diff --git a/404.html b/404.html
index 544ef66..eba6173 100644
--- a/404.html
+++ b/404.html
@@ -20,7 +20,7 @@
 
     <a class="navbar-brand me-2" href="https://grantmcdermott.com/parttree/index.html">parttree</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.1.9006</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/LICENSE-text.html b/LICENSE-text.html
index ad4658f..40465d4 100644
--- a/LICENSE-text.html
+++ b/LICENSE-text.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">parttree</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.1.9006</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/LICENSE.html b/LICENSE.html
index 470d0cf..da9c5c3 100644
--- a/LICENSE.html
+++ b/LICENSE.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">parttree</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.1.9006</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/index.html b/articles/index.html
index 7e0fec2..cc8da51 100644
--- a/articles/index.html
+++ b/articles/index.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">parttree</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.1.9006</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/parttree-art.html b/articles/parttree-art.html
index 483af8d..ca5534a 100644
--- a/articles/parttree-art.html
+++ b/articles/parttree-art.html
@@ -20,7 +20,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">parttree</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.1.9006</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -73,12 +73,15 @@ <h2 id="background">Background<a class="anchor" aria-label="anchor" href="#backg
 the same basic ideas using <strong>parttree</strong> and a few friends.
 Here are the packages that we’ll be using.</p>
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/grantmcdermott/parttree" class="external-link">parttree</a></span><span class="op">)</span> <span class="co"># This package</span></span>
+<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://grantmcdermott.com/parttree/">parttree</a></span><span class="op">)</span> <span class="co"># This package</span></span>
 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/bethatkinson/rpart" class="external-link">rpart</a></span><span class="op">)</span>    <span class="co"># For decision trees</span></span>
 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://docs.ropensci.org/magick/" class="external-link">magick</a></span><span class="op">)</span>   <span class="co"># For reading and manipulating images</span></span>
 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://asgr.github.io/imager/" class="external-link">imager</a></span><span class="op">)</span>   <span class="co"># Another image library, with some additional features</span></span>
 <span></span>
 <span><span class="va">op</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mar <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">0</span>,<span class="fl">0</span>,<span class="fl">0</span><span class="op">)</span><span class="op">)</span> <span class="co"># Remove plot margins</span></span></code></pre></div>
+<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">magick</span><span class="fu">:::</span><span class="fu">magick_threads</span><span class="op">(</span><span class="fl">2</span><span class="op">)</span></span>
+<span><span class="co">#&gt; [1] 2</span></span></code></pre></div>
 <p>While the exact details will vary depending on the image at hand, the
 essential recipe for this type of art abstraction is as follows:</p>
 <ol style="list-style-type: decimal">
@@ -116,7 +119,7 @@ <h2 id="example-1-peales-portrait-of-rosalba">Example 1: Peale’s “Portrait o
 image. Without going into the details, just know that the former is
 better for cropping the image the way I want, while the latter is better
 for R’s in-memory manipulation.&lt;/p&gt;"><sup>2</sup></a></p>
-<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Download the image</span></span>
 <span><span class="va">rosalba</span> <span class="op">=</span> <span class="fu"><a href="https://docs.ropensci.org/magick/reference/editing.html" class="external-link">image_read</a></span><span class="op">(</span><span class="st">"https://upload.wikimedia.org/wikipedia/commons/a/aa/Rembrandt_Peale_-_Portrait_of_Rosalba_Peale_-_Google_Art_Project.jpg"</span><span class="op">)</span></span>
 <span></span>
@@ -135,7 +138,7 @@ <h2 id="example-1-peales-portrait-of-rosalba">Example 1: Peale’s “Portrait o
 <p>With our cropped image in hand, let’s walk through the 4-step recipe
 from above.</p>
 <p><strong>Step 1.</strong> Convert the image into a data frame.</p>
-<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Coerce to data frame</span></span>
 <span><span class="va">rosalba_df</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/as.data.frame.html" class="external-link">as.data.frame</a></span><span class="op">(</span><span class="va">rosalba</span><span class="op">)</span></span>
 <span></span>
@@ -153,7 +156,7 @@ <h2 id="example-1-peales-portrait-of-rosalba">Example 1: Peale’s “Portrait o
 <p><strong>Step 2.</strong> Split the image by RGB colour channel. This
 is the <code>cc</code> column above, where 1=Red, 2=Green, and
 3=Blue.</p>
-<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">rosalba_ccs</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/split.html" class="external-link">split</a></span><span class="op">(</span><span class="va">rosalba_df</span>, <span class="va">rosalba_df</span><span class="op">$</span><span class="va">cc</span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># We have a list of three DFs by colour channel. Uncomment if you want to see:</span></span>
@@ -165,7 +168,7 @@ <h2 id="example-1-peales-portrait-of-rosalba">Example 1: Peale’s “Portrait o
 predictions) and trimming each tree to a maximum depth of 30 nodes. The
 next code chunk takes about 15 seconds to run on my laptop, but should
 be much quicker if you downloaded a lower-res image.</p>
-<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">## Start creating regression tree for each color channel. We'll adjust some</span></span>
 <span><span class="co">## control parameters to give us the "right" amount of resolution in the final</span></span>
 <span><span class="co">## plots.</span></span>
@@ -178,7 +181,7 @@ <h2 id="example-1-peales-portrait-of-rosalba">Example 1: Peale’s “Portrait o
 construct our abstracted art piece. I was bit glib about it earlier,
 since it really involves a few sub-steps. First, let’s grab the
 predictions for each of our trees.</p>
-<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">pred</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">trees</span>, <span class="va">predict</span><span class="op">)</span> <span class="co"># get predictions for each tree</span></span></code></pre></div>
 <p>We’re going to use these predictions to draw the abstracted
 (downscaled) version of our image.<a class="footnote-ref" tabindex="0" data-bs-toggle="popover" data-bs-content="&lt;p&gt;Remember: the tree model predictions yield “average”
@@ -188,7 +191,7 @@ <h2 id="example-1-peales-portrait-of-rosalba">Example 1: Peale’s “Portrait o
 original (pre-split) <code>rosalba_df</code> data frame. We can then
 coerce the data frame back into a <code>cimg</code> object, which comes
 with a bunch of nice plotting methods.</p>
-<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># The pred object is a list, so we convert it to a vector before overwriting the</span></span>
 <span><span class="co"># value column of the original data frame</span></span>
 <span><span class="va">rosalba_df</span><span class="op">$</span><span class="va">value</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/do.call.html" class="external-link">do.call</a></span><span class="op">(</span><span class="st">"c"</span>, <span class="va">pred</span><span class="op">)</span></span>
@@ -201,7 +204,7 @@ <h2 id="example-1-peales-portrait-of-rosalba">Example 1: Peale’s “Portrait o
 <strong>parttree</strong> will enter the fray, since this is what we’ll
 be using to highlight the partitioned areas of the downscaled pixels.
 Here’s how we can do it using base R graphics.</p>
-<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># get a list of parttree data frames (one for each tree)</span></span>
 <span><span class="va">pts</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">trees</span>, <span class="va">parttree</span><span class="op">)</span></span>
 <span></span>
@@ -226,7 +229,7 @@ <h2 id="example-1-peales-portrait-of-rosalba">Example 1: Peale’s “Portrait o
 <span><span class="co">#&gt; NULL</span></span></code></pre>
 <p>We can achieve the same effect with <strong>ggplot2</strong> if you
 prefer to use that.</p>
-<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://ggplot2.tidyverse.org" class="external-link">ggplot2</a></span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/annotation_raster.html" class="external-link">annotation_raster</a></span><span class="op">(</span><span class="va">pred_img</span>,  ymin<span class="op">=</span><span class="op">-</span><span class="cn">Inf</span>, ymax<span class="op">=</span><span class="cn">Inf</span>, xmin<span class="op">=</span><span class="op">-</span><span class="cn">Inf</span>, xmax<span class="op">=</span><span class="cn">Inf</span><span class="op">)</span> <span class="op">+</span></span>
@@ -248,11 +251,11 @@ <h2 id="example-2-parttree-logo">Example 2: parttree logo<a class="anchor" aria-
 <p>Here are the main image conversion, modeling, and prediction steps.
 These follow the same recipe that we saw in the previous portrait
 example, so I won’t repeat my explanations.</p>
-<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">bonzai</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/imager/man/load.image.html" class="external-link">load.image</a></span><span class="op">(</span><span class="st">"https://upload.wikimedia.org/wikipedia/commons/thumb/0/0c/Japanese_Zelkova_bonsai_16%2C_30_April_2012.JPG/480px-Japanese_Zelkova_bonsai_16%2C_30_April_2012.JPG"</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">bonzai</span>, axes <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
 <p><img src="parttree-art_files/figure-html/bonzai-1.png" width="90%"></p>
-<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span></span>
 <span><span class="co"># Coerce to data frame</span></span>
 <span><span class="va">bonzai_df</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/as.data.frame.html" class="external-link">as.data.frame</a></span><span class="op">(</span><span class="va">bonzai</span><span class="op">)</span></span>
@@ -286,7 +289,7 @@ <h2 id="example-2-parttree-logo">Example 2: parttree logo<a class="anchor" aria-
 or adapted this function from. But it works particularly well in cases
 like this where we want the partition lines to blend in with the main
 image.&lt;/p&gt;"><sup>4</sup></a></p>
-<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">mean_cols</span> <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">dat</span><span class="op">)</span> <span class="op">{</span></span>
 <span>  <span class="va">mcols</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/tapply.html" class="external-link">tapply</a></span><span class="op">(</span><span class="va">dat</span><span class="op">$</span><span class="va">value</span>, <span class="va">dat</span><span class="op">$</span><span class="va">cc</span>, FUN <span class="op">=</span> <span class="st">"mean"</span><span class="op">)</span></span>
 <span>  <span class="va">col1</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/grDevices/rgb.html" class="external-link">rgb</a></span><span class="op">(</span><span class="va">mcols</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span>, <span class="va">mcols</span><span class="op">[</span><span class="fl">2</span><span class="op">]</span>, <span class="va">mcols</span><span class="op">[</span><span class="fl">3</span><span class="op">]</span><span class="op">)</span></span>
@@ -300,10 +303,10 @@ <h2 id="example-2-parttree-logo">Example 2: parttree logo<a class="anchor" aria-
 <span><span class="va">bonzai_mean_cols</span> <span class="op">=</span> <span class="fu">mean_cols</span><span class="op">(</span><span class="va">bonzai_df</span><span class="op">)</span></span></code></pre></div>
 <p>The penultimate step is to generate <code>parttree</code> objects
 from each of our colour channel-based trees.</p>
-<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">bonzai_pts</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">bonzai_trees</span>, <span class="va">parttree</span><span class="op">)</span></span></code></pre></div>
 <p>And now we can plot everything together.</p>
-<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">bonzai_pred_img</span>, axes <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/funprog.html" class="external-link">Map</a></span><span class="op">(</span></span>
 <span>  <span class="kw">function</span><span class="op">(</span><span class="va">pt</span>, <span class="va">cols</span><span class="op">)</span> <span class="op">{</span></span>
@@ -326,7 +329,7 @@ <h2 id="example-2-parttree-logo">Example 2: parttree logo<a class="anchor" aria-
 <span><span class="co">#&gt; NULL</span></span></code></pre>
 <p>Again, equivalent result for those prefer
 <strong>ggplot2</strong>.</p>
-<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># library(ggplot2)</span></span>
 <span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/annotation_raster.html" class="external-link">annotation_raster</a></span><span class="op">(</span><span class="va">bonzai_pred_img</span>,  ymin<span class="op">=</span><span class="op">-</span><span class="cn">Inf</span>, ymax<span class="op">=</span><span class="cn">Inf</span>, xmin<span class="op">=</span><span class="op">-</span><span class="cn">Inf</span>, xmax<span class="op">=</span><span class="cn">Inf</span><span class="op">)</span> <span class="op">+</span></span>
@@ -342,7 +345,7 @@ <h2 id="postscript">Postscript<a class="anchor" aria-label="anchor" href="#posts
 </h2>
 <p>The individual trees for each colour channel make for nice stained
 glass prints…</p>
-<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span></span>
 <span>  <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq_along</a></span><span class="op">(</span><span class="va">bonzai_pts</span><span class="op">)</span>,</span>
 <span>  <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span>
@@ -365,7 +368,7 @@ <h2 id="postscript">Postscript<a class="anchor" aria-label="anchor" href="#posts
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; [[3]]</span></span>
 <span><span class="co">#&gt; NULL</span></span></code></pre>
-<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># reset the plot margins</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span><span class="va">op</span><span class="op">)</span></span></code></pre></div>
 </div>
diff --git a/articles/parttree-intro.html b/articles/parttree-intro.html
index 731c688..92faa41 100644
--- a/articles/parttree-intro.html
+++ b/articles/parttree-intro.html
@@ -20,7 +20,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">parttree</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.1.9006</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -74,7 +74,7 @@ <h2 id="motivating-example-classifying-penguin-species">Motivating example: Clas
 dataset via the parent package (as I have here), or import it directly
 as a CSV <a href="https://vincentarelbundock.github.io/Rdatasets/csv/palmerpenguins/penguins.csv" class="external-link">here</a>.</p>
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/grantmcdermott/parttree" class="external-link">parttree</a></span><span class="op">)</span>  <span class="co"># This package</span></span>
+<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://grantmcdermott.com/parttree/">parttree</a></span><span class="op">)</span>  <span class="co"># This package</span></span>
 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/bethatkinson/rpart" class="external-link">rpart</a></span><span class="op">)</span>     <span class="co"># For fitting decisions trees</span></span>
 <span></span>
 <span><span class="co"># install.packages("palmerpenguins")</span></span>
@@ -146,7 +146,7 @@ <h2 id="motivating-example-classifying-penguin-species">Motivating example: Clas
 <span><span class="co">#&gt; 2 206.5  -Inf 43.35</span></span>
 <span><span class="co">#&gt; 3 206.5 43.35   Inf</span></span></code></pre></div>
 <p>Speaking of visualization, underneath the hood
-<code>plot.parttree</code> calls the powerful <a href="https://grantmcdermott.com/tinyplot" class="external-link"><strong>tinyplot</strong></a>
+<code>plot.parttree</code> calls the powerful <a href="https://grantmcdermott.com/tinyplot/" class="external-link"><strong>tinyplot</strong></a>
 package. All of the latter’s various customization arguments can be
 passed on to our <code>parttree</code> plot to make it look a bit nicer.
 For example:</p>
@@ -171,9 +171,9 @@ <h3 id="continuous-predictions">Continuous predictions<a class="anchor" aria-lab
 <div class="section level2">
 <h2 id="supported-model-classes">Supported model classes<a class="anchor" aria-label="anchor" href="#supported-model-classes"></a>
 </h2>
-<p>Alongside the <a href="https://CRAN.R-project.org/web/package=rpart" class="external-link"><strong>rpart</strong></a>
+<p>Alongside the <a href="https://CRAN.R-project.org/package=rpart" class="external-link"><strong>rpart</strong></a>
 model objects that we have been working with thus far,
-<strong>parttree</strong> also supports decision trees created by the <a href="https://CRAN.R-project.org/web/package=partykit" class="external-link"><strong>partykit</strong></a>
+<strong>parttree</strong> also supports decision trees created by the <a href="https://CRAN.R-project.org/package=partykit" class="external-link"><strong>partykit</strong></a>
 package. Here we see how the latter’s <code>ctree</code> (conditional
 inference tree) algorithm yields a slightly more sophisticated
 partitioning that the former’s default.</p>
@@ -185,7 +185,7 @@ <h2 id="supported-model-classes">Supported model classes<a class="anchor" aria-l
 <span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/partykit/man/ctree.html" class="external-link">ctree</a></span><span class="op">(</span><span class="va">species</span> <span class="op">~</span> <span class="va">flipper_length_mm</span> <span class="op">+</span> <span class="va">bill_length_mm</span>, data <span class="op">=</span> <span class="va">penguins</span><span class="op">)</span> <span class="op">|&gt;</span></span>
 <span>  <span class="fu"><a href="../reference/parttree.html">parttree</a></span><span class="op">(</span><span class="op">)</span> <span class="op">|&gt;</span></span>
-<span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span>pch <span class="op">=</span> <span class="fl">19</span>, palette <span class="op">=</span> <span class="st">"classic"</span>, alpha <span class="op">=</span> <span class="fl">0.5</span><span class="op">)</span></span></code></pre></div>
+<span>  <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span>pch <span class="op">=</span> <span class="fl">16</span>, palette <span class="op">=</span> <span class="st">"classic"</span>, alpha <span class="op">=</span> <span class="fl">0.5</span><span class="op">)</span></span></code></pre></div>
 <p><img src="parttree-intro_files/figure-html/penguin_ctree_plot-1.png" width="90%"></p>
 <p><strong>parttree</strong> also supports a variety of “frontend” modes
 that call <code><a href="https://rdrr.io/pkg/rpart/man/rpart.html" class="external-link">rpart::rpart()</a></code> as the underlying engine. This
diff --git a/articles/parttree-intro_files/figure-html/penguin_ctree_plot-1.png b/articles/parttree-intro_files/figure-html/penguin_ctree_plot-1.png
index 89fe6d6..5ecf987 100644
Binary files a/articles/parttree-intro_files/figure-html/penguin_ctree_plot-1.png and b/articles/parttree-intro_files/figure-html/penguin_ctree_plot-1.png differ
diff --git a/authors.html b/authors.html
index 4efe903..2aa94fb 100644
--- a/authors.html
+++ b/authors.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">parttree</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.1.9006</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,17 +60,15 @@ <h2 id="citation">Citation</h2>
       <p><small class="dont-index">Source: <a href="https://github.com/grantmcdermott/parttree/blob/main/DESCRIPTION" class="external-link"><code>DESCRIPTION</code></a></small></p>
 
       <p>McDermott G (2025).
-<em>parttree: Visualise simple decision tree partitions</em>.
-R package version 0.0.1.9006,
-http://grantmcdermott.com/parttree, <a href="https://github.com/grantmcdermott/parttree" class="external-link">https://github.com/grantmcdermott/parttree</a>.
+<em>parttree: Visualize Simple 2-D Decision Tree Partitions</em>.
+R package version 0.1.0, <a href="https://grantmcdermott.com/parttree/">https://grantmcdermott.com/parttree/</a>.
 </p>
       <pre>@Manual{,
-  title = {parttree: Visualise simple decision tree partitions},
+  title = {parttree: Visualize Simple 2-D Decision Tree Partitions},
   author = {Grant McDermott},
   year = {2025},
-  note = {R package version 0.0.1.9006,
-    http://grantmcdermott.com/parttree},
-  url = {https://github.com/grantmcdermott/parttree},
+  note = {R package version 0.1.0},
+  url = {https://grantmcdermott.com/parttree/},
 }</pre>
     </div>
 
diff --git a/index.html b/index.html
index 42d38fe..ec91e8c 100644
--- a/index.html
+++ b/index.html
@@ -5,14 +5,14 @@
 <meta charset="utf-8">
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
 <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
-<title>Visualise simple decision tree partitions • parttree</title>
+<title>Visualize Simple 2-D Decision Tree Partitions • parttree</title>
 <script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
 <link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
 <script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
 <link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
-<script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Visualise simple decision tree partitions">
-<meta name="description" content="Simple functions for plotting 2D decision tree partition plots.">
-<meta property="og:description" content="Simple functions for plotting 2D decision tree partition plots.">
+<script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Visualize Simple 2-D Decision Tree Partitions">
+<meta name="description" content="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.">
+<meta property="og:description" content="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.">
 </head>
 <body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
@@ -22,7 +22,7 @@
 
     <a class="navbar-brand me-2" href="index.html">parttree</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.1.9006</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -60,8 +60,11 @@
 <div class="section level2">
 <h2 id="installation">Installation<a class="anchor" aria-label="anchor" href="#installation"></a>
 </h2>
-<p>This package is not on CRAN yet, but can be installed from <a href="https://grantmcdermott.r-universe.dev/parttree" class="external-link">r-universe</a>:</p>
+<p>The stable version of <strong>parttree</strong> is available on CRAN.</p>
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html" class="external-link">install.packages</a></span><span class="op">(</span><span class="st">"parttree"</span><span class="op">)</span></span></code></pre></div>
+<p>Or, you can grab the latest development version from <a href="https://grantmcdermott.r-universe.dev/parttree" class="external-link">R-universe</a>.</p>
+<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html" class="external-link">install.packages</a></span><span class="op">(</span><span class="st">"parttree"</span>, repos <span class="op">=</span> <span class="st">"https://grantmcdermott.r-universe.dev"</span><span class="op">)</span></span></code></pre></div>
 </div>
 <div class="section level2">
@@ -69,9 +72,9 @@ <h2 id="quickstart">Quickstart<a class="anchor" aria-label="anchor" href="#quick
 </h2>
 <p>The <strong>parttree</strong> <a href="https://grantmcdermott.com/parttree/index.html">homepage</a> includes an introductory vignette and detailed documentation. But here’s a quickstart example using the <a href="https://search.r-project.org/CRAN/refmans/rpart/html/kyphosis.html" class="external-link">“kyphosis”</a> dataset that comes bundled with the <strong>rpart</strong> package. In this case, we are interested in predicting kyphosis recovery after spinal surgery, as a function of 1) the number of topmost vertebra that were operated, and 2) patient age.</p>
 <p>The key function is <code><a href="reference/parttree.html">parttree()</a></code>, which comes with its own plotting method.</p>
-<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/bethatkinson/rpart" class="external-link">rpart</a></span><span class="op">)</span>     <span class="co"># For the dataset and fitting decisions trees</span></span>
-<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/grantmcdermott/parttree" class="external-link">parttree</a></span><span class="op">)</span>  <span class="co"># This package</span></span>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://grantmcdermott.com/parttree/">parttree</a></span><span class="op">)</span>  <span class="co"># This package</span></span>
 <span></span>
 <span><span class="va">fit</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/rpart/man/rpart.html" class="external-link">rpart</a></span><span class="op">(</span><span class="va">Kyphosis</span> <span class="op">~</span> <span class="va">Start</span> <span class="op">+</span> <span class="va">Age</span>, data <span class="op">=</span> <span class="va">kyphosis</span><span class="op">)</span></span>
 <span></span>
@@ -80,24 +83,24 @@ <h2 id="quickstart">Quickstart<a class="anchor" aria-label="anchor" href="#quick
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fit_pt</span><span class="op">)</span></span></code></pre></div>
 <p><img src="reference/figures/README-quickstart-1.png" width="100%"></p>
 <p>Customize your plots by passing additional arguments:</p>
-<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span></span>
-<span>  <span class="va">fit_pt</span>,</span>
-<span>  border  <span class="op">=</span> <span class="cn">NA</span>, <span class="co"># no partition borders</span></span>
-<span>  pch     <span class="op">=</span> <span class="fl">19</span>, <span class="co"># filled points</span></span>
-<span>  alpha   <span class="op">=</span> <span class="fl">0.6</span>, <span class="co"># point transparency</span></span>
-<span>  grid    <span class="op">=</span> <span class="cn">TRUE</span>, <span class="co"># background grid</span></span>
-<span>  palette <span class="op">=</span> <span class="st">"classic"</span>, <span class="co"># new colour palette</span></span>
-<span>  xlab    <span class="op">=</span> <span class="st">"Topmost vertebra operated on"</span>, <span class="co"># custom x title</span></span>
-<span>  ylab    <span class="op">=</span> <span class="st">"Patient age (months)"</span>, <span class="co"># custom y title</span></span>
-<span>  main    <span class="op">=</span> <span class="st">"Tree predictions: Kyphosis recurrence"</span> <span class="co"># custom title</span></span>
+<span>   <span class="va">fit_pt</span>,</span>
+<span>   border  <span class="op">=</span> <span class="cn">NA</span>,                                     <span class="co"># no partition borders</span></span>
+<span>   pch     <span class="op">=</span> <span class="fl">19</span>,                                     <span class="co"># filled points</span></span>
+<span>   alpha   <span class="op">=</span> <span class="fl">0.6</span>,                                    <span class="co"># point transparency</span></span>
+<span>   grid    <span class="op">=</span> <span class="cn">TRUE</span>,                                   <span class="co"># background grid</span></span>
+<span>   palette <span class="op">=</span> <span class="st">"classic"</span>,                              <span class="co"># new colour palette</span></span>
+<span>   xlab    <span class="op">=</span> <span class="st">"Topmost vertebra operated on"</span>,         <span class="co"># custom x title</span></span>
+<span>   ylab    <span class="op">=</span> <span class="st">"Patient age (months)"</span>,                 <span class="co"># custom y title</span></span>
+<span>   main    <span class="op">=</span> <span class="st">"Tree predictions: Kyphosis recurrence"</span> <span class="co"># custom title</span></span>
 <span><span class="op">)</span></span></code></pre></div>
 <p><img src="reference/figures/README-quickstart2-1.png" width="100%"></p>
 <div class="section level3">
 <h3 id="ggplot2">ggplot2<a class="anchor" aria-label="anchor" href="#ggplot2"></a>
 </h3>
 <p>For <strong>ggplot2</strong> users, we offer an equivalent workflow via the <code>geom_partree()</code> visualization layer.</p>
-<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://ggplot2.tidyverse.org" class="external-link">ggplot2</a></span><span class="op">)</span> <span class="co">## Should be loaded separately</span></span>
 <span></span>
 <span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="va">kyphosis</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>x <span class="op">=</span> <span class="va">Start</span>, y <span class="op">=</span> <span class="va">Age</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
@@ -115,6 +118,7 @@ <h3 id="ggplot2">ggplot2<a class="anchor" aria-label="anchor" href="#ggplot2"></
   </main><aside class="col-md-3"><div class="links">
 <h2 data-toc-skip>Links</h2>
 <ul class="list-unstyled">
+<li><a href="https://cloud.r-project.org/package=parttree" class="external-link">View on CRAN</a></li>
 <li><a href="https://github.com/grantmcdermott/parttree/" class="external-link">Browse source code</a></li>
 <li><a href="https://github.com/grantmcdermott/parttree/issues" class="external-link">Report a bug</a></li>
 </ul>
@@ -147,6 +151,8 @@ <h2 data-toc-skip>Developers</h2>
 <div class="dev-status">
 <h2 data-toc-skip>Dev status</h2>
 <ul class="list-unstyled">
+<li><a href="https://CRAN.R-project.org/package=parttree" class="external-link"><img src="https://www.r-pkg.org/badges/version/parttree" alt="CRAN status"></a></li>
+<li><a href="https://grantmcdermott.r-universe.dev" class="external-link"><img src="https://grantmcdermott.r-universe.dev/badges/parttree" alt="R-universe status badge"></a></li>
 <li><a href="https://github.com/grantmcdermott/parttree/actions/workflows/R-CMD-check.yaml" class="external-link"><img src="https://github.com/grantmcdermott/parttree/actions/workflows/R-CMD-check.yaml/badge.svg" alt="R-CMD-check"></a></li>
 <li><a href="https://grantmcdermott.com/parttree/index.html"><img src="https://img.shields.io/badge/docs-homepage-blue.svg" alt="Docs"></a></li>
 </ul>
diff --git a/news/index.html b/news/index.html
index 39eecc5..45bb2c2 100644
--- a/news/index.html
+++ b/news/index.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">parttree</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.1.9006</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -36,27 +36,27 @@ <h1>Changelog</h1>
     </div>
 
     <div class="section level2">
-<h2 class="pkg-version" data-toc-text="0.0.1.9006" id="parttree-0019006">parttree 0.0.1.9006<a class="anchor" aria-label="anchor" href="#parttree-0019006"></a></h2>
-<p>To be released as 0.1.0</p>
+<h2 class="pkg-version" data-toc-text="0.1.0" id="parttree-010">parttree 0.1.0<a class="anchor" aria-label="anchor" href="#parttree-010"></a></h2>
+<p>Our first CRAN submission. 🎉🎉</p>
 <div class="section level5">
-<h5 id="breaking-changes-0-0-1-9006">Breaking changes<a class="anchor" aria-label="anchor" href="#breaking-changes-0-0-1-9006"></a></h5>
-<ul><li>Move ggplot2 to Enhances, following the addition of native (base R) <code>plot.parttree</code> method. The <code><a href="../reference/geom_parttree.html">geom_parttree()</a></code> function now checks whether ggplot2 is available on the user’s system before executing any code. (<a href="https://github.com/grantmcdermott/parttree/issues/18" class="external-link">#18</a>)</li>
+<h5 id="breaking-changes-0-1-0">Breaking changes<a class="anchor" aria-label="anchor" href="#breaking-changes-0-1-0"></a></h5>
+<ul><li>Move ggplot2 to Enhances, following the addition of a dedicated (base R) <code>plot.parttree</code> method. The <code><a href="../reference/geom_parttree.html">geom_parttree()</a></code> function is still available, but requires that ggplot2 is already been installed on the user’s system. (<a href="https://github.com/grantmcdermott/parttree/issues/18" class="external-link">#18</a>)</li>
 <li>The <code>flipaxes</code> argument has been renamed to <code>flip</code>, e.g. <code>parttree(..., flip = TRUE)</code>. (<a href="https://github.com/grantmcdermott/parttree/issues/18" class="external-link">#18</a>)</li>
 </ul></div>
 <div class="section level5">
-<h5 id="improvements-0-0-1-9006">Improvements<a class="anchor" aria-label="anchor" href="#improvements-0-0-1-9006"></a></h5>
+<h5 id="improvements-0-1-0">Improvements<a class="anchor" aria-label="anchor" href="#improvements-0-1-0"></a></h5>
 <ul><li>Parttree objects now have their own class with a dedicated <code>plot.parttree</code> method, powered by tinyplot. (<a href="https://github.com/grantmcdermott/parttree/issues/18" class="external-link">#18</a>)<br></li>
 <li>Major speed-up for extracting parttree nodes and coordinates on complicated trees. (<a href="https://github.com/grantmcdermott/parttree/issues/15" class="external-link">#15</a>)</li>
 <li>Add method for tidymodels workflows objects fitted with <code>"rpart"</code> engine. (<a href="https://github.com/grantmcdermott/parttree/issues/7" class="external-link">#7</a> by <a href="https://github.com/juliasilge" class="external-link">@juliasilge</a>).</li>
 </ul></div>
 <div class="section level5">
-<h5 id="bug-fixes-0-0-1-9006">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-0-1-9006"></a></h5>
+<h5 id="bug-fixes-0-1-0">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-1-0"></a></h5>
 <ul><li>Support for negative values. (<a href="https://github.com/grantmcdermott/parttree/issues/6" class="external-link">#6</a> by <a href="https://github.com/pjgeens" class="external-link">@pjgeens</a>)</li>
 <li>Better handling of single-level factors and <code>flip(axes)</code>. (<a href="https://github.com/grantmcdermott/parttree/issues/5" class="external-link">#5</a>)</li>
 <li>Handling of complex formula expressions. (<a href="https://github.com/grantmcdermott/parttree/issues/17" class="external-link">#17</a>)</li>
 </ul></div>
 <div class="section level5">
-<h5 id="internals-0-0-1-9006">Internals<a class="anchor" aria-label="anchor" href="#internals-0-0-1-9006"></a></h5>
+<h5 id="internals-0-1-0">Internals<a class="anchor" aria-label="anchor" href="#internals-0-1-0"></a></h5>
 <ul><li>Several dependency adjustments, e.g. tinyplot to Imports and ggplot2 to Suggests. (<a href="https://github.com/grantmcdermott/parttree/issues/18" class="external-link">#18</a>)</li>
 <li>Added SVG snapshots for image-based tests. (<a href="https://github.com/grantmcdermott/parttree/issues/18" class="external-link">#18</a>)</li>
 <li>Bump ggplot2 version dependency to match deprecated functions from 3.4.0.</li>
diff --git a/pkgdown.yml b/pkgdown.yml
index 860c57f..5aaeb34 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -4,7 +4,7 @@ pkgdown_sha: ~
 articles:
   parttree-art: parttree-art.html
   parttree-intro: parttree-intro.html
-last_built: 2025-01-11T01:10Z
+last_built: 2025-01-16T16:09Z
 urls:
   reference: https://grantmcdermott.com/parttree/reference
   article: https://grantmcdermott.com/parttree/articles
diff --git a/reference/figures/README-quickstart-1.png b/reference/figures/README-quickstart-1.png
index bba8a5a..ef6a8a8 100644
Binary files a/reference/figures/README-quickstart-1.png and b/reference/figures/README-quickstart-1.png differ
diff --git a/reference/figures/README-quickstart2-1.png b/reference/figures/README-quickstart2-1.png
index e5c234e..711c798 100644
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diff --git a/reference/figures/README-quickstart_gg-1.png b/reference/figures/README-quickstart_gg-1.png
index 0445a4f..6f623b1 100644
Binary files a/reference/figures/README-quickstart_gg-1.png and b/reference/figures/README-quickstart_gg-1.png differ
diff --git a/reference/geom_parttree-5.png b/reference/geom_parttree-5.png
index ddf3940..d2e2eb5 100644
Binary files a/reference/geom_parttree-5.png and b/reference/geom_parttree-5.png differ
diff --git a/reference/geom_parttree-6.png b/reference/geom_parttree-6.png
index 54b60af..ddf3940 100644
Binary files a/reference/geom_parttree-6.png and b/reference/geom_parttree-6.png differ
diff --git a/reference/geom_parttree-7.png b/reference/geom_parttree-7.png
index 105b05b..54b60af 100644
Binary files a/reference/geom_parttree-7.png and b/reference/geom_parttree-7.png differ
diff --git a/reference/geom_parttree-8.png b/reference/geom_parttree-8.png
index 5a7fd88..105b05b 100644
Binary files a/reference/geom_parttree-8.png and b/reference/geom_parttree-8.png differ
diff --git a/reference/geom_parttree.html b/reference/geom_parttree.html
index a5dbca7..43d6526 100644
--- a/reference/geom_parttree.html
+++ b/reference/geom_parttree.html
@@ -15,7 +15,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">parttree</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.1.9006</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -171,6 +171,10 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </ul></dd>
 
 </dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>A <code><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></code>  layer.</p>
+    </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>Because of the way that <code>ggplot2</code> validates inputs and assembles
@@ -204,43 +208,45 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># install.packages("ggplot2")</span></span></span>
 <span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://ggplot2.tidyverse.org" class="external-link">ggplot2</a></span><span class="op">)</span>  <span class="co"># ggplot2 must be installed/loaded separately</span></span></span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/grantmcdermott/parttree" class="external-link">parttree</a></span><span class="op">)</span> <span class="co"># this package</span></span></span>
+<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://grantmcdermott.com/parttree/">parttree</a></span><span class="op">)</span> <span class="co"># this package</span></span></span>
 <span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/bethatkinson/rpart" class="external-link">rpart</a></span><span class="op">)</span>    <span class="co"># decision trees</span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co">### Simple decision tree (max of two predictor variables)</span></span></span>
+<span class="r-in"><span><span class="co">#</span></span></span>
+<span class="r-in"><span><span class="co">## Simple decision tree (max of two predictor variables)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">iris_tree</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/rpart/man/rpart.html" class="external-link">rpart</a></span><span class="op">(</span><span class="va">Species</span> <span class="op">~</span> <span class="va">Petal.Length</span> <span class="op">+</span> <span class="va">Petal.Width</span>, data<span class="op">=</span><span class="va">iris</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co">## Plot with original iris data only</span></span></span>
+<span class="r-in"><span><span class="co"># Plot with original iris data only</span></span></span>
 <span class="r-in"><span><span class="va">p</span> <span class="op">=</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">iris</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>x <span class="op">=</span> <span class="va">Petal.Length</span>, y <span class="op">=</span> <span class="va">Petal.Width</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_point.html" class="external-link">geom_point</a></span><span class="op">(</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>col <span class="op">=</span> <span class="va">Species</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co">## Add tree partitions to the plot (borders only)</span></span></span>
+<span class="r-in"><span><span class="co"># Add tree partitions to the plot (borders only)</span></span></span>
 <span class="r-in"><span><span class="va">p</span> <span class="op">+</span> <span class="fu">geom_parttree</span><span class="op">(</span>data <span class="op">=</span> <span class="va">iris_tree</span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="geom_parttree-1.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co">## Better to use fill and highlight predictions</span></span></span>
+<span class="r-in"><span><span class="co"># Better to use fill and highlight predictions</span></span></span>
 <span class="r-in"><span><span class="va">p</span> <span class="op">+</span> <span class="fu">geom_parttree</span><span class="op">(</span>data <span class="op">=</span> <span class="va">iris_tree</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>fill <span class="op">=</span> <span class="va">Species</span><span class="op">)</span>, alpha<span class="op">=</span><span class="fl">0.1</span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="geom_parttree-2.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co">## To drop the black border lines (i.e. fill only)</span></span></span>
+<span class="r-in"><span><span class="co"># To drop the black border lines (i.e. fill only)</span></span></span>
 <span class="r-in"><span><span class="va">p</span> <span class="op">+</span> <span class="fu">geom_parttree</span><span class="op">(</span>data <span class="op">=</span> <span class="va">iris_tree</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>fill <span class="op">=</span> <span class="va">Species</span><span class="op">)</span>, col <span class="op">=</span> <span class="cn">NA</span>, alpha <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="geom_parttree-3.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co">### Example with plot orientation mismatch</span></span></span>
+<span class="r-in"><span><span class="co">#</span></span></span>
+<span class="r-in"><span><span class="co">## Example with plot orientation mismatch</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">p2</span> <span class="op">=</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="va">iris</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>x<span class="op">=</span><span class="va">Petal.Width</span>, y<span class="op">=</span><span class="va">Petal.Length</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_point.html" class="external-link">geom_point</a></span><span class="op">(</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>col<span class="op">=</span><span class="va">Species</span><span class="op">)</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co">## Oops</span></span></span>
+<span class="r-in"><span><span class="co"># Oops</span></span></span>
 <span class="r-in"><span><span class="va">p2</span> <span class="op">+</span> <span class="fu">geom_parttree</span><span class="op">(</span>data <span class="op">=</span> <span class="va">iris_tree</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>fill<span class="op">=</span><span class="va">Species</span><span class="op">)</span>, alpha <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="geom_parttree-4.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co">## Fix with 'flip = TRUE'</span></span></span>
-<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># Fix with 'flip = TRUE'</span></span></span>
+<span class="r-in"><span><span class="va">p2</span> <span class="op">+</span> <span class="fu">geom_parttree</span><span class="op">(</span>data <span class="op">=</span> <span class="va">iris_tree</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>fill<span class="op">=</span><span class="va">Species</span><span class="op">)</span>, alpha <span class="op">=</span> <span class="fl">0.1</span>, flip <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
+<span class="r-plt img"><img src="geom_parttree-5.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co">### Various front-end frameworks are also supported, e.g.:</span></span></span>
+<span class="r-in"><span><span class="co">#</span></span></span>
+<span class="r-in"><span><span class="co">## Various front-end frameworks are also supported, e.g.:</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># install.packages("parsnip")</span></span></span>
 <span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/tidymodels/parsnip" class="external-link">parsnip</a></span><span class="op">)</span></span></span>
@@ -251,12 +257,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span>  <span class="fu"><a href="https://generics.r-lib.org/reference/fit.html" class="external-link">fit</a></span><span class="op">(</span><span class="va">Species</span> <span class="op">~</span> <span class="va">Petal.Length</span> <span class="op">+</span> <span class="va">Petal.Width</span>, data<span class="op">=</span><span class="va">iris</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">p</span> <span class="op">+</span> <span class="fu">geom_parttree</span><span class="op">(</span>data <span class="op">=</span> <span class="va">iris_tree_parsnip</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>fill<span class="op">=</span><span class="va">Species</span><span class="op">)</span>, alpha <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="geom_parttree-5.png" alt="" width="700" height="433"></span>
+<span class="r-plt img"><img src="geom_parttree-6.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co">#</span></span></span>
+<span class="r-in"><span><span class="co">## Trees with continuous independent variables are also supported.</span></span></span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co">### Trees with continuous independent variables are also supported. But you</span></span></span>
-<span class="r-in"><span><span class="co">### may need to adjust (or switch off) the fill legend to match the original</span></span></span>
-<span class="r-in"><span><span class="co">### data, e.g.:</span></span></span>
+<span class="r-in"><span><span class="co"># Note: you may need to adjust (or switch off) the fill legend to match the</span></span></span>
+<span class="r-in"><span><span class="co"># original data, e.g.:</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">iris_tree_cont</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/rpart/man/rpart.html" class="external-link">rpart</a></span><span class="op">(</span><span class="va">Petal.Length</span> <span class="op">~</span> <span class="va">Sepal.Length</span> <span class="op">+</span> <span class="va">Petal.Width</span>, data<span class="op">=</span><span class="va">iris</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">p3</span> <span class="op">=</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">iris</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>x <span class="op">=</span> <span class="va">Petal.Width</span>, y <span class="op">=</span> <span class="va">Sepal.Length</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span></span>
@@ -267,13 +274,13 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_point.html" class="external-link">geom_point</a></span><span class="op">(</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>col <span class="op">=</span> <span class="va">Petal.Length</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggtheme.html" class="external-link">theme_minimal</a></span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co">## Legend scales don't quite match here:</span></span></span>
+<span class="r-in"><span><span class="co"># Legend scales don't quite match here:</span></span></span>
 <span class="r-in"><span><span class="va">p3</span></span></span>
-<span class="r-plt img"><img src="geom_parttree-6.png" alt="" width="700" height="433"></span>
+<span class="r-plt img"><img src="geom_parttree-7.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="co">## Better to scale fill to the original data</span></span></span>
+<span class="r-in"><span><span class="co"># Better to scale fill to the original data</span></span></span>
 <span class="r-in"><span><span class="va">p3</span> <span class="op">+</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/scale_colour_continuous.html" class="external-link">scale_fill_continuous</a></span><span class="op">(</span>limits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/range.html" class="external-link">range</a></span><span class="op">(</span><span class="va">iris</span><span class="op">$</span><span class="va">Petal.Length</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-plt img"><img src="geom_parttree-7.png" alt="" width="700" height="433"></span>
+<span class="r-plt img"><img src="geom_parttree-8.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
diff --git a/reference/index.html b/reference/index.html
index 70f767f..fe070af 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">parttree</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.1.9006</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/parttree-3.png b/reference/parttree-3.png
index 3d0f1cf..3c88369 100644
Binary files a/reference/parttree-3.png and b/reference/parttree-3.png differ
diff --git a/reference/parttree.html b/reference/parttree.html
index e7e81c6..08a8462 100644
--- a/reference/parttree.html
+++ b/reference/parttree.html
@@ -13,7 +13,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">parttree</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.1.9006</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -74,7 +74,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 <dd><p>Logical. Should we flip the "x" and "y" variables in the return
 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 <code>TRUE</code> switches these around.</p></dd>
+slot. Setting to <code>TRUE</code> switches these around.</p>
+<p><em>Note:</em> This argument is primarily useful when it passed via
+<a href="geom_parttree.html">geom_parttree</a> to ensure correct axes orientation as part of a <code>ggplot2</code>
+visualization (see <a href="geom_parttree.html">geom_parttree</a> Examples). We do not expect users to
+call <code>parttree(..., flip = TRUE)</code> directly. Similarly, to switch axes
+orientation for the native (base graphics) <a href="plot.parttree.html">plot.parttree</a> method, we
+recommend calling <code>plot(..., flip = TRUE)</code> rather than flipping the
+underlying <code>parttree</code> object.</p></dd>
 
 </dl></div>
     <div class="section level2">
@@ -91,8 +98,8 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="st"><a href="https://github.com/grantmcdermott/parttree" class="external-link">"parttree"</a></span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="st"><a href="https://grantmcdermott.com/parttree/">"parttree"</a></span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="co">#</span></span></span>
 <span class="r-in"><span><span class="co">## rpart trees</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="st"><a href="https://github.com/bethatkinson/rpart" class="external-link">"rpart"</a></span><span class="op">)</span></span></span>
@@ -134,6 +141,9 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> $na.action</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> NULL</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> $flip</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $raw_data</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> NULL</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
@@ -149,17 +159,18 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co"># customize further by passing extra options to (tiny)plot</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span></span></span>
 <span class="r-in"><span>   <span class="va">rp_pt</span>,</span></span>
-<span class="r-in"><span>   border  <span class="op">=</span> <span class="cn">NA</span>, <span class="co"># no partition borders</span></span></span>
-<span class="r-in"><span>   pch     <span class="op">=</span> <span class="fl">19</span>, <span class="co"># filled points</span></span></span>
-<span class="r-in"><span>   alpha   <span class="op">=</span> <span class="fl">0.6</span>, <span class="co"># point transparency</span></span></span>
-<span class="r-in"><span>   grid    <span class="op">=</span> <span class="cn">TRUE</span>, <span class="co"># background grid</span></span></span>
-<span class="r-in"><span>   palette <span class="op">=</span> <span class="st">"classic"</span>, <span class="co"># new colour palette</span></span></span>
-<span class="r-in"><span>   xlab    <span class="op">=</span> <span class="st">"Topmost vertebra operated on"</span>, <span class="co"># custom x title</span></span></span>
-<span class="r-in"><span>   ylab    <span class="op">=</span> <span class="st">"Patient age (months)"</span>, <span class="co"># custom y title</span></span></span>
+<span class="r-in"><span>   border  <span class="op">=</span> <span class="cn">NA</span>,                                     <span class="co"># no partition borders</span></span></span>
+<span class="r-in"><span>   pch     <span class="op">=</span> <span class="fl">16</span>,                                     <span class="co"># filled points</span></span></span>
+<span class="r-in"><span>   alpha   <span class="op">=</span> <span class="fl">0.6</span>,                                    <span class="co"># point transparency</span></span></span>
+<span class="r-in"><span>   grid    <span class="op">=</span> <span class="cn">TRUE</span>,                                   <span class="co"># background grid</span></span></span>
+<span class="r-in"><span>   palette <span class="op">=</span> <span class="st">"classic"</span>,                              <span class="co"># new colour palette</span></span></span>
+<span class="r-in"><span>   xlab    <span class="op">=</span> <span class="st">"Topmost vertebra operated on"</span>,         <span class="co"># custom x title</span></span></span>
+<span class="r-in"><span>   ylab    <span class="op">=</span> <span class="st">"Patient age (months)"</span>,                 <span class="co"># custom y title</span></span></span>
 <span class="r-in"><span>   main    <span class="op">=</span> <span class="st">"Tree predictions: Kyphosis recurrence"</span> <span class="co"># custom title</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="parttree-3.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co">#</span></span></span>
 <span class="r-in"><span><span class="co">## conditional inference trees from partyit</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="st"><a href="http://partykit.r-forge.r-project.org/partykit/" class="external-link">"partykit"</a></span><span class="op">)</span></span></span>
@@ -187,10 +198,12 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> 8 14.5   55  111</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 9  8.5 -Inf  Inf</span>
 <span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co">#</span></span></span>
 <span class="r-in"><span><span class="co">## various front-end frameworks are also supported, e.g.</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># tidymodels</span></span></span>
 <span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># install.packages("parsnip")</span></span></span>
 <span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/tidymodels/parsnip" class="external-link">parsnip</a></span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://parsnip.tidymodels.org/reference/decision_tree.html" class="external-link">decision_tree</a></span><span class="op">(</span><span class="op">)</span> <span class="op">|&gt;</span></span></span>
@@ -203,6 +216,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># mlr3 (NB: use `keep_model = TRUE` for mlr3 learners)</span></span></span>
 <span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># install.packages("mlr3")</span></span></span>
 <span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://mlr3.mlr-org.com" class="external-link">mlr3</a></span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">task_iris</span> <span class="op">=</span> <span class="va"><a href="https://mlr3.mlr-org.com/reference/TaskClassif.html" class="external-link">TaskClassif</a></span><span class="op">$</span><span class="fu">new</span><span class="op">(</span><span class="st">"iris"</span>, <span class="va">iris</span>, target <span class="op">=</span> <span class="st">"Species"</span><span class="op">)</span></span></span>
diff --git a/reference/plot.parttree-3.png b/reference/plot.parttree-3.png
index 3d0f1cf..3c88369 100644
Binary files a/reference/plot.parttree-3.png and b/reference/plot.parttree-3.png differ
diff --git a/reference/plot.parttree.html b/reference/plot.parttree.html
index 04e6767..1ed9081 100644
--- a/reference/plot.parttree.html
+++ b/reference/plot.parttree.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">parttree</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.1.9006</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -106,8 +106,8 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
 
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="st"><a href="https://github.com/grantmcdermott/parttree" class="external-link">"parttree"</a></span><span class="op">)</span></span></span>
-<span class="r-in"><span></span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="st"><a href="https://grantmcdermott.com/parttree/">"parttree"</a></span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="co">#</span></span></span>
 <span class="r-in"><span><span class="co">## rpart trees</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="st"><a href="https://github.com/bethatkinson/rpart" class="external-link">"rpart"</a></span><span class="op">)</span></span></span>
@@ -149,6 +149,9 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> $na.action</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> NULL</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> $flip</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $raw_data</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> NULL</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
@@ -164,17 +167,18 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="co"># customize further by passing extra options to (tiny)plot</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span></span></span>
 <span class="r-in"><span>   <span class="va">rp_pt</span>,</span></span>
-<span class="r-in"><span>   border  <span class="op">=</span> <span class="cn">NA</span>, <span class="co"># no partition borders</span></span></span>
-<span class="r-in"><span>   pch     <span class="op">=</span> <span class="fl">19</span>, <span class="co"># filled points</span></span></span>
-<span class="r-in"><span>   alpha   <span class="op">=</span> <span class="fl">0.6</span>, <span class="co"># point transparency</span></span></span>
-<span class="r-in"><span>   grid    <span class="op">=</span> <span class="cn">TRUE</span>, <span class="co"># background grid</span></span></span>
-<span class="r-in"><span>   palette <span class="op">=</span> <span class="st">"classic"</span>, <span class="co"># new colour palette</span></span></span>
-<span class="r-in"><span>   xlab    <span class="op">=</span> <span class="st">"Topmost vertebra operated on"</span>, <span class="co"># custom x title</span></span></span>
-<span class="r-in"><span>   ylab    <span class="op">=</span> <span class="st">"Patient age (months)"</span>, <span class="co"># custom y title</span></span></span>
+<span class="r-in"><span>   border  <span class="op">=</span> <span class="cn">NA</span>,                                     <span class="co"># no partition borders</span></span></span>
+<span class="r-in"><span>   pch     <span class="op">=</span> <span class="fl">16</span>,                                     <span class="co"># filled points</span></span></span>
+<span class="r-in"><span>   alpha   <span class="op">=</span> <span class="fl">0.6</span>,                                    <span class="co"># point transparency</span></span></span>
+<span class="r-in"><span>   grid    <span class="op">=</span> <span class="cn">TRUE</span>,                                   <span class="co"># background grid</span></span></span>
+<span class="r-in"><span>   palette <span class="op">=</span> <span class="st">"classic"</span>,                              <span class="co"># new colour palette</span></span></span>
+<span class="r-in"><span>   xlab    <span class="op">=</span> <span class="st">"Topmost vertebra operated on"</span>,         <span class="co"># custom x title</span></span></span>
+<span class="r-in"><span>   ylab    <span class="op">=</span> <span class="st">"Patient age (months)"</span>,                 <span class="co"># custom y title</span></span></span>
 <span class="r-in"><span>   main    <span class="op">=</span> <span class="st">"Tree predictions: Kyphosis recurrence"</span> <span class="co"># custom title</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="plot.parttree-3.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co">#</span></span></span>
 <span class="r-in"><span><span class="co">## conditional inference trees from partyit</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="st"><a href="http://partykit.r-forge.r-project.org/partykit/" class="external-link">"partykit"</a></span><span class="op">)</span></span></span>
@@ -199,10 +203,12 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> 8 14.5   55  111</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 9  8.5 -Inf  Inf</span>
 <span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co">#</span></span></span>
 <span class="r-in"><span><span class="co">## various front-end frameworks are also supported, e.g.</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># tidymodels</span></span></span>
 <span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># install.packages("parsnip")</span></span></span>
 <span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/tidymodels/parsnip" class="external-link">parsnip</a></span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="fu"><a href="https://parsnip.tidymodels.org/reference/decision_tree.html" class="external-link">decision_tree</a></span><span class="op">(</span><span class="op">)</span> <span class="op">|&gt;</span></span></span>
@@ -215,6 +221,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># mlr3 (NB: use `keep_model = TRUE` for mlr3 learners)</span></span></span>
 <span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># install.packages("mlr3")</span></span></span>
 <span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://mlr3.mlr-org.com" class="external-link">mlr3</a></span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">task_iris</span> <span class="op">=</span> <span class="va"><a href="https://mlr3.mlr-org.com/reference/TaskClassif.html" class="external-link">TaskClassif</a></span><span class="op">$</span><span class="fu">new</span><span class="op">(</span><span class="st">"iris"</span>, <span class="va">iris</span>, target <span class="op">=</span> <span class="st">"Species"</span><span class="op">)</span></span></span>
diff --git a/search.json b/search.json
index 965daae..a52550e 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"https://grantmcdermott.com/parttree/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2020 Grant McDermott Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://grantmcdermott.com/parttree/articles/parttree-art.html","id":"background","dir":"Articles","previous_headings":"","what":"Background","title":"Abstract art with parttree and friends","text":"One fun application tree-based methods abstracting art images. really striking examples, take look designer Dimitris Ladopoulos’ portfolio. vignette show implement basic ideas using parttree friends. packages ’ll using. exact details vary depending image hand, essential recipe type art abstraction follows: Convert image matrix (data frame), rows columns correspond, respectively, X Y coordinates individual pixels. words, cell matrix (data frame) represents colour values individual pixel. Split data primary (RGB) colour channels. now three matrices (data frames), cell represents red/green/blue colour channel value individual pixel. Run tree model three RGB datasets. case, trying predict relevant colour channel value function X Y coordinates. Use predicted values plot abstracted art piece! (Okay, step requires bit work, ’re see works practice…)","code":"library(parttree) # This package library(rpart)    # For decision trees library(magick)   # For reading and manipulating images library(imager)   # Another image library, with some additional features  op = par(mar = c(0,0,0,0)) # Remove plot margins"},{"path":"https://grantmcdermott.com/parttree/articles/parttree-art.html","id":"example-1-peales-portrait-of-rosalba","dir":"Articles","previous_headings":"","what":"Example 1: Peale’s “Portrait of Rosalba”","title":"Abstract art with parttree and friends","text":"first example, mimic one Dimitri Ladopoulos’s aforementioned portrait pieces. Specifically, abstract close-(eyes ) Rembrandt Peale’s portrait daughter, Rosalba. can download digital image original artwork Wikimedia. ’ll download high-res version show , feel free go lower resolution want reduce modeling plotting times.1 However, crop image around Rosalba’s eyes get close-reduce overall complexity exercise.2  cropped image hand, let’s walk 4-step recipe . Step 1. Convert image data frame. Step 2. Split image RGB colour channel. cc column , 1=Red, 2=Green, 3=Blue. Step 3. Fit decision tree (similar model) colour channel data frames. tuning parameters give model matter experimentation. ’m giving low complexity parameter (see variation final predictions) trimming tree maximum depth 30 nodes. next code chunk takes 15 seconds run laptop, much quicker downloaded lower-res image. Step 4. Use model (colour) predictions construct abstracted art piece. bit glib earlier, since really involves sub-steps. First, let’s grab predictions trees. ’re going use predictions draw abstracted (downscaled) version image.3 Probably easiest way taking predictions overwriting “value” column original (pre-split) rosalba_df data frame. can coerce data frame back cimg object, comes bunch nice plotting methods. Now ’re ready draw abstracted art piece. ’s also parttree enter fray, since ’ll using highlight partitioned areas downscaled pixels. ’s can using base R graphics.  can achieve effect ggplot2 prefer use .","code":"# Download the image rosalba = image_read(\"https://upload.wikimedia.org/wikipedia/commons/a/aa/Rembrandt_Peale_-_Portrait_of_Rosalba_Peale_-_Google_Art_Project.jpg\")  # Crop around the eyes rosalba = image_crop(rosalba, \"850x400+890+1350\") # rosalba = image_crop(rosalba, \"750x350+890+1350\")  # Convert to cimg (better for in-memory manipulation) rosalba = magick2cimg(rosalba)  # Display rosalba #> Image. Width: 850 pix Height: 400 pix Depth: 1 Colour channels: 3 plot(rosalba, axes = FALSE) # Coerce to data frame rosalba_df = as.data.frame(rosalba)  # Round color values to ease up work for decision tree rosalba_df$value = round(rosalba_df$value, 4)  head(rosalba_df) #>   x y cc  value #> 1 1 1  1 0.3059 #> 2 2 1  1 0.3059 #> 3 3 1  1 0.2627 #> 4 4 1  1 0.2471 #> 5 5 1  1 0.3137 #> 6 6 1  1 0.2941 rosalba_ccs = split(rosalba_df, rosalba_df$cc)  # We have a list of three DFs by colour channel. Uncomment if you want to see: # str(rosalba_css) ## Start creating regression tree for each color channel. We'll adjust some ## control parameters to give us the \"right\" amount of resolution in the final ## plots. trees = lapply(   rosalba_ccs,    # function(d) rpart(value ~ x + y, data=d, control=list(cp=0.00001, maxdepth=20))   function(d) rpart(value ~ x + y, data=d, control=list(cp=0.00002, maxdepth=20))   ) pred = lapply(trees, predict) # get predictions for each tree # The pred object is a list, so we convert it to a vector before overwriting the # value column of the original data frame rosalba_df$value = do.call(\"c\", pred)  # Convert back into a cimg object, with the predictions providing pixel values pred_img = as.cimg(rosalba_df) #> Warning in as.cimg.data.frame(rosalba_df): Guessing image dimensions from #> maximum coordinate values # get a list of parttree data frames (one for each tree) pts = lapply(trees, parttree)  ## first plot the downscaled image... plot(pred_img, axes = FALSE) ## ... then layer the partitions as a series of rectangles lapply(   pts,    function(pt) plot(     pt, raw = FALSE, add = TRUE, expand = FALSE,     fill_alpha = NULL, lwd = 0.1, border = \"grey15\"   ) ) #> $`1` #> NULL #>  #> $`2` #> NULL #>  #> $`3` #> NULL library(ggplot2) ggplot() +   annotation_raster(pred_img,  ymin=-Inf, ymax=Inf, xmin=-Inf, xmax=Inf) +   lapply(trees, function(d) geom_parttree(data = d, lwd = 0.05, col = \"grey15\")) +   scale_x_continuous(limits=c(0, max(rosalba_df$x)), expand=c(0,0)) +   scale_y_reverse(limits=c(max(rosalba_df$y), 0), expand=c(0,0)) +   coord_fixed(ratio = Reduce(x = dim(rosalba)[2:1], f = \"/\") * 2) +   theme_void()"},{"path":"https://grantmcdermott.com/parttree/articles/parttree-art.html","id":"example-2-parttree-logo","dir":"Articles","previous_headings":"","what":"Example 2: parttree logo","title":"Abstract art with parttree and friends","text":"Now ’ve walked detailed example, let’s quickly go one . ’re get meta: Visualizing decision tree partitions actual tree image. also happens basis parttree logo, seems nice way bookend vignette. main image conversion, modeling, prediction steps. follow recipe saw previous portrait example, won’t repeat explanations.  point may tempted plot figure plot(bonzai_pred_img), definitely work. However, ’m going show one additional tweak make partition rectangles look bit nicer: Use mean color channel values instead black. ’s function grabbing information cimg object converted data frame.4 penultimate step generate parttree objects colour channel-based trees. now can plot everything together.  , equivalent result prefer ggplot2.","code":"bonzai = load.image(\"https://upload.wikimedia.org/wikipedia/commons/thumb/0/0c/Japanese_Zelkova_bonsai_16%2C_30_April_2012.JPG/480px-Japanese_Zelkova_bonsai_16%2C_30_April_2012.JPG\") plot(bonzai, axes = FALSE) # Coerce to data frame bonzai_df = as.data.frame(bonzai)  # Round color values to ease up work for decision trees bonzai_df$value = round(bonzai_df$value, 2)  # Split into colour channels bonzai_ccs = split(bonzai_df, bonzai_df$cc)  # Fit decision trees on each colour channel DF. Note that I'm deliberating # truncating the max tree depth to reduce the number of partitions. bonzai_trees = lapply(   bonzai_ccs,    function(d) rpart(value ~ x + y, data=d, control=list(cp=0.00001, maxdepth=10)) )  # Overwrite value column with predictions vector bonzai_df$value = do.call(\"c\", lapply(bonzai_trees, predict))  # Convert back into a cimg object, with the predictions providing pixel values bonzai_pred_img = as.cimg(bonzai_df) #> Warning in as.cimg.data.frame(bonzai_df): Guessing image dimensions from #> maximum coordinate values mean_cols = function(dat) {   mcols = tapply(dat$value, dat$cc, FUN = \"mean\")   col1 = rgb(mcols[1], mcols[2], mcols[3])   hicol = function(x) mean(x) + 0.4*sd(subset(dat, cc==1)$value)   col2 = rgb(hicol(subset(dat, cc==1)$value), hicol(subset(dat, cc==2)$value), hicol(subset(dat, cc==3)$value))   locol = function(x) mean(x) - 0.4*sd(subset(dat, cc==1)$value)   col3 = rgb(locol(subset(dat, cc==1)$value), locol(subset(dat, cc==2)$value), locol(subset(dat, cc==3)$value))   return(c(col1, col2, col3)) }  bonzai_mean_cols = mean_cols(bonzai_df) bonzai_pts = lapply(bonzai_trees, parttree) plot(bonzai_pred_img, axes = FALSE) Map(   function(pt, cols) {     plot(       pt, raw = FALSE, add = TRUE, expand = FALSE,       fill_alpha = 0, lwd = 0.2, border = cols     )   },   pt = bonzai_pts,   cols = bonzai_mean_cols ) #> $`1` #> NULL #>  #> $`2` #> NULL #>  #> $`3` #> NULL # library(ggplot2) ggplot() +   annotation_raster(bonzai_pred_img,  ymin=-Inf, ymax=Inf, xmin=-Inf, xmax=Inf) +   Map(function(d, cols) geom_parttree(data = d, lwd = 0.1, col = cols), d = bonzai_trees, cols = bonzai_mean_cols) +   scale_x_continuous(limits=c(0, max(bonzai_df$x)), expand=c(0,0)) +   scale_y_reverse(limits=c(max(bonzai_df$y), 0), expand=c(0,0)) +   coord_fixed() +   theme_void()"},{"path":"https://grantmcdermott.com/parttree/articles/parttree-art.html","id":"postscript","dir":"Articles","previous_headings":"","what":"Postscript","title":"Abstract art with parttree and friends","text":"individual trees colour channel make nice stained glass prints…","code":"lapply(   seq_along(bonzai_pts),   function(i) {     plot(       bonzai_pts[[i]], raw = FALSE, expand = FALSE,       axes = FALSE, legend = FALSE,       main = paste0(c(\"R\", \"G\", \"B\")[i]),       ## Aside: We're reversing the y-scale since higher values actually       ## correspond to points lower on the image, visually.       ylim = rev(attr(bonzai_pts[[i]], \"parttree\")[[\"yrange\"]])     )    } ) #> [[1]] #> NULL #>  #> [[2]] #> NULL #>  #> [[3]] #> NULL # reset the plot margins par(op)"},{"path":"https://grantmcdermott.com/parttree/articles/parttree-intro.html","id":"motivating-example-classifying-penguin-species","dir":"Articles","previous_headings":"","what":"Motivating example: Classifying penguin species","title":"Introduction to parttree","text":"Start loading parttree package alongside rpart, comes bundled base R installation. basic examples follow, ’ll use well-known Palmer Penguins dataset demonstrate functionality. can load dataset via parent package (), import directly CSV . Dataset hand, let’s say interested predicting penguin species function 1) flipper length 2) bill length. model simple decision tree: Like tree-based frameworks, rpart comes default plot method visualizing resulting node splits.  okay, don’t feel provides much intuition model’s prediction scale actual data. words, ’d prefer see : tree partitioned original penguin data? parttree enters fray. package named primary workhorse function parttree(), extracts information needed produce nice plot tree partitions alongside original data.  Et voila! Now can clearly see model divided Cartesian space data. Gentoo penguins typically longer flippers Chinstrap Adelie penguins, latter shortest bills. perspective end-user, ptree parttree object interesting . simply data frame contains basic information needed plot (partition coordinates, etc.). can think helpful intermediate object way visualization interest. Speaking visualization, underneath hood plot.parttree calls powerful tinyplot package. latter’s various customization arguments can passed parttree plot make look bit nicer. example:","code":"library(parttree)  # This package library(rpart)     # For fitting decisions trees  # install.packages(\"palmerpenguins\") data(\"penguins\", package = \"palmerpenguins\") head(penguins) #> # A tibble: 6 × 8 #>   species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g #>   <fct>   <fct>              <dbl>         <dbl>             <int>       <int> #> 1 Adelie  Torgersen           39.1          18.7               181        3750 #> 2 Adelie  Torgersen           39.5          17.4               186        3800 #> 3 Adelie  Torgersen           40.3          18                 195        3250 #> 4 Adelie  Torgersen           NA            NA                  NA          NA #> 5 Adelie  Torgersen           36.7          19.3               193        3450 #> 6 Adelie  Torgersen           39.3          20.6               190        3650 #> # ℹ 2 more variables: sex <fct>, year <int> tree = rpart(species ~ flipper_length_mm + bill_length_mm, data = penguins) tree #> n=342 (2 observations deleted due to missingness) #>  #> node), split, n, loss, yval, (yprob) #>       * denotes terminal node #>  #> 1) root 342 191 Adelie (0.441520468 0.198830409 0.359649123)   #>   2) flipper_length_mm< 206.5 213  64 Adelie (0.699530516 0.295774648 0.004694836)   #>     4) bill_length_mm< 43.35 150   5 Adelie (0.966666667 0.033333333 0.000000000) * #>     5) bill_length_mm>=43.35 63   5 Chinstrap (0.063492063 0.920634921 0.015873016) * #>   3) flipper_length_mm>=206.5 129   7 Gentoo (0.015503876 0.038759690 0.945736434) * plot(tree, compress = TRUE) text(tree, use.n = TRUE) ptree = parttree(tree) plot(ptree) # See also `attr(ptree, \"parttree\")` ptree #>   node   species                                                  path  xmin #> 1    3    Gentoo                            flipper_length_mm >= 206.5 206.5 #> 2    4    Adelie  flipper_length_mm < 206.5 --> bill_length_mm < 43.35  -Inf #> 3    5 Chinstrap flipper_length_mm < 206.5 --> bill_length_mm >= 43.35  -Inf #>    xmax  ymin  ymax #> 1   Inf  -Inf   Inf #> 2 206.5  -Inf 43.35 #> 3 206.5 43.35   Inf plot(ptree, pch = 16, palette = \"classic\", alpha = 0.75, grid = TRUE)"},{"path":"https://grantmcdermott.com/parttree/articles/parttree-intro.html","id":"continuous-predictions","dir":"Articles","previous_headings":"Motivating example: Classifying penguin species","what":"Continuous predictions","title":"Introduction to parttree","text":"addition discrete classification problems, parttree also supports regression trees continuous independent variables.","code":"tree_cont = rpart(body_mass_g ~ flipper_length_mm + bill_length_mm, data = penguins)  tree_cont |>   parttree() |>   plot(pch = 16, palette = \"viridis\")"},{"path":"https://grantmcdermott.com/parttree/articles/parttree-intro.html","id":"supported-model-classes","dir":"Articles","previous_headings":"","what":"Supported model classes","title":"Introduction to parttree","text":"Alongside rpart model objects working thus far, parttree also supports decision trees created partykit package. see latter’s ctree (conditional inference tree) algorithm yields slightly sophisticated partitioning former’s default.  parttree also supports variety “frontend” modes call rpart::rpart() underlying engine. includes packages mlr3 tidymodels (parsnip workflows) ecosystems. quick demonstration using parsnip, ’ll also pull different dataset just change things little.","code":"library(partykit) #> Loading required package: grid #> Loading required package: libcoin #> Loading required package: mvtnorm  ctree(species ~ flipper_length_mm + bill_length_mm, data = penguins) |>   parttree() |>   plot(pch = 19, palette = \"classic\", alpha = 0.5) set.seed(123) ## For consistent jitter  library(parsnip) library(titanic) ## Just for a different data set  titanic_train$Survived = as.factor(titanic_train$Survived)  ## Build our tree using parsnip (but with rpart as the model engine) ti_tree =   decision_tree() |>   set_engine(\"rpart\") |>   set_mode(\"classification\") |>   fit(Survived ~ Pclass + Age, data = titanic_train) ## Now pass to parttree and plot ti_tree |>   parttree() |>   plot(pch = 16, jitter = TRUE, palette = \"dark\", alpha = 0.7)"},{"path":"https://grantmcdermott.com/parttree/articles/parttree-intro.html","id":"ggplot2","dir":"Articles","previous_headings":"","what":"ggplot2","title":"Introduction to parttree","text":"default plot.parttree method produces base graphics plot. also support ggplot2 via dedicated geom_parttree() function. demonstrate initial classification tree earlier.  Compared “native” plot.parttree method, note ggplot2 workflow requires tweaks: need need plot original dataset separate layer (.e., geom_point()). geom_parttree() accepts tree object , result parttree().1 Continuous regression trees can also drawn geom_parttree. However, recommend adjusting plot fill aesthetic since model likely partition data intervals don’t match exactly raw data. easiest way setting colour fill aesthetic together part scale_colour_* call.","code":"library(ggplot2) theme_set(theme_linedraw())  ## re-using the tree model object from above... ggplot(data = penguins, aes(x = flipper_length_mm, y = bill_length_mm)) +   geom_point(aes(col = species)) +   geom_parttree(data = tree, aes(fill=species), alpha = 0.1) ## re-using the tree_cont model object from above... ggplot(data = penguins, aes(x = flipper_length_mm, y = bill_length_mm)) +   geom_parttree(data = tree_cont, aes(fill=body_mass_g), alpha = 0.3) +   geom_point(aes(col = body_mass_g)) +    scale_colour_viridis_c(aesthetics = c('colour', 'fill')) # NB: Set colour + fill together"},{"path":"https://grantmcdermott.com/parttree/articles/parttree-intro.html","id":"gotcha-ggplot-orientation","dir":"Articles","previous_headings":"ggplot2","what":"Gotcha: (gg)plot orientation","title":"Introduction to parttree","text":"already said, geom_parttree() calls companion parttree() function internally, coerces rpart tree object data frame easily understood ggplot2. example, consider initial “ptree” object earlier. , resulting data frame designed amenable ggplot2 geom layer, columns like xmin, xmax, etc. specifying aesthetics ggplot2 recognizes. (Fun fact: geom_parttree() really just thin wrapper around geom_rect().) goal parttree abstract away kinds details user, can just specify geom_parttree()—valid tree object data input—done . However, generally works well, can sometimes lead unexpected behaviour terms plot orientation. ’s ’s hard guess ahead time user specify x y variables (.e. axes) ggplot2 layers.2 see mean, let’s redo penguin plot earlier, time switch axes main ggplot() call.  case , kind orientation mismatch normally (hopefully) pretty easy recognize. fix, can use flip = TRUE argument flip orientation geom_parttree layer.","code":"# ptree = parttree(tree) ptree #>   node   species                                                  path  xmin #> 1    3    Gentoo                            flipper_length_mm >= 206.5 206.5 #> 2    4    Adelie  flipper_length_mm < 206.5 --> bill_length_mm < 43.35  -Inf #> 3    5 Chinstrap flipper_length_mm < 206.5 --> bill_length_mm >= 43.35  -Inf #>    xmax  ymin  ymax #> 1   Inf  -Inf   Inf #> 2 206.5  -Inf 43.35 #> 3 206.5 43.35   Inf ## First, redo our first plot but this time switch the x and y variables p3 = ggplot(   data = penguins,    aes(x = bill_length_mm, y = flipper_length_mm) ## Switched!   ) +   geom_point(aes(col = species))  ## Add on our tree (and some preemptive titling..) p3 +   geom_parttree(data = tree, aes(fill = species), alpha = 0.1) +   labs(     title = \"Oops!\",     subtitle = \"Looks like a mismatch between our x and y axes...\"   ) p3 +   geom_parttree(     data = tree, aes(fill = species), alpha = 0.1,     flip = TRUE  ## Flip the orientation   ) +   labs(title = \"That's better\")"},{"path":"https://grantmcdermott.com/parttree/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Grant McDermott. Author, maintainer. Achim Zeileis. Contributor. Brian Heseung Kim. Contributor. Julia Silge. Contributor.","code":""},{"path":"https://grantmcdermott.com/parttree/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"McDermott G (2025). parttree: Visualise simple decision tree partitions. R package version 0.0.1.9006, http://grantmcdermott.com/parttree, https://github.com/grantmcdermott/parttree.","code":"@Manual{,   title = {parttree: Visualise simple decision tree partitions},   author = {Grant McDermott},   year = {2025},   note = {R package version 0.0.1.9006,     http://grantmcdermott.com/parttree},   url = {https://github.com/grantmcdermott/parttree}, }"},{"path":"https://grantmcdermott.com/parttree/index.html","id":"parttree-","dir":"","previous_headings":"","what":"Visualise simple decision tree partitions","title":"Visualise simple decision tree partitions","text":"Visualize simple 2-D decision tree partitions R. parttree package provides visualization methods base R graphics (via tinyplot) ggplot2.","code":""},{"path":"https://grantmcdermott.com/parttree/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Visualise simple decision tree partitions","text":"package CRAN yet, can installed r-universe:","code":"install.packages(\"parttree\", repos = \"https://grantmcdermott.r-universe.dev\")"},{"path":"https://grantmcdermott.com/parttree/index.html","id":"quickstart","dir":"","previous_headings":"","what":"Quickstart","title":"Visualise simple decision tree partitions","text":"parttree homepage includes introductory vignette detailed documentation. ’s quickstart example using “kyphosis” dataset comes bundled rpart package. case, interested predicting kyphosis recovery spinal surgery, function 1) number topmost vertebra operated, 2) patient age. key function parttree(), comes plotting method.  Customize plots passing additional arguments:","code":"library(rpart)     # For the dataset and fitting decisions trees library(parttree)  # This package  fit = rpart(Kyphosis ~ Start + Age, data = kyphosis)  # Grab the partitions and plot fit_pt = parttree(fit) plot(fit_pt) 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 )"},{"path":"https://grantmcdermott.com/parttree/index.html","id":"ggplot2","dir":"","previous_headings":"Quickstart","what":"ggplot2","title":"Visualise simple decision tree partitions","text":"ggplot2 users, offer equivalent workflow via geom_partree() visualization layer.","code":"library(ggplot2) ## Should be loaded separately  ggplot(kyphosis, aes(x = Start, y = Age)) +   geom_parttree(data = fit, alpha = 0.1, aes(fill = Kyphosis)) + # <-- key layer   geom_point(aes(col = Kyphosis)) +   labs(     x = \"No. of topmost vertebra operated on\", y = \"Patient age (months)\",     caption = \"Note: Points denote observations. Shading denotes model predictions.\"     ) +   theme_minimal()"},{"path":"https://grantmcdermott.com/parttree/reference/geom_parttree.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualize tree partitions with ggplot2 — geom_parttree","title":"Visualize tree partitions with ggplot2 — geom_parttree","text":"geom_parttree() simple wrapper around parttree() takes tree model object converts amenable data frame ggplot2 knows plot. Please note ggplot2 hard dependency parttree must thus installed separately user's system calling geom_parttree.","code":""},{"path":"https://grantmcdermott.com/parttree/reference/geom_parttree.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualize tree partitions with ggplot2 — geom_parttree","text":"","code":"geom_parttree(   mapping = NULL,   data = NULL,   stat = \"identity\",   position = \"identity\",   linejoin = \"mitre\",   na.rm = FALSE,   show.legend = NA,   inherit.aes = TRUE,   flip = FALSE,   ... )"},{"path":"https://grantmcdermott.com/parttree/reference/geom_parttree.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualize tree partitions with ggplot2 — geom_parttree","text":"mapping Set aesthetic mappings created aes(). specified inherit.aes = TRUE (default), combined default mapping top level plot. must supply mapping plot mapping. data rpart::rpart.object object compatible type (e.g. decision tree constructed via partykit, tidymodels, mlr3 front-ends). stat statistical transformation use data layer. using geom_*() function construct layer, stat argument can used override default coupling geoms stats. stat argument accepts following: Stat ggproto subclass, example StatCount. string naming stat. give stat string, strip function name stat_ prefix. example, use stat_count(), give stat \"count\". information ways specify stat, see layer stat documentation. position position adjustment use data layer. can used various ways, including prevent overplotting improving display. position argument accepts following: result calling position function, position_jitter(). method allows passing extra arguments position. string naming position adjustment. give position string, strip function name position_ prefix. example, use position_jitter(), give position \"jitter\". information ways specify position, see layer position documentation. linejoin Line join style (round, mitre, bevel). na.rm FALSE, default, missing values removed warning. TRUE, missing values silently removed. show.legend logical. layer included legends? NA, default, includes aesthetics mapped. FALSE never includes, TRUE always includes. can also named logical vector finely select aesthetics display. inherit.aes FALSE, overrides default aesthetics, rather combining . useful helper functions define data aesthetics inherit behaviour default plot specification, e.g. borders(). flip Logical. default, \"x\" \"y\" axes variables plotting determined first split tree. can cause plot orientation mismatches depending users specify layers plot. Setting TRUE flip \"x\" \"y\" variables geom_parttree layer. ... arguments passed layer()'s params argument. arguments broadly fall one 4 categories . Notably, arguments position argument, aesthetics required can passed .... Unknown arguments part 4 categories ignored. Static aesthetics mapped scale, fixed value apply layer whole. example, colour = \"red\" linewidth = 3. geom's documentation Aesthetics section lists available options. 'required' aesthetics passed params. Please note passing unmapped aesthetics vectors technically possible, order required length guaranteed parallel input data. constructing layer using stat_*() function, ... argument can used pass parameters geom part layer. example stat_density(geom = \"area\", outline.type = \"\"). geom's documentation lists parameters can accept. Inversely, constructing layer using geom_*() function, ... argument can used pass parameters stat part layer. example geom_area(stat = \"density\", adjust = 0.5). stat's documentation lists parameters can accept. key_glyph argument layer() may also passed .... can one functions described key glyphs, change display layer legend.","code":""},{"path":"https://grantmcdermott.com/parttree/reference/geom_parttree.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Visualize tree partitions with ggplot2 — geom_parttree","text":"way ggplot2 validates inputs assembles plot layers, note data input geom_parttree() (.e. decision tree object) must assigned layer ; initialising ggplot2::ggplot() call. See Examples.","code":""},{"path":"https://grantmcdermott.com/parttree/reference/geom_parttree.html","id":"aesthetics","dir":"Reference","previous_headings":"","what":"Aesthetics","title":"Visualize tree partitions with ggplot2 — geom_parttree","text":"geom_parttree() aims \"work----box\" minimal input user's side, apart specifying data object. includes taking care data transformation way , generally, produces optimal corner coordinates partition (.e. xmin, xmax, ymin, ymax). However, also understands following aesthetics users may choose specify manually: fill (particularly encouraged, since provide visual cue regarding prediction partition region) colour alpha linetype size","code":""},{"path":[]},{"path":"https://grantmcdermott.com/parttree/reference/geom_parttree.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visualize tree partitions with ggplot2 — geom_parttree","text":"","code":"# install.packages(\"ggplot2\") library(ggplot2)  # ggplot2 must be installed/loaded separately  library(parttree) # this package library(rpart)    # decision trees  ### Simple decision tree (max of two predictor variables)  iris_tree = rpart(Species ~ Petal.Length + Petal.Width, data=iris)  ## 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) p + geom_parttree(data = iris_tree)   ## 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) p + geom_parttree(data = iris_tree, aes(fill = Species), col = NA, alpha = 0.1)    ### Example with plot orientation mismatch  p2 = ggplot(iris, aes(x=Petal.Width, y=Petal.Length)) +   geom_point(aes(col=Species))  ## Oops p2 + geom_parttree(data = iris_tree, aes(fill=Species), alpha = 0.1)   ## Fix with 'flip = TRUE'   ### Various front-end frameworks are also supported, e.g.:  # install.packages(\"parsnip\") library(parsnip)  iris_tree_parsnip = decision_tree() |>   set_engine(\"rpart\") |>   set_mode(\"classification\") |>   fit(Species ~ Petal.Length + Petal.Width, data=iris)  p + geom_parttree(data = iris_tree_parsnip, aes(fill=Species), alpha = 0.1)    ### 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.:  iris_tree_cont = rpart(Petal.Length ~ Sepal.Length + Petal.Width, data=iris) p3 = ggplot(data = iris, aes(x = Petal.Width, y = Sepal.Length)) +  geom_parttree(    data = iris_tree_cont,    aes(fill = Petal.Length), alpha=0.5    ) +   geom_point(aes(col = Petal.Length)) +   theme_minimal()  ## Legend scales don't quite match here: p3   ## Better to scale fill to the original data p3 + scale_fill_continuous(limits = range(iris$Petal.Length))"},{"path":"https://grantmcdermott.com/parttree/reference/parttree.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert a decision tree into a data frame of partition coordinates — parttree","title":"Convert a decision tree into a data frame of partition coordinates — parttree","text":"Extracts terminal leaf nodes decision tree contains two numeric predictor variables. leaf nodes converted data frame, row represents partition (leaf terminal node) can easily plotted 2-D coordinate space.","code":""},{"path":"https://grantmcdermott.com/parttree/reference/parttree.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert a decision tree into a data frame of partition coordinates — parttree","text":"","code":"parttree(tree, keep_as_dt = FALSE, flip = FALSE)"},{"path":"https://grantmcdermott.com/parttree/reference/parttree.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert a decision tree into a data frame of partition coordinates — parttree","text":"tree rpart.object alike. includes compatible classes mlr3 tidymodels frontends, constparty class inheriting party. keep_as_dt Logical. function relies data.table internal data manipulation. coerce final return object regular data frame (default behavior) unless user specifies TRUE. flip Logical. flip \"x\" \"y\" variables return data frame? default behaviour first split variable tree take \"y\" slot, second split variable take \"x\" slot. Setting TRUE switches around.","code":""},{"path":"https://grantmcdermott.com/parttree/reference/parttree.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert a decision tree into a data frame of partition coordinates — parttree","text":"data frame comprising seven columns: leaf node, path, set rectangle limits (.e., xmin, xmax, ymin, ymax), final column corresponding predicted value leaf.","code":""},{"path":[]},{"path":"https://grantmcdermott.com/parttree/reference/parttree.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert a decision tree into a data frame of partition coordinates — parttree","text":"","code":"library(\"parttree\")  ## rpart trees  library(\"rpart\") rp = rpart(Kyphosis ~ Start + Age, data = kyphosis)  # A parttree object is just a data frame with additional attributes (rp_pt = parttree(rp)) #>   node Kyphosis                                                       path xmin #> 1    3  present                                                Start < 8.5 -Inf #> 2    4   absent                             Start >= 8.5 --> Start >= 14.5 14.5 #> 3   10   absent                 Start >= 8.5 --> Start < 14.5 --> Age < 55  8.5 #> 4   22   absent Start >= 8.5 --> Start < 14.5 --> Age >= 55 --> Age >= 111  8.5 #> 5   23  present  Start >= 8.5 --> Start < 14.5 --> Age >= 55 --> Age < 111  8.5 #>   xmax ymin ymax #> 1  8.5 -Inf  Inf #> 2  Inf -Inf  Inf #> 3 14.5 -Inf   55 #> 4 14.5  111  Inf #> 5 14.5   55  111 attr(rp_pt, \"parttree\") #> $xvar #> [1] \"Start\" #>  #> $yvar #> [1] \"Age\" #>  #> $xrange #> [1]  1 18 #>  #> $yrange #> [1]   1 206 #>  #> $response #> [1] \"Kyphosis\" #>  #> $call #> rpart(formula = Kyphosis ~ Start + Age, data = kyphosis) #>  #> $na.action #> NULL #>  #> $raw_data #> NULL #>   # simple plot plot(rp_pt)   # removing the (recursive) partition borders helps to emphasise overall fit plot(rp_pt, border = NA)   # 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    main    = \"Tree predictions: Kyphosis recurrence\" # custom title )   ## conditional inference trees from partyit  library(\"partykit\") #> Loading required package: grid #> Loading required package: libcoin #> Loading required package: mvtnorm ct = ctree(Species ~ Petal.Length + Petal.Width, data = iris) ct_pt = parttree(ct) plot(ct_pt, pch = 19, palette = \"okabe\", main = \"ctree predictions: iris species\") #> Error in eval(raw_data): object 'ct' not found  ## rpart via partykit rp2 = as.party(rp) parttree(rp2) #>   node Kyphosis                                                       path xmin #> 3    3   absent                               Start < 8.5 --> Start < 14.5 14.5 #> 5    5   absent                 Start < 8.5 --> Start >= 14.5 --> Age < 55  8.5 #> 7    7   absent  Start < 8.5 --> Start >= 14.5 --> Age >= 55 --> Age < 111  8.5 #> 8    8  present Start < 8.5 --> Start >= 14.5 --> Age >= 55 --> Age >= 111  8.5 #> 9    9  present                                               Start >= 8.5 -Inf #>   xmax ymin ymax #> 3  Inf -Inf  Inf #> 5 14.5 -Inf   55 #> 7 14.5  111  Inf #> 8 14.5   55  111 #> 9  8.5 -Inf  Inf  ## various front-end frameworks are also supported, e.g.  # tidymodels  library(parsnip)  decision_tree() |>   set_engine(\"rpart\") |>   set_mode(\"classification\") |>   fit(Species ~ Petal.Length + Petal.Width, data=iris) |>   parttree() |>   plot(main = \"This time brought to you via parsnip...\")   # mlr3 (NB: use `keep_model = TRUE` for mlr3 learners)  library(mlr3)  task_iris = TaskClassif$new(\"iris\", iris, target = \"Species\") task_iris$formula(rhs = \"Petal.Length + Petal.Width\") #> Species ~ `Petal.Length + Petal.Width` #> NULL fit_iris = lrn(\"classif.rpart\", keep_model = TRUE) # NB! fit_iris$train(task_iris) plot(parttree(fit_iris), main = \"... and now mlr3\")"},{"path":"https://grantmcdermott.com/parttree/reference/plot.parttree.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot decision tree partitions — plot.parttree","title":"Plot decision tree partitions — plot.parttree","text":"Provides plot method parttree objects.","code":""},{"path":"https://grantmcdermott.com/parttree/reference/plot.parttree.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot decision tree partitions — plot.parttree","text":"","code":"# S3 method for class 'parttree' plot(   x,   raw = TRUE,   border = \"black\",   fill_alpha = 0.3,   expand = TRUE,   jitter = FALSE,   add = FALSE,   ... )"},{"path":"https://grantmcdermott.com/parttree/reference/plot.parttree.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot decision tree partitions — plot.parttree","text":"x parttree data frame. raw Logical. raw (original) data points plotted ? Default TRUE. border Colour partition borders (edges). Default \"black\". remove borders altogether, specify NA. fill_alpha Numeric range [0,1]. Alpha transparency filled partition rectangles. Default 0.3. expand Logical. partition limits expanded meet edge plot axes? Default TRUE. FALSE, partition limits extend range raw data. jitter Logical. raw points jittered? Default FALSE. evaluated raw = TRUE. add Logical. Add existing plot? Default FALSE. ... Additional arguments passed tinyplot.","code":""},{"path":"https://grantmcdermott.com/parttree/reference/plot.parttree.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot decision tree partitions — plot.parttree","text":"return value, called side effect producing plot. return value; called side effect producing plot.","code":""},{"path":"https://grantmcdermott.com/parttree/reference/plot.parttree.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot decision tree partitions — plot.parttree","text":"","code":"library(\"parttree\")  ## rpart trees  library(\"rpart\") rp = rpart(Kyphosis ~ Start + Age, data = kyphosis)  # A parttree object is just a data frame with additional attributes (rp_pt = parttree(rp)) #>   node Kyphosis                                                       path xmin #> 1    3  present                                                Start < 8.5 -Inf #> 2    4   absent                             Start >= 8.5 --> Start >= 14.5 14.5 #> 3   10   absent                 Start >= 8.5 --> Start < 14.5 --> Age < 55  8.5 #> 4   22   absent Start >= 8.5 --> Start < 14.5 --> Age >= 55 --> Age >= 111  8.5 #> 5   23  present  Start >= 8.5 --> Start < 14.5 --> Age >= 55 --> Age < 111  8.5 #>   xmax ymin ymax #> 1  8.5 -Inf  Inf #> 2  Inf -Inf  Inf #> 3 14.5 -Inf   55 #> 4 14.5  111  Inf #> 5 14.5   55  111 attr(rp_pt, \"parttree\") #> $xvar #> [1] \"Start\" #>  #> $yvar #> [1] \"Age\" #>  #> $xrange #> [1]  1 18 #>  #> $yrange #> [1]   1 206 #>  #> $response #> [1] \"Kyphosis\" #>  #> $call #> rpart(formula = Kyphosis ~ Start + Age, data = kyphosis) #>  #> $na.action #> NULL #>  #> $raw_data #> NULL #>   # simple plot plot(rp_pt)   # removing the (recursive) partition borders helps to emphasise overall fit plot(rp_pt, border = NA)   # customize further by passing extra options to (tiny)plot plot(    rp_pt,    border  = NA, # no partition borders    pch     = 19, # filled points    alpha   = 0.6, # point transparency    grid    = TRUE, # background grid    palette = \"classic\", # new colour palette    xlab    = \"Topmost vertebra operated on\", # custom x title    ylab    = \"Patient age (months)\", # custom y title    main    = \"Tree predictions: Kyphosis recurrence\" # custom title )   ## conditional inference trees from partyit  library(\"partykit\") ct = ctree(Species ~ Petal.Length + Petal.Width, data = iris) ct_pt = parttree(ct) plot(ct_pt, pch = 19, palette = \"okabe\", main = \"ctree predictions: iris species\") #> Error in eval(raw_data): object 'ct' not found  ## rpart via partykit rp2 = as.party(rp) parttree(rp2) #>   node Kyphosis                                                       path xmin #> 3    3   absent                               Start < 8.5 --> Start < 14.5 14.5 #> 5    5   absent                 Start < 8.5 --> Start >= 14.5 --> Age < 55  8.5 #> 7    7   absent  Start < 8.5 --> Start >= 14.5 --> Age >= 55 --> Age < 111  8.5 #> 8    8  present Start < 8.5 --> Start >= 14.5 --> Age >= 55 --> Age >= 111  8.5 #> 9    9  present                                               Start >= 8.5 -Inf #>   xmax ymin ymax #> 3  Inf -Inf  Inf #> 5 14.5 -Inf   55 #> 7 14.5  111  Inf #> 8 14.5   55  111 #> 9  8.5 -Inf  Inf  ## various front-end frameworks are also supported, e.g.  # tidymodels  library(parsnip)  decision_tree() |>   set_engine(\"rpart\") |>   set_mode(\"classification\") |>   fit(Species ~ Petal.Length + Petal.Width, data=iris) |>   parttree() |>   plot(main = \"This time brought to you via parsnip...\")   # mlr3 (NB: use `keep_model = TRUE` for mlr3 learners)  library(mlr3)  task_iris = TaskClassif$new(\"iris\", iris, target = \"Species\") task_iris$formula(rhs = \"Petal.Length + Petal.Width\") #> Species ~ `Petal.Length + Petal.Width` #> NULL fit_iris = lrn(\"classif.rpart\", keep_model = TRUE) # NB! fit_iris$train(task_iris) plot(parttree(fit_iris), main = \"... and now mlr3\")"},{"path":"https://grantmcdermott.com/parttree/news/index.html","id":"parttree-0019006","dir":"Changelog","previous_headings":"","what":"parttree 0.0.1.9006","title":"parttree 0.0.1.9006","text":"released 0.1.0","code":""},{"path":"https://grantmcdermott.com/parttree/news/index.html","id":"breaking-changes-0-0-1-9006","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"parttree 0.0.1.9006","text":"Move ggplot2 Enhances, following addition native (base R) plot.parttree method. geom_parttree() function now checks whether ggplot2 available user’s system executing code. (#18) flipaxes argument renamed flip, e.g. parttree(..., flip = TRUE). (#18)","code":""},{"path":"https://grantmcdermott.com/parttree/news/index.html","id":"improvements-0-0-1-9006","dir":"Changelog","previous_headings":"","what":"Improvements","title":"parttree 0.0.1.9006","text":"Parttree objects now class dedicated plot.parttree method, powered tinyplot. (#18) Major speed-extracting parttree nodes coordinates complicated trees. (#15) Add method tidymodels workflows objects fitted \"rpart\" engine. (#7 @juliasilge).","code":""},{"path":"https://grantmcdermott.com/parttree/news/index.html","id":"bug-fixes-0-0-1-9006","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"parttree 0.0.1.9006","text":"Support negative values. (#6 @pjgeens) Better handling single-level factors flip(axes). (#5) Handling complex formula expressions. (#17)","code":""},{"path":"https://grantmcdermott.com/parttree/news/index.html","id":"internals-0-0-1-9006","dir":"Changelog","previous_headings":"","what":"Internals","title":"parttree 0.0.1.9006","text":"Several dependency adjustments, e.g. tinyplot Imports ggplot2 Suggests. (#18) Added SVG snapshots image-based tests. (#18) Bump ggplot2 version dependency match deprecated functions 3.4.0. Switched “main” primary GitHub branch development. Added two dedicated vignettes.","code":""},{"path":"https://grantmcdermott.com/parttree/news/index.html","id":"parttree-001","dir":"Changelog","previous_headings":"","what":"parttree 0.0.1","title":"parttree 0.0.1","text":"Create parttree() generic. (, #3 @zeileis) Support partykit objects (.e. constparty class), turn allows support base plot methods. (#3 @zeileis) ggplot2 moves imports depends now automatically loaded parttree. Add flipaxes argument easy switching case mismatch plot orientation. (#2 @brhkim) Added NEWS.md file track changes package.","code":""},{"path":"https://grantmcdermott.com/parttree/news/index.html","id":"parttree-0009000","dir":"Changelog","previous_headings":"","what":"parttree 0.0.0.9000","title":"parttree 0.0.0.9000","text":"Initial set functions.","code":""}]
+[{"path":"https://grantmcdermott.com/parttree/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2020 Grant McDermott Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://grantmcdermott.com/parttree/articles/parttree-art.html","id":"background","dir":"Articles","previous_headings":"","what":"Background","title":"Abstract art with parttree and friends","text":"One fun application tree-based methods abstracting art images. really striking examples, take look designer Dimitris Ladopoulos’ portfolio. vignette show implement basic ideas using parttree friends. packages ’ll using. exact details vary depending image hand, essential recipe type art abstraction follows: Convert image matrix (data frame), rows columns correspond, respectively, X Y coordinates individual pixels. words, cell matrix (data frame) represents colour values individual pixel. Split data primary (RGB) colour channels. now three matrices (data frames), cell represents red/green/blue colour channel value individual pixel. Run tree model three RGB datasets. case, trying predict relevant colour channel value function X Y coordinates. Use predicted values plot abstracted art piece! (Okay, step requires bit work, ’re see works practice…)","code":"library(parttree) # This package library(rpart)    # For decision trees library(magick)   # For reading and manipulating images library(imager)   # Another image library, with some additional features  op = par(mar = c(0,0,0,0)) # Remove plot margins magick:::magick_threads(2) #> [1] 2"},{"path":"https://grantmcdermott.com/parttree/articles/parttree-art.html","id":"example-1-peales-portrait-of-rosalba","dir":"Articles","previous_headings":"","what":"Example 1: Peale’s “Portrait of Rosalba”","title":"Abstract art with parttree and friends","text":"first example, mimic one Dimitri Ladopoulos’s aforementioned portrait pieces. Specifically, abstract close-(eyes ) Rembrandt Peale’s portrait daughter, Rosalba. can download digital image original artwork Wikimedia. ’ll download high-res version show , feel free go lower resolution want reduce modeling plotting times.1 However, crop image around Rosalba’s eyes get close-reduce overall complexity exercise.2  cropped image hand, let’s walk 4-step recipe . Step 1. Convert image data frame. Step 2. Split image RGB colour channel. cc column , 1=Red, 2=Green, 3=Blue. Step 3. Fit decision tree (similar model) colour channel data frames. tuning parameters give model matter experimentation. ’m giving low complexity parameter (see variation final predictions) trimming tree maximum depth 30 nodes. next code chunk takes 15 seconds run laptop, much quicker downloaded lower-res image. Step 4. Use model (colour) predictions construct abstracted art piece. bit glib earlier, since really involves sub-steps. First, let’s grab predictions trees. ’re going use predictions draw abstracted (downscaled) version image.3 Probably easiest way taking predictions overwriting “value” column original (pre-split) rosalba_df data frame. can coerce data frame back cimg object, comes bunch nice plotting methods. Now ’re ready draw abstracted art piece. ’s also parttree enter fray, since ’ll using highlight partitioned areas downscaled pixels. ’s can using base R graphics.  can achieve effect ggplot2 prefer use .","code":"# Download the image rosalba = image_read(\"https://upload.wikimedia.org/wikipedia/commons/a/aa/Rembrandt_Peale_-_Portrait_of_Rosalba_Peale_-_Google_Art_Project.jpg\")  # Crop around the eyes rosalba = image_crop(rosalba, \"850x400+890+1350\") # rosalba = image_crop(rosalba, \"750x350+890+1350\")  # Convert to cimg (better for in-memory manipulation) rosalba = magick2cimg(rosalba)  # Display rosalba #> Image. Width: 850 pix Height: 400 pix Depth: 1 Colour channels: 3 plot(rosalba, axes = FALSE) # Coerce to data frame rosalba_df = as.data.frame(rosalba)  # Round color values to ease up work for decision tree rosalba_df$value = round(rosalba_df$value, 4)  head(rosalba_df) #>   x y cc  value #> 1 1 1  1 0.3059 #> 2 2 1  1 0.3059 #> 3 3 1  1 0.2627 #> 4 4 1  1 0.2471 #> 5 5 1  1 0.3137 #> 6 6 1  1 0.2941 rosalba_ccs = split(rosalba_df, rosalba_df$cc)  # We have a list of three DFs by colour channel. Uncomment if you want to see: # str(rosalba_css) ## Start creating regression tree for each color channel. We'll adjust some ## control parameters to give us the \"right\" amount of resolution in the final ## plots. trees = lapply(   rosalba_ccs,    # function(d) rpart(value ~ x + y, data=d, control=list(cp=0.00001, maxdepth=20))   function(d) rpart(value ~ x + y, data=d, control=list(cp=0.00002, maxdepth=20))   ) pred = lapply(trees, predict) # get predictions for each tree # The pred object is a list, so we convert it to a vector before overwriting the # value column of the original data frame rosalba_df$value = do.call(\"c\", pred)  # Convert back into a cimg object, with the predictions providing pixel values pred_img = as.cimg(rosalba_df) #> Warning in as.cimg.data.frame(rosalba_df): Guessing image dimensions from #> maximum coordinate values # get a list of parttree data frames (one for each tree) pts = lapply(trees, parttree)  ## first plot the downscaled image... plot(pred_img, axes = FALSE) ## ... then layer the partitions as a series of rectangles lapply(   pts,    function(pt) plot(     pt, raw = FALSE, add = TRUE, expand = FALSE,     fill_alpha = NULL, lwd = 0.1, border = \"grey15\"   ) ) #> $`1` #> NULL #>  #> $`2` #> NULL #>  #> $`3` #> NULL library(ggplot2) ggplot() +   annotation_raster(pred_img,  ymin=-Inf, ymax=Inf, xmin=-Inf, xmax=Inf) +   lapply(trees, function(d) geom_parttree(data = d, lwd = 0.05, col = \"grey15\")) +   scale_x_continuous(limits=c(0, max(rosalba_df$x)), expand=c(0,0)) +   scale_y_reverse(limits=c(max(rosalba_df$y), 0), expand=c(0,0)) +   coord_fixed(ratio = Reduce(x = dim(rosalba)[2:1], f = \"/\") * 2) +   theme_void()"},{"path":"https://grantmcdermott.com/parttree/articles/parttree-art.html","id":"example-2-parttree-logo","dir":"Articles","previous_headings":"","what":"Example 2: parttree logo","title":"Abstract art with parttree and friends","text":"Now ’ve walked detailed example, let’s quickly go one . ’re get meta: Visualizing decision tree partitions actual tree image. also happens basis parttree logo, seems nice way bookend vignette. main image conversion, modeling, prediction steps. follow recipe saw previous portrait example, won’t repeat explanations.  point may tempted plot figure plot(bonzai_pred_img), definitely work. However, ’m going show one additional tweak make partition rectangles look bit nicer: Use mean color channel values instead black. ’s function grabbing information cimg object converted data frame.4 penultimate step generate parttree objects colour channel-based trees. now can plot everything together.  , equivalent result prefer ggplot2.","code":"bonzai = load.image(\"https://upload.wikimedia.org/wikipedia/commons/thumb/0/0c/Japanese_Zelkova_bonsai_16%2C_30_April_2012.JPG/480px-Japanese_Zelkova_bonsai_16%2C_30_April_2012.JPG\") plot(bonzai, axes = FALSE) # Coerce to data frame bonzai_df = as.data.frame(bonzai)  # Round color values to ease up work for decision trees bonzai_df$value = round(bonzai_df$value, 2)  # Split into colour channels bonzai_ccs = split(bonzai_df, bonzai_df$cc)  # Fit decision trees on each colour channel DF. Note that I'm deliberating # truncating the max tree depth to reduce the number of partitions. bonzai_trees = lapply(   bonzai_ccs,    function(d) rpart(value ~ x + y, data=d, control=list(cp=0.00001, maxdepth=10)) )  # Overwrite value column with predictions vector bonzai_df$value = do.call(\"c\", lapply(bonzai_trees, predict))  # Convert back into a cimg object, with the predictions providing pixel values bonzai_pred_img = as.cimg(bonzai_df) #> Warning in as.cimg.data.frame(bonzai_df): Guessing image dimensions from #> maximum coordinate values mean_cols = function(dat) {   mcols = tapply(dat$value, dat$cc, FUN = \"mean\")   col1 = rgb(mcols[1], mcols[2], mcols[3])   hicol = function(x) mean(x) + 0.4*sd(subset(dat, cc==1)$value)   col2 = rgb(hicol(subset(dat, cc==1)$value), hicol(subset(dat, cc==2)$value), hicol(subset(dat, cc==3)$value))   locol = function(x) mean(x) - 0.4*sd(subset(dat, cc==1)$value)   col3 = rgb(locol(subset(dat, cc==1)$value), locol(subset(dat, cc==2)$value), locol(subset(dat, cc==3)$value))   return(c(col1, col2, col3)) }  bonzai_mean_cols = mean_cols(bonzai_df) bonzai_pts = lapply(bonzai_trees, parttree) plot(bonzai_pred_img, axes = FALSE) Map(   function(pt, cols) {     plot(       pt, raw = FALSE, add = TRUE, expand = FALSE,       fill_alpha = 0, lwd = 0.2, border = cols     )   },   pt = bonzai_pts,   cols = bonzai_mean_cols ) #> $`1` #> NULL #>  #> $`2` #> NULL #>  #> $`3` #> NULL # library(ggplot2) ggplot() +   annotation_raster(bonzai_pred_img,  ymin=-Inf, ymax=Inf, xmin=-Inf, xmax=Inf) +   Map(function(d, cols) geom_parttree(data = d, lwd = 0.1, col = cols), d = bonzai_trees, cols = bonzai_mean_cols) +   scale_x_continuous(limits=c(0, max(bonzai_df$x)), expand=c(0,0)) +   scale_y_reverse(limits=c(max(bonzai_df$y), 0), expand=c(0,0)) +   coord_fixed() +   theme_void()"},{"path":"https://grantmcdermott.com/parttree/articles/parttree-art.html","id":"postscript","dir":"Articles","previous_headings":"","what":"Postscript","title":"Abstract art with parttree and friends","text":"individual trees colour channel make nice stained glass prints…","code":"lapply(   seq_along(bonzai_pts),   function(i) {     plot(       bonzai_pts[[i]], raw = FALSE, expand = FALSE,       axes = FALSE, legend = FALSE,       main = paste0(c(\"R\", \"G\", \"B\")[i]),       ## Aside: We're reversing the y-scale since higher values actually       ## correspond to points lower on the image, visually.       ylim = rev(attr(bonzai_pts[[i]], \"parttree\")[[\"yrange\"]])     )    } ) #> [[1]] #> NULL #>  #> [[2]] #> NULL #>  #> [[3]] #> NULL # reset the plot margins par(op)"},{"path":"https://grantmcdermott.com/parttree/articles/parttree-intro.html","id":"motivating-example-classifying-penguin-species","dir":"Articles","previous_headings":"","what":"Motivating example: Classifying penguin species","title":"Introduction to parttree","text":"Start loading parttree package alongside rpart, comes bundled base R installation. basic examples follow, ’ll use well-known Palmer Penguins dataset demonstrate functionality. can load dataset via parent package (), import directly CSV . Dataset hand, let’s say interested predicting penguin species function 1) flipper length 2) bill length. model simple decision tree: Like tree-based frameworks, rpart comes default plot method visualizing resulting node splits.  okay, don’t feel provides much intuition model’s prediction scale actual data. words, ’d prefer see : tree partitioned original penguin data? parttree enters fray. package named primary workhorse function parttree(), extracts information needed produce nice plot tree partitions alongside original data.  Et voila! Now can clearly see model divided Cartesian space data. Gentoo penguins typically longer flippers Chinstrap Adelie penguins, latter shortest bills. perspective end-user, ptree parttree object interesting . simply data frame contains basic information needed plot (partition coordinates, etc.). can think helpful intermediate object way visualization interest. Speaking visualization, underneath hood plot.parttree calls powerful tinyplot package. latter’s various customization arguments can passed parttree plot make look bit nicer. example:","code":"library(parttree)  # This package library(rpart)     # For fitting decisions trees  # install.packages(\"palmerpenguins\") data(\"penguins\", package = \"palmerpenguins\") head(penguins) #> # A tibble: 6 × 8 #>   species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g #>   <fct>   <fct>              <dbl>         <dbl>             <int>       <int> #> 1 Adelie  Torgersen           39.1          18.7               181        3750 #> 2 Adelie  Torgersen           39.5          17.4               186        3800 #> 3 Adelie  Torgersen           40.3          18                 195        3250 #> 4 Adelie  Torgersen           NA            NA                  NA          NA #> 5 Adelie  Torgersen           36.7          19.3               193        3450 #> 6 Adelie  Torgersen           39.3          20.6               190        3650 #> # ℹ 2 more variables: sex <fct>, year <int> tree = rpart(species ~ flipper_length_mm + bill_length_mm, data = penguins) tree #> n=342 (2 observations deleted due to missingness) #>  #> node), split, n, loss, yval, (yprob) #>       * denotes terminal node #>  #> 1) root 342 191 Adelie (0.441520468 0.198830409 0.359649123)   #>   2) flipper_length_mm< 206.5 213  64 Adelie (0.699530516 0.295774648 0.004694836)   #>     4) bill_length_mm< 43.35 150   5 Adelie (0.966666667 0.033333333 0.000000000) * #>     5) bill_length_mm>=43.35 63   5 Chinstrap (0.063492063 0.920634921 0.015873016) * #>   3) flipper_length_mm>=206.5 129   7 Gentoo (0.015503876 0.038759690 0.945736434) * plot(tree, compress = TRUE) text(tree, use.n = TRUE) ptree = parttree(tree) plot(ptree) # See also `attr(ptree, \"parttree\")` ptree #>   node   species                                                  path  xmin #> 1    3    Gentoo                            flipper_length_mm >= 206.5 206.5 #> 2    4    Adelie  flipper_length_mm < 206.5 --> bill_length_mm < 43.35  -Inf #> 3    5 Chinstrap flipper_length_mm < 206.5 --> bill_length_mm >= 43.35  -Inf #>    xmax  ymin  ymax #> 1   Inf  -Inf   Inf #> 2 206.5  -Inf 43.35 #> 3 206.5 43.35   Inf plot(ptree, pch = 16, palette = \"classic\", alpha = 0.75, grid = TRUE)"},{"path":"https://grantmcdermott.com/parttree/articles/parttree-intro.html","id":"continuous-predictions","dir":"Articles","previous_headings":"Motivating example: Classifying penguin species","what":"Continuous predictions","title":"Introduction to parttree","text":"addition discrete classification problems, parttree also supports regression trees continuous independent variables.","code":"tree_cont = rpart(body_mass_g ~ flipper_length_mm + bill_length_mm, data = penguins)  tree_cont |>   parttree() |>   plot(pch = 16, palette = \"viridis\")"},{"path":"https://grantmcdermott.com/parttree/articles/parttree-intro.html","id":"supported-model-classes","dir":"Articles","previous_headings":"","what":"Supported model classes","title":"Introduction to parttree","text":"Alongside rpart model objects working thus far, parttree also supports decision trees created partykit package. see latter’s ctree (conditional inference tree) algorithm yields slightly sophisticated partitioning former’s default.  parttree also supports variety “frontend” modes call rpart::rpart() underlying engine. includes packages mlr3 tidymodels (parsnip workflows) ecosystems. quick demonstration using parsnip, ’ll also pull different dataset just change things little.","code":"library(partykit) #> Loading required package: grid #> Loading required package: libcoin #> Loading required package: mvtnorm  ctree(species ~ flipper_length_mm + bill_length_mm, data = penguins) |>   parttree() |>   plot(pch = 16, palette = \"classic\", alpha = 0.5) set.seed(123) ## For consistent jitter  library(parsnip) library(titanic) ## Just for a different data set  titanic_train$Survived = as.factor(titanic_train$Survived)  ## Build our tree using parsnip (but with rpart as the model engine) ti_tree =   decision_tree() |>   set_engine(\"rpart\") |>   set_mode(\"classification\") |>   fit(Survived ~ Pclass + Age, data = titanic_train) ## Now pass to parttree and plot ti_tree |>   parttree() |>   plot(pch = 16, jitter = TRUE, palette = \"dark\", alpha = 0.7)"},{"path":"https://grantmcdermott.com/parttree/articles/parttree-intro.html","id":"ggplot2","dir":"Articles","previous_headings":"","what":"ggplot2","title":"Introduction to parttree","text":"default plot.parttree method produces base graphics plot. also support ggplot2 via dedicated geom_parttree() function. demonstrate initial classification tree earlier.  Compared “native” plot.parttree method, note ggplot2 workflow requires tweaks: need need plot original dataset separate layer (.e., geom_point()). geom_parttree() accepts tree object , result parttree().1 Continuous regression trees can also drawn geom_parttree. However, recommend adjusting plot fill aesthetic since model likely partition data intervals don’t match exactly raw data. easiest way setting colour fill aesthetic together part scale_colour_* call.","code":"library(ggplot2) theme_set(theme_linedraw())  ## re-using the tree model object from above... ggplot(data = penguins, aes(x = flipper_length_mm, y = bill_length_mm)) +   geom_point(aes(col = species)) +   geom_parttree(data = tree, aes(fill=species), alpha = 0.1) ## re-using the tree_cont model object from above... ggplot(data = penguins, aes(x = flipper_length_mm, y = bill_length_mm)) +   geom_parttree(data = tree_cont, aes(fill=body_mass_g), alpha = 0.3) +   geom_point(aes(col = body_mass_g)) +    scale_colour_viridis_c(aesthetics = c('colour', 'fill')) # NB: Set colour + fill together"},{"path":"https://grantmcdermott.com/parttree/articles/parttree-intro.html","id":"gotcha-ggplot-orientation","dir":"Articles","previous_headings":"ggplot2","what":"Gotcha: (gg)plot orientation","title":"Introduction to parttree","text":"already said, geom_parttree() calls companion parttree() function internally, coerces rpart tree object data frame easily understood ggplot2. example, consider initial “ptree” object earlier. , resulting data frame designed amenable ggplot2 geom layer, columns like xmin, xmax, etc. specifying aesthetics ggplot2 recognizes. (Fun fact: geom_parttree() really just thin wrapper around geom_rect().) goal parttree abstract away kinds details user, can just specify geom_parttree()—valid tree object data input—done . However, generally works well, can sometimes lead unexpected behaviour terms plot orientation. ’s ’s hard guess ahead time user specify x y variables (.e. axes) ggplot2 layers.2 see mean, let’s redo penguin plot earlier, time switch axes main ggplot() call.  case , kind orientation mismatch normally (hopefully) pretty easy recognize. fix, can use flip = TRUE argument flip orientation geom_parttree layer.","code":"# ptree = parttree(tree) ptree #>   node   species                                                  path  xmin #> 1    3    Gentoo                            flipper_length_mm >= 206.5 206.5 #> 2    4    Adelie  flipper_length_mm < 206.5 --> bill_length_mm < 43.35  -Inf #> 3    5 Chinstrap flipper_length_mm < 206.5 --> bill_length_mm >= 43.35  -Inf #>    xmax  ymin  ymax #> 1   Inf  -Inf   Inf #> 2 206.5  -Inf 43.35 #> 3 206.5 43.35   Inf ## First, redo our first plot but this time switch the x and y variables p3 = ggplot(   data = penguins,    aes(x = bill_length_mm, y = flipper_length_mm) ## Switched!   ) +   geom_point(aes(col = species))  ## Add on our tree (and some preemptive titling..) p3 +   geom_parttree(data = tree, aes(fill = species), alpha = 0.1) +   labs(     title = \"Oops!\",     subtitle = \"Looks like a mismatch between our x and y axes...\"   ) p3 +   geom_parttree(     data = tree, aes(fill = species), alpha = 0.1,     flip = TRUE  ## Flip the orientation   ) +   labs(title = \"That's better\")"},{"path":"https://grantmcdermott.com/parttree/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Grant McDermott. Author, maintainer. Achim Zeileis. Contributor. Brian Heseung Kim. Contributor. Julia Silge. Contributor.","code":""},{"path":"https://grantmcdermott.com/parttree/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"McDermott G (2025). parttree: Visualize Simple 2-D Decision Tree Partitions. R package version 0.1.0, https://grantmcdermott.com/parttree/.","code":"@Manual{,   title = {parttree: Visualize Simple 2-D Decision Tree Partitions},   author = {Grant McDermott},   year = {2025},   note = {R package version 0.1.0},   url = {https://grantmcdermott.com/parttree/}, }"},{"path":"https://grantmcdermott.com/parttree/index.html","id":"parttree-","dir":"","previous_headings":"","what":"Visualize Simple 2-D Decision Tree Partitions","title":"Visualize Simple 2-D Decision Tree Partitions","text":"Visualize simple 2-D decision tree partitions R. parttree package provides visualization methods base R graphics (via tinyplot) ggplot2.","code":""},{"path":"https://grantmcdermott.com/parttree/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Visualize Simple 2-D Decision Tree Partitions","text":"stable version parttree available CRAN. , can grab latest development version R-universe.","code":"install.packages(\"parttree\") install.packages(\"parttree\", repos = \"https://grantmcdermott.r-universe.dev\")"},{"path":"https://grantmcdermott.com/parttree/index.html","id":"quickstart","dir":"","previous_headings":"","what":"Quickstart","title":"Visualize Simple 2-D Decision Tree Partitions","text":"parttree homepage includes introductory vignette detailed documentation. ’s quickstart example using “kyphosis” dataset comes bundled rpart package. case, interested predicting kyphosis recovery spinal surgery, function 1) number topmost vertebra operated, 2) patient age. key function parttree(), comes plotting method.  Customize plots passing additional arguments:","code":"library(rpart)     # For the dataset and fitting decisions trees library(parttree)  # This package  fit = rpart(Kyphosis ~ Start + Age, data = kyphosis)  # Grab the partitions and plot fit_pt = parttree(fit) plot(fit_pt) 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 )"},{"path":"https://grantmcdermott.com/parttree/index.html","id":"ggplot2","dir":"","previous_headings":"Quickstart","what":"ggplot2","title":"Visualize Simple 2-D Decision Tree Partitions","text":"ggplot2 users, offer equivalent workflow via geom_partree() visualization layer.","code":"library(ggplot2) ## Should be loaded separately  ggplot(kyphosis, aes(x = Start, y = Age)) +   geom_parttree(data = fit, alpha = 0.1, aes(fill = Kyphosis)) + # <-- key layer   geom_point(aes(col = Kyphosis)) +   labs(     x = \"No. of topmost vertebra operated on\", y = \"Patient age (months)\",     caption = \"Note: Points denote observations. Shading denotes model predictions.\"     ) +   theme_minimal()"},{"path":"https://grantmcdermott.com/parttree/reference/geom_parttree.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualize tree partitions with ggplot2 — geom_parttree","title":"Visualize tree partitions with ggplot2 — geom_parttree","text":"geom_parttree() simple wrapper around parttree() takes tree model object converts amenable data frame ggplot2 knows plot. Please note ggplot2 hard dependency parttree must thus installed separately user's system calling geom_parttree.","code":""},{"path":"https://grantmcdermott.com/parttree/reference/geom_parttree.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualize tree partitions with ggplot2 — geom_parttree","text":"","code":"geom_parttree(   mapping = NULL,   data = NULL,   stat = \"identity\",   position = \"identity\",   linejoin = \"mitre\",   na.rm = FALSE,   show.legend = NA,   inherit.aes = TRUE,   flip = FALSE,   ... )"},{"path":"https://grantmcdermott.com/parttree/reference/geom_parttree.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualize tree partitions with ggplot2 — geom_parttree","text":"mapping Set aesthetic mappings created aes(). specified inherit.aes = TRUE (default), combined default mapping top level plot. must supply mapping plot mapping. data rpart::rpart.object object compatible type (e.g. decision tree constructed via partykit, tidymodels, mlr3 front-ends). stat statistical transformation use data layer. using geom_*() function construct layer, stat argument can used override default coupling geoms stats. stat argument accepts following: Stat ggproto subclass, example StatCount. string naming stat. give stat string, strip function name stat_ prefix. example, use stat_count(), give stat \"count\". information ways specify stat, see layer stat documentation. position position adjustment use data layer. can used various ways, including prevent overplotting improving display. position argument accepts following: result calling position function, position_jitter(). method allows passing extra arguments position. string naming position adjustment. give position string, strip function name position_ prefix. example, use position_jitter(), give position \"jitter\". information ways specify position, see layer position documentation. linejoin Line join style (round, mitre, bevel). na.rm FALSE, default, missing values removed warning. TRUE, missing values silently removed. show.legend logical. layer included legends? NA, default, includes aesthetics mapped. FALSE never includes, TRUE always includes. can also named logical vector finely select aesthetics display. inherit.aes FALSE, overrides default aesthetics, rather combining . useful helper functions define data aesthetics inherit behaviour default plot specification, e.g. borders(). flip Logical. default, \"x\" \"y\" axes variables plotting determined first split tree. can cause plot orientation mismatches depending users specify layers plot. Setting TRUE flip \"x\" \"y\" variables geom_parttree layer. ... arguments passed layer()'s params argument. arguments broadly fall one 4 categories . Notably, arguments position argument, aesthetics required can passed .... Unknown arguments part 4 categories ignored. Static aesthetics mapped scale, fixed value apply layer whole. example, colour = \"red\" linewidth = 3. geom's documentation Aesthetics section lists available options. 'required' aesthetics passed params. Please note passing unmapped aesthetics vectors technically possible, order required length guaranteed parallel input data. constructing layer using stat_*() function, ... argument can used pass parameters geom part layer. example stat_density(geom = \"area\", outline.type = \"\"). geom's documentation lists parameters can accept. Inversely, constructing layer using geom_*() function, ... argument can used pass parameters stat part layer. example geom_area(stat = \"density\", adjust = 0.5). stat's documentation lists parameters can accept. key_glyph argument layer() may also passed .... can one functions described key glyphs, change display layer legend.","code":""},{"path":"https://grantmcdermott.com/parttree/reference/geom_parttree.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Visualize tree partitions with ggplot2 — geom_parttree","text":"ggplot  layer.","code":""},{"path":"https://grantmcdermott.com/parttree/reference/geom_parttree.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Visualize tree partitions with ggplot2 — geom_parttree","text":"way ggplot2 validates inputs assembles plot layers, note data input geom_parttree() (.e. decision tree object) must assigned layer ; initialising ggplot2::ggplot() call. See Examples.","code":""},{"path":"https://grantmcdermott.com/parttree/reference/geom_parttree.html","id":"aesthetics","dir":"Reference","previous_headings":"","what":"Aesthetics","title":"Visualize tree partitions with ggplot2 — geom_parttree","text":"geom_parttree() aims \"work----box\" minimal input user's side, apart specifying data object. includes taking care data transformation way , generally, produces optimal corner coordinates partition (.e. xmin, xmax, ymin, ymax). However, also understands following aesthetics users may choose specify manually: fill (particularly encouraged, since provide visual cue regarding prediction partition region) colour alpha linetype size","code":""},{"path":[]},{"path":"https://grantmcdermott.com/parttree/reference/geom_parttree.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visualize tree partitions with ggplot2 — geom_parttree","text":"","code":"# install.packages(\"ggplot2\") library(ggplot2)  # ggplot2 must be installed/loaded separately  library(parttree) # this package library(rpart)    # decision trees # ## Simple decision tree (max of two predictor variables)  iris_tree = rpart(Species ~ Petal.Length + Petal.Width, data=iris)  # 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) p + geom_parttree(data = iris_tree)   # 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) p + geom_parttree(data = iris_tree, aes(fill = Species), col = NA, alpha = 0.1)   # ## Example with plot orientation mismatch  p2 = ggplot(iris, aes(x=Petal.Width, y=Petal.Length)) +   geom_point(aes(col=Species))  # Oops p2 + geom_parttree(data = iris_tree, aes(fill=Species), alpha = 0.1)   # 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.:  # install.packages(\"parsnip\") library(parsnip)  iris_tree_parsnip = decision_tree() |>   set_engine(\"rpart\") |>   set_mode(\"classification\") |>   fit(Species ~ Petal.Length + Petal.Width, data=iris)  p + geom_parttree(data = iris_tree_parsnip, aes(fill=Species), alpha = 0.1)   # ## Trees with continuous independent variables are also supported.  # 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)) +  geom_parttree(    data = iris_tree_cont,    aes(fill = Petal.Length), alpha=0.5    ) +   geom_point(aes(col = Petal.Length)) +   theme_minimal()  # Legend scales don't quite match here: p3   # Better to scale fill to the original data p3 + scale_fill_continuous(limits = range(iris$Petal.Length))"},{"path":"https://grantmcdermott.com/parttree/reference/parttree.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert a decision tree into a data frame of partition coordinates — parttree","title":"Convert a decision tree into a data frame of partition coordinates — parttree","text":"Extracts terminal leaf nodes decision tree contains two numeric predictor variables. leaf nodes converted data frame, row represents partition (leaf terminal node) can easily plotted 2-D coordinate space.","code":""},{"path":"https://grantmcdermott.com/parttree/reference/parttree.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert a decision tree into a data frame of partition coordinates — parttree","text":"","code":"parttree(tree, keep_as_dt = FALSE, flip = FALSE)"},{"path":"https://grantmcdermott.com/parttree/reference/parttree.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert a decision tree into a data frame of partition coordinates — parttree","text":"tree rpart.object alike. includes compatible classes mlr3 tidymodels frontends, constparty class inheriting party. keep_as_dt Logical. function relies data.table internal data manipulation. coerce final return object regular data frame (default behavior) unless user specifies TRUE. flip Logical. flip \"x\" \"y\" variables return data frame? default behaviour first split variable tree take \"y\" slot, second split variable take \"x\" slot. Setting TRUE switches around. Note: argument primarily useful passed via geom_parttree ensure correct axes orientation part ggplot2 visualization (see geom_parttree Examples). expect users call parttree(..., flip = TRUE) directly. Similarly, switch axes orientation native (base graphics) plot.parttree method, recommend calling plot(..., flip = TRUE) rather flipping underlying parttree object.","code":""},{"path":"https://grantmcdermott.com/parttree/reference/parttree.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert a decision tree into a data frame of partition coordinates — parttree","text":"data frame comprising seven columns: leaf node, path, set rectangle limits (.e., xmin, xmax, ymin, ymax), final column corresponding predicted value leaf.","code":""},{"path":[]},{"path":"https://grantmcdermott.com/parttree/reference/parttree.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert a decision tree into a data frame of partition coordinates — parttree","text":"","code":"library(\"parttree\") # ## rpart trees  library(\"rpart\") rp = rpart(Kyphosis ~ Start + Age, data = kyphosis)  # A parttree object is just a data frame with additional attributes (rp_pt = parttree(rp)) #>   node Kyphosis                                                       path xmin #> 1    3  present                                                Start < 8.5 -Inf #> 2    4   absent                             Start >= 8.5 --> Start >= 14.5 14.5 #> 3   10   absent                 Start >= 8.5 --> Start < 14.5 --> Age < 55  8.5 #> 4   22   absent Start >= 8.5 --> Start < 14.5 --> Age >= 55 --> Age >= 111  8.5 #> 5   23  present  Start >= 8.5 --> Start < 14.5 --> Age >= 55 --> Age < 111  8.5 #>   xmax ymin ymax #> 1  8.5 -Inf  Inf #> 2  Inf -Inf  Inf #> 3 14.5 -Inf   55 #> 4 14.5  111  Inf #> 5 14.5   55  111 attr(rp_pt, \"parttree\") #> $xvar #> [1] \"Start\" #>  #> $yvar #> [1] \"Age\" #>  #> $xrange #> [1]  1 18 #>  #> $yrange #> [1]   1 206 #>  #> $response #> [1] \"Kyphosis\" #>  #> $call #> rpart(formula = Kyphosis ~ Start + Age, data = kyphosis) #>  #> $na.action #> NULL #>  #> $flip #> [1] FALSE #>  #> $raw_data #> NULL #>   # simple plot plot(rp_pt)   # removing the (recursive) partition borders helps to emphasise overall fit plot(rp_pt, border = NA)   # customize further by passing extra options to (tiny)plot plot(    rp_pt,    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\") #> Loading required package: grid #> Loading required package: libcoin #> Loading required package: mvtnorm ct = ctree(Species ~ Petal.Length + Petal.Width, data = iris) ct_pt = parttree(ct) plot(ct_pt, pch = 19, palette = \"okabe\", main = \"ctree predictions: iris species\") #> Error in eval(raw_data): object 'ct' not found  ## rpart via partykit rp2 = as.party(rp) parttree(rp2) #>   node Kyphosis                                                       path xmin #> 3    3   absent                               Start < 8.5 --> Start < 14.5 14.5 #> 5    5   absent                 Start < 8.5 --> Start >= 14.5 --> Age < 55  8.5 #> 7    7   absent  Start < 8.5 --> Start >= 14.5 --> Age >= 55 --> Age < 111  8.5 #> 8    8  present Start < 8.5 --> Start >= 14.5 --> Age >= 55 --> Age >= 111  8.5 #> 9    9  present                                               Start >= 8.5 -Inf #>   xmax ymin ymax #> 3  Inf -Inf  Inf #> 5 14.5 -Inf   55 #> 7 14.5  111  Inf #> 8 14.5   55  111 #> 9  8.5 -Inf  Inf  # ## various front-end frameworks are also supported, e.g.  # tidymodels  # install.packages(\"parsnip\") library(parsnip)  decision_tree() |>   set_engine(\"rpart\") |>   set_mode(\"classification\") |>   fit(Species ~ Petal.Length + Petal.Width, data=iris) |>   parttree() |>   plot(main = \"This time brought to you via parsnip...\")   # mlr3 (NB: use `keep_model = TRUE` for mlr3 learners)  # install.packages(\"mlr3\") library(mlr3)  task_iris = TaskClassif$new(\"iris\", iris, target = \"Species\") task_iris$formula(rhs = \"Petal.Length + Petal.Width\") #> Species ~ `Petal.Length + Petal.Width` #> NULL fit_iris = lrn(\"classif.rpart\", keep_model = TRUE) # NB! fit_iris$train(task_iris) plot(parttree(fit_iris), main = \"... and now mlr3\")"},{"path":"https://grantmcdermott.com/parttree/reference/plot.parttree.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot decision tree partitions — plot.parttree","title":"Plot decision tree partitions — plot.parttree","text":"Provides plot method parttree objects.","code":""},{"path":"https://grantmcdermott.com/parttree/reference/plot.parttree.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot decision tree partitions — plot.parttree","text":"","code":"# S3 method for class 'parttree' plot(   x,   raw = TRUE,   border = \"black\",   fill_alpha = 0.3,   expand = TRUE,   jitter = FALSE,   add = FALSE,   ... )"},{"path":"https://grantmcdermott.com/parttree/reference/plot.parttree.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot decision tree partitions — plot.parttree","text":"x parttree data frame. raw Logical. raw (original) data points plotted ? Default TRUE. border Colour partition borders (edges). Default \"black\". remove borders altogether, specify NA. fill_alpha Numeric range [0,1]. Alpha transparency filled partition rectangles. Default 0.3. expand Logical. partition limits expanded meet edge plot axes? Default TRUE. FALSE, partition limits extend range raw data. jitter Logical. raw points jittered? Default FALSE. evaluated raw = TRUE. add Logical. Add existing plot? Default FALSE. ... Additional arguments passed tinyplot.","code":""},{"path":"https://grantmcdermott.com/parttree/reference/plot.parttree.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot decision tree partitions — plot.parttree","text":"return value, called side effect producing plot. return value; called side effect producing plot.","code":""},{"path":"https://grantmcdermott.com/parttree/reference/plot.parttree.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot decision tree partitions — plot.parttree","text":"","code":"library(\"parttree\") # ## rpart trees  library(\"rpart\") rp = rpart(Kyphosis ~ Start + Age, data = kyphosis)  # A parttree object is just a data frame with additional attributes (rp_pt = parttree(rp)) #>   node Kyphosis                                                       path xmin #> 1    3  present                                                Start < 8.5 -Inf #> 2    4   absent                             Start >= 8.5 --> Start >= 14.5 14.5 #> 3   10   absent                 Start >= 8.5 --> Start < 14.5 --> Age < 55  8.5 #> 4   22   absent Start >= 8.5 --> Start < 14.5 --> Age >= 55 --> Age >= 111  8.5 #> 5   23  present  Start >= 8.5 --> Start < 14.5 --> Age >= 55 --> Age < 111  8.5 #>   xmax ymin ymax #> 1  8.5 -Inf  Inf #> 2  Inf -Inf  Inf #> 3 14.5 -Inf   55 #> 4 14.5  111  Inf #> 5 14.5   55  111 attr(rp_pt, \"parttree\") #> $xvar #> [1] \"Start\" #>  #> $yvar #> [1] \"Age\" #>  #> $xrange #> [1]  1 18 #>  #> $yrange #> [1]   1 206 #>  #> $response #> [1] \"Kyphosis\" #>  #> $call #> rpart(formula = Kyphosis ~ Start + Age, data = kyphosis) #>  #> $na.action #> NULL #>  #> $flip #> [1] FALSE #>  #> $raw_data #> NULL #>   # simple plot plot(rp_pt)   # removing the (recursive) partition borders helps to emphasise overall fit plot(rp_pt, border = NA)   # customize further by passing extra options to (tiny)plot plot(    rp_pt,    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\") ct = ctree(Species ~ Petal.Length + Petal.Width, data = iris) ct_pt = parttree(ct) plot(ct_pt, pch = 19, palette = \"okabe\", main = \"ctree predictions: iris species\") #> Error in eval(raw_data): object 'ct' not found  ## rpart via partykit rp2 = as.party(rp) parttree(rp2) #>   node Kyphosis                                                       path xmin #> 3    3   absent                               Start < 8.5 --> Start < 14.5 14.5 #> 5    5   absent                 Start < 8.5 --> Start >= 14.5 --> Age < 55  8.5 #> 7    7   absent  Start < 8.5 --> Start >= 14.5 --> Age >= 55 --> Age < 111  8.5 #> 8    8  present Start < 8.5 --> Start >= 14.5 --> Age >= 55 --> Age >= 111  8.5 #> 9    9  present                                               Start >= 8.5 -Inf #>   xmax ymin ymax #> 3  Inf -Inf  Inf #> 5 14.5 -Inf   55 #> 7 14.5  111  Inf #> 8 14.5   55  111 #> 9  8.5 -Inf  Inf  # ## various front-end frameworks are also supported, e.g.  # tidymodels  # install.packages(\"parsnip\") library(parsnip)  decision_tree() |>   set_engine(\"rpart\") |>   set_mode(\"classification\") |>   fit(Species ~ Petal.Length + Petal.Width, data=iris) |>   parttree() |>   plot(main = \"This time brought to you via parsnip...\")   # mlr3 (NB: use `keep_model = TRUE` for mlr3 learners)  # install.packages(\"mlr3\") library(mlr3)  task_iris = TaskClassif$new(\"iris\", iris, target = \"Species\") task_iris$formula(rhs = \"Petal.Length + Petal.Width\") #> Species ~ `Petal.Length + Petal.Width` #> NULL fit_iris = lrn(\"classif.rpart\", keep_model = TRUE) # NB! fit_iris$train(task_iris) plot(parttree(fit_iris), main = \"... and now mlr3\")"},{"path":"https://grantmcdermott.com/parttree/news/index.html","id":"parttree-010","dir":"Changelog","previous_headings":"","what":"parttree 0.1.0","title":"parttree 0.1.0","text":"first CRAN submission. 🎉🎉","code":""},{"path":"https://grantmcdermott.com/parttree/news/index.html","id":"breaking-changes-0-1-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"parttree 0.1.0","text":"Move ggplot2 Enhances, following addition dedicated (base R) plot.parttree method. geom_parttree() function still available, requires ggplot2 already installed user’s system. (#18) flipaxes argument renamed flip, e.g. parttree(..., flip = TRUE). (#18)","code":""},{"path":"https://grantmcdermott.com/parttree/news/index.html","id":"improvements-0-1-0","dir":"Changelog","previous_headings":"","what":"Improvements","title":"parttree 0.1.0","text":"Parttree objects now class dedicated plot.parttree method, powered tinyplot. (#18) Major speed-extracting parttree nodes coordinates complicated trees. (#15) Add method tidymodels workflows objects fitted \"rpart\" engine. (#7 @juliasilge).","code":""},{"path":"https://grantmcdermott.com/parttree/news/index.html","id":"bug-fixes-0-1-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"parttree 0.1.0","text":"Support negative values. (#6 @pjgeens) Better handling single-level factors flip(axes). (#5) Handling complex formula expressions. (#17)","code":""},{"path":"https://grantmcdermott.com/parttree/news/index.html","id":"internals-0-1-0","dir":"Changelog","previous_headings":"","what":"Internals","title":"parttree 0.1.0","text":"Several dependency adjustments, e.g. tinyplot Imports ggplot2 Suggests. (#18) Added SVG snapshots image-based tests. (#18) Bump ggplot2 version dependency match deprecated functions 3.4.0. Switched “main” primary GitHub branch development. Added two dedicated vignettes.","code":""},{"path":"https://grantmcdermott.com/parttree/news/index.html","id":"parttree-001","dir":"Changelog","previous_headings":"","what":"parttree 0.0.1","title":"parttree 0.0.1","text":"Create parttree() generic. (, #3 @zeileis) Support partykit objects (.e. constparty class), turn allows support base plot methods. (#3 @zeileis) ggplot2 moves imports depends now automatically loaded parttree. Add flipaxes argument easy switching case mismatch plot orientation. (#2 @brhkim) Added NEWS.md file track changes package.","code":""},{"path":"https://grantmcdermott.com/parttree/news/index.html","id":"parttree-0009000","dir":"Changelog","previous_headings":"","what":"parttree 0.0.0.9000","title":"parttree 0.0.0.9000","text":"Initial set functions.","code":""}]