diff --git a/docs/hanamicloth_book.datasets.html b/docs/hanamicloth_book.datasets.html
index c0340ba..a291307 100644
--- a/docs/hanamicloth_book.datasets.html
+++ b/docs/hanamicloth_book.datasets.html
@@ -155,13 +155,13 @@
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="./hanamicloth_book.walkthrough.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Walkthrough</span></span></a>
+ <span class="menu-text"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Walkthrough 👣</span></span></a>
   </div>
 </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="./hanamicloth_book.plotlycloth_walkthrough.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Plotlycloth Walkthrough (experimental 🛠)</span></span></a>
+ <span class="menu-text"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Plotlycloth Walkthrough 👣 - experimental 🛠</span></span></a>
   </div>
 </li>
     </ul>
@@ -722,8 +722,8 @@ <h2 data-number="2.3" class="anchored" data-anchor-id="us-economic-time-series">
       </a>          
   </div>
   <div class="nav-page nav-page-next">
-      <a href="./hanamicloth_book.walkthrough.html" class="pagination-link" aria-label="Walkthrough">
-        <span class="nav-page-text"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Walkthrough</span></span> <i class="bi bi-arrow-right-short"></i>
+      <a href="./hanamicloth_book.walkthrough.html" class="pagination-link" aria-label="Walkthrough 👣">
+        <span class="nav-page-text"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Walkthrough 👣</span></span> <i class="bi bi-arrow-right-short"></i>
       </a>
   </div>
 </nav>
diff --git a/docs/hanamicloth_book.plotlycloth_walkthrough.html b/docs/hanamicloth_book.plotlycloth_walkthrough.html
index 447b654..a42d361 100644
--- a/docs/hanamicloth_book.plotlycloth_walkthrough.html
+++ b/docs/hanamicloth_book.plotlycloth_walkthrough.html
@@ -7,7 +7,7 @@
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 
-<title>Hanamicloth – 4&nbsp; Plotlycloth Walkthrough (experimental 🛠)</title>
+<title>Hanamicloth – 4&nbsp; Plotlycloth Walkthrough 👣 - experimental 🛠</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -114,7 +114,7 @@
       <button type="button" class="quarto-btn-toggle btn" data-bs-toggle="collapse" role="button" data-bs-target=".quarto-sidebar-collapse-item" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">
         <i class="bi bi-layout-text-sidebar-reverse"></i>
       </button>
-        <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="./hanamicloth_book.plotlycloth_walkthrough.html"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Plotlycloth Walkthrough (experimental 🛠)</span></a></li></ol></nav>
+        <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="./hanamicloth_book.plotlycloth_walkthrough.html"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Plotlycloth Walkthrough 👣 - experimental 🛠</span></a></li></ol></nav>
         <a class="flex-grow-1" role="navigation" data-bs-toggle="collapse" data-bs-target=".quarto-sidebar-collapse-item" aria-controls="quarto-sidebar" aria-expanded="false" aria-label="Toggle sidebar navigation" onclick="if (window.quartoToggleHeadroom) { window.quartoToggleHeadroom(); }">      
         </a>
       <button type="button" class="btn quarto-search-button" aria-label="Search" onclick="window.quartoOpenSearch();">
@@ -154,13 +154,13 @@
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="./hanamicloth_book.walkthrough.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Walkthrough</span></span></a>
+ <span class="menu-text"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Walkthrough 👣</span></span></a>
   </div>
 </li>
         <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="./hanamicloth_book.plotlycloth_walkthrough.html" class="sidebar-item-text sidebar-link active">
- <span class="menu-text"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Plotlycloth Walkthrough (experimental 🛠)</span></span></a>
+ <span class="menu-text"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Plotlycloth Walkthrough 👣 - experimental 🛠</span></span></a>
   </div>
 </li>
     </ul>
@@ -199,7 +199,7 @@ <h2 id="toc-title">Table of contents</h2>
 
 <header id="title-block-header" class="quarto-title-block default">
 <div class="quarto-title">
-<h1 class="title"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Plotlycloth Walkthrough (experimental 🛠)</span></h1>
+<h1 class="title"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Plotlycloth Walkthrough 👣 - experimental 🛠</span></h1>
 </div>
 
 
@@ -366,7 +366,7 @@ <h2 data-number="4.3" class="anchored" data-anchor-id="templates-and-parameters"
 <span id="cb5-70"><a href="#cb5-70" aria-hidden="true" tabindex="-1"></a>     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--<span class="dv">48211</span>],</span>
 <span id="cb5-71"><a href="#cb5-71" aria-hidden="true" tabindex="-1"></a>     :=y <span class="at">:sepal-length</span>,</span>
 <span id="cb5-72"><a href="#cb5-72" aria-hidden="true" tabindex="-1"></a>     :=x1-after-stat :=x1,</span>
-<span id="cb5-73"><a href="#cb5-73" aria-hidden="true" tabindex="-1"></a>     :=dataset #&lt;WrappedValue<span class="at">@28277720:</span> datasets/iris [<span class="dv">10</span> <span class="dv">6</span>]:</span>
+<span id="cb5-73"><a href="#cb5-73" aria-hidden="true" tabindex="-1"></a>     :=dataset #&lt;WrappedValue<span class="at">@1fe6d211:</span> datasets/iris [<span class="dv">10</span> <span class="dv">6</span>]:</span>
 <span id="cb5-74"><a href="#cb5-74" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb5-75"><a href="#cb5-75" aria-hidden="true" tabindex="-1"></a>| <span class="at">:rownames</span> | <span class="at">:sepal-length</span> | <span class="at">:sepal-width</span> | <span class="at">:petal-length</span> | <span class="at">:petal-width</span> |   <span class="at">:species</span> |</span>
 <span id="cb5-76"><a href="#cb5-76" aria-hidden="true" tabindex="-1"></a>|----------:|--------------:|-------------:|--------------:|-------------:|------------|</span>
@@ -443,7 +443,7 @@ <h2 data-number="4.3" class="anchored" data-anchor-id="templates-and-parameters"
 <span id="cb5-147"><a href="#cb5-147" aria-hidden="true" tabindex="-1"></a>  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--<span class="dv">48211</span>],</span>
 <span id="cb5-148"><a href="#cb5-148" aria-hidden="true" tabindex="-1"></a>  :=y <span class="at">:y</span>,</span>
 <span id="cb5-149"><a href="#cb5-149" aria-hidden="true" tabindex="-1"></a>  :=x1-after-stat :=x1,</span>
-<span id="cb5-150"><a href="#cb5-150" aria-hidden="true" tabindex="-1"></a>  :=dataset #&lt;WrappedValue<span class="at">@28277720:</span> datasets/iris [<span class="dv">10</span> <span class="dv">6</span>]:</span>
+<span id="cb5-150"><a href="#cb5-150" aria-hidden="true" tabindex="-1"></a>  :=dataset #&lt;WrappedValue<span class="at">@1fe6d211:</span> datasets/iris [<span class="dv">10</span> <span class="dv">6</span>]:</span>
 <span id="cb5-151"><a href="#cb5-151" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb5-152"><a href="#cb5-152" aria-hidden="true" tabindex="-1"></a>| <span class="at">:rownames</span> | <span class="at">:sepal-length</span> | <span class="at">:sepal-width</span> | <span class="at">:petal-length</span> | <span class="at">:petal-width</span> |   <span class="at">:species</span> |</span>
 <span id="cb5-153"><a href="#cb5-153" aria-hidden="true" tabindex="-1"></a>|----------:|--------------:|-------------:|--------------:|-------------:|------------|</span>
@@ -684,7 +684,7 @@ <h2 data-number="4.10" class="anchored" data-anchor-id="updating-data"><span cla
 <span id="cb23-9"><a href="#cb23-9" aria-hidden="true" tabindex="-1"></a>                         :=mark-opacity <span class="fl">0.5</span>}))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <div style="height:400px;width:100%;"><script>Plotly.newPlot(document.currentScript.parentElement,
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 </div>
 
 
@@ -617,135 +617,136 @@ <h2 data-number="3.7" class="anchored" data-anchor-id="delayed-transformation"><
 <span id="cb31-13"><a href="#cb31-13" aria-hidden="true" tabindex="-1"></a> <span class="at">:kindly/f</span> <span class="va">#'scicloj.hanamicloth.v1.api/vega-lite-xform,</span></span>
 <span id="cb31-14"><a href="#cb31-14" aria-hidden="true" tabindex="-1"></a> <span class="at">:aerial.hanami.templates/defaults</span></span>
 <span id="cb31-15"><a href="#cb31-15" aria-hidden="true" tabindex="-1"></a> {:=x2 <span class="at">:com.rpl.specter.impl/NONE</span>,</span>
-<span id="cb31-16"><a href="#cb31-16" aria-hidden="true" tabindex="-1"></a>  :=base-dataset #&lt;WrappedValue<span class="at">@37e5aed:</span> ggplot2/economics_long [<span class="dv">574</span> <span class="dv">5</span>]:</span>
-<span id="cb31-17"><a href="#cb31-17" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb31-18"><a href="#cb31-18" aria-hidden="true" tabindex="-1"></a>| <span class="at">:rownames</span> |      <span class="at">:date</span> | <span class="at">:variable</span> | <span class="at">:value</span> |   <span class="at">:value01</span> |</span>
-<span id="cb31-19"><a href="#cb31-19" aria-hidden="true" tabindex="-1"></a>|----------:|------------|-----------|-------:|-----------:|</span>
-<span id="cb31-20"><a href="#cb31-20" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2297</span> | <span class="dv">1967-07-01</span> |  unemploy | <span class="fl">2944.0</span> | <span class="fl">0.02044683</span> |</span>
-<span id="cb31-21"><a href="#cb31-21" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2298</span> | <span class="dv">1967-08-01</span> |  unemploy | <span class="fl">2945.0</span> | <span class="fl">0.02052578</span> |</span>
-<span id="cb31-22"><a href="#cb31-22" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2299</span> | <span class="dv">1967-09-01</span> |  unemploy | <span class="fl">2958.0</span> | <span class="fl">0.02155206</span> |</span>
-<span id="cb31-23"><a href="#cb31-23" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2300</span> | <span class="dv">1967-10-01</span> |  unemploy | <span class="fl">3143.0</span> | <span class="fl">0.03615694</span> |</span>
-<span id="cb31-24"><a href="#cb31-24" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2301</span> | <span class="dv">1967-11-01</span> |  unemploy | <span class="fl">3066.0</span> | <span class="fl">0.03007816</span> |</span>
-<span id="cb31-25"><a href="#cb31-25" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2302</span> | <span class="dv">1967-12-01</span> |  unemploy | <span class="fl">3018.0</span> | <span class="fl">0.02628878</span> |</span>
-<span id="cb31-26"><a href="#cb31-26" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2303</span> | <span class="dv">1968-01-01</span> |  unemploy | <span class="fl">2878.0</span> | <span class="fl">0.01523644</span> |</span>
-<span id="cb31-27"><a href="#cb31-27" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2304</span> | <span class="dv">1968-02-01</span> |  unemploy | <span class="fl">3001.0</span> | <span class="fl">0.02494671</span> |</span>
-<span id="cb31-28"><a href="#cb31-28" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2305</span> | <span class="dv">1968-03-01</span> |  unemploy | <span class="fl">2877.0</span> | <span class="fl">0.01515750</span> |</span>
-<span id="cb31-29"><a href="#cb31-29" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2306</span> | <span class="dv">1968-04-01</span> |  unemploy | <span class="fl">2709.0</span> | <span class="fl">0.00189469</span> |</span>
-<span id="cb31-30"><a href="#cb31-30" aria-hidden="true" tabindex="-1"></a>|       ... |        ... |       ... |    ... |        ... |</span>
-<span id="cb31-31"><a href="#cb31-31" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2860</span> | <span class="dv">2014-06-01</span> |  unemploy | <span class="fl">9460.0</span> | <span class="fl">0.53485435</span> |</span>
-<span id="cb31-32"><a href="#cb31-32" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2861</span> | <span class="dv">2014-07-01</span> |  unemploy | <span class="fl">9608.0</span> | <span class="fl">0.54653825</span> |</span>
-<span id="cb31-33"><a href="#cb31-33" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2862</span> | <span class="dv">2014-08-01</span> |  unemploy | <span class="fl">9599.0</span> | <span class="fl">0.54582774</span> |</span>
-<span id="cb31-34"><a href="#cb31-34" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2863</span> | <span class="dv">2014-09-01</span> |  unemploy | <span class="fl">9262.0</span> | <span class="fl">0.51922318</span> |</span>
-<span id="cb31-35"><a href="#cb31-35" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2864</span> | <span class="dv">2014-10-01</span> |  unemploy | <span class="fl">8990.0</span> | <span class="fl">0.49775006</span> |</span>
-<span id="cb31-36"><a href="#cb31-36" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2865</span> | <span class="dv">2014-11-01</span> |  unemploy | <span class="fl">9090.0</span> | <span class="fl">0.50564459</span> |</span>
-<span id="cb31-37"><a href="#cb31-37" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2866</span> | <span class="dv">2014-12-01</span> |  unemploy | <span class="fl">8717.0</span> | <span class="fl">0.47619799</span> |</span>
-<span id="cb31-38"><a href="#cb31-38" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2867</span> | <span class="dv">2015-01-01</span> |  unemploy | <span class="fl">8903.0</span> | <span class="fl">0.49088182</span> |</span>
-<span id="cb31-39"><a href="#cb31-39" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2868</span> | <span class="dv">2015-02-01</span> |  unemploy | <span class="fl">8610.0</span> | <span class="fl">0.46775085</span> |</span>
-<span id="cb31-40"><a href="#cb31-40" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2869</span> | <span class="dv">2015-03-01</span> |  unemploy | <span class="fl">8504.0</span> | <span class="fl">0.45938265</span> |</span>
-<span id="cb31-41"><a href="#cb31-41" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2870</span> | <span class="dv">2015-04-01</span> |  unemploy | <span class="fl">8526.0</span> | <span class="fl">0.46111944</span> |</span>
-<span id="cb31-42"><a href="#cb31-42" aria-hidden="true" tabindex="-1"></a><span class="kw">&gt;</span>,</span>
-<span id="cb31-43"><a href="#cb31-43" aria-hidden="true" tabindex="-1"></a>  :=layer [],</span>
-<span id="cb31-44"><a href="#cb31-44" aria-hidden="true" tabindex="-1"></a>  :=y-type</span>
-<span id="cb31-45"><a href="#cb31-45" aria-hidden="true" tabindex="-1"></a>  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--<span class="dv">48211</span>],</span>
-<span id="cb31-46"><a href="#cb31-46" aria-hidden="true" tabindex="-1"></a>  :=x2-encoding</span>
-<span id="cb31-47"><a href="#cb31-47" aria-hidden="true" tabindex="-1"></a>  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--<span class="dv">48211</span>],</span>
-<span id="cb31-48"><a href="#cb31-48" aria-hidden="true" tabindex="-1"></a>  :=opacity <span class="at">:com.rpl.specter.impl/NONE</span>,</span>
-<span id="cb31-49"><a href="#cb31-49" aria-hidden="true" tabindex="-1"></a>  :=y2-after-stat :=y2,</span>
-<span id="cb31-50"><a href="#cb31-50" aria-hidden="true" tabindex="-1"></a>  :=x2-after-stat :=x2,</span>
-<span id="cb31-51"><a href="#cb31-51" aria-hidden="true" tabindex="-1"></a>  :=usermeta {<span class="at">:embedOptions</span> {<span class="at">:renderer</span> :=renderer}},</span>
-<span id="cb31-52"><a href="#cb31-52" aria-hidden="true" tabindex="-1"></a>  :=title <span class="at">:com.rpl.specter.impl/NONE</span>,</span>
-<span id="cb31-53"><a href="#cb31-53" aria-hidden="true" tabindex="-1"></a>  :=y-type-after-stat</span>
-<span id="cb31-54"><a href="#cb31-54" aria-hidden="true" tabindex="-1"></a>  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--<span class="dv">48211</span>],</span>
-<span id="cb31-55"><a href="#cb31-55" aria-hidden="true" tabindex="-1"></a>  :=height <span class="dv">300</span>,</span>
-<span id="cb31-56"><a href="#cb31-56" aria-hidden="true" tabindex="-1"></a>  :=renderer <span class="at">:svg</span>,</span>
-<span id="cb31-57"><a href="#cb31-57" aria-hidden="true" tabindex="-1"></a>  :=mark-opacity <span class="at">:com.rpl.specter.impl/NONE</span>,</span>
-<span id="cb31-58"><a href="#cb31-58" aria-hidden="true" tabindex="-1"></a>  :=y-title <span class="at">:com.rpl.specter.impl/NONE</span>,</span>
-<span id="cb31-59"><a href="#cb31-59" aria-hidden="true" tabindex="-1"></a>  :=size <span class="at">:com.rpl.specter.impl/NONE</span>,</span>
-<span id="cb31-60"><a href="#cb31-60" aria-hidden="true" tabindex="-1"></a>  :=group</span>
-<span id="cb31-61"><a href="#cb31-61" aria-hidden="true" tabindex="-1"></a>  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--<span class="dv">48211</span>],</span>
-<span id="cb31-62"><a href="#cb31-62" aria-hidden="true" tabindex="-1"></a>  :=mark-size <span class="at">:com.rpl.specter.impl/NONE</span>,</span>
-<span id="cb31-63"><a href="#cb31-63" aria-hidden="true" tabindex="-1"></a>  :=layer? <span class="at">:com.rpl.specter.impl/NONE</span>,</span>
-<span id="cb31-64"><a href="#cb31-64" aria-hidden="true" tabindex="-1"></a>  :=size-type</span>
-<span id="cb31-65"><a href="#cb31-65" aria-hidden="true" tabindex="-1"></a>  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--<span class="dv">48211</span>],</span>
-<span id="cb31-66"><a href="#cb31-66" aria-hidden="true" tabindex="-1"></a>  :=layer-dataset</span>
-<span id="cb31-67"><a href="#cb31-67" aria-hidden="true" tabindex="-1"></a>  #&lt;WrappedValue<span class="at">@37e5aed:</span> ggplot2/economics_long [<span class="dv">574</span> <span class="dv">5</span>]:</span>
-<span id="cb31-68"><a href="#cb31-68" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb31-69"><a href="#cb31-69" aria-hidden="true" tabindex="-1"></a>| <span class="at">:rownames</span> |      <span class="at">:date</span> | <span class="at">:variable</span> | <span class="at">:value</span> |   <span class="at">:value01</span> |</span>
-<span id="cb31-70"><a href="#cb31-70" aria-hidden="true" tabindex="-1"></a>|----------:|------------|-----------|-------:|-----------:|</span>
-<span id="cb31-71"><a href="#cb31-71" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2297</span> | <span class="dv">1967-07-01</span> |  unemploy | <span class="fl">2944.0</span> | <span class="fl">0.02044683</span> |</span>
-<span id="cb31-72"><a href="#cb31-72" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2298</span> | <span class="dv">1967-08-01</span> |  unemploy | <span class="fl">2945.0</span> | <span class="fl">0.02052578</span> |</span>
-<span id="cb31-73"><a href="#cb31-73" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2299</span> | <span class="dv">1967-09-01</span> |  unemploy | <span class="fl">2958.0</span> | <span class="fl">0.02155206</span> |</span>
-<span id="cb31-74"><a href="#cb31-74" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2300</span> | <span class="dv">1967-10-01</span> |  unemploy | <span class="fl">3143.0</span> | <span class="fl">0.03615694</span> |</span>
-<span id="cb31-75"><a href="#cb31-75" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2301</span> | <span class="dv">1967-11-01</span> |  unemploy | <span class="fl">3066.0</span> | <span class="fl">0.03007816</span> |</span>
-<span id="cb31-76"><a href="#cb31-76" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2302</span> | <span class="dv">1967-12-01</span> |  unemploy | <span class="fl">3018.0</span> | <span class="fl">0.02628878</span> |</span>
-<span id="cb31-77"><a href="#cb31-77" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2303</span> | <span class="dv">1968-01-01</span> |  unemploy | <span class="fl">2878.0</span> | <span class="fl">0.01523644</span> |</span>
-<span id="cb31-78"><a href="#cb31-78" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2304</span> | <span class="dv">1968-02-01</span> |  unemploy | <span class="fl">3001.0</span> | <span class="fl">0.02494671</span> |</span>
-<span id="cb31-79"><a href="#cb31-79" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2305</span> | <span class="dv">1968-03-01</span> |  unemploy | <span class="fl">2877.0</span> | <span class="fl">0.01515750</span> |</span>
-<span id="cb31-80"><a href="#cb31-80" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2306</span> | <span class="dv">1968-04-01</span> |  unemploy | <span class="fl">2709.0</span> | <span class="fl">0.00189469</span> |</span>
-<span id="cb31-81"><a href="#cb31-81" aria-hidden="true" tabindex="-1"></a>|       ... |        ... |       ... |    ... |        ... |</span>
-<span id="cb31-82"><a href="#cb31-82" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2860</span> | <span class="dv">2014-06-01</span> |  unemploy | <span class="fl">9460.0</span> | <span class="fl">0.53485435</span> |</span>
-<span id="cb31-83"><a href="#cb31-83" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2861</span> | <span class="dv">2014-07-01</span> |  unemploy | <span class="fl">9608.0</span> | <span class="fl">0.54653825</span> |</span>
-<span id="cb31-84"><a href="#cb31-84" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2862</span> | <span class="dv">2014-08-01</span> |  unemploy | <span class="fl">9599.0</span> | <span class="fl">0.54582774</span> |</span>
-<span id="cb31-85"><a href="#cb31-85" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2863</span> | <span class="dv">2014-09-01</span> |  unemploy | <span class="fl">9262.0</span> | <span class="fl">0.51922318</span> |</span>
-<span id="cb31-86"><a href="#cb31-86" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2864</span> | <span class="dv">2014-10-01</span> |  unemploy | <span class="fl">8990.0</span> | <span class="fl">0.49775006</span> |</span>
-<span id="cb31-87"><a href="#cb31-87" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2865</span> | <span class="dv">2014-11-01</span> |  unemploy | <span class="fl">9090.0</span> | <span class="fl">0.50564459</span> |</span>
-<span id="cb31-88"><a href="#cb31-88" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2866</span> | <span class="dv">2014-12-01</span> |  unemploy | <span class="fl">8717.0</span> | <span class="fl">0.47619799</span> |</span>
-<span id="cb31-89"><a href="#cb31-89" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2867</span> | <span class="dv">2015-01-01</span> |  unemploy | <span class="fl">8903.0</span> | <span class="fl">0.49088182</span> |</span>
-<span id="cb31-90"><a href="#cb31-90" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2868</span> | <span class="dv">2015-02-01</span> |  unemploy | <span class="fl">8610.0</span> | <span class="fl">0.46775085</span> |</span>
-<span id="cb31-91"><a href="#cb31-91" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2869</span> | <span class="dv">2015-03-01</span> |  unemploy | <span class="fl">8504.0</span> | <span class="fl">0.45938265</span> |</span>
-<span id="cb31-92"><a href="#cb31-92" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2870</span> | <span class="dv">2015-04-01</span> |  unemploy | <span class="fl">8526.0</span> | <span class="fl">0.46111944</span> |</span>
-<span id="cb31-93"><a href="#cb31-93" aria-hidden="true" tabindex="-1"></a><span class="kw">&gt;</span>,</span>
-<span id="cb31-94"><a href="#cb31-94" aria-hidden="true" tabindex="-1"></a>  :=color <span class="at">:com.rpl.specter.impl/NONE</span>,</span>
-<span id="cb31-95"><a href="#cb31-95" aria-hidden="true" tabindex="-1"></a>  :=mark-color <span class="st">"purple"</span>,</span>
-<span id="cb31-96"><a href="#cb31-96" aria-hidden="true" tabindex="-1"></a>  :=x <span class="at">:date</span>,</span>
-<span id="cb31-97"><a href="#cb31-97" aria-hidden="true" tabindex="-1"></a>  :=y2 <span class="at">:com.rpl.specter.impl/NONE</span>,</span>
-<span id="cb31-98"><a href="#cb31-98" aria-hidden="true" tabindex="-1"></a>  :=x-after-stat :=x,</span>
-<span id="cb31-99"><a href="#cb31-99" aria-hidden="true" tabindex="-1"></a>  <span class="at">:DFMT</span> {<span class="at">:type</span> <span class="st">"csv"</span>},</span>
-<span id="cb31-100"><a href="#cb31-100" aria-hidden="true" tabindex="-1"></a>  :=encoding</span>
-<span id="cb31-101"><a href="#cb31-101" aria-hidden="true" tabindex="-1"></a>  {<span class="at">:color</span> {<span class="at">:field</span> :=color, <span class="at">:type</span> :=color-type},</span>
-<span id="cb31-102"><a href="#cb31-102" aria-hidden="true" tabindex="-1"></a>   <span class="at">:size</span> {<span class="at">:field</span> :=size, <span class="at">:type</span> :=size-type},</span>
-<span id="cb31-103"><a href="#cb31-103" aria-hidden="true" tabindex="-1"></a>   <span class="at">:x</span></span>
-<span id="cb31-104"><a href="#cb31-104" aria-hidden="true" tabindex="-1"></a>   {<span class="at">:field</span> :=x-after-stat,</span>
-<span id="cb31-105"><a href="#cb31-105" aria-hidden="true" tabindex="-1"></a>    <span class="at">:type</span> :=x-type-after-stat,</span>
-<span id="cb31-106"><a href="#cb31-106" aria-hidden="true" tabindex="-1"></a>    <span class="at">:title</span> :=x-title,</span>
-<span id="cb31-107"><a href="#cb31-107" aria-hidden="true" tabindex="-1"></a>    <span class="at">:bin</span> :=x-bin},</span>
-<span id="cb31-108"><a href="#cb31-108" aria-hidden="true" tabindex="-1"></a>   <span class="at">:y</span></span>
-<span id="cb31-109"><a href="#cb31-109" aria-hidden="true" tabindex="-1"></a>   {<span class="at">:field</span> :=y-after-stat,</span>
-<span id="cb31-110"><a href="#cb31-110" aria-hidden="true" tabindex="-1"></a>    <span class="at">:type</span> :=y-type-after-stat,</span>
-<span id="cb31-111"><a href="#cb31-111" aria-hidden="true" tabindex="-1"></a>    <span class="at">:title</span> :=y-title,</span>
-<span id="cb31-112"><a href="#cb31-112" aria-hidden="true" tabindex="-1"></a>    <span class="at">:bin</span> :=y-bin},</span>
-<span id="cb31-113"><a href="#cb31-113" aria-hidden="true" tabindex="-1"></a>   <span class="at">:x2</span> :=x2-encoding,</span>
-<span id="cb31-114"><a href="#cb31-114" aria-hidden="true" tabindex="-1"></a>   <span class="at">:y2</span> :=y2-encoding},</span>
-<span id="cb31-115"><a href="#cb31-115" aria-hidden="true" tabindex="-1"></a>  :=x-type-after-stat</span>
-<span id="cb31-116"><a href="#cb31-116" aria-hidden="true" tabindex="-1"></a>  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--<span class="dv">48211</span>],</span>
-<span id="cb31-117"><a href="#cb31-117" aria-hidden="true" tabindex="-1"></a>  :=x-bin <span class="at">:com.rpl.specter.impl/NONE</span>,</span>
-<span id="cb31-118"><a href="#cb31-118" aria-hidden="true" tabindex="-1"></a>  :=x-type</span>
-<span id="cb31-119"><a href="#cb31-119" aria-hidden="true" tabindex="-1"></a>  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--<span class="dv">48211</span>],</span>
-<span id="cb31-120"><a href="#cb31-120" aria-hidden="true" tabindex="-1"></a>  :=histogram-nbins <span class="dv">10</span>,</span>
-<span id="cb31-121"><a href="#cb31-121" aria-hidden="true" tabindex="-1"></a>  :=stat <span class="at">:com.rpl.specter.impl/NONE</span>,</span>
-<span id="cb31-122"><a href="#cb31-122" aria-hidden="true" tabindex="-1"></a>  :=width <span class="dv">400</span>,</span>
-<span id="cb31-123"><a href="#cb31-123" aria-hidden="true" tabindex="-1"></a>  :=color-type</span>
-<span id="cb31-124"><a href="#cb31-124" aria-hidden="true" tabindex="-1"></a>  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--<span class="dv">48211</span>],</span>
-<span id="cb31-125"><a href="#cb31-125" aria-hidden="true" tabindex="-1"></a>  :=mark <span class="st">"circle"</span>,</span>
-<span id="cb31-126"><a href="#cb31-126" aria-hidden="true" tabindex="-1"></a>  :=dataset-after-stat</span>
-<span id="cb31-127"><a href="#cb31-127" aria-hidden="true" tabindex="-1"></a>  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--<span class="dv">48211</span>],</span>
-<span id="cb31-128"><a href="#cb31-128" aria-hidden="true" tabindex="-1"></a>  :=y-bin <span class="at">:com.rpl.specter.impl/NONE</span>,</span>
-<span id="cb31-129"><a href="#cb31-129" aria-hidden="true" tabindex="-1"></a>  :=x-title <span class="at">:com.rpl.specter.impl/NONE</span>,</span>
-<span id="cb31-130"><a href="#cb31-130" aria-hidden="true" tabindex="-1"></a>  :=csv-data</span>
-<span id="cb31-131"><a href="#cb31-131" aria-hidden="true" tabindex="-1"></a>  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--<span class="dv">48211</span>],</span>
-<span id="cb31-132"><a href="#cb31-132" aria-hidden="true" tabindex="-1"></a>  :=y <span class="at">:value</span>,</span>
-<span id="cb31-133"><a href="#cb31-133" aria-hidden="true" tabindex="-1"></a>  :=data {<span class="at">:values</span> :=csv-data, <span class="at">:format</span> {<span class="at">:type</span> <span class="st">"csv"</span>}},</span>
-<span id="cb31-134"><a href="#cb31-134" aria-hidden="true" tabindex="-1"></a>  :=dataset</span>
-<span id="cb31-135"><a href="#cb31-135" aria-hidden="true" tabindex="-1"></a>  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--<span class="dv">48211</span>],</span>
-<span id="cb31-136"><a href="#cb31-136" aria-hidden="true" tabindex="-1"></a>  <span class="at">:VALDATA</span> :=csv-data,</span>
-<span id="cb31-137"><a href="#cb31-137" aria-hidden="true" tabindex="-1"></a>  :=mark-tooltip <span class="va">true</span>,</span>
-<span id="cb31-138"><a href="#cb31-138" aria-hidden="true" tabindex="-1"></a>  :=background <span class="st">"floralwhite"</span>,</span>
-<span id="cb31-139"><a href="#cb31-139" aria-hidden="true" tabindex="-1"></a>  :=y-after-stat :=y,</span>
-<span id="cb31-140"><a href="#cb31-140" aria-hidden="true" tabindex="-1"></a>  :=predictors [:=x],</span>
-<span id="cb31-141"><a href="#cb31-141" aria-hidden="true" tabindex="-1"></a>  :=y2-encoding</span>
-<span id="cb31-142"><a href="#cb31-142" aria-hidden="true" tabindex="-1"></a>  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--<span class="dv">48211</span>]},</span>
-<span id="cb31-143"><a href="#cb31-143" aria-hidden="true" tabindex="-1"></a> <span class="at">:height</span> :=height,</span>
-<span id="cb31-144"><a href="#cb31-144" aria-hidden="true" tabindex="-1"></a> <span class="at">:data</span> :=data}</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<span id="cb31-16"><a href="#cb31-16" aria-hidden="true" tabindex="-1"></a>  :=base-dataset</span>
+<span id="cb31-17"><a href="#cb31-17" aria-hidden="true" tabindex="-1"></a>  #&lt;WrappedValue<span class="at">@43dcbcc6:</span> ggplot2/economics_long [<span class="dv">574</span> <span class="dv">5</span>]:</span>
+<span id="cb31-18"><a href="#cb31-18" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb31-19"><a href="#cb31-19" aria-hidden="true" tabindex="-1"></a>| <span class="at">:rownames</span> |      <span class="at">:date</span> | <span class="at">:variable</span> | <span class="at">:value</span> |   <span class="at">:value01</span> |</span>
+<span id="cb31-20"><a href="#cb31-20" aria-hidden="true" tabindex="-1"></a>|----------:|------------|-----------|-------:|-----------:|</span>
+<span id="cb31-21"><a href="#cb31-21" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2297</span> | <span class="dv">1967-07-01</span> |  unemploy | <span class="fl">2944.0</span> | <span class="fl">0.02044683</span> |</span>
+<span id="cb31-22"><a href="#cb31-22" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2298</span> | <span class="dv">1967-08-01</span> |  unemploy | <span class="fl">2945.0</span> | <span class="fl">0.02052578</span> |</span>
+<span id="cb31-23"><a href="#cb31-23" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2299</span> | <span class="dv">1967-09-01</span> |  unemploy | <span class="fl">2958.0</span> | <span class="fl">0.02155206</span> |</span>
+<span id="cb31-24"><a href="#cb31-24" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2300</span> | <span class="dv">1967-10-01</span> |  unemploy | <span class="fl">3143.0</span> | <span class="fl">0.03615694</span> |</span>
+<span id="cb31-25"><a href="#cb31-25" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2301</span> | <span class="dv">1967-11-01</span> |  unemploy | <span class="fl">3066.0</span> | <span class="fl">0.03007816</span> |</span>
+<span id="cb31-26"><a href="#cb31-26" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2302</span> | <span class="dv">1967-12-01</span> |  unemploy | <span class="fl">3018.0</span> | <span class="fl">0.02628878</span> |</span>
+<span id="cb31-27"><a href="#cb31-27" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2303</span> | <span class="dv">1968-01-01</span> |  unemploy | <span class="fl">2878.0</span> | <span class="fl">0.01523644</span> |</span>
+<span id="cb31-28"><a href="#cb31-28" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2304</span> | <span class="dv">1968-02-01</span> |  unemploy | <span class="fl">3001.0</span> | <span class="fl">0.02494671</span> |</span>
+<span id="cb31-29"><a href="#cb31-29" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2305</span> | <span class="dv">1968-03-01</span> |  unemploy | <span class="fl">2877.0</span> | <span class="fl">0.01515750</span> |</span>
+<span id="cb31-30"><a href="#cb31-30" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2306</span> | <span class="dv">1968-04-01</span> |  unemploy | <span class="fl">2709.0</span> | <span class="fl">0.00189469</span> |</span>
+<span id="cb31-31"><a href="#cb31-31" aria-hidden="true" tabindex="-1"></a>|       ... |        ... |       ... |    ... |        ... |</span>
+<span id="cb31-32"><a href="#cb31-32" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2860</span> | <span class="dv">2014-06-01</span> |  unemploy | <span class="fl">9460.0</span> | <span class="fl">0.53485435</span> |</span>
+<span id="cb31-33"><a href="#cb31-33" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2861</span> | <span class="dv">2014-07-01</span> |  unemploy | <span class="fl">9608.0</span> | <span class="fl">0.54653825</span> |</span>
+<span id="cb31-34"><a href="#cb31-34" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2862</span> | <span class="dv">2014-08-01</span> |  unemploy | <span class="fl">9599.0</span> | <span class="fl">0.54582774</span> |</span>
+<span id="cb31-35"><a href="#cb31-35" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2863</span> | <span class="dv">2014-09-01</span> |  unemploy | <span class="fl">9262.0</span> | <span class="fl">0.51922318</span> |</span>
+<span id="cb31-36"><a href="#cb31-36" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2864</span> | <span class="dv">2014-10-01</span> |  unemploy | <span class="fl">8990.0</span> | <span class="fl">0.49775006</span> |</span>
+<span id="cb31-37"><a href="#cb31-37" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2865</span> | <span class="dv">2014-11-01</span> |  unemploy | <span class="fl">9090.0</span> | <span class="fl">0.50564459</span> |</span>
+<span id="cb31-38"><a href="#cb31-38" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2866</span> | <span class="dv">2014-12-01</span> |  unemploy | <span class="fl">8717.0</span> | <span class="fl">0.47619799</span> |</span>
+<span id="cb31-39"><a href="#cb31-39" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2867</span> | <span class="dv">2015-01-01</span> |  unemploy | <span class="fl">8903.0</span> | <span class="fl">0.49088182</span> |</span>
+<span id="cb31-40"><a href="#cb31-40" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2868</span> | <span class="dv">2015-02-01</span> |  unemploy | <span class="fl">8610.0</span> | <span class="fl">0.46775085</span> |</span>
+<span id="cb31-41"><a href="#cb31-41" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2869</span> | <span class="dv">2015-03-01</span> |  unemploy | <span class="fl">8504.0</span> | <span class="fl">0.45938265</span> |</span>
+<span id="cb31-42"><a href="#cb31-42" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2870</span> | <span class="dv">2015-04-01</span> |  unemploy | <span class="fl">8526.0</span> | <span class="fl">0.46111944</span> |</span>
+<span id="cb31-43"><a href="#cb31-43" aria-hidden="true" tabindex="-1"></a><span class="kw">&gt;</span>,</span>
+<span id="cb31-44"><a href="#cb31-44" aria-hidden="true" tabindex="-1"></a>  :=layer [],</span>
+<span id="cb31-45"><a href="#cb31-45" aria-hidden="true" tabindex="-1"></a>  :=y-type</span>
+<span id="cb31-46"><a href="#cb31-46" aria-hidden="true" tabindex="-1"></a>  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--<span class="dv">48211</span>],</span>
+<span id="cb31-47"><a href="#cb31-47" aria-hidden="true" tabindex="-1"></a>  :=x2-encoding</span>
+<span id="cb31-48"><a href="#cb31-48" aria-hidden="true" tabindex="-1"></a>  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--<span class="dv">48211</span>],</span>
+<span id="cb31-49"><a href="#cb31-49" aria-hidden="true" tabindex="-1"></a>  :=opacity <span class="at">:com.rpl.specter.impl/NONE</span>,</span>
+<span id="cb31-50"><a href="#cb31-50" aria-hidden="true" tabindex="-1"></a>  :=y2-after-stat :=y2,</span>
+<span id="cb31-51"><a href="#cb31-51" aria-hidden="true" tabindex="-1"></a>  :=x2-after-stat :=x2,</span>
+<span id="cb31-52"><a href="#cb31-52" aria-hidden="true" tabindex="-1"></a>  :=usermeta {<span class="at">:embedOptions</span> {<span class="at">:renderer</span> :=renderer}},</span>
+<span id="cb31-53"><a href="#cb31-53" aria-hidden="true" tabindex="-1"></a>  :=title <span class="at">:com.rpl.specter.impl/NONE</span>,</span>
+<span id="cb31-54"><a href="#cb31-54" aria-hidden="true" tabindex="-1"></a>  :=y-type-after-stat</span>
+<span id="cb31-55"><a href="#cb31-55" aria-hidden="true" tabindex="-1"></a>  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--<span class="dv">48211</span>],</span>
+<span id="cb31-56"><a href="#cb31-56" aria-hidden="true" tabindex="-1"></a>  :=height <span class="dv">300</span>,</span>
+<span id="cb31-57"><a href="#cb31-57" aria-hidden="true" tabindex="-1"></a>  :=renderer <span class="at">:svg</span>,</span>
+<span id="cb31-58"><a href="#cb31-58" aria-hidden="true" tabindex="-1"></a>  :=mark-opacity <span class="at">:com.rpl.specter.impl/NONE</span>,</span>
+<span id="cb31-59"><a href="#cb31-59" aria-hidden="true" tabindex="-1"></a>  :=y-title <span class="at">:com.rpl.specter.impl/NONE</span>,</span>
+<span id="cb31-60"><a href="#cb31-60" aria-hidden="true" tabindex="-1"></a>  :=size <span class="at">:com.rpl.specter.impl/NONE</span>,</span>
+<span id="cb31-61"><a href="#cb31-61" aria-hidden="true" tabindex="-1"></a>  :=group</span>
+<span id="cb31-62"><a href="#cb31-62" aria-hidden="true" tabindex="-1"></a>  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--<span class="dv">48211</span>],</span>
+<span id="cb31-63"><a href="#cb31-63" aria-hidden="true" tabindex="-1"></a>  :=mark-size <span class="at">:com.rpl.specter.impl/NONE</span>,</span>
+<span id="cb31-64"><a href="#cb31-64" aria-hidden="true" tabindex="-1"></a>  :=layer? <span class="at">:com.rpl.specter.impl/NONE</span>,</span>
+<span id="cb31-65"><a href="#cb31-65" aria-hidden="true" tabindex="-1"></a>  :=size-type</span>
+<span id="cb31-66"><a href="#cb31-66" aria-hidden="true" tabindex="-1"></a>  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--<span class="dv">48211</span>],</span>
+<span id="cb31-67"><a href="#cb31-67" aria-hidden="true" tabindex="-1"></a>  :=layer-dataset</span>
+<span id="cb31-68"><a href="#cb31-68" aria-hidden="true" tabindex="-1"></a>  #&lt;WrappedValue<span class="at">@43dcbcc6:</span> ggplot2/economics_long [<span class="dv">574</span> <span class="dv">5</span>]:</span>
+<span id="cb31-69"><a href="#cb31-69" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb31-70"><a href="#cb31-70" aria-hidden="true" tabindex="-1"></a>| <span class="at">:rownames</span> |      <span class="at">:date</span> | <span class="at">:variable</span> | <span class="at">:value</span> |   <span class="at">:value01</span> |</span>
+<span id="cb31-71"><a href="#cb31-71" aria-hidden="true" tabindex="-1"></a>|----------:|------------|-----------|-------:|-----------:|</span>
+<span id="cb31-72"><a href="#cb31-72" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2297</span> | <span class="dv">1967-07-01</span> |  unemploy | <span class="fl">2944.0</span> | <span class="fl">0.02044683</span> |</span>
+<span id="cb31-73"><a href="#cb31-73" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2298</span> | <span class="dv">1967-08-01</span> |  unemploy | <span class="fl">2945.0</span> | <span class="fl">0.02052578</span> |</span>
+<span id="cb31-74"><a href="#cb31-74" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2299</span> | <span class="dv">1967-09-01</span> |  unemploy | <span class="fl">2958.0</span> | <span class="fl">0.02155206</span> |</span>
+<span id="cb31-75"><a href="#cb31-75" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2300</span> | <span class="dv">1967-10-01</span> |  unemploy | <span class="fl">3143.0</span> | <span class="fl">0.03615694</span> |</span>
+<span id="cb31-76"><a href="#cb31-76" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2301</span> | <span class="dv">1967-11-01</span> |  unemploy | <span class="fl">3066.0</span> | <span class="fl">0.03007816</span> |</span>
+<span id="cb31-77"><a href="#cb31-77" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2302</span> | <span class="dv">1967-12-01</span> |  unemploy | <span class="fl">3018.0</span> | <span class="fl">0.02628878</span> |</span>
+<span id="cb31-78"><a href="#cb31-78" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2303</span> | <span class="dv">1968-01-01</span> |  unemploy | <span class="fl">2878.0</span> | <span class="fl">0.01523644</span> |</span>
+<span id="cb31-79"><a href="#cb31-79" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2304</span> | <span class="dv">1968-02-01</span> |  unemploy | <span class="fl">3001.0</span> | <span class="fl">0.02494671</span> |</span>
+<span id="cb31-80"><a href="#cb31-80" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2305</span> | <span class="dv">1968-03-01</span> |  unemploy | <span class="fl">2877.0</span> | <span class="fl">0.01515750</span> |</span>
+<span id="cb31-81"><a href="#cb31-81" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2306</span> | <span class="dv">1968-04-01</span> |  unemploy | <span class="fl">2709.0</span> | <span class="fl">0.00189469</span> |</span>
+<span id="cb31-82"><a href="#cb31-82" aria-hidden="true" tabindex="-1"></a>|       ... |        ... |       ... |    ... |        ... |</span>
+<span id="cb31-83"><a href="#cb31-83" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2860</span> | <span class="dv">2014-06-01</span> |  unemploy | <span class="fl">9460.0</span> | <span class="fl">0.53485435</span> |</span>
+<span id="cb31-84"><a href="#cb31-84" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2861</span> | <span class="dv">2014-07-01</span> |  unemploy | <span class="fl">9608.0</span> | <span class="fl">0.54653825</span> |</span>
+<span id="cb31-85"><a href="#cb31-85" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2862</span> | <span class="dv">2014-08-01</span> |  unemploy | <span class="fl">9599.0</span> | <span class="fl">0.54582774</span> |</span>
+<span id="cb31-86"><a href="#cb31-86" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2863</span> | <span class="dv">2014-09-01</span> |  unemploy | <span class="fl">9262.0</span> | <span class="fl">0.51922318</span> |</span>
+<span id="cb31-87"><a href="#cb31-87" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2864</span> | <span class="dv">2014-10-01</span> |  unemploy | <span class="fl">8990.0</span> | <span class="fl">0.49775006</span> |</span>
+<span id="cb31-88"><a href="#cb31-88" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2865</span> | <span class="dv">2014-11-01</span> |  unemploy | <span class="fl">9090.0</span> | <span class="fl">0.50564459</span> |</span>
+<span id="cb31-89"><a href="#cb31-89" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2866</span> | <span class="dv">2014-12-01</span> |  unemploy | <span class="fl">8717.0</span> | <span class="fl">0.47619799</span> |</span>
+<span id="cb31-90"><a href="#cb31-90" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2867</span> | <span class="dv">2015-01-01</span> |  unemploy | <span class="fl">8903.0</span> | <span class="fl">0.49088182</span> |</span>
+<span id="cb31-91"><a href="#cb31-91" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2868</span> | <span class="dv">2015-02-01</span> |  unemploy | <span class="fl">8610.0</span> | <span class="fl">0.46775085</span> |</span>
+<span id="cb31-92"><a href="#cb31-92" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2869</span> | <span class="dv">2015-03-01</span> |  unemploy | <span class="fl">8504.0</span> | <span class="fl">0.45938265</span> |</span>
+<span id="cb31-93"><a href="#cb31-93" aria-hidden="true" tabindex="-1"></a>|      <span class="dv">2870</span> | <span class="dv">2015-04-01</span> |  unemploy | <span class="fl">8526.0</span> | <span class="fl">0.46111944</span> |</span>
+<span id="cb31-94"><a href="#cb31-94" aria-hidden="true" tabindex="-1"></a><span class="kw">&gt;</span>,</span>
+<span id="cb31-95"><a href="#cb31-95" aria-hidden="true" tabindex="-1"></a>  :=color <span class="at">:com.rpl.specter.impl/NONE</span>,</span>
+<span id="cb31-96"><a href="#cb31-96" aria-hidden="true" tabindex="-1"></a>  :=mark-color <span class="st">"purple"</span>,</span>
+<span id="cb31-97"><a href="#cb31-97" aria-hidden="true" tabindex="-1"></a>  :=x <span class="at">:date</span>,</span>
+<span id="cb31-98"><a href="#cb31-98" aria-hidden="true" tabindex="-1"></a>  :=y2 <span class="at">:com.rpl.specter.impl/NONE</span>,</span>
+<span id="cb31-99"><a href="#cb31-99" aria-hidden="true" tabindex="-1"></a>  :=x-after-stat :=x,</span>
+<span id="cb31-100"><a href="#cb31-100" aria-hidden="true" tabindex="-1"></a>  <span class="at">:DFMT</span> {<span class="at">:type</span> <span class="st">"csv"</span>},</span>
+<span id="cb31-101"><a href="#cb31-101" aria-hidden="true" tabindex="-1"></a>  :=encoding</span>
+<span id="cb31-102"><a href="#cb31-102" aria-hidden="true" tabindex="-1"></a>  {<span class="at">:color</span> {<span class="at">:field</span> :=color, <span class="at">:type</span> :=color-type},</span>
+<span id="cb31-103"><a href="#cb31-103" aria-hidden="true" tabindex="-1"></a>   <span class="at">:size</span> {<span class="at">:field</span> :=size, <span class="at">:type</span> :=size-type},</span>
+<span id="cb31-104"><a href="#cb31-104" aria-hidden="true" tabindex="-1"></a>   <span class="at">:x</span></span>
+<span id="cb31-105"><a href="#cb31-105" aria-hidden="true" tabindex="-1"></a>   {<span class="at">:field</span> :=x-after-stat,</span>
+<span id="cb31-106"><a href="#cb31-106" aria-hidden="true" tabindex="-1"></a>    <span class="at">:type</span> :=x-type-after-stat,</span>
+<span id="cb31-107"><a href="#cb31-107" aria-hidden="true" tabindex="-1"></a>    <span class="at">:title</span> :=x-title,</span>
+<span id="cb31-108"><a href="#cb31-108" aria-hidden="true" tabindex="-1"></a>    <span class="at">:bin</span> :=x-bin},</span>
+<span id="cb31-109"><a href="#cb31-109" aria-hidden="true" tabindex="-1"></a>   <span class="at">:y</span></span>
+<span id="cb31-110"><a href="#cb31-110" aria-hidden="true" tabindex="-1"></a>   {<span class="at">:field</span> :=y-after-stat,</span>
+<span id="cb31-111"><a href="#cb31-111" aria-hidden="true" tabindex="-1"></a>    <span class="at">:type</span> :=y-type-after-stat,</span>
+<span id="cb31-112"><a href="#cb31-112" aria-hidden="true" tabindex="-1"></a>    <span class="at">:title</span> :=y-title,</span>
+<span id="cb31-113"><a href="#cb31-113" aria-hidden="true" tabindex="-1"></a>    <span class="at">:bin</span> :=y-bin},</span>
+<span id="cb31-114"><a href="#cb31-114" aria-hidden="true" tabindex="-1"></a>   <span class="at">:x2</span> :=x2-encoding,</span>
+<span id="cb31-115"><a href="#cb31-115" aria-hidden="true" tabindex="-1"></a>   <span class="at">:y2</span> :=y2-encoding},</span>
+<span id="cb31-116"><a href="#cb31-116" aria-hidden="true" tabindex="-1"></a>  :=x-type-after-stat</span>
+<span id="cb31-117"><a href="#cb31-117" aria-hidden="true" tabindex="-1"></a>  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--<span class="dv">48211</span>],</span>
+<span id="cb31-118"><a href="#cb31-118" aria-hidden="true" tabindex="-1"></a>  :=x-bin <span class="at">:com.rpl.specter.impl/NONE</span>,</span>
+<span id="cb31-119"><a href="#cb31-119" aria-hidden="true" tabindex="-1"></a>  :=x-type</span>
+<span id="cb31-120"><a href="#cb31-120" aria-hidden="true" tabindex="-1"></a>  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--<span class="dv">48211</span>],</span>
+<span id="cb31-121"><a href="#cb31-121" aria-hidden="true" tabindex="-1"></a>  :=histogram-nbins <span class="dv">10</span>,</span>
+<span id="cb31-122"><a href="#cb31-122" aria-hidden="true" tabindex="-1"></a>  :=stat <span class="at">:com.rpl.specter.impl/NONE</span>,</span>
+<span id="cb31-123"><a href="#cb31-123" aria-hidden="true" tabindex="-1"></a>  :=width <span class="dv">400</span>,</span>
+<span id="cb31-124"><a href="#cb31-124" aria-hidden="true" tabindex="-1"></a>  :=color-type</span>
+<span id="cb31-125"><a href="#cb31-125" aria-hidden="true" tabindex="-1"></a>  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--<span class="dv">48211</span>],</span>
+<span id="cb31-126"><a href="#cb31-126" aria-hidden="true" tabindex="-1"></a>  :=mark <span class="st">"circle"</span>,</span>
+<span id="cb31-127"><a href="#cb31-127" aria-hidden="true" tabindex="-1"></a>  :=dataset-after-stat</span>
+<span id="cb31-128"><a href="#cb31-128" aria-hidden="true" tabindex="-1"></a>  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--<span class="dv">48211</span>],</span>
+<span id="cb31-129"><a href="#cb31-129" aria-hidden="true" tabindex="-1"></a>  :=y-bin <span class="at">:com.rpl.specter.impl/NONE</span>,</span>
+<span id="cb31-130"><a href="#cb31-130" aria-hidden="true" tabindex="-1"></a>  :=x-title <span class="at">:com.rpl.specter.impl/NONE</span>,</span>
+<span id="cb31-131"><a href="#cb31-131" aria-hidden="true" tabindex="-1"></a>  :=csv-data</span>
+<span id="cb31-132"><a href="#cb31-132" aria-hidden="true" tabindex="-1"></a>  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--<span class="dv">48211</span>],</span>
+<span id="cb31-133"><a href="#cb31-133" aria-hidden="true" tabindex="-1"></a>  :=y <span class="at">:value</span>,</span>
+<span id="cb31-134"><a href="#cb31-134" aria-hidden="true" tabindex="-1"></a>  :=data {<span class="at">:values</span> :=csv-data, <span class="at">:format</span> {<span class="at">:type</span> <span class="st">"csv"</span>}},</span>
+<span id="cb31-135"><a href="#cb31-135" aria-hidden="true" tabindex="-1"></a>  :=dataset</span>
+<span id="cb31-136"><a href="#cb31-136" aria-hidden="true" tabindex="-1"></a>  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--<span class="dv">48211</span>],</span>
+<span id="cb31-137"><a href="#cb31-137" aria-hidden="true" tabindex="-1"></a>  <span class="at">:VALDATA</span> :=csv-data,</span>
+<span id="cb31-138"><a href="#cb31-138" aria-hidden="true" tabindex="-1"></a>  :=mark-tooltip <span class="va">true</span>,</span>
+<span id="cb31-139"><a href="#cb31-139" aria-hidden="true" tabindex="-1"></a>  :=background <span class="st">"floralwhite"</span>,</span>
+<span id="cb31-140"><a href="#cb31-140" aria-hidden="true" tabindex="-1"></a>  :=y-after-stat :=y,</span>
+<span id="cb31-141"><a href="#cb31-141" aria-hidden="true" tabindex="-1"></a>  :=predictors [:=x],</span>
+<span id="cb31-142"><a href="#cb31-142" aria-hidden="true" tabindex="-1"></a>  :=y2-encoding</span>
+<span id="cb31-143"><a href="#cb31-143" aria-hidden="true" tabindex="-1"></a>  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--<span class="dv">48211</span>]},</span>
+<span id="cb31-144"><a href="#cb31-144" aria-hidden="true" tabindex="-1"></a> <span class="at">:height</span> :=height,</span>
+<span id="cb31-145"><a href="#cb31-145" aria-hidden="true" tabindex="-1"></a> <span class="at">:data</span> :=data}</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>The structure returned by <code>haclo/base</code> is a Hanami template (with <a href="https://github.com/jsa-aerial/hanami?tab=readme-ov-file#template-local-defaults">local defaults</a>). When it is displayed, it goes through the Hanami transform to recieve the Vega-Lite spec.</p>
 <p>When we use base, we can keep processing the template in a pipeline of transformations. We will use it soon with layers.</p>
@@ -807,7 +808,7 @@ <h2 data-number="3.9" class="anchored" data-anchor-id="updating-data"><span clas
 <span id="cb36-7"><a href="#cb36-7" aria-hidden="true" tabindex="-1"></a>    (haclo/layer-point {:=mark-color <span class="st">"green"</span></span>
 <span id="cb36-8"><a href="#cb36-8" aria-hidden="true" tabindex="-1"></a>                        :=mark-size <span class="dv">200</span>}))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
-<div><script>vegaEmbed(document.currentScript.parentElement, 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0,0.5348543459382648\n2861,2014-07-01,unemploy,9608.0,0.5465382489934475\n2862,2014-08-01,unemploy,9599.0,0.5458277413752269\n2863,2014-09-01,unemploy,9262.0,0.5192231783374122\n2864,2014-10-01,unemploy,8990.0,0.4977500592089682\n2865,2014-11-01,unemploy,9090.0,0.5056445883003079\n2866,2014-12-01,unemploy,8717.0,0.4761979947896108\n2867,2015-01-01,unemploy,8903.0,0.49088181889950266\n2868,2015-02-01,unemploy,8610.0,0.4677508486618773\n2869,2015-03-01,unemploy,8504.0,0.4593826478250572\n2870,2015-04-01,unemploy,8526.0,0.46111944422515194\n","format":{"type":"csv"}}},{"mark":{"type":"circle","color":"green","size":200,"tooltip":true},"encoding":{"x":{"field":"date","type":"temporal"},"y":{"field":"value","type":"quantitative"}},"data":{"values":"rownames,date,variable,value,value01\n2686,1999-12-01,unemploy,5653.0,0.23430962343096234\n2309,1968-07-01,unemploy,2883.0,0.01563116760085261\n2794,2008-12-01,unemploy,11286.0,0.6790084471461277\n2570,1990-04-01,unemploy,6797.0,0.3246230362358885\n2751,2005-05-01,unemploy,7651.0,0.3920423146759296\n","format":{"type":"csv"}}}],"height":300,"data":{"url":"hanamicloth_book.walkthrough_files\/2.csv","format":{"type":"csv"}}});</script></div>
 <p>You see, we have lots of data for the lines, but only five random points.</p>
 </section>
 <section id="processing-raw-vega-lite" class="level2" data-number="3.10">
@@ -847,7 +848,7 @@ <h2 data-number="3.10" class="anchored" data-anchor-id="processing-raw-vega-lite
 <span id="cb38-19"><a href="#cb38-19" aria-hidden="true" tabindex="-1"></a>    <span class="at">:y</span> {<span class="at">:field</span> <span class="at">:value</span>, <span class="at">:type</span> <span class="at">:quantitative</span>}},</span>
 <span id="cb38-20"><a href="#cb38-20" aria-hidden="true" tabindex="-1"></a>   <span class="at">:data</span></span>
 <span id="cb38-21"><a href="#cb38-21" aria-hidden="true" tabindex="-1"></a>   {<span class="at">:values</span></span>
-<span id="cb38-22"><a href="#cb38-22" aria-hidden="true" tabindex="-1"></a>    <span class="st">"rownames,date,variable,value,value01</span><span class="sc">\n</span><span class="st">2851,2013-09-01,unemploy,11270.0,0.6777453224915134</span><span class="sc">\n</span><span class="st">2833,2012-03-01,unemploy,12713.0,0.7916633772795453</span><span class="sc">\n</span><span class="st">2636,1995-10-01,unemploy,7328.0,0.3665429857109023</span><span class="sc">\n</span><span class="st">2419,1977-09-01,unemploy,6751.0,0.32099155285387226</span><span class="sc">\n</span><span class="st">2574,1990-08-01,unemploy,7188.0,0.35549064498302674</span><span class="sc">\n</span><span class="st">"</span>,</span>
+<span id="cb38-22"><a href="#cb38-22" aria-hidden="true" tabindex="-1"></a>    <span class="st">"rownames,date,variable,value,value01</span><span class="sc">\n</span><span class="st">2362,1972-12-01,unemploy,4543.0,0.14668035051709166</span><span class="sc">\n</span><span class="st">2609,1993-07-01,unemploy,8930.0,0.4930133417541644</span><span class="sc">\n</span><span class="st">2741,2004-07-01,unemploy,8136.0,0.4303307807689271</span><span class="sc">\n</span><span class="st">2399,1976-01-01,unemploy,7534.0,0.3828057156390621</span><span class="sc">\n</span><span class="st">2698,2000-12-01,unemploy,5634.0,0.2328096629036078</span><span class="sc">\n</span><span class="st">"</span>,</span>
 <span id="cb38-23"><a href="#cb38-23" aria-hidden="true" tabindex="-1"></a>    <span class="at">:format</span> {<span class="at">:type</span> <span class="st">"csv"</span>}}}],</span>
 <span id="cb38-24"><a href="#cb38-24" aria-hidden="true" tabindex="-1"></a> <span class="at">:height</span> <span class="dv">300</span>,</span>
 <span id="cb38-25"><a href="#cb38-25" aria-hidden="true" tabindex="-1"></a> <span class="at">:data</span></span>
@@ -868,7 +869,7 @@ <h2 data-number="3.10" class="anchored" data-anchor-id="processing-raw-vega-lite
 <span id="cb39-9"><a href="#cb39-9" aria-hidden="true" tabindex="-1"></a>    haclo/plot</span>
 <span id="cb39-10"><a href="#cb39-10" aria-hidden="true" tabindex="-1"></a>    (<span class="kw">assoc</span> <span class="at">:background</span> <span class="st">"lightgrey"</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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 <section id="smoothing" class="level2" data-number="3.11">
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-      <a href="./hanamicloth_book.plotlycloth_walkthrough.html" class="pagination-link" aria-label="Plotlycloth Walkthrough (experimental 🛠)">
-        <span class="nav-page-text"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Plotlycloth Walkthrough (experimental 🛠)</span></span> <i class="bi bi-arrow-right-short"></i>
+      <a href="./hanamicloth_book.plotlycloth_walkthrough.html" class="pagination-link" aria-label="Plotlycloth Walkthrough 👣 - experimental 🛠">
+        <span class="nav-page-text"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Plotlycloth Walkthrough 👣 - experimental 🛠</span></span> <i class="bi bi-arrow-right-short"></i>
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- <span class="menu-text"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Walkthrough</span></span></a>
+ <span class="menu-text"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Walkthrough 👣</span></span></a>
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   <a href="./hanamicloth_book.plotlycloth_walkthrough.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Plotlycloth Walkthrough (experimental 🛠)</span></span></a>
+ <span class="menu-text"><span class="chapter-number">4</span>&nbsp; <span class="chapter-title">Plotlycloth Walkthrough 👣 - experimental 🛠</span></span></a>
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   <ul>
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   <ul>
-  <li><a href="#near-term-plan" id="toc-near-term-plan" class="nav-link" data-scroll-target="#near-term-plan"><span class="header-section-number">1.1</span> Near term plan</a></li>
-  <li><a href="#goals" id="toc-goals" class="nav-link" data-scroll-target="#goals"><span class="header-section-number">1.2</span> Goals</a></li>
-  <li><a href="#discussion" id="toc-discussion" class="nav-link" data-scroll-target="#discussion"><span class="header-section-number">1.3</span> Discussion</a></li>
-  <li><a href="#chapters-in-this-book" id="toc-chapters-in-this-book" class="nav-link" data-scroll-target="#chapters-in-this-book"><span class="header-section-number">1.4</span> Chapters in this book:</a></li>
+  <li><a href="#two-apis" id="toc-two-apis" class="nav-link" data-scroll-target="#two-apis"><span class="header-section-number">1.1</span> Two APIs</a></li>
+  <li><a href="#near-term-plan" id="toc-near-term-plan" class="nav-link" data-scroll-target="#near-term-plan"><span class="header-section-number">1.2</span> Near term plan</a></li>
+  <li><a href="#goals" id="toc-goals" class="nav-link" data-scroll-target="#goals"><span class="header-section-number">1.3</span> Goals</a></li>
+  <li><a href="#discussion" id="toc-discussion" class="nav-link" data-scroll-target="#discussion"><span class="header-section-number">1.4</span> Discussion</a></li>
+  <li><a href="#chapters-in-this-book" id="toc-chapters-in-this-book" class="nav-link" data-scroll-target="#chapters-in-this-book"><span class="header-section-number">1.5</span> Chapters in this book:</a></li>
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@@ -231,11 +232,11 @@ <h1 class="title">Hanamicloth</h1>
 <span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a>    (haclo/layer-point {:=mark-color <span class="st">"green"</span></span>
 <span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a>                        :=mark-size <span class="dv">200</span>}))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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 <p>See more examples:</p>
 <ul>
-<li><p>main API <a href="./hanamicloth_book.walkthrough.html">Walkthrough</a> 👣</p></li>
-<li><p>experimental plotlycloth API <a href="./hanamicloth_book.plotlycloth_walkthrough.html">Walkthrough</a></p></li>
+<li><p><a href="./hanamicloth_book.walkthrough.html">main API Walkthrough</a> 👣</p></li>
+<li><p><a href="./hanamicloth_book.plotlycloth_walkthrough.html">plotlycloth Walkthrough</a> 👣 - experimental 🛠</p></li>
 </ul>
 <section id="preface" class="level1" data-number="1">
 <h1 data-number="1"><span class="header-section-number">1</span> Preface</h1>
@@ -248,15 +249,24 @@ <h1 data-number="1"><span class="header-section-number">1</span> Preface</h1>
 <hr>
 <p>An early version of this library was demonstrated in Kira Mclean’s April 2024 talk at London Clojurians:</p>
 <iframe allowfullscreen="allowfullscreen" src="https://www.youtube.com/embed/eUFf3-og_-Y"></iframe>
-<section id="near-term-plan" class="level2" data-number="1.1">
-<h2 data-number="1.1" class="anchored" data-anchor-id="near-term-plan"><span class="header-section-number">1.1</span> Near term plan</h2>
+<section id="two-apis" class="level2" data-number="1.1">
+<h2 data-number="1.1" class="anchored" data-anchor-id="two-apis"><span class="header-section-number">1.1</span> Two APIs</h2>
+<p>Hanamicloth currently supports two APIs:</p>
 <ul>
-<li>Stabilize both the <a href="https://vega.github.io/vega-lite/">Vega-Lite</a>-based API and the <a href="https://plotly.com/javascript/">Plotly.js</a>-based API as Beta stage.</li>
+<li><p><code>scicloj.hanamicloth.v1.api</code> generates <a href="https://vega.github.io/vega-lite/">Vega-Lite</a> plots.</p></li>
+<li><p><code>scicloj.hanamicloth.v1.plotlycloth</code> generates the <a href="https://plotly.com/javascript/">Plotly.js</a> plots.</p></li>
+</ul>
+<p>Each of these APIs builds upon the strengths of its target platform and partially uses its naming and concepts. Thus, the two APIs are not completely compatible. The Plotly-based API is expected to grow a little further in terms of its flexibility and the kinds of idioms it can express.</p>
+</section>
+<section id="near-term-plan" class="level2" data-number="1.2">
+<h2 data-number="1.2" class="anchored" data-anchor-id="near-term-plan"><span class="header-section-number">1.2</span> Near term plan</h2>
+<ul>
+<li>Stabilize both the Vega-Lite-based API and the Plotly.js-based API as Beta stage.</li>
 <li>Keep developing main ly the Plotly.js-based API (as it will be more flexible to extend).</li>
 </ul>
 </section>
-<section id="goals" class="level2" data-number="1.2">
-<h2 data-number="1.2" class="anchored" data-anchor-id="goals"><span class="header-section-number">1.2</span> Goals</h2>
+<section id="goals" class="level2" data-number="1.3">
+<h2 data-number="1.3" class="anchored" data-anchor-id="goals"><span class="header-section-number">1.3</span> Goals</h2>
 <ul>
 <li>Have a functional grammar for common plotting tasks (but not all tasks).</li>
 <li>In particular, provide an easy way to work with layers.</li>
@@ -265,26 +275,26 @@ <h2 data-number="1.2" class="anchored" data-anchor-id="goals"><span class="heade
 <li>By default, infer relevant information from the data (e.g., field types).</li>
 <li>Catch common errors using the data (e.g., missing fields).</li>
 <li>Be able to use backend Clojure for relevant statistical tasks (e.g., smoothing by regression, histograms, density estimation).</li>
-<li>Be able to rely on <a href="https://vega.github.io/vega-lite/">Vega-Lite</a> for other some components of the pipeline (e.g., scales and coordinates).</li>
+<li>Be able to rely on Vega-Lite/Plotly.js for other some components of the pipeline (e.g., scales and coordinates).</li>
 <li>Provide simpler Hanami templates, compared to the original ones.</li>
 <li>Still have the option of using the original Hanami templates.</li>
-<li>Still be able to use all of Vega-Lite in its raw format for the highest flexibility.</li>
+<li>Still be able to use all of Vega-Lite/Plotly.js in its raw format for the highest flexibility.</li>
 </ul>
 <p>In the longer term, this project is part of the Scicloj effort to create a grammar-of-graphics visualization library in Clojure.</p>
 </section>
-<section id="discussion" class="level2" data-number="1.3">
-<h2 data-number="1.3" class="anchored" data-anchor-id="discussion"><span class="header-section-number">1.3</span> Discussion</h2>
+<section id="discussion" class="level2" data-number="1.4">
+<h2 data-number="1.4" class="anchored" data-anchor-id="discussion"><span class="header-section-number">1.4</span> Discussion</h2>
 <ul>
 <li>development - topic threads under <a href="https://clojurians.zulipchat.com/#narrow/stream/443101-hanamicloth-dev">#hanamicloth-dev</a> at the <a href="https://scicloj.github.io/docs/community/chat/">Clojurians Zulip chat</a> or <a href="https://github.com/scicloj/hanamicloth/issues">Github Issues</a></li>
 <li>usage - topic threads under <a href="https://clojurians.zulipchat.com/#narrow/stream/151924-data-science/">#data-science</a></li>
 </ul>
 </section>
-<section id="chapters-in-this-book" class="level2" data-number="1.4">
-<h2 data-number="1.4" class="anchored" data-anchor-id="chapters-in-this-book"><span class="header-section-number">1.4</span> Chapters in this book:</h2>
+<section id="chapters-in-this-book" class="level2" data-number="1.5">
+<h2 data-number="1.5" class="anchored" data-anchor-id="chapters-in-this-book"><span class="header-section-number">1.5</span> Chapters in this book:</h2>
 <ul>
 <li><p><a href="hanamicloth_book.datasets.html">Some datasets</a></p></li>
-<li><p><a href="hanamicloth_book.walkthrough.html">Walkthrough</a></p></li>
-<li><p><a href="hanamicloth_book.plotlycloth_walkthrough.html">Plotlycloth Walkthrough (experimental 🛠)</a></p></li>
+<li><p><a href="hanamicloth_book.walkthrough.html">Walkthrough 👣</a></p></li>
+<li><p><a href="hanamicloth_book.plotlycloth_walkthrough.html">Plotlycloth Walkthrough 👣 - experimental 🛠</a></p></li>
 </ul>
 <div style="background-color:grey;height:2px;width:100%;"></div>
 <div><pre><small><small>source: <a href="https://github.com/scicloj/hanamicloth/blob/main/notebooks/index.clj">notebooks/index.clj</a></small></small></pre></div>
diff --git a/docs/search.json b/docs/search.json
index 2f5884f..1878e97 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -9,12 +9,22 @@
       "<span class='chapter-number'>1</span>  <span class='chapter-title'>Preface</span>"
     ]
   },
+  {
+    "objectID": "index.html#two-apis",
+    "href": "index.html#two-apis",
+    "title": "Hanamicloth",
+    "section": "1.1 Two APIs",
+    "text": "1.1 Two APIs\nHanamicloth currently supports two APIs:\n\nscicloj.hanamicloth.v1.api generates Vega-Lite plots.\nscicloj.hanamicloth.v1.plotlycloth generates the Plotly.js plots.\n\nEach of these APIs builds upon the strengths of its target platform and partially uses its naming and concepts. Thus, the two APIs are not completely compatible. The Plotly-based API is expected to grow a little further in terms of its flexibility and the kinds of idioms it can express.",
+    "crumbs": [
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     "title": "Hanamicloth",
-    "section": "1.1 Near term plan",
-    "text": "1.1 Near term plan\n\nStabilize both the Vega-Lite-based API and the Plotly.js-based API as Beta stage.\nKeep developing main ly the Plotly.js-based API (as it will be more flexible to extend).",
+    "section": "1.2 Near term plan",
+    "text": "1.2 Near term plan\n\nStabilize both the Vega-Lite-based API and the Plotly.js-based API as Beta stage.\nKeep developing main ly the Plotly.js-based API (as it will be more flexible to extend).",
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@@ -23,8 +33,8 @@
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-    "section": "1.2 Goals",
-    "text": "1.2 Goals\n\nHave a functional grammar for common plotting tasks (but not all tasks).\nIn particular, provide an easy way to work with layers.\nBe able to pass Tablecloth/tech.ml.dataset datasets directly to the plotting functions.\nWork out-of-the box in Kindly-supporting tools.\nBy default, infer relevant information from the data (e.g., field types).\nCatch common errors using the data (e.g., missing fields).\nBe able to use backend Clojure for relevant statistical tasks (e.g., smoothing by regression, histograms, density estimation).\nBe able to rely on Vega-Lite for other some components of the pipeline (e.g., scales and coordinates).\nProvide simpler Hanami templates, compared to the original ones.\nStill have the option of using the original Hanami templates.\nStill be able to use all of Vega-Lite in its raw format for the highest flexibility.\n\nIn the longer term, this project is part of the Scicloj effort to create a grammar-of-graphics visualization library in Clojure.",
+    "section": "1.3 Goals",
+    "text": "1.3 Goals\n\nHave a functional grammar for common plotting tasks (but not all tasks).\nIn particular, provide an easy way to work with layers.\nBe able to pass Tablecloth/tech.ml.dataset datasets directly to the plotting functions.\nWork out-of-the box in Kindly-supporting tools.\nBy default, infer relevant information from the data (e.g., field types).\nCatch common errors using the data (e.g., missing fields).\nBe able to use backend Clojure for relevant statistical tasks (e.g., smoothing by regression, histograms, density estimation).\nBe able to rely on Vega-Lite/Plotly.js for other some components of the pipeline (e.g., scales and coordinates).\nProvide simpler Hanami templates, compared to the original ones.\nStill have the option of using the original Hanami templates.\nStill be able to use all of Vega-Lite/Plotly.js in its raw format for the highest flexibility.\n\nIn the longer term, this project is part of the Scicloj effort to create a grammar-of-graphics visualization library in Clojure.",
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@@ -33,8 +43,8 @@
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     "title": "Hanamicloth",
-    "section": "1.3 Discussion",
-    "text": "1.3 Discussion\n\ndevelopment - topic threads under #hanamicloth-dev at the Clojurians Zulip chat or Github Issues\nusage - topic threads under #data-science",
+    "section": "1.4 Discussion",
+    "text": "1.4 Discussion\n\ndevelopment - topic threads under #hanamicloth-dev at the Clojurians Zulip chat or Github Issues\nusage - topic threads under #data-science",
     "crumbs": [
       "<span class='chapter-number'>1</span>  <span class='chapter-title'>Preface</span>"
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     "objectID": "index.html#chapters-in-this-book",
     "href": "index.html#chapters-in-this-book",
     "title": "Hanamicloth",
-    "section": "1.4 Chapters in this book:",
-    "text": "1.4 Chapters in this book:\n\nSome datasets\nWalkthrough\nPlotlycloth Walkthrough (experimental 🛠)\n\n\nsource: notebooks/index.clj",
+    "section": "1.5 Chapters in this book:",
+    "text": "1.5 Chapters in this book:\n\nSome datasets\nWalkthrough 👣\nPlotlycloth Walkthrough 👣 - experimental 🛠\n\n\nsource: notebooks/index.clj",
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-    "title": "3  Walkthrough",
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     "section": "",
     "text": "3.1 Setup\nFor this tutorial, we require:",
     "crumbs": [
-      "<span class='chapter-number'>3</span>  <span class='chapter-title'>Walkthrough</span>"
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     "href": "hanamicloth_book.walkthrough.html#setup",
-    "title": "3  Walkthrough",
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     "section": "",
     "text": "Hanamicloth’s main API namepace\nHanami\nTablecloth\nthe datetime namespace of dtype-next\nthe print namespace of tech.ml.dataset for customized dataset printing\nKindly (to specify how certaiun values should be visualized)\nthe datasets defined in the Datasets chapter\n\n\n(ns hanamicloth-book.walkthrough\n  (:require [scicloj.hanamicloth.v1.api :as haclo]\n            [aerial.hanami.templates :as ht]\n            [tablecloth.api :as tc]\n            [tech.v3.datatype.datetime :as datetime]\n            [tech.v3.dataset.print :as print]\n            [scicloj.kindly.v4.kind :as kind]\n            [clojure.string :as str]\n            [scicloj.kindly.v4.api :as kindly]\n            [hanamicloth-book.datasets :as datasets]))",
     "crumbs": [
-      "<span class='chapter-number'>3</span>  <span class='chapter-title'>Walkthrough</span>"
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     "section": "3.2 Basic usage",
     "text": "3.2 Basic usage\nLet us create a scatter plot from the Iris dataset. We pass a Tablecloth dataset to a Hanamicloth function with a Hanamicloth template.\n\n(-&gt; datasets/iris\n    (haclo/plot haclo/point-chart\n                {:=x :sepal-width\n                 :=y :sepal-length\n                 :=color :species\n                 :=mark-size 200}))\n\n\nSoon, the Hanamicloth docs will offer an introduction to the use of Hanami templates and substitution keys. For now, please see the Hanami documentation.\nWhile Hanamicloth allows using the classic Hanami templates and substitution keys, it also offers its own sets of templates, that we just used here.\nUnlike the classic Hanami keys of using capital letter substitution keys (e.g. :COLOR) Hanamicloth uses the convention of substitution keys beginning with = (e.g. :=color)\nThe templates of Hanamicloth also support a layered grammar which is demonstrated later in this document.\n(Here is how we can express the same plot with the layered grammar:)\n\n(-&gt; datasets/iris\n    (haclo/layer-point\n     {:=x :sepal-width\n      :=y :sepal-length\n      :=color :species\n      :=mark-size 200}))\n\n\nThe value returned by a haclo/plot function is a Vega-Lite spec:\n\n(-&gt; datasets/iris\n    (haclo/plot haclo/point-chart\n                {:=x :sepal-width\n                 :=y :sepal-length\n                 :=color :species\n                 :=mark-size 200})\n    kind/pprint)\n\n\n{:encoding\n {:color {:field :species, :type :nominal},\n  :x {:field :sepal-width, :type :quantitative},\n  :y {:field :sepal-length, :type :quantitative}},\n :usermeta {:embedOptions {:renderer :svg}},\n :mark {:type \"circle\", :size 200, :tooltip true},\n :width 400,\n :background \"floralwhite\",\n :height 300,\n :data\n {:values\n  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 :format {:type \"csv\"}}}\n\nBy looking at the :values key above, you can see that the dataset was implicitly represented as CSV, and that it was defined to be rendered as :svg by default.\nThe resulting plot is displayed correctly, as it is annotated by Kindly as a Vega-lite plot:\n\n(-&gt; datasets/iris\n    (haclo/plot haclo/point-chart\n                {:=x :sepal-width\n                 :=y :sepal-length\n                 :=color :species\n                 :=mark-size 200})\n    meta)\n\n\n#:kindly{:kind :kind/vega-lite}",
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     "section": "3.3 Using classic Hanami templates and defaults",
     "text": "3.3 Using classic Hanami templates and defaults\nWe can also use Hanami’s original templates (ht/chart) and substitution keys (:X, :Y, :MSIZE).\n\n(-&gt; datasets/iris\n    (haclo/plot ht/point-chart\n                {:X :sepal-width\n                 :Y :sepal-length\n                 :MSIZE 200\n                 :COLOR \"species\"}))\n\n\n\n(-&gt; datasets/iris\n    (haclo/plot ht/point-chart\n                {:X :sepal-width\n                 :Y :sepal-length\n                 :MSIZE 200\n                 :COLOR \"species\"})\n    kind/pprint)\n\n\n{:encoding\n {:y {:field :sepal-length, :type \"quantitative\"},\n  :color {:field \"species\", :type \"nominal\"},\n  :x {:field :sepal-width, :type \"quantitative\"}},\n :mark {:type \"circle\", :size 200, :tooltip true},\n :width 400,\n :background \"floralwhite\",\n :height 300,\n :data\n {:values\n  \"rownames,sepal-length,sepal-width,petal-length,petal-width,species\\n1,5.1,3.5,1.4,0.2,setosa\\n2,4.9,3.0,1.4,0.2,setosa\\n3,4.7,3.2,1.3,0.2,setosa\\n4,4.6,3.1,1.5,0.2,setosa\\n5,5.0,3.6,1.4,0.2,setosa\\n6,5.4,3.9,1.7,0.4,setosa\\n7,4.6,3.4,1.4,0.3,setosa\\n8,5.0,3.4,1.5,0.2,setosa\\n9,4.4,2.9,1.4,0.2,setosa\\n10,4.9,3.1,1.5,0.1,setosa\\n11,5.4,3.7,1.5,0.2,setosa\\n12,4.8,3.4,1.6,0.2,setosa\\n13,4.8,3.0,1.4,0.1,setosa\\n14,4.3,3.0,1.1,0.1,setosa\\n15,5.8,4.0,1.2,0.2,setosa\\n16,5.7,4.4,1.5,0.4,setosa\\n17,5.4,3.9,1.3,0.4,setosa\\n18,5.1,3.5,1.4,0.3,setosa\\n19,5.7,3.8,1.7,0.3,setosa\\n20,5.1,3.8,1.5,0.3,setosa\\n21,5.4,3.4,1.7,0.2,setosa\\n22,5.1,3.7,1.5,0.4,setosa\\n23,4.6,3.6,1.0,0.2,setosa\\n24,5.1,3.3,1.7,0.5,setosa\\n25,4.8,3.4,1.9,0.2,setosa\\n26,5.0,3.0,1.6,0.2,setosa\\n27,5.0,3.4,1.6,0.4,setosa\\n28,5.2,3.5,1.5,0.2,setosa\\n29,5.2,3.4,1.4,0.2,setosa\\n30,4.7,3.2,1.6,0.2,setosa\\n31,4.8,3.1,1.6,0.2,setosa\\n32,5.4,3.4,1.5,0.4,setosa\\n33,5.2,4.1,1.5,0.1,setosa\\n34,5.5,4.2,1.4,0.2,setosa\\n35,4.9,3.1,1.5,0.2,setosa\\n36,5.0,3.2,1.2,0.2,setosa\\n37,5.5,3.5,1.3,0.2,setosa\\n38,4.9,3.6,1.4,0.1,setosa\\n39,4.4,3.0,1.3,0.2,setosa\\n40,5.1,3.4,1.5,0.2,setosa\\n41,5.0,3.5,1.3,0.3,setosa\\n42,4.5,2.3,1.3,0.3,setosa\\n43,4.4,3.2,1.3,0.2,setosa\\n44,5.0,3.5,1.6,0.6,setosa\\n45,5.1,3.8,1.9,0.4,setosa\\n46,4.8,3.0,1.4,0.3,setosa\\n47,5.1,3.8,1.6,0.2,setosa\\n48,4.6,3.2,1.4,0.2,setosa\\n49,5.3,3.7,1.5,0.2,setosa\\n50,5.0,3.3,1.4,0.2,setosa\\n51,7.0,3.2,4.7,1.4,versicolor\\n52,6.4,3.2,4.5,1.5,versicolor\\n53,6.9,3.1,4.9,1.5,versicolor\\n54,5.5,2.3,4.0,1.3,versicolor\\n55,6.5,2.8,4.6,1.5,versicolor\\n56,5.7,2.8,4.5,1.3,versicolor\\n57,6.3,3.3,4.7,1.6,versicolor\\n58,4.9,2.4,3.3,1.0,versicolor\\n59,6.6,2.9,4.6,1.3,versicolor\\n60,5.2,2.7,3.9,1.4,versicolor\\n61,5.0,2.0,3.5,1.0,versicolor\\n62,5.9,3.0,4.2,1.5,versicolor\\n63,6.0,2.2,4.0,1.0,versicolor\\n64,6.1,2.9,4.7,1.4,versicolor\\n65,5.6,2.9,3.6,1.3,versicolor\\n66,6.7,3.1,4.4,1.4,versicolor\\n67,5.6,3.0,4.5,1.5,versicolor\\n68,5.8,2.7,4.1,1.0,versicolor\\n69,6.2,2.2,4.5,1.5,versicolor\\n70,5.6,2.5,3.9,1.1,versicolor\\n71,5.9,3.2,4.8,1.8,versicolor\\n72,6.1,2.8,4.0,1.3,versicolor\\n73,6.3,2.5,4.9,1.5,versicolor\\n74,6.1,2.8,4.7,1.2,versicolor\\n75,6.4,2.9,4.3,1.3,versicolor\\n76,6.6,3.0,4.4,1.4,versicolor\\n77,6.8,2.8,4.8,1.4,versicolor\\n78,6.7,3.0,5.0,1.7,versicolor\\n79,6.0,2.9,4.5,1.5,versicolor\\n80,5.7,2.6,3.5,1.0,versicolor\\n81,5.5,2.4,3.8,1.1,versicolor\\n82,5.5,2.4,3.7,1.0,versicolor\\n83,5.8,2.7,3.9,1.2,versicolor\\n84,6.0,2.7,5.1,1.6,versicolor\\n85,5.4,3.0,4.5,1.5,versicolor\\n86,6.0,3.4,4.5,1.6,versicolor\\n87,6.7,3.1,4.7,1.5,versicolor\\n88,6.3,2.3,4.4,1.3,versicolor\\n89,5.6,3.0,4.1,1.3,versicolor\\n90,5.5,2.5,4.0,1.3,versicolor\\n91,5.5,2.6,4.4,1.2,versicolor\\n92,6.1,3.0,4.6,1.4,versicolor\\n93,5.8,2.6,4.0,1.2,versicolor\\n94,5.0,2.3,3.3,1.0,versicolor\\n95,5.6,2.7,4.2,1.3,versicolor\\n96,5.7,3.0,4.2,1.2,versicolor\\n97,5.7,2.9,4.2,1.3,versicolor\\n98,6.2,2.9,4.3,1.3,versicolor\\n99,5.1,2.5,3.0,1.1,versicolor\\n100,5.7,2.8,4.1,1.3,versicolor\\n101,6.3,3.3,6.0,2.5,virginica\\n102,5.8,2.7,5.1,1.9,virginica\\n103,7.1,3.0,5.9,2.1,virginica\\n104,6.3,2.9,5.6,1.8,virginica\\n105,6.5,3.0,5.8,2.2,virginica\\n106,7.6,3.0,6.6,2.1,virginica\\n107,4.9,2.5,4.5,1.7,virginica\\n108,7.3,2.9,6.3,1.8,virginica\\n109,6.7,2.5,5.8,1.8,virginica\\n110,7.2,3.6,6.1,2.5,virginica\\n111,6.5,3.2,5.1,2.0,virginica\\n112,6.4,2.7,5.3,1.9,virginica\\n113,6.8,3.0,5.5,2.1,virginica\\n114,5.7,2.5,5.0,2.0,virginica\\n115,5.8,2.8,5.1,2.4,virginica\\n116,6.4,3.2,5.3,2.3,virginica\\n117,6.5,3.0,5.5,1.8,virginica\\n118,7.7,3.8,6.7,2.2,virginica\\n119,7.7,2.6,6.9,2.3,virginica\\n120,6.0,2.2,5.0,1.5,virginica\\n121,6.9,3.2,5.7,2.3,virginica\\n122,5.6,2.8,4.9,2.0,virginica\\n123,7.7,2.8,6.7,2.0,virginica\\n124,6.3,2.7,4.9,1.8,virginica\\n125,6.7,3.3,5.7,2.1,virginica\\n126,7.2,3.2,6.0,1.8,virginica\\n127,6.2,2.8,4.8,1.8,virginica\\n128,6.1,3.0,4.9,1.8,virginica\\n129,6.4,2.8,5.6,2.1,virginica\\n130,7.2,3.0,5.8,1.6,virginica\\n131,7.4,2.8,6.1,1.9,virginica\\n132,7.9,3.8,6.4,2.0,virginica\\n133,6.4,2.8,5.6,2.2,virginica\\n134,6.3,2.8,5.1,1.5,virginica\\n135,6.1,2.6,5.6,1.4,virginica\\n136,7.7,3.0,6.1,2.3,virginica\\n137,6.3,3.4,5.6,2.4,virginica\\n138,6.4,3.1,5.5,1.8,virginica\\n139,6.0,3.0,4.8,1.8,virginica\\n140,6.9,3.1,5.4,2.1,virginica\\n141,6.7,3.1,5.6,2.4,virginica\\n142,6.9,3.1,5.1,2.3,virginica\\n143,5.8,2.7,5.1,1.9,virginica\\n144,6.8,3.2,5.9,2.3,virginica\\n145,6.7,3.3,5.7,2.5,virginica\\n146,6.7,3.0,5.2,2.3,virginica\\n147,6.3,2.5,5.0,1.9,virginica\\n148,6.5,3.0,5.2,2.0,virginica\\n149,6.2,3.4,5.4,2.3,virginica\\n150,5.9,3.0,5.1,1.8,virginica\\n\",\n  :format {:type \"csv\"}}}",
     "crumbs": [
-      "<span class='chapter-number'>3</span>  <span class='chapter-title'>Walkthrough</span>"
+      "<span class='chapter-number'>3</span>  <span class='chapter-title'>Walkthrough 👣</span>"
     ]
   },
   {
     "objectID": "hanamicloth_book.walkthrough.html#inferring-and-overriding-field-types",
     "href": "hanamicloth_book.walkthrough.html#inferring-and-overriding-field-types",
-    "title": "3  Walkthrough",
+    "title": "3  Walkthrough 👣",
     "section": "3.4 Inferring and overriding field types",
     "text": "3.4 Inferring and overriding field types\nField types are inferred from the dataset’s column type. Here, for example, x and y are :quantitative, and color is :nominal (and is thus coloured with distinct colours rather than a gradient).\n\n(-&gt; datasets/iris\n    (haclo/plot haclo/point-chart\n                {:=x :sepal-width\n                 :=y :sepal-length\n                 :=color :species\n                 :=mark-size 200}))\n\n\n\n(-&gt; datasets/iris\n    (haclo/plot haclo/point-chart\n                {:=x :sepal-width\n                 :=y :sepal-length\n                 :=color :species\n                 :=mark-size 200})\n    kind/pprint)\n\n\n{:encoding\n {:color {:field :species, :type :nominal},\n  :x {:field :sepal-width, :type :quantitative},\n  :y {:field :sepal-length, :type :quantitative}},\n :usermeta {:embedOptions {:renderer :svg}},\n :mark {:type \"circle\", :size 200, :tooltip true},\n :width 400,\n :background \"floralwhite\",\n :height 300,\n :data\n {:values\n  \"rownames,sepal-length,sepal-width,petal-length,petal-width,species\\n1,5.1,3.5,1.4,0.2,setosa\\n2,4.9,3.0,1.4,0.2,setosa\\n3,4.7,3.2,1.3,0.2,setosa\\n4,4.6,3.1,1.5,0.2,setosa\\n5,5.0,3.6,1.4,0.2,setosa\\n6,5.4,3.9,1.7,0.4,setosa\\n7,4.6,3.4,1.4,0.3,setosa\\n8,5.0,3.4,1.5,0.2,setosa\\n9,4.4,2.9,1.4,0.2,setosa\\n10,4.9,3.1,1.5,0.1,setosa\\n11,5.4,3.7,1.5,0.2,setosa\\n12,4.8,3.4,1.6,0.2,setosa\\n13,4.8,3.0,1.4,0.1,setosa\\n14,4.3,3.0,1.1,0.1,setosa\\n15,5.8,4.0,1.2,0.2,setosa\\n16,5.7,4.4,1.5,0.4,setosa\\n17,5.4,3.9,1.3,0.4,setosa\\n18,5.1,3.5,1.4,0.3,setosa\\n19,5.7,3.8,1.7,0.3,setosa\\n20,5.1,3.8,1.5,0.3,setosa\\n21,5.4,3.4,1.7,0.2,setosa\\n22,5.1,3.7,1.5,0.4,setosa\\n23,4.6,3.6,1.0,0.2,setosa\\n24,5.1,3.3,1.7,0.5,setosa\\n25,4.8,3.4,1.9,0.2,setosa\\n26,5.0,3.0,1.6,0.2,setosa\\n27,5.0,3.4,1.6,0.4,setosa\\n28,5.2,3.5,1.5,0.2,setosa\\n29,5.2,3.4,1.4,0.2,setosa\\n30,4.7,3.2,1.6,0.2,setosa\\n31,4.8,3.1,1.6,0.2,setosa\\n32,5.4,3.4,1.5,0.4,setosa\\n33,5.2,4.1,1.5,0.1,setosa\\n34,5.5,4.2,1.4,0.2,setosa\\n35,4.9,3.1,1.5,0.2,setosa\\n36,5.0,3.2,1.2,0.2,setosa\\n37,5.5,3.5,1.3,0.2,setosa\\n38,4.9,3.6,1.4,0.1,setosa\\n39,4.4,3.0,1.3,0.2,setosa\\n40,5.1,3.4,1.5,0.2,setosa\\n41,5.0,3.5,1.3,0.3,setosa\\n42,4.5,2.3,1.3,0.3,setosa\\n43,4.4,3.2,1.3,0.2,setosa\\n44,5.0,3.5,1.6,0.6,setosa\\n45,5.1,3.8,1.9,0.4,setosa\\n46,4.8,3.0,1.4,0.3,setosa\\n47,5.1,3.8,1.6,0.2,setosa\\n48,4.6,3.2,1.4,0.2,setosa\\n49,5.3,3.7,1.5,0.2,setosa\\n50,5.0,3.3,1.4,0.2,setosa\\n51,7.0,3.2,4.7,1.4,versicolor\\n52,6.4,3.2,4.5,1.5,versicolor\\n53,6.9,3.1,4.9,1.5,versicolor\\n54,5.5,2.3,4.0,1.3,versicolor\\n55,6.5,2.8,4.6,1.5,versicolor\\n56,5.7,2.8,4.5,1.3,versicolor\\n57,6.3,3.3,4.7,1.6,versicolor\\n58,4.9,2.4,3.3,1.0,versicolor\\n59,6.6,2.9,4.6,1.3,versicolor\\n60,5.2,2.7,3.9,1.4,versicolor\\n61,5.0,2.0,3.5,1.0,versicolor\\n62,5.9,3.0,4.2,1.5,versicolor\\n63,6.0,2.2,4.0,1.0,versicolor\\n64,6.1,2.9,4.7,1.4,versicolor\\n65,5.6,2.9,3.6,1.3,versicolor\\n66,6.7,3.1,4.4,1.4,versicolor\\n67,5.6,3.0,4.5,1.5,versicolor\\n68,5.8,2.7,4.1,1.0,versicolor\\n69,6.2,2.2,4.5,1.5,versicolor\\n70,5.6,2.5,3.9,1.1,versicolor\\n71,5.9,3.2,4.8,1.8,versicolor\\n72,6.1,2.8,4.0,1.3,versicolor\\n73,6.3,2.5,4.9,1.5,versicolor\\n74,6.1,2.8,4.7,1.2,versicolor\\n75,6.4,2.9,4.3,1.3,versicolor\\n76,6.6,3.0,4.4,1.4,versicolor\\n77,6.8,2.8,4.8,1.4,versicolor\\n78,6.7,3.0,5.0,1.7,versicolor\\n79,6.0,2.9,4.5,1.5,versicolor\\n80,5.7,2.6,3.5,1.0,versicolor\\n81,5.5,2.4,3.8,1.1,versicolor\\n82,5.5,2.4,3.7,1.0,versicolor\\n83,5.8,2.7,3.9,1.2,versicolor\\n84,6.0,2.7,5.1,1.6,versicolor\\n85,5.4,3.0,4.5,1.5,versicolor\\n86,6.0,3.4,4.5,1.6,versicolor\\n87,6.7,3.1,4.7,1.5,versicolor\\n88,6.3,2.3,4.4,1.3,versicolor\\n89,5.6,3.0,4.1,1.3,versicolor\\n90,5.5,2.5,4.0,1.3,versicolor\\n91,5.5,2.6,4.4,1.2,versicolor\\n92,6.1,3.0,4.6,1.4,versicolor\\n93,5.8,2.6,4.0,1.2,versicolor\\n94,5.0,2.3,3.3,1.0,versicolor\\n95,5.6,2.7,4.2,1.3,versicolor\\n96,5.7,3.0,4.2,1.2,versicolor\\n97,5.7,2.9,4.2,1.3,versicolor\\n98,6.2,2.9,4.3,1.3,versicolor\\n99,5.1,2.5,3.0,1.1,versicolor\\n100,5.7,2.8,4.1,1.3,versicolor\\n101,6.3,3.3,6.0,2.5,virginica\\n102,5.8,2.7,5.1,1.9,virginica\\n103,7.1,3.0,5.9,2.1,virginica\\n104,6.3,2.9,5.6,1.8,virginica\\n105,6.5,3.0,5.8,2.2,virginica\\n106,7.6,3.0,6.6,2.1,virginica\\n107,4.9,2.5,4.5,1.7,virginica\\n108,7.3,2.9,6.3,1.8,virginica\\n109,6.7,2.5,5.8,1.8,virginica\\n110,7.2,3.6,6.1,2.5,virginica\\n111,6.5,3.2,5.1,2.0,virginica\\n112,6.4,2.7,5.3,1.9,virginica\\n113,6.8,3.0,5.5,2.1,virginica\\n114,5.7,2.5,5.0,2.0,virginica\\n115,5.8,2.8,5.1,2.4,virginica\\n116,6.4,3.2,5.3,2.3,virginica\\n117,6.5,3.0,5.5,1.8,virginica\\n118,7.7,3.8,6.7,2.2,virginica\\n119,7.7,2.6,6.9,2.3,virginica\\n120,6.0,2.2,5.0,1.5,virginica\\n121,6.9,3.2,5.7,2.3,virginica\\n122,5.6,2.8,4.9,2.0,virginica\\n123,7.7,2.8,6.7,2.0,virginica\\n124,6.3,2.7,4.9,1.8,virginica\\n125,6.7,3.3,5.7,2.1,virginica\\n126,7.2,3.2,6.0,1.8,virginica\\n127,6.2,2.8,4.8,1.8,virginica\\n128,6.1,3.0,4.9,1.8,virginica\\n129,6.4,2.8,5.6,2.1,virginica\\n130,7.2,3.0,5.8,1.6,virginica\\n131,7.4,2.8,6.1,1.9,virginica\\n132,7.9,3.8,6.4,2.0,virginica\\n133,6.4,2.8,5.6,2.2,virginica\\n134,6.3,2.8,5.1,1.5,virginica\\n135,6.1,2.6,5.6,1.4,virginica\\n136,7.7,3.0,6.1,2.3,virginica\\n137,6.3,3.4,5.6,2.4,virginica\\n138,6.4,3.1,5.5,1.8,virginica\\n139,6.0,3.0,4.8,1.8,virginica\\n140,6.9,3.1,5.4,2.1,virginica\\n141,6.7,3.1,5.6,2.4,virginica\\n142,6.9,3.1,5.1,2.3,virginica\\n143,5.8,2.7,5.1,1.9,virginica\\n144,6.8,3.2,5.9,2.3,virginica\\n145,6.7,3.3,5.7,2.5,virginica\\n146,6.7,3.0,5.2,2.3,virginica\\n147,6.3,2.5,5.0,1.9,virginica\\n148,6.5,3.0,5.2,2.0,virginica\\n149,6.2,3.4,5.4,2.3,virginica\\n150,5.9,3.0,5.1,1.8,virginica\\n\",\n  :format {:type \"csv\"}}}\n\nOn the other hand, in the following example, color is :quantitative:\n\n(-&gt; datasets/mtcars\n    (haclo/plot haclo/point-chart\n                {:=x :mpg\n                 :=y :disp\n                 :=color :cyl\n                 :=mark-size 200}))\n\n\n\n(-&gt; datasets/mtcars\n    (haclo/plot haclo/point-chart\n                {:=x :mpg\n                 :=y :disp\n                 :=color :cyl\n                 :=mark-size 200})\n    kind/pprint)\n\n\n{:encoding\n {:color {:field :cyl, :type :quantitative},\n  :x {:field :mpg, :type :quantitative},\n  :y {:field :disp, :type :quantitative}},\n :usermeta {:embedOptions {:renderer :svg}},\n :mark {:type \"circle\", :size 200, :tooltip true},\n :width 400,\n :background \"floralwhite\",\n :height 300,\n :data\n {:values\n  \"rownames,mpg,cyl,disp,hp,drat,wt,qsec,vs,am,gear,carb\\nMazda RX4,21.0,6,160.0,110,3.9,2.62,16.46,0,1,4,4\\nMazda RX4 Wag,21.0,6,160.0,110,3.9,2.875,17.02,0,1,4,4\\nDatsun 710,22.8,4,108.0,93,3.85,2.32,18.61,1,1,4,1\\nHornet 4 Drive,21.4,6,258.0,110,3.08,3.215,19.44,1,0,3,1\\nHornet Sportabout,18.7,8,360.0,175,3.15,3.44,17.02,0,0,3,2\\nValiant,18.1,6,225.0,105,2.76,3.46,20.22,1,0,3,1\\nDuster 360,14.3,8,360.0,245,3.21,3.57,15.84,0,0,3,4\\nMerc 240D,24.4,4,146.7,62,3.69,3.19,20.0,1,0,4,2\\nMerc 230,22.8,4,140.8,95,3.92,3.15,22.9,1,0,4,2\\nMerc 280,19.2,6,167.6,123,3.92,3.44,18.3,1,0,4,4\\nMerc 280C,17.8,6,167.6,123,3.92,3.44,18.9,1,0,4,4\\nMerc 450SE,16.4,8,275.8,180,3.07,4.07,17.4,0,0,3,3\\nMerc 450SL,17.3,8,275.8,180,3.07,3.73,17.6,0,0,3,3\\nMerc 450SLC,15.2,8,275.8,180,3.07,3.78,18.0,0,0,3,3\\nCadillac Fleetwood,10.4,8,472.0,205,2.93,5.25,17.98,0,0,3,4\\nLincoln Continental,10.4,8,460.0,215,3.0,5.424,17.82,0,0,3,4\\nChrysler Imperial,14.7,8,440.0,230,3.23,5.345,17.42,0,0,3,4\\nFiat 128,32.4,4,78.7,66,4.08,2.2,19.47,1,1,4,1\\nHonda Civic,30.4,4,75.7,52,4.93,1.615,18.52,1,1,4,2\\nToyota Corolla,33.9,4,71.1,65,4.22,1.835,19.9,1,1,4,1\\nToyota Corona,21.5,4,120.1,97,3.7,2.465,20.01,1,0,3,1\\nDodge Challenger,15.5,8,318.0,150,2.76,3.52,16.87,0,0,3,2\\nAMC Javelin,15.2,8,304.0,150,3.15,3.435,17.3,0,0,3,2\\nCamaro Z28,13.3,8,350.0,245,3.73,3.84,15.41,0,0,3,4\\nPontiac Firebird,19.2,8,400.0,175,3.08,3.845,17.05,0,0,3,2\\nFiat X1-9,27.3,4,79.0,66,4.08,1.935,18.9,1,1,4,1\\nPorsche 914-2,26.0,4,120.3,91,4.43,2.14,16.7,0,1,5,2\\nLotus Europa,30.4,4,95.1,113,3.77,1.513,16.9,1,1,5,2\\nFord Pantera L,15.8,8,351.0,264,4.22,3.17,14.5,0,1,5,4\\nFerrari Dino,19.7,6,145.0,175,3.62,2.77,15.5,0,1,5,6\\nMaserati Bora,15.0,8,301.0,335,3.54,3.57,14.6,0,1,5,8\\nVolvo 142E,21.4,4,121.0,109,4.11,2.78,18.6,1,1,4,2\\n\",\n  :format {:type \"csv\"}}}\n\nThis can be overridden to define color as :noninal:\n\n(-&gt; datasets/mtcars\n    (haclo/plot haclo/point-chart\n                {:=x :mpg\n                 :=y :disp\n                 :=color :cyl\n                 :=color-type :nominal\n                 :=mark-size 200}))\n\n\n\n(-&gt; datasets/mtcars\n    (haclo/plot haclo/point-chart\n                {:=x :mpg\n                 :=y :disp\n                 :=color :cyl\n                 :=color-type :nominal\n                 :=mark-size 200})\n    kind/pprint)\n\n\n{:encoding\n {:color {:field :cyl, :type :nominal},\n  :x {:field :mpg, :type :quantitative},\n  :y {:field :disp, :type :quantitative}},\n :usermeta {:embedOptions {:renderer :svg}},\n :mark {:type \"circle\", :size 200, :tooltip true},\n :width 400,\n :background \"floralwhite\",\n :height 300,\n :data\n {:values\n  \"rownames,mpg,cyl,disp,hp,drat,wt,qsec,vs,am,gear,carb\\nMazda RX4,21.0,6,160.0,110,3.9,2.62,16.46,0,1,4,4\\nMazda RX4 Wag,21.0,6,160.0,110,3.9,2.875,17.02,0,1,4,4\\nDatsun 710,22.8,4,108.0,93,3.85,2.32,18.61,1,1,4,1\\nHornet 4 Drive,21.4,6,258.0,110,3.08,3.215,19.44,1,0,3,1\\nHornet Sportabout,18.7,8,360.0,175,3.15,3.44,17.02,0,0,3,2\\nValiant,18.1,6,225.0,105,2.76,3.46,20.22,1,0,3,1\\nDuster 360,14.3,8,360.0,245,3.21,3.57,15.84,0,0,3,4\\nMerc 240D,24.4,4,146.7,62,3.69,3.19,20.0,1,0,4,2\\nMerc 230,22.8,4,140.8,95,3.92,3.15,22.9,1,0,4,2\\nMerc 280,19.2,6,167.6,123,3.92,3.44,18.3,1,0,4,4\\nMerc 280C,17.8,6,167.6,123,3.92,3.44,18.9,1,0,4,4\\nMerc 450SE,16.4,8,275.8,180,3.07,4.07,17.4,0,0,3,3\\nMerc 450SL,17.3,8,275.8,180,3.07,3.73,17.6,0,0,3,3\\nMerc 450SLC,15.2,8,275.8,180,3.07,3.78,18.0,0,0,3,3\\nCadillac Fleetwood,10.4,8,472.0,205,2.93,5.25,17.98,0,0,3,4\\nLincoln Continental,10.4,8,460.0,215,3.0,5.424,17.82,0,0,3,4\\nChrysler Imperial,14.7,8,440.0,230,3.23,5.345,17.42,0,0,3,4\\nFiat 128,32.4,4,78.7,66,4.08,2.2,19.47,1,1,4,1\\nHonda Civic,30.4,4,75.7,52,4.93,1.615,18.52,1,1,4,2\\nToyota Corolla,33.9,4,71.1,65,4.22,1.835,19.9,1,1,4,1\\nToyota Corona,21.5,4,120.1,97,3.7,2.465,20.01,1,0,3,1\\nDodge Challenger,15.5,8,318.0,150,2.76,3.52,16.87,0,0,3,2\\nAMC Javelin,15.2,8,304.0,150,3.15,3.435,17.3,0,0,3,2\\nCamaro Z28,13.3,8,350.0,245,3.73,3.84,15.41,0,0,3,4\\nPontiac Firebird,19.2,8,400.0,175,3.08,3.845,17.05,0,0,3,2\\nFiat X1-9,27.3,4,79.0,66,4.08,1.935,18.9,1,1,4,1\\nPorsche 914-2,26.0,4,120.3,91,4.43,2.14,16.7,0,1,5,2\\nLotus Europa,30.4,4,95.1,113,3.77,1.513,16.9,1,1,5,2\\nFord Pantera L,15.8,8,351.0,264,4.22,3.17,14.5,0,1,5,4\\nFerrari Dino,19.7,6,145.0,175,3.62,2.77,15.5,0,1,5,6\\nMaserati Bora,15.0,8,301.0,335,3.54,3.57,14.6,0,1,5,8\\nVolvo 142E,21.4,4,121.0,109,4.11,2.78,18.6,1,1,4,2\\n\",\n  :format {:type \"csv\"}}}",
     "crumbs": [
-      "<span class='chapter-number'>3</span>  <span class='chapter-title'>Walkthrough</span>"
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     "section": "3.5 More examples",
     "text": "3.5 More examples\nA Hanamicloth boxplot:\n\n(-&gt; datasets/mtcars\n    (haclo/plot haclo/boxplot-chart\n                {:=x :cyl\n                 :=x-type :nominal\n                 :=y :disp}))\n\n\nAn original Hanami boxplot:\n\n(-&gt; datasets/mtcars\n    (haclo/plot ht/boxplot-chart\n                {:X :cyl\n                 :XTYPE :nominal\n                 :Y :disp}))\n\n\nPlotting segments with Hanamicloth:\n\n(-&gt; datasets/iris\n    (haclo/plot haclo/rule-chart\n                {:=x :sepal-width\n                 :=y :sepal-length\n                 :=x2 :petal-width\n                 :=y2 :petal-length\n                 :=mark-opacity 0.5\n                 :=mark-size 3\n                 :=color :species}))\n\n\nPlotting segments with original Hanami:\n\n(-&gt; datasets/iris\n    (haclo/plot ht/rule-chart\n                {:X :sepal-width\n                 :Y :sepal-length\n                 :X2 :petal-width\n                 :Y2 :petal-length\n                 :OPACITY 0.5\n                 :SIZE 3\n                 :COLOR \"species\"}))",
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-    "title": "3  Walkthrough",
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     "section": "3.6 Time series",
     "text": "3.6 Time series\nLet us plot a time series:\n\n(-&gt; datasets/economics-long\n    (tc/select-rows #(-&gt; % :variable (= \"unemploy\")))\n    (haclo/plot haclo/line-chart\n                {:=x :date\n                 :=y :value\n                 :=mark-color \"purple\"}))\n\n\nYou see, the :date field was correctly inferred to be of the :temporal kind.\n\n(-&gt; datasets/economics-long\n    (tc/select-rows #(-&gt; % :variable (= \"unemploy\")))\n    (haclo/plot haclo/line-chart\n                {:=x :date\n                 :=y :value\n                 :=mark-color \"purple\"})\n    kind/pprint)\n\n\n{:encoding\n {:x {:field :date, :type :temporal},\n  :y {:field :value, :type :quantitative}},\n :usermeta {:embedOptions {:renderer :svg}},\n :mark {:type \"line\", :color \"purple\", :tooltip true},\n :width 400,\n :background \"floralwhite\",\n :height 300,\n :data\n {:values\n  \"rownames,date,variable,value,value01\\n2297,1967-07-01,unemploy,2944.0,0.020446830346569828\\n2298,1967-08-01,unemploy,2945.0,0.020525775637483226\\n2299,1967-09-01,unemploy,2958.0,0.021552064419357385\\n2300,1967-10-01,unemploy,3143.0,0.036156943238335834\\n2301,1967-11-01,unemploy,3066.0,0.030078155838004262\\n2302,1967-12-01,unemploy,3018.0,0.026288781874161206\\n2303,1968-01-01,unemploy,2878.0,0.015236441146285624\\n2304,1968-02-01,unemploy,3001.0,0.024946711928633458\\n2305,1968-03-01,unemploy,2877.0,0.015157495855372228\\n2306,1968-04-01,unemploy,2709.0,0.0018946869819215285\\n2307,1968-05-01,unemploy,2740.0,0.004341991000236836\\n2308,1968-06-01,unemploy,2938.0,0.019973158601089443\\n2309,1968-07-01,unemploy,2883.0,0.01563116760085261\\n2310,1968-08-01,unemploy,2768.0,0.006552459145811952\\n2311,1968-09-01,unemploy,2686.0,7.894529091339702E-5\\n2312,1968-10-01,unemploy,2689.0,3.157811636535881E-4\\n2313,1968-11-01,unemploy,2715.0,0.0023683587274019105\\n2314,1968-12-01,unemploy,2685.0,0.0\\n2315,1969-01-01,unemploy,2718.0,0.0026051946001421017\\n2316,1969-02-01,unemploy,2692.0,5.526170363937791E-4\\n2317,1969-03-01,unemploy,2712.0,0.0021315228546617193\\n2318,1969-04-01,unemploy,2758.0,0.005763006236677982\\n2319,1969-05-01,unemploy,2713.0,0.0022104681455751164\\n2320,1969-06-01,unemploy,2816.0,0.010341833109655009\\n2321,1969-07-01,unemploy,2868.0,0.014446988237151653\\n2322,1969-08-01,unemploy,2856.0,0.01349964474619089\\n2323,1969-09-01,unemploy,3040.0,0.02802557827425594\\n2324,1969-10-01,unemploy,3049.0,0.028736085892476514\\n2325,1969-11-01,unemploy,2856.0,0.01349964474619089\\n2326,1969-12-01,unemploy,2884.0,0.015710112891766007\\n2327,1970-01-01,unemploy,3201.0,0.04073577011131286\\n2328,1970-02-01,unemploy,3453.0,0.06062998342148891\\n2329,1970-03-01,unemploy,3635.0,0.07499802636772716\\n2330,1970-04-01,unemploy,3797.0,0.08778716349569748\\n2331,1970-05-01,unemploy,3919.0,0.09741848898713192\\n2332,1970-06-01,unemploy,4071.0,0.10941817320596826\\n2333,1970-07-01,unemploy,4175.0,0.11762848346096155\\n2334,1970-08-01,unemploy,4256.0,0.1240230520249467\\n2335,1970-09-01,unemploy,4456.0,0.13981211020762613\\n2336,1970-10-01,unemploy,4591.0,0.1504697244809347\\n2337,1970-11-01,unemploy,4898.0,0.1747059287913476\\n2338,1970-12-01,unemploy,5076.0,0.18875819057393226\\n2339,1971-01-01,unemploy,4986.0,0.18165311439172654\\n2340,1971-02-01,unemploy,4903.0,0.1751006552459146\\n2341,1971-03-01,unemploy,4987.0,0.18173205968263992\\n2342,1971-04-01,unemploy,4959.0,0.17952159153706482\\n2343,1971-05-01,unemploy,4996.0,0.1824425673008605\\n2344,1971-06-01,unemploy,4949.0,0.17873213862793083\\n2345,1971-07-01,unemploy,5035.0,0.185521433646483\\n2346,1971-08-01,unemploy,5134.0,0.1933370174469093\\n2347,1971-09-01,unemploy,5042.0,0.18607405068287677\\n2348,1971-10-01,unemploy,4954.0,0.17912686508249784\\n2349,1971-11-01,unemploy,5161.0,0.19546854030157101\\n2350,1971-12-01,unemploy,5154.0,0.19491592326517723\\n2351,1972-01-01,unemploy,5019.0,0.18425830899186862\\n2352,1972-02-01,unemploy,4928.0,0.1770742875187495\\n2353,1972-03-01,unemploy,5038.0,0.18575826951922317\\n2354,1972-04-01,unemploy,4959.0,0.17952159153706482\\n2355,1972-05-01,unemploy,4922.0,0.17660061577326913\\n2356,1972-06-01,unemploy,4923.0,0.17667956106418253\\n2357,1972-07-01,unemploy,4913.0,0.17589010815504855\\n2358,1972-08-01,unemploy,4939.0,0.17794268571879687\\n2359,1972-09-01,unemploy,4849.0,0.17083760953659113\\n2360,1972-10-01,unemploy,4875.0,0.17289018710033946\\n2361,1972-11-01,unemploy,4602.0,0.15133812268098207\\n2362,1972-12-01,unemploy,4543.0,0.14668035051709166\\n2363,1973-01-01,unemploy,4326.0,0.1295492223888845\\n2364,1973-02-01,unemploy,4452.0,0.13949632904397252\\n2365,1973-03-01,unemploy,4394.0,0.1349175021709955\\n2366,1973-04-01,unemploy,4459.0,0.1400489460803663\\n2367,1973-05-01,unemploy,4329.0,0.1297860582616247\\n2368,1973-06-01,unemploy,4363.0,0.13247019815268019\\n2369,1973-07-01,unemploy,4305.0,0.12789137127970315\\n2370,1973-08-01,unemploy,4305.0,0.12789137127970315\\n2371,1973-09-01,unemploy,4350.0,0.13144390937080602\\n2372,1973-10-01,unemploy,4144.0,0.11518117944264625\\n2373,1973-11-01,unemploy,4396.0,0.1350753927528223\\n2374,1973-12-01,unemploy,4489.0,0.1424173048077682\\n2375,1974-01-01,unemploy,4644.0,0.15465382489934476\\n2376,1974-02-01,unemploy,4731.0,0.1615220652088103\\n2377,1974-03-01,unemploy,4634.0,0.15386437199021077\\n2378,1974-04-01,unemploy,4618.0,0.15260124733559643\\n2379,1974-05-01,unemploy,4705.0,0.15946948764506197\\n2380,1974-06-01,unemploy,4927.0,0.1769953422278361\\n2381,1974-07-01,unemploy,5063.0,0.1877319017920581\\n2382,1974-08-01,unemploy,5022.0,0.18449514486460883\\n2383,1974-09-01,unemploy,5437.0,0.21725744059366858\\n2384,1974-10-01,unemploy,5523.0,0.22404673561222074\\n2385,1974-11-01,unemploy,6140.0,0.27275598010578667\\n2386,1974-12-01,unemploy,6636.0,0.3119128443988316\\n2387,1975-01-01,unemploy,7501.0,0.38020052103892005\\n2388,1975-02-01,unemploy,7520.0,0.3817004815662745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 :format {:type \"csv\"}}}",
     "crumbs": [
-      "<span class='chapter-number'>3</span>  <span class='chapter-title'>Walkthrough</span>"
+      "<span class='chapter-number'>3</span>  <span class='chapter-title'>Walkthrough 👣</span>"
     ]
   },
   {
     "objectID": "hanamicloth_book.walkthrough.html#delayed-transformation",
     "href": "hanamicloth_book.walkthrough.html#delayed-transformation",
-    "title": "3  Walkthrough",
+    "title": "3  Walkthrough 👣",
     "section": "3.7 Delayed transformation",
-    "text": "3.7 Delayed transformation\nInstead of the haclo/plot function, it is possible to used haclo/base:\n\n(-&gt; datasets/economics-long\n    (tc/select-rows #(-&gt; % :variable (= \"unemploy\")))\n    (haclo/base haclo/line-chart\n                {:=x :date\n                 :=y :value\n                 :=mark-color \"purple\"}))\n\n\nThe result is displayed the same way, but the internal representation delays the Hanami transformation of templates.\nLet us compare the two:\n\n(-&gt; datasets/economics-long\n    (tc/select-rows #(-&gt; % :variable (= \"unemploy\")))\n    (haclo/plot haclo/line-chart\n                {:=x :date\n                 :=y :value\n                 :=mark-color \"purple\"})\n    kind/pprint)\n\n\n{:encoding\n {:x {:field :date, :type :temporal},\n  :y {:field :value, :type :quantitative}},\n :usermeta {:embedOptions {:renderer :svg}},\n :mark {:type \"line\", :color \"purple\", :tooltip true},\n :width 400,\n :background \"floralwhite\",\n :height 300,\n :data\n {:values\n  \"rownames,date,variable,value,value01\\n2297,1967-07-01,unemploy,2944.0,0.020446830346569828\\n2298,1967-08-01,unemploy,2945.0,0.020525775637483226\\n2299,1967-09-01,unemploy,2958.0,0.021552064419357385\\n2300,1967-10-01,unemploy,3143.0,0.036156943238335834\\n2301,1967-11-01,unemploy,3066.0,0.030078155838004262\\n2302,1967-12-01,unemploy,3018.0,0.026288781874161206\\n2303,1968-01-01,unemploy,2878.0,0.015236441146285624\\n2304,1968-02-01,unemploy,3001.0,0.024946711928633458\\n2305,1968-03-01,unemploy,2877.0,0.015157495855372228\\n2306,1968-04-01,unemploy,2709.0,0.0018946869819215285\\n2307,1968-05-01,unemploy,2740.0,0.004341991000236836\\n2308,1968-06-01,unemploy,2938.0,0.019973158601089443\\n2309,1968-07-01,unemploy,2883.0,0.01563116760085261\\n2310,1968-08-01,unemploy,2768.0,0.006552459145811952\\n2311,1968-09-01,unemploy,2686.0,7.894529091339702E-5\\n2312,1968-10-01,unemploy,2689.0,3.157811636535881E-4\\n2313,1968-11-01,unemploy,2715.0,0.0023683587274019105\\n2314,1968-12-01,unemploy,2685.0,0.0\\n2315,1969-01-01,unemploy,2718.0,0.0026051946001421017\\n2316,1969-02-01,unemploy,2692.0,5.526170363937791E-4\\n2317,1969-03-01,unemploy,2712.0,0.0021315228546617193\\n2318,1969-04-01,unemploy,2758.0,0.005763006236677982\\n2319,1969-05-01,unemploy,2713.0,0.0022104681455751164\\n2320,1969-06-01,unemploy,2816.0,0.010341833109655009\\n2321,1969-07-01,unemploy,2868.0,0.014446988237151653\\n2322,1969-08-01,unemploy,2856.0,0.01349964474619089\\n2323,1969-09-01,unemploy,3040.0,0.02802557827425594\\n2324,1969-10-01,unemploy,3049.0,0.028736085892476514\\n2325,1969-11-01,unemploy,2856.0,0.01349964474619089\\n2326,1969-12-01,unemploy,2884.0,0.015710112891766007\\n2327,1970-01-01,unemploy,3201.0,0.04073577011131286\\n2328,1970-02-01,unemploy,3453.0,0.06062998342148891\\n2329,1970-03-01,unemploy,3635.0,0.07499802636772716\\n2330,1970-04-01,unemploy,3797.0,0.08778716349569748\\n2331,1970-05-01,unemploy,3919.0,0.09741848898713192\\n2332,1970-06-01,unemploy,4071.0,0.10941817320596826\\n2333,1970-07-01,unemploy,4175.0,0.11762848346096155\\n2334,1970-08-01,unemploy,4256.0,0.1240230520249467\\n2335,1970-09-01,unemploy,4456.0,0.13981211020762613\\n2336,1970-10-01,unemploy,4591.0,0.1504697244809347\\n2337,1970-11-01,unemploy,4898.0,0.1747059287913476\\n2338,1970-12-01,unemploy,5076.0,0.18875819057393226\\n2339,1971-01-01,unemploy,4986.0,0.18165311439172654\\n2340,1971-02-01,unemploy,4903.0,0.1751006552459146\\n2341,1971-03-01,unemploy,4987.0,0.18173205968263992\\n2342,1971-04-01,unemploy,4959.0,0.17952159153706482\\n2343,1971-05-01,unemploy,4996.0,0.1824425673008605\\n2344,1971-06-01,unemploy,4949.0,0.17873213862793083\\n2345,1971-07-01,unemploy,5035.0,0.185521433646483\\n2346,1971-08-01,unemploy,5134.0,0.1933370174469093\\n2347,1971-09-01,unemploy,5042.0,0.18607405068287677\\n2348,1971-10-01,unemploy,4954.0,0.17912686508249784\\n2349,1971-11-01,unemploy,5161.0,0.19546854030157101\\n2350,1971-12-01,unemploy,5154.0,0.19491592326517723\\n2351,1972-01-01,unemploy,5019.0,0.18425830899186862\\n2352,1972-02-01,unemploy,4928.0,0.1770742875187495\\n2353,1972-03-01,unemploy,5038.0,0.18575826951922317\\n2354,1972-04-01,unemploy,4959.0,0.17952159153706482\\n2355,1972-05-01,unemploy,4922.0,0.17660061577326913\\n2356,1972-06-01,unemploy,4923.0,0.17667956106418253\\n2357,1972-07-01,unemploy,4913.0,0.17589010815504855\\n2358,1972-08-01,unemploy,4939.0,0.17794268571879687\\n2359,1972-09-01,unemploy,4849.0,0.17083760953659113\\n2360,1972-10-01,unemploy,4875.0,0.17289018710033946\\n2361,1972-11-01,unemploy,4602.0,0.15133812268098207\\n2362,1972-12-01,unemploy,4543.0,0.14668035051709166\\n2363,1973-01-01,unemploy,4326.0,0.1295492223888845\\n2364,1973-02-01,unemploy,4452.0,0.13949632904397252\\n2365,1973-03-01,unemploy,4394.0,0.1349175021709955\\n2366,1973-04-01,unemploy,4459.0,0.1400489460803663\\n2367,1973-05-01,unemploy,4329.0,0.1297860582616247\\n2368,1973-06-01,unemploy,4363.0,0.13247019815268019\\n2369,1973-07-01,unemploy,4305.0,0.12789137127970315\\n2370,1973-08-01,unemploy,4305.0,0.12789137127970315\\n2371,1973-09-01,unemploy,4350.0,0.13144390937080602\\n2372,1973-10-01,unemploy,4144.0,0.11518117944264625\\n2373,1973-11-01,unemploy,4396.0,0.1350753927528223\\n2374,1973-12-01,unemploy,4489.0,0.1424173048077682\\n2375,1974-01-01,unemploy,4644.0,0.15465382489934476\\n2376,1974-02-01,unemploy,4731.0,0.1615220652088103\\n2377,1974-03-01,unemploy,4634.0,0.15386437199021077\\n2378,1974-04-01,unemploy,4618.0,0.15260124733559643\\n2379,1974-05-01,unemploy,4705.0,0.15946948764506197\\n2380,1974-06-01,unemploy,4927.0,0.1769953422278361\\n2381,1974-07-01,unemploy,5063.0,0.1877319017920581\\n2382,1974-08-01,unemploy,5022.0,0.18449514486460883\\n2383,1974-09-01,unemploy,5437.0,0.21725744059366858\\n2384,1974-10-01,unemploy,5523.0,0.22404673561222074\\n2385,1974-11-01,unemploy,6140.0,0.27275598010578667\\n2386,1974-12-01,unemploy,6636.0,0.3119128443988316\\n2387,1975-01-01,unemploy,7501.0,0.38020052103892005\\n2388,1975-02-01,unemploy,7520.0,0.3817004815662745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0367095602748\\n2857,2014-03-01,unemploy,10380.0,0.60748401357859\\n2858,2014-04-01,unemploy,9702.0,0.5539591063393069\\n2859,2014-05-01,unemploy,9859.0,0.5663535170127102\\n2860,2014-06-01,unemploy,9460.0,0.5348543459382648\\n2861,2014-07-01,unemploy,9608.0,0.5465382489934475\\n2862,2014-08-01,unemploy,9599.0,0.5458277413752269\\n2863,2014-09-01,unemploy,9262.0,0.5192231783374122\\n2864,2014-10-01,unemploy,8990.0,0.4977500592089682\\n2865,2014-11-01,unemploy,9090.0,0.5056445883003079\\n2866,2014-12-01,unemploy,8717.0,0.4761979947896108\\n2867,2015-01-01,unemploy,8903.0,0.49088181889950266\\n2868,2015-02-01,unemploy,8610.0,0.4677508486618773\\n2869,2015-03-01,unemploy,8504.0,0.4593826478250572\\n2870,2015-04-01,unemploy,8526.0,0.46111944422515194\\n\",\n  :format {:type \"csv\"}}}\n\n\n(-&gt; datasets/economics-long\n    (tc/select-rows #(-&gt; % :variable (= \"unemploy\")))\n    (haclo/base haclo/line-chart\n                {:=x :date\n                 :=y :value\n                 :=mark-color \"purple\"})\n    kind/pprint)\n\n\n{:encoding :=encoding,\n :usermeta :=usermeta,\n :mark\n {:type \"line\",\n  :color :=mark-color,\n  :size :=mark-size,\n  :opacity :=mark-opacity,\n  :tooltip :=mark-tooltip},\n :width :=width,\n :background :=background,\n :title :=title,\n :layer :=layer,\n :kindly/f #'scicloj.hanamicloth.v1.api/vega-lite-xform,\n :aerial.hanami.templates/defaults\n {:=x2 :com.rpl.specter.impl/NONE,\n  :=base-dataset\n  #&lt;WrappedValue@5e6ef666: ggplot2/economics_long [574 5]:\n\n| :rownames |      :date | :variable | :value |   :value01 |\n|----------:|------------|-----------|-------:|-----------:|\n|      2297 | 1967-07-01 |  unemploy | 2944.0 | 0.02044683 |\n|      2298 | 1967-08-01 |  unemploy | 2945.0 | 0.02052578 |\n|      2299 | 1967-09-01 |  unemploy | 2958.0 | 0.02155206 |\n|      2300 | 1967-10-01 |  unemploy | 3143.0 | 0.03615694 |\n|      2301 | 1967-11-01 |  unemploy | 3066.0 | 0.03007816 |\n|      2302 | 1967-12-01 |  unemploy | 3018.0 | 0.02628878 |\n|      2303 | 1968-01-01 |  unemploy | 2878.0 | 0.01523644 |\n|      2304 | 1968-02-01 |  unemploy | 3001.0 | 0.02494671 |\n|      2305 | 1968-03-01 |  unemploy | 2877.0 | 0.01515750 |\n|      2306 | 1968-04-01 |  unemploy | 2709.0 | 0.00189469 |\n|       ... |        ... |       ... |    ... |        ... |\n|      2860 | 2014-06-01 |  unemploy | 9460.0 | 0.53485435 |\n|      2861 | 2014-07-01 |  unemploy | 9608.0 | 0.54653825 |\n|      2862 | 2014-08-01 |  unemploy | 9599.0 | 0.54582774 |\n|      2863 | 2014-09-01 |  unemploy | 9262.0 | 0.51922318 |\n|      2864 | 2014-10-01 |  unemploy | 8990.0 | 0.49775006 |\n|      2865 | 2014-11-01 |  unemploy | 9090.0 | 0.50564459 |\n|      2866 | 2014-12-01 |  unemploy | 8717.0 | 0.47619799 |\n|      2867 | 2015-01-01 |  unemploy | 8903.0 | 0.49088182 |\n|      2868 | 2015-02-01 |  unemploy | 8610.0 | 0.46775085 |\n|      2869 | 2015-03-01 |  unemploy | 8504.0 | 0.45938265 |\n|      2870 | 2015-04-01 |  unemploy | 8526.0 | 0.46111944 |\n&gt;,\n  :=layer [],\n  :=y-type\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=x2-encoding\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=opacity :com.rpl.specter.impl/NONE,\n  :=y2-after-stat :=y2,\n  :=x2-after-stat :=x2,\n  :=usermeta {:embedOptions {:renderer :=renderer}},\n  :=title :com.rpl.specter.impl/NONE,\n  :=y-type-after-stat\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=height 300,\n  :=renderer :svg,\n  :=mark-opacity :com.rpl.specter.impl/NONE,\n  :=y-title :com.rpl.specter.impl/NONE,\n  :=size :com.rpl.specter.impl/NONE,\n  :=group\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=mark-size :com.rpl.specter.impl/NONE,\n  :=layer? :com.rpl.specter.impl/NONE,\n  :=size-type\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=layer-dataset\n  #&lt;WrappedValue@5e6ef666: ggplot2/economics_long [574 5]:\n\n| :rownames |      :date | :variable | :value |   :value01 |\n|----------:|------------|-----------|-------:|-----------:|\n|      2297 | 1967-07-01 |  unemploy | 2944.0 | 0.02044683 |\n|      2298 | 1967-08-01 |  unemploy | 2945.0 | 0.02052578 |\n|      2299 | 1967-09-01 |  unemploy | 2958.0 | 0.02155206 |\n|      2300 | 1967-10-01 |  unemploy | 3143.0 | 0.03615694 |\n|      2301 | 1967-11-01 |  unemploy | 3066.0 | 0.03007816 |\n|      2302 | 1967-12-01 |  unemploy | 3018.0 | 0.02628878 |\n|      2303 | 1968-01-01 |  unemploy | 2878.0 | 0.01523644 |\n|      2304 | 1968-02-01 |  unemploy | 3001.0 | 0.02494671 |\n|      2305 | 1968-03-01 |  unemploy | 2877.0 | 0.01515750 |\n|      2306 | 1968-04-01 |  unemploy | 2709.0 | 0.00189469 |\n|       ... |        ... |       ... |    ... |        ... |\n|      2860 | 2014-06-01 |  unemploy | 9460.0 | 0.53485435 |\n|      2861 | 2014-07-01 |  unemploy | 9608.0 | 0.54653825 |\n|      2862 | 2014-08-01 |  unemploy | 9599.0 | 0.54582774 |\n|      2863 | 2014-09-01 |  unemploy | 9262.0 | 0.51922318 |\n|      2864 | 2014-10-01 |  unemploy | 8990.0 | 0.49775006 |\n|      2865 | 2014-11-01 |  unemploy | 9090.0 | 0.50564459 |\n|      2866 | 2014-12-01 |  unemploy | 8717.0 | 0.47619799 |\n|      2867 | 2015-01-01 |  unemploy | 8903.0 | 0.49088182 |\n|      2868 | 2015-02-01 |  unemploy | 8610.0 | 0.46775085 |\n|      2869 | 2015-03-01 |  unemploy | 8504.0 | 0.45938265 |\n|      2870 | 2015-04-01 |  unemploy | 8526.0 | 0.46111944 |\n&gt;,\n  :=color :com.rpl.specter.impl/NONE,\n  :=mark-color \"purple\",\n  :=x :date,\n  :=y2 :com.rpl.specter.impl/NONE,\n  :=x-after-stat :=x,\n  :DFMT {:type \"csv\"},\n  :=encoding\n  {:color {:field :=color, :type :=color-type},\n   :size {:field :=size, :type :=size-type},\n   :x\n   {:field :=x-after-stat,\n    :type :=x-type-after-stat,\n    :title :=x-title,\n    :bin :=x-bin},\n   :y\n   {:field :=y-after-stat,\n    :type :=y-type-after-stat,\n    :title :=y-title,\n    :bin :=y-bin},\n   :x2 :=x2-encoding,\n   :y2 :=y2-encoding},\n  :=x-type-after-stat\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=x-bin :com.rpl.specter.impl/NONE,\n  :=x-type\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=histogram-nbins 10,\n  :=stat :com.rpl.specter.impl/NONE,\n  :=width 400,\n  :=color-type\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=mark \"circle\",\n  :=dataset-after-stat\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=y-bin :com.rpl.specter.impl/NONE,\n  :=x-title :com.rpl.specter.impl/NONE,\n  :=csv-data\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=y :value,\n  :=data {:values :=csv-data, :format {:type \"csv\"}},\n  :=dataset\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :VALDATA :=csv-data,\n  :=mark-tooltip true,\n  :=background \"floralwhite\",\n  :=y-after-stat :=y,\n  :=predictors [:=x],\n  :=y2-encoding\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211]},\n :height :=height,\n :data :=data}\n\nThe structure returned by haclo/base is a Hanami template (with local defaults). When it is displayed, it goes through the Hanami transform to recieve the Vega-Lite spec.\nWhen we use base, we can keep processing the template in a pipeline of transformations. We will use it soon with layers.",
+    "text": "3.7 Delayed transformation\nInstead of the haclo/plot function, it is possible to used haclo/base:\n\n(-&gt; datasets/economics-long\n    (tc/select-rows #(-&gt; % :variable (= \"unemploy\")))\n    (haclo/base haclo/line-chart\n                {:=x :date\n                 :=y :value\n                 :=mark-color \"purple\"}))\n\n\nThe result is displayed the same way, but the internal representation delays the Hanami transformation of templates.\nLet us compare the two:\n\n(-&gt; datasets/economics-long\n    (tc/select-rows #(-&gt; % :variable (= \"unemploy\")))\n    (haclo/plot haclo/line-chart\n                {:=x :date\n                 :=y :value\n                 :=mark-color \"purple\"})\n    kind/pprint)\n\n\n{:encoding\n {:x {:field :date, :type :temporal},\n  :y {:field :value, :type :quantitative}},\n :usermeta {:embedOptions {:renderer :svg}},\n :mark {:type \"line\", :color \"purple\", :tooltip true},\n :width 400,\n :background \"floralwhite\",\n :height 300,\n :data\n {:values\n  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0367095602748\\n2857,2014-03-01,unemploy,10380.0,0.60748401357859\\n2858,2014-04-01,unemploy,9702.0,0.5539591063393069\\n2859,2014-05-01,unemploy,9859.0,0.5663535170127102\\n2860,2014-06-01,unemploy,9460.0,0.5348543459382648\\n2861,2014-07-01,unemploy,9608.0,0.5465382489934475\\n2862,2014-08-01,unemploy,9599.0,0.5458277413752269\\n2863,2014-09-01,unemploy,9262.0,0.5192231783374122\\n2864,2014-10-01,unemploy,8990.0,0.4977500592089682\\n2865,2014-11-01,unemploy,9090.0,0.5056445883003079\\n2866,2014-12-01,unemploy,8717.0,0.4761979947896108\\n2867,2015-01-01,unemploy,8903.0,0.49088181889950266\\n2868,2015-02-01,unemploy,8610.0,0.4677508486618773\\n2869,2015-03-01,unemploy,8504.0,0.4593826478250572\\n2870,2015-04-01,unemploy,8526.0,0.46111944422515194\\n\",\n  :format {:type \"csv\"}}}\n\n\n(-&gt; datasets/economics-long\n    (tc/select-rows #(-&gt; % :variable (= \"unemploy\")))\n    (haclo/base haclo/line-chart\n                {:=x :date\n                 :=y :value\n                 :=mark-color \"purple\"})\n    kind/pprint)\n\n\n{:encoding :=encoding,\n :usermeta :=usermeta,\n :mark\n {:type \"line\",\n  :color :=mark-color,\n  :size :=mark-size,\n  :opacity :=mark-opacity,\n  :tooltip :=mark-tooltip},\n :width :=width,\n :background :=background,\n :title :=title,\n :layer :=layer,\n :kindly/f #'scicloj.hanamicloth.v1.api/vega-lite-xform,\n :aerial.hanami.templates/defaults\n {:=x2 :com.rpl.specter.impl/NONE,\n  :=base-dataset\n  #&lt;WrappedValue@43dcbcc6: ggplot2/economics_long [574 5]:\n\n| :rownames |      :date | :variable | :value |   :value01 |\n|----------:|------------|-----------|-------:|-----------:|\n|      2297 | 1967-07-01 |  unemploy | 2944.0 | 0.02044683 |\n|      2298 | 1967-08-01 |  unemploy | 2945.0 | 0.02052578 |\n|      2299 | 1967-09-01 |  unemploy | 2958.0 | 0.02155206 |\n|      2300 | 1967-10-01 |  unemploy | 3143.0 | 0.03615694 |\n|      2301 | 1967-11-01 |  unemploy | 3066.0 | 0.03007816 |\n|      2302 | 1967-12-01 |  unemploy | 3018.0 | 0.02628878 |\n|      2303 | 1968-01-01 |  unemploy | 2878.0 | 0.01523644 |\n|      2304 | 1968-02-01 |  unemploy | 3001.0 | 0.02494671 |\n|      2305 | 1968-03-01 |  unemploy | 2877.0 | 0.01515750 |\n|      2306 | 1968-04-01 |  unemploy | 2709.0 | 0.00189469 |\n|       ... |        ... |       ... |    ... |        ... |\n|      2860 | 2014-06-01 |  unemploy | 9460.0 | 0.53485435 |\n|      2861 | 2014-07-01 |  unemploy | 9608.0 | 0.54653825 |\n|      2862 | 2014-08-01 |  unemploy | 9599.0 | 0.54582774 |\n|      2863 | 2014-09-01 |  unemploy | 9262.0 | 0.51922318 |\n|      2864 | 2014-10-01 |  unemploy | 8990.0 | 0.49775006 |\n|      2865 | 2014-11-01 |  unemploy | 9090.0 | 0.50564459 |\n|      2866 | 2014-12-01 |  unemploy | 8717.0 | 0.47619799 |\n|      2867 | 2015-01-01 |  unemploy | 8903.0 | 0.49088182 |\n|      2868 | 2015-02-01 |  unemploy | 8610.0 | 0.46775085 |\n|      2869 | 2015-03-01 |  unemploy | 8504.0 | 0.45938265 |\n|      2870 | 2015-04-01 |  unemploy | 8526.0 | 0.46111944 |\n&gt;,\n  :=layer [],\n  :=y-type\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=x2-encoding\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=opacity :com.rpl.specter.impl/NONE,\n  :=y2-after-stat :=y2,\n  :=x2-after-stat :=x2,\n  :=usermeta {:embedOptions {:renderer :=renderer}},\n  :=title :com.rpl.specter.impl/NONE,\n  :=y-type-after-stat\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=height 300,\n  :=renderer :svg,\n  :=mark-opacity :com.rpl.specter.impl/NONE,\n  :=y-title :com.rpl.specter.impl/NONE,\n  :=size :com.rpl.specter.impl/NONE,\n  :=group\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=mark-size :com.rpl.specter.impl/NONE,\n  :=layer? :com.rpl.specter.impl/NONE,\n  :=size-type\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=layer-dataset\n  #&lt;WrappedValue@43dcbcc6: ggplot2/economics_long [574 5]:\n\n| :rownames |      :date | :variable | :value |   :value01 |\n|----------:|------------|-----------|-------:|-----------:|\n|      2297 | 1967-07-01 |  unemploy | 2944.0 | 0.02044683 |\n|      2298 | 1967-08-01 |  unemploy | 2945.0 | 0.02052578 |\n|      2299 | 1967-09-01 |  unemploy | 2958.0 | 0.02155206 |\n|      2300 | 1967-10-01 |  unemploy | 3143.0 | 0.03615694 |\n|      2301 | 1967-11-01 |  unemploy | 3066.0 | 0.03007816 |\n|      2302 | 1967-12-01 |  unemploy | 3018.0 | 0.02628878 |\n|      2303 | 1968-01-01 |  unemploy | 2878.0 | 0.01523644 |\n|      2304 | 1968-02-01 |  unemploy | 3001.0 | 0.02494671 |\n|      2305 | 1968-03-01 |  unemploy | 2877.0 | 0.01515750 |\n|      2306 | 1968-04-01 |  unemploy | 2709.0 | 0.00189469 |\n|       ... |        ... |       ... |    ... |        ... |\n|      2860 | 2014-06-01 |  unemploy | 9460.0 | 0.53485435 |\n|      2861 | 2014-07-01 |  unemploy | 9608.0 | 0.54653825 |\n|      2862 | 2014-08-01 |  unemploy | 9599.0 | 0.54582774 |\n|      2863 | 2014-09-01 |  unemploy | 9262.0 | 0.51922318 |\n|      2864 | 2014-10-01 |  unemploy | 8990.0 | 0.49775006 |\n|      2865 | 2014-11-01 |  unemploy | 9090.0 | 0.50564459 |\n|      2866 | 2014-12-01 |  unemploy | 8717.0 | 0.47619799 |\n|      2867 | 2015-01-01 |  unemploy | 8903.0 | 0.49088182 |\n|      2868 | 2015-02-01 |  unemploy | 8610.0 | 0.46775085 |\n|      2869 | 2015-03-01 |  unemploy | 8504.0 | 0.45938265 |\n|      2870 | 2015-04-01 |  unemploy | 8526.0 | 0.46111944 |\n&gt;,\n  :=color :com.rpl.specter.impl/NONE,\n  :=mark-color \"purple\",\n  :=x :date,\n  :=y2 :com.rpl.specter.impl/NONE,\n  :=x-after-stat :=x,\n  :DFMT {:type \"csv\"},\n  :=encoding\n  {:color {:field :=color, :type :=color-type},\n   :size {:field :=size, :type :=size-type},\n   :x\n   {:field :=x-after-stat,\n    :type :=x-type-after-stat,\n    :title :=x-title,\n    :bin :=x-bin},\n   :y\n   {:field :=y-after-stat,\n    :type :=y-type-after-stat,\n    :title :=y-title,\n    :bin :=y-bin},\n   :x2 :=x2-encoding,\n   :y2 :=y2-encoding},\n  :=x-type-after-stat\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=x-bin :com.rpl.specter.impl/NONE,\n  :=x-type\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=histogram-nbins 10,\n  :=stat :com.rpl.specter.impl/NONE,\n  :=width 400,\n  :=color-type\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=mark \"circle\",\n  :=dataset-after-stat\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=y-bin :com.rpl.specter.impl/NONE,\n  :=x-title :com.rpl.specter.impl/NONE,\n  :=csv-data\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=y :value,\n  :=data {:values :=csv-data, :format {:type \"csv\"}},\n  :=dataset\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :VALDATA :=csv-data,\n  :=mark-tooltip true,\n  :=background \"floralwhite\",\n  :=y-after-stat :=y,\n  :=predictors [:=x],\n  :=y2-encoding\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211]},\n :height :=height,\n :data :=data}\n\nThe structure returned by haclo/base is a Hanami template (with local defaults). When it is displayed, it goes through the Hanami transform to recieve the Vega-Lite spec.\nWhen we use base, we can keep processing the template in a pipeline of transformations. We will use it soon with layers.",
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     "section": "3.8 Adding layers",
     "text": "3.8 Adding layers\nA base plot does not need to have a specified chart. Instead, we may add layers:\n\n(-&gt; datasets/economics-long\n    (tc/select-rows #(-&gt; % :variable (= \"unemploy\")))\n    (haclo/base {:=x :date\n                 :=y :value\n                 :=mark-color \"purple\"})\n    haclo/layer-line)\n\n\nThe substitution keys can also be specified on the layer level:\n\n(-&gt; datasets/economics-long\n    (tc/select-rows #(-&gt; % :variable (= \"unemploy\")))\n    (haclo/base {:=x :date\n                 :=y :value})\n    (haclo/layer-line {:=mark-color \"purple\"}))\n\n\nThis allows us to create, e.g., aesthetic differences between layers:\n\n(-&gt; datasets/economics-long\n    (tc/select-rows #(-&gt; % :variable (= \"unemploy\")))\n    (haclo/base {:=x :date\n                 :=y :value})\n    (haclo/layer-point {:=mark-color \"green\"\n                        :=mark-size 200\n                        :=mark-opacity 0.1})\n    (haclo/layer-line {:=mark-color \"purple\"}))\n\n\nWe can also skip the base and have everything in the layer:\n\n(-&gt; datasets/economics-long\n    (tc/select-rows #(-&gt; % :variable (= \"unemploy\")))\n    (haclo/layer-line {:=x :date\n                       :=y :value\n                       :=mark-color \"purple\"}))",
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     "section": "3.9 Updating data",
     "text": "3.9 Updating data\nUsing haclo/update-data, we may process the dataset during the pipeline, affecting only the layers added further down the pipeline.\nThis functionality is inspired by ggbuilder and metamorph.\n\n(-&gt; datasets/economics-long\n    (tc/select-rows #(-&gt; % :variable (= \"unemploy\")))\n    (haclo/base {:=x :date\n                 :=y :value})\n    (haclo/layer-line {:=mark-color \"purple\"})\n    (haclo/update-data tc/random 5)\n    (haclo/layer-point {:=mark-color \"green\"\n                        :=mark-size 200}))\n\n\nYou see, we have lots of data for the lines, but only five random points.",
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     "section": "3.10 Processing raw vega-lite",
-    "text": "3.10 Processing raw vega-lite\nDuring a pipeline, we may call haclo/plot to apply the Hanami transform and realize the Vega-Lite spec.\n\n(-&gt; datasets/economics-long\n    (tc/select-rows #(-&gt; % :variable (= \"unemploy\")))\n    (haclo/base {:=x :date\n                 :=y :value})\n    (haclo/layer-line {:=mark-color \"purple\"})\n    (haclo/update-data tc/random 5)\n    (haclo/layer-point {:=mark-color \"green\"\n                        :=mark-size 200})\n    haclo/plot\n    kind/pprint)\n\n\n{:encoding\n {:x {:field :date, :type :temporal},\n  :y {:field :value, :type :quantitative}},\n :usermeta {:embedOptions {:renderer :svg}},\n :width 400,\n :background \"floralwhite\",\n :layer\n [{:mark {:type \"line\", :color \"purple\", :tooltip true},\n   :encoding\n   {:x {:field :date, :type :temporal},\n    :y {:field :value, :type :quantitative}},\n   :data\n   {:values\n    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 :format {:type \"csv\"}}}\n\nWhile this in itself does not affect the display of the plot, it allows us to keep editing it as a Vega-Lite spec. For example, let us change the backgound colour this way:\n\n(-&gt; datasets/economics-long\n    (tc/select-rows #(-&gt; % :variable (= \"unemploy\")))\n    (haclo/base {:=x :date\n                 :=y :value})\n    (haclo/layer-line {:=mark-color \"purple\"})\n    (haclo/update-data tc/random 5)\n    (haclo/layer-point {:=mark-color \"green\"\n                        :=mark-size 200})\n    haclo/plot\n    (assoc :background \"lightgrey\"))",
+    "text": "3.10 Processing raw vega-lite\nDuring a pipeline, we may call haclo/plot to apply the Hanami transform and realize the Vega-Lite spec.\n\n(-&gt; datasets/economics-long\n    (tc/select-rows #(-&gt; % :variable (= \"unemploy\")))\n    (haclo/base {:=x :date\n                 :=y :value})\n    (haclo/layer-line {:=mark-color \"purple\"})\n    (haclo/update-data tc/random 5)\n    (haclo/layer-point {:=mark-color \"green\"\n                        :=mark-size 200})\n    haclo/plot\n    kind/pprint)\n\n\n{:encoding\n {:x {:field :date, :type :temporal},\n  :y {:field :value, :type :quantitative}},\n :usermeta {:embedOptions {:renderer :svg}},\n :width 400,\n :background \"floralwhite\",\n :layer\n [{:mark {:type \"line\", :color \"purple\", :tooltip true},\n   :encoding\n   {:x {:field :date, :type :temporal},\n    :y {:field :value, :type :quantitative}},\n   :data\n   {:values\n    \"rownames,date,variable,value,value01\\n2297,1967-07-01,unemploy,2944.0,0.020446830346569828\\n2298,1967-08-01,unemploy,2945.0,0.020525775637483226\\n2299,1967-09-01,unemploy,2958.0,0.021552064419357385\\n2300,1967-10-01,unemploy,3143.0,0.036156943238335834\\n2301,1967-11-01,unemploy,3066.0,0.030078155838004262\\n2302,1967-12-01,unemploy,3018.0,0.026288781874161206\\n2303,1968-01-01,unemploy,2878.0,0.015236441146285624\\n2304,1968-02-01,unemploy,3001.0,0.024946711928633458\\n2305,1968-03-01,unemploy,2877.0,0.015157495855372228\\n2306,1968-04-01,unemploy,2709.0,0.0018946869819215285\\n2307,1968-05-01,unemploy,2740.0,0.004341991000236836\\n2308,1968-06-01,unemploy,2938.0,0.019973158601089443\\n2309,1968-07-01,unemploy,2883.0,0.01563116760085261\\n2310,1968-08-01,unemploy,2768.0,0.006552459145811952\\n2311,1968-09-01,unemploy,2686.0,7.894529091339702E-5\\n2312,1968-10-01,unemploy,2689.0,3.157811636535881E-4\\n2313,1968-11-01,unemploy,2715.0,0.0023683587274019105\\n2314,1968-12-01,unemploy,2685.0,0.0\\n2315,1969-01-01,unemploy,2718.0,0.0026051946001421017\\n2316,1969-02-01,unemploy,2692.0,5.526170363937791E-4\\n2317,1969-03-01,unemploy,2712.0,0.0021315228546617193\\n2318,1969-04-01,unemploy,2758.0,0.005763006236677982\\n2319,1969-05-01,unemploy,2713.0,0.0022104681455751164\\n2320,1969-06-01,unemploy,2816.0,0.010341833109655009\\n2321,1969-07-01,unemploy,2868.0,0.014446988237151653\\n2322,1969-08-01,unemploy,2856.0,0.01349964474619089\\n2323,1969-09-01,unemploy,3040.0,0.02802557827425594\\n2324,1969-10-01,unemploy,3049.0,0.028736085892476514\\n2325,1969-11-01,unemploy,2856.0,0.01349964474619089\\n2326,1969-12-01,unemploy,2884.0,0.015710112891766007\\n2327,1970-01-01,unemploy,3201.0,0.04073577011131286\\n2328,1970-02-01,unemploy,3453.0,0.06062998342148891\\n2329,1970-03-01,unemploy,3635.0,0.07499802636772716\\n2330,1970-04-01,unemploy,3797.0,0.08778716349569748\\n2331,1970-05-01,unemploy,3919.0,0.09741848898713192\\n2332,1970-06-01,unemploy,4071.0,0.10941817320596826\\n2333,1970-07-01,unemploy,4175.0,0.11762848346096155\\n2334,1970-08-01,unemploy,4256.0,0.1240230520249467\\n2335,1970-09-01,unemploy,4456.0,0.13981211020762613\\n2336,1970-10-01,unemploy,4591.0,0.1504697244809347\\n2337,1970-11-01,unemploy,4898.0,0.1747059287913476\\n2338,1970-12-01,unemploy,5076.0,0.18875819057393226\\n2339,1971-01-01,unemploy,4986.0,0.18165311439172654\\n2340,1971-02-01,unemploy,4903.0,0.1751006552459146\\n2341,1971-03-01,unemploy,4987.0,0.18173205968263992\\n2342,1971-04-01,unemploy,4959.0,0.17952159153706482\\n2343,1971-05-01,unemploy,4996.0,0.1824425673008605\\n2344,1971-06-01,unemploy,4949.0,0.17873213862793083\\n2345,1971-07-01,unemploy,5035.0,0.185521433646483\\n2346,1971-08-01,unemploy,5134.0,0.1933370174469093\\n2347,1971-09-01,unemploy,5042.0,0.18607405068287677\\n2348,1971-10-01,unemploy,4954.0,0.17912686508249784\\n2349,1971-11-01,unemploy,5161.0,0.19546854030157101\\n2350,1971-12-01,unemploy,5154.0,0.19491592326517723\\n2351,1972-01-01,unemploy,5019.0,0.18425830899186862\\n2352,1972-02-01,unemploy,4928.0,0.1770742875187495\\n2353,1972-03-01,unemploy,5038.0,0.18575826951922317\\n2354,1972-04-01,unemploy,4959.0,0.17952159153706482\\n2355,1972-05-01,unemploy,4922.0,0.17660061577326913\\n2356,1972-06-01,unemploy,4923.0,0.17667956106418253\\n2357,1972-07-01,unemploy,4913.0,0.17589010815504855\\n2358,1972-08-01,unemploy,4939.0,0.17794268571879687\\n2359,1972-09-01,unemploy,4849.0,0.17083760953659113\\n2360,1972-10-01,unemploy,4875.0,0.17289018710033946\\n2361,1972-11-01,unemploy,4602.0,0.15133812268098207\\n2362,1972-12-01,unemploy,4543.0,0.14668035051709166\\n2363,1973-01-01,unemploy,4326.0,0.1295492223888845\\n2364,1973-02-01,unemploy,4452.0,0.13949632904397252\\n2365,1973-03-01,unemploy,4394.0,0.1349175021709955\\n2366,1973-04-01,unemploy,4459.0,0.1400489460803663\\n2367,1973-05-01,unemploy,4329.0,0.1297860582616247\\n2368,1973-06-01,unemploy,4363.0,0.13247019815268019\\n2369,1973-07-01,unemploy,4305.0,0.12789137127970315\\n2370,1973-08-01,unemploy,4305.0,0.12789137127970315\\n2371,1973-09-01,unemploy,4350.0,0.13144390937080602\\n2372,1973-10-01,unemploy,4144.0,0.11518117944264625\\n2373,1973-11-01,unemploy,4396.0,0.1350753927528223\\n2374,1973-12-01,unemploy,4489.0,0.1424173048077682\\n2375,1974-01-01,unemploy,4644.0,0.15465382489934476\\n2376,1974-02-01,unemploy,4731.0,0.1615220652088103\\n2377,1974-03-01,unemploy,4634.0,0.15386437199021077\\n2378,1974-04-01,unemploy,4618.0,0.15260124733559643\\n2379,1974-05-01,unemploy,4705.0,0.15946948764506197\\n2380,1974-06-01,unemploy,4927.0,0.1769953422278361\\n2381,1974-07-01,unemploy,5063.0,0.1877319017920581\\n2382,1974-08-01,unemploy,5022.0,0.18449514486460883\\n2383,1974-09-01,unemploy,5437.0,0.21725744059366858\\n2384,1974-10-01,unemploy,5523.0,0.22404673561222074\\n2385,1974-11-01,unemploy,6140.0,0.27275598010578667\\n2386,1974-12-01,unemploy,6636.0,0.3119128443988316\\n2387,1975-01-01,unemploy,7501.0,0.38020052103892005\\n2388,1975-02-01,unemploy,7520.0,0.3817004815662745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 :format {:type \"csv\"}}}\n\nWhile this in itself does not affect the display of the plot, it allows us to keep editing it as a Vega-Lite spec. For example, let us change the backgound colour this way:\n\n(-&gt; datasets/economics-long\n    (tc/select-rows #(-&gt; % :variable (= \"unemploy\")))\n    (haclo/base {:=x :date\n                 :=y :value})\n    (haclo/layer-line {:=mark-color \"purple\"})\n    (haclo/update-data tc/random 5)\n    (haclo/layer-point {:=mark-color \"green\"\n                        :=mark-size 200})\n    haclo/plot\n    (assoc :background \"lightgrey\"))",
     "crumbs": [
-      "<span class='chapter-number'>3</span>  <span class='chapter-title'>Walkthrough</span>"
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     "href": "hanamicloth_book.walkthrough.html#smoothing",
-    "title": "3  Walkthrough",
+    "title": "3  Walkthrough 👣",
     "section": "3.11 Smoothing",
     "text": "3.11 Smoothing\nhaclo/layer-smooth is a layer that applies some statistical processing to the dataset to model it as a smooth shape. It is inspired by ggplot’s geom_smooth.\nAt the moment, it can only be used to model :=y by linear regression. Soon we will add more ways of modelling the data.\n\n(-&gt; datasets/iris\n    (haclo/base {:=title \"dummy\"\n                 :=mark-color \"green\"\n                 :=x :sepal-width\n                 :=y :sepal-length})\n    haclo/layer-point\n    (haclo/layer-smooth {:=mark-color \"orange\"}))\n\n\nBy default, the regression is computed with only one predictor variable, which is :=x. But this can be overriden using the :predictors key. We may compute a regression with more than one predictor.\n\n(-&gt; datasets/iris\n    (haclo/base {:=x :sepal-width\n                 :=y :sepal-length})\n    haclo/layer-point\n    (haclo/layer-smooth {:=predictors [:petal-width\n                                       :petal-length]}))",
     "crumbs": [
-      "<span class='chapter-number'>3</span>  <span class='chapter-title'>Walkthrough</span>"
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     "objectID": "hanamicloth_book.walkthrough.html#grouping",
     "href": "hanamicloth_book.walkthrough.html#grouping",
-    "title": "3  Walkthrough",
+    "title": "3  Walkthrough 👣",
     "section": "3.12 Grouping",
     "text": "3.12 Grouping\nThe regression computed by haclo/layer-smooth is affected by the inferred grouping of the data.\nFor example, here we recieve three regression lines, each for every species.\n\n(-&gt; datasets/iris\n    (haclo/base {:=title \"dummy\"\n                 :=color :species\n                 :=x :sepal-width\n                 :=y :sepal-length})\n    haclo/layer-point\n    haclo/layer-smooth)\n\n\nThis happened because the :color field was :species, which is of :nominal type.\nBut we may override this using the :group key. For example, let us avoid grouping:\n\n(-&gt; datasets/iris\n    (haclo/base {:=title \"dummy\"\n                 :=mark-color \"green\"\n                 :=color :species\n                 :=group []\n                 :=x :sepal-width\n                 :=y :sepal-length})\n    haclo/layer-point\n    haclo/layer-smooth)",
     "crumbs": [
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-    "title": "3  Walkthrough",
+    "title": "3  Walkthrough 👣",
     "section": "3.13 Example: out-of-sample predictions",
     "text": "3.13 Example: out-of-sample predictions\nHere is a slighly more elaborate example inpired by the London Clojurians talk mentioned in the preface.\nAssume we wish to predict the unemployment rate for 96 months. Let us add those months to our dataset, and mark them as Future (considering the original data as Past):\n\n(-&gt; datasets/economics-long\n    (tc/select-rows #(-&gt; % :variable (= \"unemploy\")))\n    (tc/add-column :relative-time \"Past\")\n    (tc/concat (tc/dataset {:date (-&gt; datasets/economics-long\n                                      :date\n                                      last\n                                      (datetime/plus-temporal-amount (range 96) :days))\n                            :relative-time \"Future\"}))\n    (print/print-range 6))\n\n\nggplot2/economics_long [670 6]:\n\n\n\n\n\n\n\n\n\n\n\n:rownames\n:date\n:variable\n:value\n:value01\n:relative-time\n\n\n\n\n2297\n1967-07-01\nunemploy\n2944.0\n0.02044683\nPast\n\n\n2298\n1967-08-01\nunemploy\n2945.0\n0.02052578\nPast\n\n\n2299\n1967-09-01\nunemploy\n2958.0\n0.02155206\nPast\n\n\n…\n…\n…\n…\n…\n…\n\n\n\n2015-07-02\n\n\n\nFuture\n\n\n\n2015-07-03\n\n\n\nFuture\n\n\n\n2015-07-04\n\n\n\nFuture\n\n\n\n2015-07-05\n\n\n\nFuture\n\n\n\n\nLet us represent our dates as numbers, so that we can use them in linear regression:\n\n(-&gt; datasets/economics-long\n    (tc/select-rows #(-&gt; % :variable (= \"unemploy\")))\n    (tc/add-column :relative-time \"Past\")\n    (tc/concat (tc/dataset {:date (-&gt; datasets/economics-long\n                                      :date\n                                      last\n                                      (datetime/plus-temporal-amount (range 96) :months))\n                            :relative-time \"Future\"}))\n    (tc/add-column :year #(datetime/long-temporal-field :years (:date %)))\n    (tc/add-column :month #(datetime/long-temporal-field :months (:date %)))\n    (tc/map-columns :yearmonth [:year :month] (fn [y m] (+ m (* 12 y))))\n    (print/print-range 6))\n\n\nggplot2/economics_long [670 9]:\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n:rownames\n:date\n:variable\n:value\n:value01\n:relative-time\n:year\n:month\n:yearmonth\n\n\n\n\n2297\n1967-07-01\nunemploy\n2944.0\n0.02044683\nPast\n1967\n7\n23611\n\n\n2298\n1967-08-01\nunemploy\n2945.0\n0.02052578\nPast\n1967\n8\n23612\n\n\n2299\n1967-09-01\nunemploy\n2958.0\n0.02155206\nPast\n1967\n9\n23613\n\n\n…\n…\n…\n…\n…\n…\n…\n…\n…\n\n\n\n2022-12-01\n\n\n\nFuture\n2022\n12\n24276\n\n\n\n2023-01-01\n\n\n\nFuture\n2023\n1\n24277\n\n\n\n2023-02-01\n\n\n\nFuture\n2023\n2\n24278\n\n\n\n2023-03-01\n\n\n\nFuture\n2023\n3\n24279\n\n\n\n\nLet us use the same regression line for the Past and Future groups. To do this, we avoid grouping by assigning [] to :=group. The line is affected only by the past, since in the Future, :=y is missing. We use the numerical field :yearmonth as the regression predictor, but for plotting, we still use the :temporal field :date.\n\n(-&gt; datasets/economics-long\n    (tc/select-rows #(-&gt; % :variable (= \"unemploy\")))\n    (tc/add-column :relative-time \"Past\")\n    (tc/concat (tc/dataset {:date (-&gt; datasets/economics-long\n                                      :date\n                                      last\n                                      (datetime/plus-temporal-amount (range 96) :months))\n                            :relative-time \"Future\"}))\n    (tc/add-column :year #(datetime/long-temporal-field :years (:date %)))\n    (tc/add-column :month #(datetime/long-temporal-field :months (:date %)))\n    (tc/map-columns :yearmonth [:year :month] (fn [y m] (+ m (* 12 y))))\n    (haclo/base {:=x :date\n                 :=y :value})\n    (haclo/layer-smooth {:=color :relative-time\n                         :=mark-size 10\n                         :=group []\n                         :=predictors [:yearmonth]})\n    ;; Keep only the past for the following layer:\n    (haclo/update-data (fn [dataset]\n                         (-&gt; dataset\n                             (tc/select-rows (fn [row]\n                                               (-&gt; row :relative-time (= \"Past\")))))))\n    (haclo/layer-line {:=mark-color \"purple\"\n                       :=mark-size 3}))",
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     "section": "3.14 Histograms",
     "text": "3.14 Histograms\nHistograms can also be represented as layers with statistical processing:\n\n(-&gt; datasets/iris\n    (haclo/layer-histogram {:=x :sepal-width}))\n\n\n\n(-&gt; datasets/iris\n    (haclo/layer-histogram {:=x :sepal-width\n                            :=histogram-nbins 30}))",
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     "text": "3.15 Coming soon\n\n3.15.1 Facets\n\n\n3.15.2 Coordinates\n\n\n3.15.3 Scales\n\n\n3.15.4 Generating Plotly plots\n\nsource: notebooks/hanamicloth_book/walkthrough.clj",
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     "text": "4.1 Setup\nFor this tutorial, we require:",
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     "text": "The plotlycloth API namepace\nTablecloth for dataset processing\nthe datetime namespace of dtype-next\nthe print namespace of tech.ml.dataset for customized dataset printing\nKindly (to specify how certaiun values should be visualized)\nthe datasets defined in the Datasets chapter\n\n\n(ns hanamicloth-book.plotlycloth-walkthrough\n  (:require [scicloj.hanamicloth.v1.plotlycloth :as ploclo]\n            [tablecloth.api :as tc]\n            [tech.v3.datatype.datetime :as datetime]\n            [tech.v3.dataset.print :as print]\n            [scicloj.kindly.v4.kind :as kind]\n            [clojure.string :as str]\n            [scicloj.kindly.v4.api :as kindly]\n            [hanamicloth-book.datasets :as datasets]))",
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     "section": "4.2 Basic usage",
     "text": "4.2 Basic usage\nPlotlycloth plots are created by passing datasets to a pipeline of layer functions.\nAdditional parameters to the functions are passed as maps. By convention, the map keys begin with = (e.g., :=color).\nFor example, let us plot a scatterplot (a layer of points) of 10 random items from the Iris dataset.\n\n(-&gt; datasets/iris\n    (tc/random 10 {:seed 1})\n    (ploclo/layer-point\n     {:=x :sepal-width\n      :=y :sepal-length\n      :=color :species\n      :=mark-size 20\n      :=mark-opacity 0.6}))",
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     "section": "4.3 Templates and parameters",
-    "text": "4.3 Templates and parameters\n(💡 You do neet need to understand these details for basic usage.)\nTechnically, the parameter maps contain Hanami substitution keys, which means they are processed by a simple set of rules, but you do not need to understand what this means yet.\nThe layer functions return a Hanami template. Let us print the resulting structure of the previous plot.\n\n(def example1\n  (-&gt; datasets/iris\n      (tc/random 10 {:seed 1})\n      (ploclo/layer-point\n       {:=x :sepal-width\n        :=y :sepal-length\n        :=color :species\n        :=mark-size 20\n        :=mark-opacity 0.6})))\n\n\n(kind/pprint example1)\n\n\n{:data :=traces,\n :layout :=layout,\n :aerial.hanami.templates/defaults\n {:=x0 :com.rpl.specter.impl/NONE,\n  :=y-type\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=x0-after-stat :=x0,\n  :=layers\n  [{:y :=y-after-stat,\n    :trace-base {:mode :=mode, :type :=type, :opacity :=mark-opacity},\n    :color-type :=color-type,\n    :group :=group,\n    :color :=color,\n    :mark :=mark,\n    :x-title :=x-title,\n    :name :=name,\n    :y1 :=y1-after-stat,\n    :size :=size,\n    :size-type :=size-type,\n    :aerial.hanami.templates/defaults\n    {:=x0 :com.rpl.specter.impl/NONE,\n     :=y-type\n     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n     :=x0-after-stat :=x0,\n     :=layers [],\n     :=x1 :com.rpl.specter.impl/NONE,\n     :=title :com.rpl.specter.impl/NONE,\n     :=y1 :com.rpl.specter.impl/NONE,\n     :=y-type-after-stat\n     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n     :=height :com.rpl.specter.impl/NONE,\n     :=name :com.rpl.specter.impl/NONE,\n     :=mark-opacity 0.6,\n     :=inferred-group\n     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n     :=mode\n     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n     :=y-title :com.rpl.specter.impl/NONE,\n     :=size :com.rpl.specter.impl/NONE,\n     :=group :=inferred-group,\n     :=y0 :com.rpl.specter.impl/NONE,\n     :=mark-size 20,\n     :=size-type\n     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n     :=color :species,\n     :=mark-color :com.rpl.specter.impl/NONE,\n     :=y1-after-stat :=y1,\n     :=x :sepal-width,\n     :=x-after-stat :=x,\n     :=yaxis-gridcolor \"rgb(255,255,255)\",\n     :=type\n     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n     :=x-type-after-stat\n     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n     :=traces\n     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n     :=x-type\n     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n     :=histogram-nbins 10,\n     :=stat :com.rpl.specter.impl/NONE,\n     :=width :com.rpl.specter.impl/NONE,\n     :=color-type\n     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n     :=xaxis-gridcolor \"rgb(255,255,255)\",\n     :=mark :point,\n     :=dataset-after-stat\n     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n     :=x-title :com.rpl.specter.impl/NONE,\n     :=layout\n     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n     :=y :sepal-length,\n     :=x1-after-stat :=x1,\n     :=dataset #&lt;WrappedValue@28277720: datasets/iris [10 6]:\n\n| :rownames | :sepal-length | :sepal-width | :petal-length | :petal-width |   :species |\n|----------:|--------------:|-------------:|--------------:|-------------:|------------|\n|        27 |           5.0 |          3.4 |           1.6 |          0.4 |     setosa |\n|        97 |           5.7 |          2.9 |           4.2 |          1.3 | versicolor |\n|       127 |           6.2 |          2.8 |           4.8 |          1.8 |  virginica |\n|        92 |           6.1 |          3.0 |           4.6 |          1.4 | versicolor |\n|         7 |           4.6 |          3.4 |           1.4 |          0.3 |     setosa |\n|        95 |           5.6 |          2.7 |           4.2 |          1.3 | versicolor |\n|       125 |           6.7 |          3.3 |           5.7 |          2.1 |  virginica |\n|        61 |           5.0 |          2.0 |           3.5 |          1.0 | versicolor |\n|        73 |           6.3 |          2.5 |           4.9 |          1.5 | versicolor |\n|        42 |           4.5 |          2.3 |           1.3 |          0.3 |     setosa |\n&gt;,\n     :=background \"rgb(229,229,229)\",\n     :=y0-after-stat :=y0,\n     :=y-after-stat :=y,\n     :=predictors [:=x],\n     :=marker-size-key\n     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211]},\n    :y0 :=y0-after-stat,\n    :inferred-group :=inferred-group,\n    :marker-override\n    {:color :=mark-color, :=marker-size-key :=mark-size},\n    :x :=x-after-stat,\n    :x1 :=x1-after-stat,\n    :x0 :=x0-after-stat,\n    :y-title :=y-title,\n    :dataset :=dataset-after-stat}],\n  :=x1 :com.rpl.specter.impl/NONE,\n  :=title :com.rpl.specter.impl/NONE,\n  :=y1 :com.rpl.specter.impl/NONE,\n  :=y-type-after-stat\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=height :com.rpl.specter.impl/NONE,\n  :=name :com.rpl.specter.impl/NONE,\n  :=mark-opacity :com.rpl.specter.impl/NONE,\n  :=inferred-group\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=mode\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=y-title :com.rpl.specter.impl/NONE,\n  :=size :com.rpl.specter.impl/NONE,\n  :=group :=inferred-group,\n  :=y0 :com.rpl.specter.impl/NONE,\n  :=mark-size :com.rpl.specter.impl/NONE,\n  :=size-type\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=color :com.rpl.specter.impl/NONE,\n  :=mark-color :com.rpl.specter.impl/NONE,\n  :=y1-after-stat :=y1,\n  :=x :x,\n  :=x-after-stat :=x,\n  :=yaxis-gridcolor \"rgb(255,255,255)\",\n  :=type\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=x-type-after-stat\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=traces\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=x-type\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=histogram-nbins 10,\n  :=stat :com.rpl.specter.impl/NONE,\n  :=width :com.rpl.specter.impl/NONE,\n  :=color-type\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=xaxis-gridcolor \"rgb(255,255,255)\",\n  :=mark :point,\n  :=dataset-after-stat\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=x-title :com.rpl.specter.impl/NONE,\n  :=layout\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=y :y,\n  :=x1-after-stat :=x1,\n  :=dataset #&lt;WrappedValue@28277720: datasets/iris [10 6]:\n\n| :rownames | :sepal-length | :sepal-width | :petal-length | :petal-width |   :species |\n|----------:|--------------:|-------------:|--------------:|-------------:|------------|\n|        27 |           5.0 |          3.4 |           1.6 |          0.4 |     setosa |\n|        97 |           5.7 |          2.9 |           4.2 |          1.3 | versicolor |\n|       127 |           6.2 |          2.8 |           4.8 |          1.8 |  virginica |\n|        92 |           6.1 |          3.0 |           4.6 |          1.4 | versicolor |\n|         7 |           4.6 |          3.4 |           1.4 |          0.3 |     setosa |\n|        95 |           5.6 |          2.7 |           4.2 |          1.3 | versicolor |\n|       125 |           6.7 |          3.3 |           5.7 |          2.1 |  virginica |\n|        61 |           5.0 |          2.0 |           3.5 |          1.0 | versicolor |\n|        73 |           6.3 |          2.5 |           4.9 |          1.5 | versicolor |\n|        42 |           4.5 |          2.3 |           1.3 |          0.3 |     setosa |\n&gt;,\n  :=background \"rgb(229,229,229)\",\n  :=y0-after-stat :=y0,\n  :=y-after-stat :=y,\n  :=predictors [:=x],\n  :=marker-size-key\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211]},\n :kindly/f #'scicloj.hanamicloth.v1.plotlycloth/plotly-xform}\n\nThis template has all the necessary knowledge, including the substitution keys, to turn into a plot. This happens when your visual tool (e.g., Clay) displays the plot. The tool knows what to do thanks to the Kindly metadata and a special function attached to the plot.\n\n(meta example1)\n\n\n#:kindly{:kind :kind/fn}\n\n\n(:kindly/f example1)\n\n\n#'scicloj.hanamicloth.v1.plotlycloth/plotly-xform",
+    "text": "4.3 Templates and parameters\n(💡 You do neet need to understand these details for basic usage.)\nTechnically, the parameter maps contain Hanami substitution keys, which means they are processed by a simple set of rules, but you do not need to understand what this means yet.\nThe layer functions return a Hanami template. Let us print the resulting structure of the previous plot.\n\n(def example1\n  (-&gt; datasets/iris\n      (tc/random 10 {:seed 1})\n      (ploclo/layer-point\n       {:=x :sepal-width\n        :=y :sepal-length\n        :=color :species\n        :=mark-size 20\n        :=mark-opacity 0.6})))\n\n\n(kind/pprint example1)\n\n\n{:data :=traces,\n :layout :=layout,\n :aerial.hanami.templates/defaults\n {:=x0 :com.rpl.specter.impl/NONE,\n  :=y-type\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=x0-after-stat :=x0,\n  :=layers\n  [{:y :=y-after-stat,\n    :trace-base {:mode :=mode, :type :=type, :opacity :=mark-opacity},\n    :color-type :=color-type,\n    :group :=group,\n    :color :=color,\n    :mark :=mark,\n    :x-title :=x-title,\n    :name :=name,\n    :y1 :=y1-after-stat,\n    :size :=size,\n    :size-type :=size-type,\n    :aerial.hanami.templates/defaults\n    {:=x0 :com.rpl.specter.impl/NONE,\n     :=y-type\n     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n     :=x0-after-stat :=x0,\n     :=layers [],\n     :=x1 :com.rpl.specter.impl/NONE,\n     :=title :com.rpl.specter.impl/NONE,\n     :=y1 :com.rpl.specter.impl/NONE,\n     :=y-type-after-stat\n     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n     :=height :com.rpl.specter.impl/NONE,\n     :=name :com.rpl.specter.impl/NONE,\n     :=mark-opacity 0.6,\n     :=inferred-group\n     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n     :=mode\n     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n     :=y-title :com.rpl.specter.impl/NONE,\n     :=size :com.rpl.specter.impl/NONE,\n     :=group :=inferred-group,\n     :=y0 :com.rpl.specter.impl/NONE,\n     :=mark-size 20,\n     :=size-type\n     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n     :=color :species,\n     :=mark-color :com.rpl.specter.impl/NONE,\n     :=y1-after-stat :=y1,\n     :=x :sepal-width,\n     :=x-after-stat :=x,\n     :=yaxis-gridcolor \"rgb(255,255,255)\",\n     :=type\n     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n     :=x-type-after-stat\n     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n     :=traces\n     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n     :=x-type\n     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n     :=histogram-nbins 10,\n     :=stat :com.rpl.specter.impl/NONE,\n     :=width :com.rpl.specter.impl/NONE,\n     :=color-type\n     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n     :=xaxis-gridcolor \"rgb(255,255,255)\",\n     :=mark :point,\n     :=dataset-after-stat\n     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n     :=x-title :com.rpl.specter.impl/NONE,\n     :=layout\n     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n     :=y :sepal-length,\n     :=x1-after-stat :=x1,\n     :=dataset #&lt;WrappedValue@1fe6d211: datasets/iris [10 6]:\n\n| :rownames | :sepal-length | :sepal-width | :petal-length | :petal-width |   :species |\n|----------:|--------------:|-------------:|--------------:|-------------:|------------|\n|        27 |           5.0 |          3.4 |           1.6 |          0.4 |     setosa |\n|        97 |           5.7 |          2.9 |           4.2 |          1.3 | versicolor |\n|       127 |           6.2 |          2.8 |           4.8 |          1.8 |  virginica |\n|        92 |           6.1 |          3.0 |           4.6 |          1.4 | versicolor |\n|         7 |           4.6 |          3.4 |           1.4 |          0.3 |     setosa |\n|        95 |           5.6 |          2.7 |           4.2 |          1.3 | versicolor |\n|       125 |           6.7 |          3.3 |           5.7 |          2.1 |  virginica |\n|        61 |           5.0 |          2.0 |           3.5 |          1.0 | versicolor |\n|        73 |           6.3 |          2.5 |           4.9 |          1.5 | versicolor |\n|        42 |           4.5 |          2.3 |           1.3 |          0.3 |     setosa |\n&gt;,\n     :=background \"rgb(229,229,229)\",\n     :=y0-after-stat :=y0,\n     :=y-after-stat :=y,\n     :=predictors [:=x],\n     :=marker-size-key\n     #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211]},\n    :y0 :=y0-after-stat,\n    :inferred-group :=inferred-group,\n    :marker-override\n    {:color :=mark-color, :=marker-size-key :=mark-size},\n    :x :=x-after-stat,\n    :x1 :=x1-after-stat,\n    :x0 :=x0-after-stat,\n    :y-title :=y-title,\n    :dataset :=dataset-after-stat}],\n  :=x1 :com.rpl.specter.impl/NONE,\n  :=title :com.rpl.specter.impl/NONE,\n  :=y1 :com.rpl.specter.impl/NONE,\n  :=y-type-after-stat\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=height :com.rpl.specter.impl/NONE,\n  :=name :com.rpl.specter.impl/NONE,\n  :=mark-opacity :com.rpl.specter.impl/NONE,\n  :=inferred-group\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=mode\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=y-title :com.rpl.specter.impl/NONE,\n  :=size :com.rpl.specter.impl/NONE,\n  :=group :=inferred-group,\n  :=y0 :com.rpl.specter.impl/NONE,\n  :=mark-size :com.rpl.specter.impl/NONE,\n  :=size-type\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=color :com.rpl.specter.impl/NONE,\n  :=mark-color :com.rpl.specter.impl/NONE,\n  :=y1-after-stat :=y1,\n  :=x :x,\n  :=x-after-stat :=x,\n  :=yaxis-gridcolor \"rgb(255,255,255)\",\n  :=type\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=x-type-after-stat\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=traces\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=x-type\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=histogram-nbins 10,\n  :=stat :com.rpl.specter.impl/NONE,\n  :=width :com.rpl.specter.impl/NONE,\n  :=color-type\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=xaxis-gridcolor \"rgb(255,255,255)\",\n  :=mark :point,\n  :=dataset-after-stat\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=x-title :com.rpl.specter.impl/NONE,\n  :=layout\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211],\n  :=y :y,\n  :=x1-after-stat :=x1,\n  :=dataset #&lt;WrappedValue@1fe6d211: datasets/iris [10 6]:\n\n| :rownames | :sepal-length | :sepal-width | :petal-length | :petal-width |   :species |\n|----------:|--------------:|-------------:|--------------:|-------------:|------------|\n|        27 |           5.0 |          3.4 |           1.6 |          0.4 |     setosa |\n|        97 |           5.7 |          2.9 |           4.2 |          1.3 | versicolor |\n|       127 |           6.2 |          2.8 |           4.8 |          1.8 |  virginica |\n|        92 |           6.1 |          3.0 |           4.6 |          1.4 | versicolor |\n|         7 |           4.6 |          3.4 |           1.4 |          0.3 |     setosa |\n|        95 |           5.6 |          2.7 |           4.2 |          1.3 | versicolor |\n|       125 |           6.7 |          3.3 |           5.7 |          2.1 |  virginica |\n|        61 |           5.0 |          2.0 |           3.5 |          1.0 | versicolor |\n|        73 |           6.3 |          2.5 |           4.9 |          1.5 | versicolor |\n|        42 |           4.5 |          2.3 |           1.3 |          0.3 |     setosa |\n&gt;,\n  :=background \"rgb(229,229,229)\",\n  :=y0-after-stat :=y0,\n  :=y-after-stat :=y,\n  :=predictors [:=x],\n  :=marker-size-key\n  #function[scicloj.hanamicloth.v1.dag/fn-with-deps-keys/fn--48211]},\n :kindly/f #'scicloj.hanamicloth.v1.plotlycloth/plotly-xform}\n\nThis template has all the necessary knowledge, including the substitution keys, to turn into a plot. This happens when your visual tool (e.g., Clay) displays the plot. The tool knows what to do thanks to the Kindly metadata and a special function attached to the plot.\n\n(meta example1)\n\n\n#:kindly{:kind :kind/fn}\n\n\n(:kindly/f example1)\n\n\n#'scicloj.hanamicloth.v1.plotlycloth/plotly-xform",
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     "section": "4.4 Realizing the plot",
     "text": "4.4 Realizing the plot\nIf you wish to see the resulting plot specification before displaying it as a plot, you can use the plot function. In this case, it generates a Plotly.js plot:\n\n(-&gt; example1\n    ploclo/plot\n    kind/pprint)\n\n\n{:data\n [{:mode :markers,\n   :type :scatter,\n   :opacity 0.6,\n   :name \"setosa\",\n   :x [3.4 3.4 2.3],\n   :y [5.0 4.6 4.5],\n   :marker {:color \"#1B9E77\", :size 20}}\n  {:mode :markers,\n   :type :scatter,\n   :opacity 0.6,\n   :name \"versicolor\",\n   :x [2.9 3.0 2.7 2.0 2.5],\n   :y [5.7 6.1 5.6 5.0 6.3],\n   :marker {:color \"#D95F02\", :size 20}}\n  {:mode :markers,\n   :type :scatter,\n   :opacity 0.6,\n   :name \"virginica\",\n   :x [2.8 3.3],\n   :y [6.2 6.7],\n   :marker {:color \"#7570B3\", :size 20}}],\n :layout\n {:width nil,\n  :height nil,\n  :plot_bgcolor \"rgb(229,229,229)\",\n  :xaxis {:gridcolor \"rgb(255,255,255)\", :title :sepal-width},\n  :yaxis {:gridcolor \"rgb(255,255,255)\", :title :sepal-length},\n  :title nil}}\n\nIt is annotated as kind/plotly, so that visual tools know how to render it.\n\n(-&gt; example1\n    ploclo/plot\n    meta)\n\n\n#:kindly{:kind :kind/plotly}\n\nThis can be useful if you wish to process the actual Plotly.js spec rather than use Plotlycloth’s API. Let us change the background colour, for example:\n\n(-&gt; example1\n    ploclo/plot\n    (assoc-in [:layout :plot_bgcolor] \"#eeeedd\"))",
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     "section": "4.5 Field type inference",
     "text": "4.5 Field type inference\nPlotlycloth infers the type of relevant fields from the data.\nThe example above was colored as it were since :species column was nominal, so it was assigned distinct colours.\nIn the following example, the coloring is by a quantitative column, so a color gradient is used:\n\n(-&gt; datasets/mtcars\n    (ploclo/layer-point\n     {:=x :mpg\n      :=y :disp\n      :=color :cyl\n      :=mark-size 20}))\n\n\nWe can override the inferred types and thus affect the generated plot:\n\n(-&gt; datasets/mtcars\n    (ploclo/layer-point\n     {:=x :mpg\n      :=y :disp\n      :=color :cyl\n      :=color-type :nominal\n      :=mark-size 20}))",
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     "section": "4.6 More examples",
     "text": "4.6 More examples\n\n4.6.1 Boxplot\n\n(-&gt; datasets/mtcars\n    (ploclo/layer-boxplot\n     {:=x :cyl\n      :=y :disp}))\n\n\n\n\n4.6.2 Segment plot\n\n(-&gt; datasets/iris\n    (ploclo/layer-segment\n     {:=x0 :sepal-width\n      :=y0 :sepal-length\n      :=x1 :petal-width\n      :=y1 :petal-length\n      :=mark-opacity 0.4\n      :=mark-size 3\n      :=color :species}))",
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     "section": "4.7 Varying color and size",
     "text": "4.7 Varying color and size\n\n(-&gt; {:ABCD (range 1 11)\n     :EFGH [5 2.5 5 7.5 5 2.5 7.5 4.5 5.5 5]\n     :IJKL [:A :A :A :A :A :B :B :B :B :B]\n     :MNOP [:C :D :C :D :C :D :C :D :C :D]}\n    tc/dataset\n    (ploclo/base {:=title \"IJKLMNOP\"})\n    (ploclo/layer-point {:=x :ABCD\n                         :=y :EFGH\n                         :=color :IJKL\n                         :=size :MNOP\n                         :=name \"QRST1\"})\n    (ploclo/layer-line\n     {:=title \"IJKL MNOP\"\n      :=x :ABCD\n      :=y :ABCD\n      :=name \"QRST2\"\n      :=mark-color \"magenta\"\n      :=mark-size 20\n      :=mark-opacity 0.2})\n    ploclo/plot)",
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     "section": "4.8 Time series",
     "text": "4.8 Time series\nDate and time fields are handle appropriately. Let us, for example, draw the time series of unemployment counts.\n\n(-&gt; datasets/economics-long\n    (tc/select-rows #(-&gt; % :variable (= \"unemploy\")))\n    (ploclo/layer-line\n     {:=x :date\n      :=y :value\n      :=mark-color \"purple\"}))",
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     "section": "4.9 Multiple layers",
     "text": "4.9 Multiple layers\nWe can draw more than one layer:\n\n(-&gt; datasets/economics-long\n    (tc/select-rows #(-&gt; % :variable (= \"unemploy\")))\n    (ploclo/layer-point {:=x :date\n                         :=y :value\n                         :=mark-color \"green\"\n                         :=mark-size 20\n                         :=mark-opacity 0.5})\n    (ploclo/layer-line {:=x :date\n                        :=y :value\n                        :=mark-color \"purple\"}))\n\n\nWe can also use the base function for the common parameters across layers:\n\n(-&gt; datasets/economics-long\n    (tc/select-rows #(-&gt; % :variable (= \"unemploy\")))\n    (ploclo/base {:=x :date\n                  :=y :value})\n    (ploclo/layer-point {:=mark-color \"green\"\n                         :=mark-size 20\n                         :=mark-opacity 0.5})\n    (ploclo/layer-line {:=mark-color \"purple\"}))",
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     "section": "4.10 Updating data",
     "text": "4.10 Updating data\nWe can use the update-data function to vary the dataset along a plotting pipeline, affecting the layers that follow.\nThis functionality is inspired by ggbuilder and metamorph.\nHere, for example, we draw a line, then sample 5 data rows, and draw them as points:\n\n(-&gt; datasets/economics-long\n    (tc/select-rows #(-&gt; % :variable (= \"unemploy\")))\n    (ploclo/base {:=x :date\n                  :=y :value})\n    (ploclo/layer-line {:=mark-color \"purple\"})\n    (ploclo/update-data tc/random 5)\n    (ploclo/layer-point {:=mark-color \"green\"\n                         :=mark-size 15\n                         :=mark-opacity 0.5}))",
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     "section": "4.11 Smoothing",
     "text": "4.11 Smoothing\nlayer-smooth is a layer that applies some statistical processing to the dataset to model it as a smooth shape. It is inspired by ggplot’s geom_smooth.\nAt the moment, it can only be used to model :=y by linear regression. Soon we will add more ways of modelling the data.\n\n(-&gt; datasets/iris\n    (ploclo/base {:=title \"dummy\"\n                  :=mark-color \"green\"\n                  :=x :sepal-width\n                  :=y :sepal-length})\n    ploclo/layer-point\n    (ploclo/layer-smooth {:=mark-color \"orange\"})\n    ploclo/plot)\n\n\nBy default, the regression is computed with only one predictor variable, which is :=x. But this can be overriden using the :predictors key. We may compute a regression with more than one predictor.\n\n(-&gt; datasets/iris\n    (ploclo/base {:=x :sepal-width\n                  :=y :sepal-length})\n    ploclo/layer-point\n    (ploclo/layer-smooth {:=predictors [:petal-width\n                                        :petal-length]\n                          :=mark-opacity 0.5})\n    ploclo/plot)",
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     "section": "4.12 Grouping",
     "text": "4.12 Grouping\nThe regression computed by haclo/layer-smooth is affected by the inferred grouping of the data.\nFor example, here we recieve three regression lines, each for every species.\n\n(-&gt; datasets/iris\n    (ploclo/base {:=title \"dummy\"\n                  :=color :species\n                  :=x :sepal-width\n                  :=y :sepal-length})\n    ploclo/layer-point\n    ploclo/layer-smooth)\n\n\nThis happened because the :color field was :species, which is of :nominal type.\nBut we may override this using the :group key. For example, let us avoid grouping:\n\n(-&gt; datasets/iris\n    (ploclo/base {:=title \"dummy\"\n                  :=color :species\n                  :=group []\n                  :=x :sepal-width\n                  :=y :sepal-length})\n    ploclo/layer-point\n    ploclo/layer-smooth)",
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+    "title": "4  Plotlycloth Walkthrough 👣 - experimental 🛠",
     "section": "4.13 Example: out-of-sample predictions",
     "text": "4.13 Example: out-of-sample predictions\nHere is a slighly more elaborate example inpired by the London Clojurians talk mentioned in the preface.\nAssume we wish to predict the unemployment rate for 96 months. Let us add those months to our dataset, and mark them as Future (considering the original data as Past):\n\n(-&gt; datasets/economics-long\n    (tc/select-rows #(-&gt; % :variable (= \"unemploy\")))\n    (tc/add-column :relative-time \"Past\")\n    (tc/concat (tc/dataset {:date (-&gt; datasets/economics-long\n                                      :date\n                                      last\n                                      (datetime/plus-temporal-amount (range 96) :days))\n                            :relative-time \"Future\"}))\n    (print/print-range 6))\n\n\nggplot2/economics_long [670 6]:\n\n\n\n\n\n\n\n\n\n\n\n:rownames\n:date\n:variable\n:value\n:value01\n:relative-time\n\n\n\n\n2297\n1967-07-01\nunemploy\n2944.0\n0.02044683\nPast\n\n\n2298\n1967-08-01\nunemploy\n2945.0\n0.02052578\nPast\n\n\n2299\n1967-09-01\nunemploy\n2958.0\n0.02155206\nPast\n\n\n…\n…\n…\n…\n…\n…\n\n\n\n2015-07-02\n\n\n\nFuture\n\n\n\n2015-07-03\n\n\n\nFuture\n\n\n\n2015-07-04\n\n\n\nFuture\n\n\n\n2015-07-05\n\n\n\nFuture\n\n\n\n\nLet us represent our dates as numbers, so that we can use them in linear regression:\n\n(-&gt; datasets/economics-long\n    (tc/select-rows #(-&gt; % :variable (= \"unemploy\")))\n    (tc/add-column :relative-time \"Past\")\n    (tc/concat (tc/dataset {:date (-&gt; datasets/economics-long\n                                      :date\n                                      last\n                                      (datetime/plus-temporal-amount (range 96) :months))\n                            :relative-time \"Future\"}))\n    (tc/add-column :year #(datetime/long-temporal-field :years (:date %)))\n    (tc/add-column :month #(datetime/long-temporal-field :months (:date %)))\n    (tc/map-columns :yearmonth [:year :month] (fn [y m] (+ m (* 12 y))))\n    (print/print-range 6))\n\n\nggplot2/economics_long [670 9]:\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n:rownames\n:date\n:variable\n:value\n:value01\n:relative-time\n:year\n:month\n:yearmonth\n\n\n\n\n2297\n1967-07-01\nunemploy\n2944.0\n0.02044683\nPast\n1967\n7\n23611\n\n\n2298\n1967-08-01\nunemploy\n2945.0\n0.02052578\nPast\n1967\n8\n23612\n\n\n2299\n1967-09-01\nunemploy\n2958.0\n0.02155206\nPast\n1967\n9\n23613\n\n\n…\n…\n…\n…\n…\n…\n…\n…\n…\n\n\n\n2022-12-01\n\n\n\nFuture\n2022\n12\n24276\n\n\n\n2023-01-01\n\n\n\nFuture\n2023\n1\n24277\n\n\n\n2023-02-01\n\n\n\nFuture\n2023\n2\n24278\n\n\n\n2023-03-01\n\n\n\nFuture\n2023\n3\n24279\n\n\n\n\nLet us use the same regression line for the Past and Future groups. To do this, we avoid grouping by assigning [] to :=group. The line is affected only by the past, since in the Future, :=y is missing. We use the numerical field :yearmonth as the regression predictor, but for plotting, we still use the :temporal field :date.\n\n(-&gt; datasets/economics-long\n    (tc/select-rows #(-&gt; % :variable (= \"unemploy\")))\n    (tc/add-column :relative-time \"Past\")\n    (tc/concat (tc/dataset {:date (-&gt; datasets/economics-long\n                                      :date\n                                      last\n                                      (datetime/plus-temporal-amount (range 96) :months))\n                            :relative-time \"Future\"}))\n    (tc/add-column :year #(datetime/long-temporal-field :years (:date %)))\n    (tc/add-column :month #(datetime/long-temporal-field :months (:date %)))\n    (tc/map-columns :yearmonth [:year :month] (fn [y m] (+ m (* 12 y))))\n    (ploclo/base {:=x :date\n                  :=y :value})\n    (ploclo/layer-smooth {:=color :relative-time\n                          :=mark-size 20\n                          :=group []\n                          :=predictors [:yearmonth]})\n    ;; Keep only the past for the following layer:\n    (ploclo/update-data (fn [dataset]\n                          (-&gt; dataset\n                              (tc/select-rows (fn [row]\n                                                (-&gt; row :relative-time (= \"Past\")))))))\n    (ploclo/layer-line {:=mark-color \"purple\"\n                        :=mark-size 3}))",
     "crumbs": [
-      "<span class='chapter-number'>4</span>  <span class='chapter-title'>Plotlycloth Walkthrough (experimental 🛠)</span>"
+      "<span class='chapter-number'>4</span>  <span class='chapter-title'>Plotlycloth Walkthrough 👣 - experimental 🛠</span>"
     ]
   },
   {
     "objectID": "hanamicloth_book.plotlycloth_walkthrough.html#histograms",
     "href": "hanamicloth_book.plotlycloth_walkthrough.html#histograms",
-    "title": "4  Plotlycloth Walkthrough (experimental 🛠)",
+    "title": "4  Plotlycloth Walkthrough 👣 - experimental 🛠",
     "section": "4.14 Histograms",
     "text": "4.14 Histograms\nHistograms can also be represented as layers with statistical processing:\n\n(-&gt; datasets/iris\n    (ploclo/layer-histogram {:=x :sepal-width}))\n\n\n\n(-&gt; datasets/iris\n    (ploclo/layer-histogram {:=x :sepal-width\n                             :=histogram-nbins 30}))\n\n\n\nsource: notebooks/hanamicloth_book/plotlycloth_walkthrough.clj",
     "crumbs": [
-      "<span class='chapter-number'>4</span>  <span class='chapter-title'>Plotlycloth Walkthrough (experimental 🛠)</span>"
+      "<span class='chapter-number'>4</span>  <span class='chapter-title'>Plotlycloth Walkthrough 👣 - experimental 🛠</span>"
     ]
   }
 ]
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diff --git a/notebooks/index.clj b/notebooks/index.clj
index 67f8d99..548596e 100644
--- a/notebooks/index.clj
+++ b/notebooks/index.clj
@@ -29,7 +29,7 @@
 
 * [main API Walkthrough](./hanamicloth_book.walkthrough.html) 👣
 
-* [plotlycloth Walkthrough](./hanamicloth_book.plotlycloth_walkthrough.html) - experimental 🛠")
+* [plotlycloth Walkthrough](./hanamicloth_book.plotlycloth_walkthrough.html) 👣 - experimental 🛠")
 
 ;; # Preface