Merge pull request #22 from grantmcdermott/cran

CRAN v0.1.0 submission

.Rbuildignore

@@ -7,3 +7,5 @@
 ^docs$
 ^pkgdown$
 ^SCRATCH$
+^cran-comments\.md$
+^CRAN-SUBMISSION$

.gitignore

@@ -4,3 +4,5 @@
 docs
 inst/doc
 SCRATCH/
+**/*/.DS_Store
+.DS_Store

CRAN-SUBMISSION

@@ -0,0 +1,3 @@
+Version: 0.1.0
+Date: 2025-01-15 02:46:58 UTC
+SHA: f5833ac6b32f3e95843b0dc9c44aff7e1b4c1dc3

DESCRIPTION

@@ -1,11 +1,12 @@
 Package: parttree
-Title: Visualise simple decision tree partitions
-Version: 0.0.1.9006
+Title: Visualize Simple 2-D Decision Tree Partitions
+Version: 0.1.0
+Date: 2025-10-14
 Authors@R: c(
     person(given = "Grant",
            family = "McDermott",
            role = c("aut", "cre"),
-           email = "grantmcd@uoregon.edu",
+           email = "gmcd@amazon.com",
            comment = c(ORCID = "0000-0001-7883-8573")),
     person(given = "Achim",
            family = "Zeileis",
@@ -22,14 +23,20 @@ Authors@R: c(
            role = "ctb", 
            email = "julia.silge@gmail.com", 
            comment = c(ORCID = "0000-0002-3671-836X")))
-Description: Simple functions for plotting 2D decision tree partition plots.
+Description: Visualize the partitions of simple decision trees, involving one or
+    two predictors, on the scale of the original data. Provides an intuitive
+    alternative to traditional tree diagrams, by visualizing how a decision tree
+    divides the predictor space in a simple 2D plot alongside the original data.
+    The 'parttree' package supports both classification and regression trees
+    from 'rpart' and 'partykit', as well as trees produced by popular frontend
+    systems like 'tidymodels' and 'mlr3'. Visualization methods are provided for
+    both base R graphics and 'ggplot2'.
 License: MIT + file LICENSE
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.3.2
 LazyData: true
-URL: https://github.com/grantmcdermott/parttree,
-    http://grantmcdermott.com/parttree
+URL: https://grantmcdermott.com/parttree/
 BugReports: https://github.com/grantmcdermott/parttree/issues
 Imports:
     graphics,

NEWS.md

@@ -1,12 +1,12 @@
-# parttree 0.0.1.9006
+# parttree 0.1.0
 
-To be released as 0.1.0
+Our first CRAN submission. 🎉🎉
 
 #### Breaking changes
 
-* Move ggplot2 to Enhances, following the addition of native (base R)
-`plot.parttree` method. The `geom_parttree()` function now checks whether
-ggplot2 is available on the user's system before executing any code. (#18)
+* Move ggplot2 to Enhances, following the addition of a dedicated (base R)
+`plot.parttree` method. The `geom_parttree()` function is still available, but
+requires that ggplot2 is already been installed on the user's system. (#18)
 * The `flipaxes` argument has been renamed to `flip`, e.g.
 `parttree(..., flip = TRUE)`. (#18)
 

R/geom_parttree.R

@@ -20,6 +20,7 @@
 #'   plot layers, note that the data input for `geom_parttree()` (i.e. decision
 #'   tree object) must assigned in the layer itself; not in the initialising
 #'   [ggplot2::ggplot()] call. See Examples.
+#' @returns A \code{\link[ggplot2]{ggplot}}  layer.
 #' @section Aesthetics:
 #' \code{geom_parttree()} aims to "work-out-of-the-box" with minimal input from
 #' the user's side, apart from specifying the data object. This includes taking
@@ -42,37 +43,39 @@
 #'
 #' library(parttree) # this package
 #' library(rpart)    # decision trees
-#'
-#' ### Simple decision tree (max of two predictor variables)
+#' \dontshow{data.table::setDTthreads(2)}
+#' #
+#' ## Simple decision tree (max of two predictor variables)
 #'
 #' iris_tree = rpart(Species ~ Petal.Length + Petal.Width, data=iris)
 #'
-#' ## Plot with original iris data only
+#' # Plot with original iris data only
 #' p = ggplot(data = iris, aes(x = Petal.Length, y = Petal.Width)) +
 #'   geom_point(aes(col = Species))
 #'
-#' ## Add tree partitions to the plot (borders only)
+#' # Add tree partitions to the plot (borders only)
 #' p + geom_parttree(data = iris_tree)
 #'
-#' ## Better to use fill and highlight predictions
+#' # Better to use fill and highlight predictions
 #' p + geom_parttree(data = iris_tree, aes(fill = Species), alpha=0.1)
 #'
-#' ## To drop the black border lines (i.e. fill only)
+#' # To drop the black border lines (i.e. fill only)
 #' p + geom_parttree(data = iris_tree, aes(fill = Species), col = NA, alpha = 0.1)
 #'
-#'
-#' ### Example with plot orientation mismatch
+#' #
+#' ## Example with plot orientation mismatch
 #'
 #' p2 = ggplot(iris, aes(x=Petal.Width, y=Petal.Length)) +
 #'   geom_point(aes(col=Species))
 #'
-#' ## Oops
+#' # Oops
 #' p2 + geom_parttree(data = iris_tree, aes(fill=Species), alpha = 0.1)
 #'
-#' ## Fix with 'flip = TRUE'
-#'
+#' # Fix with 'flip = TRUE'
+#' p2 + geom_parttree(data = iris_tree, aes(fill=Species), alpha = 0.1, flip = TRUE)
 #'
-#' ### Various front-end frameworks are also supported, e.g.:
+#' #
+#' ## Various front-end frameworks are also supported, e.g.:
 #'
 #' # install.packages("parsnip")
 #' library(parsnip)
@@ -84,10 +87,11 @@
 #'
 #' p + geom_parttree(data = iris_tree_parsnip, aes(fill=Species), alpha = 0.1)
 #'
+#' #
+#' ## Trees with continuous independent variables are also supported.
 #'
-#' ### Trees with continuous independent variables are also supported. But you
-#' ### may need to adjust (or switch off) the fill legend to match the original
-#' ### data, e.g.:
+#' # Note: you may need to adjust (or switch off) the fill legend to match the
+#' # original data, e.g.:
 #'
 #' iris_tree_cont = rpart(Petal.Length ~ Sepal.Length + Petal.Width, data=iris)
 #' p3 = ggplot(data = iris, aes(x = Petal.Width, y = Sepal.Length)) +
@@ -98,10 +102,10 @@
 #'   geom_point(aes(col = Petal.Length)) +
 #'   theme_minimal()
 #'
-#' ## Legend scales don't quite match here:
+#' # Legend scales don't quite match here:
 #' p3
 #'
-#' ## Better to scale fill to the original data
+#' # Better to scale fill to the original data
 #' p3 + scale_fill_continuous(limits = range(iris$Petal.Length))
 #'
 #' @seealso [plot.parttree()], which provides an alternative plotting method using base R graphics.

R/parttree.R

@@ -15,6 +15,14 @@
 #'   data frame? The default behaviour is for the first split variable in the
 #'   tree to take the "y" slot, and any second split variable to take the "x"
 #'   slot. Setting to `TRUE` switches these around.
+#'
+#'   _Note:_ This argument is primarily useful when it passed via
+#'   [geom_parttree] to ensure correct axes orientation as part of a `ggplot2`
+#'   visualization (see [geom_parttree] Examples). We do not expect users to
+#'   call `parttree(..., flip = TRUE)` directly. Similarly, to switch axes
+#'   orientation for the native (base graphics) [plot.parttree] method, we
+#'   recommend calling `plot(..., flip = TRUE)` rather than flipping the
+#'   underlying `parttree` object.
 #' @seealso [plot.parttree], [geom_parttree], \code{\link[rpart]{rpart}},
 #'   \code{\link[partykit]{ctree}} [partykit::ctree].
 #' @returns A data frame comprising seven columns: the leaf node, its path, a
@@ -26,7 +34,8 @@
 #' @export
 #' @examples
 #' library("parttree")
-#'
+#' \dontshow{data.table::setDTthreads(2)}
+#' #
 #' ## rpart trees
 #'
 #' library("rpart")
@@ -45,16 +54,17 @@
 #' # customize further by passing extra options to (tiny)plot
 #' plot(
 #'    rp_pt,
-#'    border  = NA, # no partition borders
-#'    pch     = 19, # filled points
-#'    alpha   = 0.6, # point transparency
-#'    grid    = TRUE, # background grid
-#'    palette = "classic", # new colour palette
-#'    xlab    = "Topmost vertebra operated on", # custom x title
-#'    ylab    = "Patient age (months)", # custom y title
+#'    border  = NA,                                     # no partition borders
+#'    pch     = 16,                                     # filled points
+#'    alpha   = 0.6,                                    # point transparency
+#'    grid    = TRUE,                                   # background grid
+#'    palette = "classic",                              # new colour palette
+#'    xlab    = "Topmost vertebra operated on",         # custom x title
+#'    ylab    = "Patient age (months)",                 # custom y title
 #'    main    = "Tree predictions: Kyphosis recurrence" # custom title
 #' )
 #'
+#' #
 #' ## conditional inference trees from partyit
 #'
 #' library("partykit")
@@ -66,10 +76,12 @@
 #' rp2 = as.party(rp)
 #' parttree(rp2)
 #'
+#' #
 #' ## various front-end frameworks are also supported, e.g.
 #'
 #' # tidymodels
 #'
+#' # install.packages("parsnip")
 #' library(parsnip)
 #'
 #' decision_tree() |>
@@ -81,6 +93,7 @@
 #'
 #' # mlr3 (NB: use `keep_model = TRUE` for mlr3 learners)
 #'
+#' # install.packages("mlr3")
 #' library(mlr3)
 #'
 #' task_iris = TaskClassif$new("iris", iris, target = "Species")
@@ -157,15 +170,12 @@ parttree.rpart =
     if (length(vars)==2) {
       ## special case we can assume is likely wrong, notwithstanding ability to still flip axes
       if (vars[1]=="y" & vars[2]=="x") vars = rev(vars)
-    }
-    if (flip) {
-      vars = rev(vars)
+    } else if (length(vars)==1) {
       ## Handle edge cases with only 1 level
-      if (length(vars)==1) {
-        missing_var = setdiff(attr(tree$terms, 'term.labels'), vars)
-        vars = c(missing_var, vars)
-      }
+      missing_var = setdiff(attr(tree$terms, 'term.labels'), vars)
+      vars = c(missing_var, vars)
     }
+    if (flip) vars = rev(vars)
 
     part_coords =
       part_dt[
@@ -242,6 +252,7 @@ parttree.rpart =
       response = y_var,
       call = orig_call,
       na.action = orig_na.action,
+      flip = flip,
       raw_data = raw_data
       )
 
@@ -472,6 +483,7 @@ parttree.constparty =
       response = my,
       call = NULL,
       na.action = NULL,
+      flip = flip,
       raw_data = substitute(tree$data) # Or, partykit::model_frame_rpart?
       )
 

R/plot.R

@@ -35,11 +35,20 @@ plot.parttree = function(
     add = FALSE,
     ...
     ) {
+    dots = list(...)
     object = x
-    xvar = attr(object, "parttree")[["xvar"]]
-    yvar = attr(object, "parttree")[["yvar"]]
-    xrange = attr(object, "parttree")[["xrange"]]
-    yrange = attr(object, "parttree")[["yrange"]]
+    flip_data = isTRUE(attr(object, "parttree")[["flip"]])
+    if (flip_data) {
+      xvar = attr(object, "parttree")[["yvar"]]
+      yvar = attr(object, "parttree")[["xvar"]]
+      xrange = attr(object, "parttree")[["yrange"]]
+      yrange = attr(object, "parttree")[["xrange"]]
+    } else {
+      xvar = attr(object, "parttree")[["xvar"]]
+      yvar = attr(object, "parttree")[["yvar"]]
+      xrange = attr(object, "parttree")[["xrange"]]
+      yrange = attr(object, "parttree")[["yrange"]]
+    }
     response = attr(object, "parttree")[["response"]]
     raw_data = attr(object, "parttree")[["raw_data"]]
     orig_call = attr(object, "parttree")[["call"]]
@@ -109,10 +118,17 @@ plot.parttree = function(
     # Grab the plot corners and adjust the partition limits
     if (isTRUE(expand)) {
         corners = par("usr")
-        object$xmin[xmin_idxr] = corners[1]
-        object$xmax[xmax_idxr] = corners[2]
-        object$ymin[ymin_idxr] = corners[3]
-        object$ymax[ymax_idxr] = corners[4]
+        if (isTRUE(dots[["flip"]])) {
+          object$xmin[xmin_idxr] = corners[3]
+          object$xmax[xmax_idxr] = corners[4]
+          object$ymin[ymin_idxr] = corners[1]
+          object$ymax[ymax_idxr] = corners[2]
+        } else {
+          object$xmin[xmin_idxr] = corners[1]
+          object$xmax[xmax_idxr] = corners[2]
+          object$ymin[ymin_idxr] = corners[3]
+          object$ymax[ymax_idxr] = corners[4]
+        }
     }
 
 

README.Rmd

@@ -16,6 +16,8 @@ knitr::opts_chunk$set(
 # parttree <a href='https://grantmcdermott.com/parttree/'><img src='man/figures/hex.png' align="right" width="120" /></a>
 
 <!-- badges: start -->
+[![CRAN status](https://www.r-pkg.org/badges/version/parttree)](https://CRAN.R-project.org/package=parttree)
+[![R-universe status badge](https://grantmcdermott.r-universe.dev/badges/parttree)](https://grantmcdermott.r-universe.dev)
 [![R-CMD-check](https://github.com/grantmcdermott/parttree/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/grantmcdermott/parttree/actions/workflows/R-CMD-check.yaml)
 [![Docs](https://img.shields.io/badge/docs-homepage-blue.svg)](https://grantmcdermott.com/parttree/index.html)
 <!-- badges: end -->
@@ -27,8 +29,14 @@ package provides visualization methods for both base R graphics (via
 
 ## Installation
 
-This package is not on CRAN yet, but can be installed from
-[r-universe](https://grantmcdermott.r-universe.dev/parttree):
+The stable version of **parttree** is available on CRAN.
+
+``` r
+install.packages("parttree")
+```
+
+Or, you can grab the latest development version from
+[R-universe](https://grantmcdermott.r-universe.dev/parttree).
 
 ``` r
 install.packages("parttree", repos = "https://grantmcdermott.r-universe.dev")
@@ -60,15 +68,15 @@ Customize your plots by passing additional arguments:
 
 ```{r quickstart2}
 plot(
-  fit_pt,
-  border  = NA, # no partition borders
-  pch     = 19, # filled points
-  alpha   = 0.6, # point transparency
-  grid    = TRUE, # background grid
-  palette = "classic", # new colour palette
-  xlab    = "Topmost vertebra operated on", # custom x title
-  ylab    = "Patient age (months)", # custom y title
-  main    = "Tree predictions: Kyphosis recurrence" # custom title
+   fit_pt,
+   border  = NA,                                     # no partition borders
+   pch     = 19,                                     # filled points
+   alpha   = 0.6,                                    # point transparency
+   grid    = TRUE,                                   # background grid
+   palette = "classic",                              # new colour palette
+   xlab    = "Topmost vertebra operated on",         # custom x title
+   ylab    = "Patient age (months)",                 # custom y title
+   main    = "Tree predictions: Kyphosis recurrence" # custom title
 )
 ```