Merge my fork?

.Rbuildignore

@@ -2,3 +2,11 @@
 ^\.Rproj\.user$
 ^README\.Rmd$
 ^README-.*\.png$
+examples/
+discussion/
+^cran-comments\.md$
+docs/
+pkgdown/
+_pkgdown.yaml
+^CRAN-SUBMISSION$
+dev/

DESCRIPTION

@@ -1,17 +1,37 @@
 Package: ggbiplot
 Type: Package
-Title: A ggplot2 based biplot
-Version: 0.55
-Date: 2011-10-23
-Author: Vincent Q. Vu <[email protected]>
-Maintainer: Vincent Q. Vu <[email protected]>
-Description: A ggplot2 based biplot.  It provides a drop-in
-    replacement for biplot.princomp(). It implements a
-    biplot and scree plot using ggplot2.
-Depends:
-    ggplot2, plyr, scales, grid
+Title: A Grammar of Graphics Implementation of Biplots
+Version: 0.6.5
+Date: 2025-09-16
+Authors@R: c(
+    person(c("Vincent", "Q."), "Vu", , "[email protected]", role = c("aut"),
+            comment = c(ORCID = "0000-0002-4689-0497")),
+    person("Michael", "Friendly", , "[email protected]", role = c("aut", "cre"),
+           comment = c(ORCID = "0000-0002-3237-0941")),
+    person("Aghasi", "Tavadyan", ,  "[email protected]", role = "ctb")
+           )
+Description: A 'ggplot2' based implementation of biplots, giving a representation of a dataset in
+    a two dimensional space accounting for the greatest variance, together with variable vectors
+    showing how the data variables relate to this space. It provides a 
+    replacement for stats::biplot(), but with many enhancements to control the analysis and
+    graphical display. It implements 
+    biplot and scree plot methods which can be used with the results of prcomp(), princomp(),
+    FactoMineR::PCA(), ade4::dudi.pca() or MASS::lda() and can be customized using 'ggplot2' techniques.
+Depends: 
+    R (>= 4.1.0),
+    ggplot2
+Imports:
+    scales
+Suggests: 
+    corrplot,
+    dplyr,
+    MASS,
+    broom,
+    tidyr
 License: GPL-2
-URL: http://github.com/vqv/ggbiplot
-Collate:
-    'ggbiplot.r'
-    'ggscreeplot.r'
+Encoding: UTF-8
+Language: en-US
+URL: https://github.com/friendly/ggbiplot, https://friendly.github.io/ggbiplot/
+BugReports: https://github.com/friendly/ggbiplot/issues
+RoxygenNote: 7.3.2
+LazyData: true

NAMESPACE

@@ -1,2 +1,11 @@
+# Generated by roxygen2: do not edit by hand
+
+export(get_SVD)
 export(ggbiplot)
 export(ggscreeplot)
+export(reflect)
+import(ggplot2)
+importFrom(scales,muted)
+importFrom(stats,predict)
+importFrom(stats,qchisq)
+importFrom(stats,var)

NEWS.md

@@ -0,0 +1,59 @@
+## Version 0.6.5
+
+- Now Depends R (>= 4.1.0) to handle |>
+- Added lda() objects to those handled by reflect()
+- Illustrate reflection in ggbiplot examples
+
+## Version 0.6.4
+
+- Documented solution to 'scale_color_discrete() produces two legends' #2
+- Fix glitch with axis labels
+- `ggbiplot` gains `geom.ind` and `geom.var` arguments for more flexlible handling of the geometries used to display the
+observation points and variable labels.
+
+## Version 0.6.3
+
+- Fix axis label spacing
+- `ggbiplot` gains a `clip` argument to control if points, labels, etc. are clipped to the axis limits.
+
+## Version 0.6.2
+
+This is a modest upgrade to the initial release, adding a number of features.
+
+- corrected small bugs in `ggbiplot() and `ggscreeplot()`
+- `reflect()` now also reflects supplementary variables from `FactoMineR::PCA()`
+- added support for `ade4::dudi.pca()`
+- `ggbiplot()` gains an `axis.title` argument
+- `ggscreeplot()` gains `color`, `shape`, `linetype` and `linewidth` arguments
+- Added `get_SVD()` intended the simplify the interface to various PCA functions.
+- Now use `get_SVD()` in `ggbiplot()` and `ggscreeplot()`
+- Extend some examples, requiring Depends:ggplot2
+
+## Version 0.6.1
+
+- Revised documentation for `ggscreeplot()`
+- `data(crime)` used in README giving a more complete example.
+- Fixed links reported as 301s
+- Published pkgdown site
+
+## Version 0.6.0
+
+- Use `geom_polygon()` rather than `geom_path()` for ellipses to allow them to be filled.
+- Added `var.factor` argument to expand or reflect the variable vectors
+- Moved points/labels code earlier so ellipses and variable vectors are not obscured
+- Replaced internal calculation of ellipses with `stat_ellipse()`
+- Now allow ellipses to be filled (`geom_polygon()`) or unfilled (`geom_path()`)
+- Added hex logo
+- Added iris example to README
+- Removed Imports: dplyr, purrr as no longer needed
+
+## Version 0.56
+
+- Fixed many documentation errors and warnings
+- Use roxygen2 for documentation
+- added `varname.color` to replace fixed `muted("red")`
+- tweaked arrow style
+- increased default thickness of variable vectors
+- add `point.size` argument
+
+

R/crime.R

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+#' @title U. S. Crimes
+#' @name crime
+#' @description 
+#' This dataset gives rates of occurrence (per 100,000 people)
+#' various serious crimes in each of the 50 U. S. states, 
+#' originally from the United States Statistical Abstracts (1970). The data
+#' were analyzed by John Hartigan (1975) in his book \emph{Clustering Algorithms} and 
+#' were later reanalyzed by Friendly (1991). 
+#' 
+#' @docType data
+#' @usage data(crime)
+#' @format 
+#' A data frame with 50 observations on the following 10 variables.
+#' \describe{
+#'   \item{\code{state}}{state name, a character vector}
+#'   \item{\code{murder}}{a numeric vector}
+#'   \item{\code{rape}}{a numeric vector}
+#'   \item{\code{robbery}}{a numeric vector}
+#'   \item{\code{assault}}{a numeric vector}
+#'   \item{\code{burglary}}{a numeric vector}
+#'   \item{\code{larceny}}{a numeric vector}
+#'   \item{\code{auto}}{auto thefts, a numeric vector}
+#'   \item{\code{st}}{state abbreviation, a character vector}
+#'   \item{\code{region}}{region of the U.S., a factor with levels \code{Northeast} \code{South} \code{North Central} \code{West}}
+#' }
+#' @keywords dataset
+#' @source 
+#' The data are originally from the United States Statistical Abstracts (1970).
+#' This dataset also appears in the SAS/Stat Sample library,
+#' \emph{Getting Started Example for PROC PRINCOMP}, 
+#' \url{https://support.sas.com/documentation/onlinedoc/stat/ex_code/131/princgs.html}, from which the
+#' current copy was derived.
+#' @references 
+#' Friendly, M. (1991). \emph{SAS System for Statistical Graphics}. SAS Institute.
+#'
+#' Hartigan, J. A. (1975). \emph{Clustering Algorithms}. John Wiley and Sons.
+
+#' @examples 
+#' data(crime)
+#' library(ggplot2)
+#' crime.pca <- 
+#'   crime |> 
+#'   dplyr::select(where(is.numeric)) |>
+#'   prcomp(scale. = TRUE)
+#'
+#' ggbiplot(crime.pca,
+#'      labels = crime$st ,
+#'      circle = TRUE,
+#'      varname.size = 4,
+#'      varname.color = "red") +
+#'  theme_minimal(base_size = 14) 
+
+NULL

R/get_SVD.R

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+#' Extract the SVD components from a PCA-like object
+#'
+#' @description
+#' 
+#' Biplots are based on the Singular Value Decomposition, which for a data matrix is
+#' \deqn{\mathbf{X} / \sqrt{n} = \mathbf{U} \mathbf{D} \mathbf{V}^T}
+#' but these are computed and returned in quite different
+#' forms by various PCA-like methods. This function provides a common interface, returning
+#' the components with standard names.
+#'
+#'
+#' @param pcobj  an object returned by \code{\link[stats]{prcomp}}, \code{\link[stats]{princomp}}, 
+#'               \code{\link[FactoMineR]{PCA}}, \code{\link[ade4]{dudi.pca}}, or \code{\link[MASS]{lda}}
+#'
+#' @return  A list of four elements
+#'   \describe{
+#'     \item{n}{The sample size on which the analysis was based}
+#'     \item{U}{Left singular vectors, giving observation scores}
+#'     \item{D}{vector of singular values, the diagonal elements of the matrix \eqn{\mathbf{D}}, which are also the square roots
+#'         of the eigenvalues of \eqn{\mathbf{X} \mathbf{X}'}}
+#'     \item{V}{Right singular vectors, giving variable loadings}
+#'   }
+#' @export
+#'
+#' @examples
+#' data(crime)
+#' crime.pca <- 
+#'   crime |> 
+#'   dplyr::select(where(is.numeric)) |>
+#'   prcomp(scale. = TRUE)
+#' 
+#' crime.svd <- get_SVD(crime.pca)
+#' names(crime.svd)
+#  # singular values
+#' crime.svd$D
+#'
+get_SVD <- function(pcobj) {
+
+# Recover the SVD from a PCA-like object
+  if(inherits(pcobj, 'prcomp')){
+    n <- nrow(pcobj$x)
+    D <- pcobj$sdev
+    U <- sweep(pcobj$x, 2, 1 / (D * sqrt(n)), FUN = '*')
+    V <- pcobj$rotation
+  } else if(inherits(pcobj, 'princomp')) {
+    n <- pcobj$n.obs
+    D <- pcobj$sdev
+    U <- sweep(pcobj$scores, 2, 1 / (D * sqrt(n)), FUN = '*')
+    V <- pcobj$loadings
+  } else if(inherits(pcobj, 'PCA')) {
+    n <- nrow(pcobj$call$X)
+    D <- unlist(sqrt(pcobj$eig)[1])
+    U <- sweep(pcobj$ind$coord, 2, 1 / (D * sqrt(n)), FUN = '*')
+    V <- sweep(pcobj$var$coord, 2, sqrt(pcobj$eig[1:ncol(pcobj$var$coord),1]), FUN="/")
+  } else if(inherits(pcobj, "lda")) {
+    n <- pcobj$N
+    D <- pcobj$svd
+    U <- predict(pcobj)$x/sqrt(n)
+    V <- pcobj$scaling
+  } else if(inherits(pcobj, 'pca') & inherits(pcobj, 'dudi')){
+    n <- nrow(pcobj$tab)
+    D <- sqrt(pcobj$eig)
+    U <- pcobj$li
+    V <- pcobj$co
+  }
+  else {
+    stop('Expected a object of class "prcomp", "princomp", "PCA", c("pca", "dudi") or "lda"')
+  }
+
+  list(n=n, U=U, D=D, V=V)
+
+}