplots*](https://www.google.com/books/edition/Biplots/lTxiedIxRpgC)
 and the textbook [*Understanding
 Biplots*](https://www.wiley.com/en-us/Understanding+Biplots-p-9781119972907)
-by John C. Gower, David J. Hand, Sugnet Gardner–Lubbe, and Niel J. Le
+by John C. Gower, David J. Hand, Sugnet Gardner–Lubbe, and Niël J. Le
 Roux, and by the volume [*Principal Components
 Analysis*](https://link.springer.com/book/10.1007/b98835) by I. T.
 Jolliffe.

README.rmd

@@ -135,7 +135,7 @@ For legibility, the axes are limited to the data range and offset from the origi
 
 ```{r predictive biplot}
 ggbiplot(iris_pca, axis.type = "predictive", axis.percents = FALSE) +
-  theme_biplot() +
+  theme_scaffold() +
   geom_rows_point(aes(color = Species, shape = Species)) +
   stat_rows_center(
     aes(color = Species, shape = Species),
@@ -167,7 +167,7 @@ This package was originally inspired by the **ggbiplot** extension developed by
 The motivation to unify a variety of ordination methods came from several books and articles by [Michael Greenacre](https://www.fbbva.es/microsite/multivariate-statistics/resources.html), in particular [_Biplots in Practice_](https://www.fbbva.es/microsite/multivariate-statistics/resources.html#biplots).
 Several answers at CrossValidated, in particular by [amoeba](https://stats.stackexchange.com/users/28666/amoeba) and [ttnphns](https://stats.stackexchange.com/users/3277/ttnphns), provided theoretical insights and informed design choices.
 Thomas Lin Pedersen's [**tidygraph**](https://github.com/thomasp85/tidygraph) prequel to **ggraph** finally induced the shift from the downstream generation of scatterplots to the upstream handling and manipulating of models.
-Additional design elements and features have been informed by the monograph [_Biplots_](https://www.google.com/books/edition/Biplots/lTxiedIxRpgC) and the textbook [_Understanding Biplots_](https://www.wiley.com/en-us/Understanding+Biplots-p-9781119972907) by John C. Gower, David J. Hand, Sugnet Gardner--Lubbe, and Niel J. Le Roux, and by the volume [_Principal Components Analysis_](https://link.springer.com/book/10.1007/b98835) by I. T. Jolliffe.
+Additional design elements and features have been informed by the monograph [_Biplots_](https://www.google.com/books/edition/Biplots/lTxiedIxRpgC) and the textbook [_Understanding Biplots_](https://www.wiley.com/en-us/Understanding+Biplots-p-9781119972907) by John C. Gower, David J. Hand, Sugnet Gardner--Lubbe, and Niël J. Le Roux, and by the volume [_Principal Components Analysis_](https://link.springer.com/book/10.1007/b98835) by I. T. Jolliffe.
 
 ### exposition
 

inst/examples/ex-stat-rule-glass.r

@@ -40,7 +40,7 @@ glass_lda %>%
   ggbiplot() +
   theme_bw() + theme_biplot() +
   geom_rows_point(aes(shape = grouping, alpha = discriminant)) +
-  stat_cols_rule(aes(label = name), color = "#888888", num = 8L,
+  stat_cols_rule(aes(label = name), geom = "axis", color = "#888888", num = 8L,
                  fun.offset = \(x) minabspp(x, p = .1),
                  text_size = 2.5, text_dodge = .025) +
   scale_shape_manual(values = c(16L, 17L, 15L, 18L)) +

man-roxygen/ref-gardner2001.r

@@ -0,0 +1,6 @@
+#' @references
+#'
+#' Gardner S (2001) _Extensions of biplot methodology to discriminant analysis
+#' with applications of non-parametric principal components_. PhD thesis,
+#' Stellenbosch University. <http://hdl.handle.net/10019.1/52264>
+#'