New Upstream Snapshot - r-cran-forcats

• QA Page
• Maintainer email: edd@debian.org
• Automatic publish policy: main: push-derived , pristine-tar: push-derived , upstream: push-derived
• Last processed: 2023-02-01T19:16 (took 31m24s)
• Branch URL: https://salsa.debian.org/edd/r-cran-forcats.git -b master (taken from version 1.0.0-1)
• Queue position: 144668 (a 62w1d wait)

Schedule a new run

Ready changes

Publish now

Summary

Merged new upstream version: 1.0.0+git20230130.1.4a8525a (was: 1.0.0).

Resulting package

Built on 2023-02-01T19:16 (took 31m24s)

The resulting binary packages can be installed (if you have the apt repository enabled) by running one of:

apt install -t fresh-snapshots r-cran-forcats

Lintian Result

• r-cran-forcats_1.0.0+git20230130.1.4a8525a-1~jan+nus1.dsc
• r-cran-forcats_1.0.0+git20230130.1.4a8525a-1~jan+nus1_all.deb
• r-cran-forcats_1.0.0+git20230130.1.4a8525a-1~jan+nus1_amd64.buildinfo
• r-cran-forcats_1.0.0+git20230130.1.4a8525a-1~jan+nus1_amd64.changes

Diff

diff --git a/DESCRIPTION b/DESCRIPTION
index 4b85ea3..62daa9b 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: forcats
 Title: Tools for Working with Categorical Variables (Factors)
-Version: 1.0.0
+Version: 1.0.0.9000
 Authors@R: c(
     person("Hadley", "Wickham", , "hadley@rstudio.com", role = c("aut", "cre")),
     person("RStudio", role = c("cph", "fnd"))
@@ -24,11 +24,10 @@ Config/Needs/website: tidyverse/tidytemplate
 Config/testthat/edition: 3
 Encoding: UTF-8
 LazyData: true
+Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.2.3
 NeedsCompilation: no
-Packaged: 2023-01-27 14:11:11 UTC; hadleywickham
+Packaged: 2023-02-01 19:04:14 UTC; janitor
 Author: Hadley Wickham [aut, cre],
   RStudio [cph, fnd]
 Maintainer: Hadley Wickham <hadley@rstudio.com>
-Repository: CRAN
-Date/Publication: 2023-01-29 22:20:02 UTC
diff --git a/MD5 b/MD5
deleted file mode 100644
index d40944d..0000000
--- a/MD5
+++ /dev/null
@@ -1,125 +0,0 @@
-a4f2bfc739dd8db1f4ca5c7fda94fb5b *DESCRIPTION
-500bcbd26c47066e09614bf631a00918 *LICENSE
-d8643b38be14ae3050e4c946bcf48fd0 *NAMESPACE
-46e72ec4a8e25f9aadb701c78ed9802c *NEWS.md
-45bcddef681bf2f519865ab1db6d3603 *R/anon.R
-e12ba39cd7eff746d1c2dafffe6bcc76 *R/as_factor.R
-57138a5d89f0c3efc4b45a642fbeef7b *R/c.R
-7ce872c518a3ab355ec1933732dfdfb0 *R/collapse.R
-07c06e6be0443b7d5b9094f11daa406f *R/compat-obj-type.R
-0fa60b49bc17479ffacbdf7e1d738a31 *R/compat-purrr.R
-e39d35d817dec590fcb8b9a2715ba226 *R/compat-types-check.R
-7cd532c336e275a55c1363c1875cfe1c *R/count.R
-1ab292b28151c8eaf6a7230e3acf625e *R/cross.R
-9fa75f8a9b75947ae8c96b056f2aa14d *R/data.R
-e863fa1fb87c673cf59b74efe9f648d3 *R/drop.R
-67fefb42acff5d243ffc343159a6732f *R/expand.R
-f28c779b0d5425a71ebb352a0aa559a6 *R/explicit_na.R
-ea44ffa298e055dda7e3d08d2672e0ce *R/fct.R
-26855623d2ed5f6bb59935f6e0b4433e *R/forcats-package.R
-8e8af8e0dfa91679a26142054c49e374 *R/lump.R
-d56bc9d2a8dc0a40b2563e21e26a769e *R/lvls.R
-96614e2742061692d0f02dae5a98a4fa *R/match.R
-5572d511d7b5bb0a9cf55d5b3d9965d6 *R/na.R
-49d497d7e7e1a4c718adc3a28f315abb *R/other.R
-75559f555d9f11e3570338a1e92d074c *R/recode.R
-231a197dff1dd14402de380e8190a250 *R/relabel.R
-a6b48ecf867ff1761b504d6fb51e8dba *R/relevel.R
-8c4baa85cf5ada9e01926644143ef92b *R/reorder.R
-2513b5d4ddb3af53f57f9ef368200112 *R/rev.R
-d55288e95054eef5890808392e382ed7 *R/shift.R
-9aebfd5ecc427de639e076a022cb0a53 *R/shuffle.R
-4041fd2a6104c1a3de19103c8e31da13 *R/unique.R
-f1809804fc7f0a9c532172065c273635 *R/utils.R
-5dbb0234ec23547b315e778c3110bc46 *README.md
-f3b930e8d049201ca0dcb6acc97dc69b *build/vignette.rds
-a6984b4f6c2e70deebeca86c7b3d76ea *data/gss_cat.rda
-95f06af1f237da638a0b355e04eab4c7 *inst/doc/forcats.R
-46f3fdb2904c70a13a191d3a6c4286cb *inst/doc/forcats.Rmd
-de81b94175bc6a44fc6dc74869ac5922 *inst/doc/forcats.html
-b7ee1e2072b474ba8ea48f495361cc2f *man/as_factor.Rd
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-679f67140aa7a12ffa8e2644324a1231 *man/lvls.Rd
-50ed89e2798ad529ca69dad8d85851dd *man/lvls_union.Rd
-0f020b37daf27c2fd4c78c574285ef1b *man/pipe.Rd
-7b0f13301043822509977d02384cb6e9 *tests/testthat.R
-a9fa58319df7a1eb56ccc0035180403b *tests/testthat/_snaps/anon.md
-1f3c82f622263d1b7d194e57a0f633ca *tests/testthat/_snaps/c.md
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-2653e9a498d2a6797ac8422e90c910dd *tests/testthat/_snaps/shift.md
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-66091c3cb1f0f78860ad430b40b637ca *tests/testthat/test-anon.R
-488e881283f84aec6fb0acf6aa107c28 *tests/testthat/test-as_factor.R
-1b6a13e44c0137fb07f41919aaeda0f5 *tests/testthat/test-c.R
-b8a73a7395a395422776a00bed5d23c4 *tests/testthat/test-collapse.R
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-c9ccc31b9ce68abbf1c3cb47e1b6c0df *tests/testthat/test-explicit_na.R
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-f5ca3fea7e341b725200debb6fad4a7a *tests/testthat/test-lump.R
-d199afcedfd14af1eb67c7ffd0aa48fb *tests/testthat/test-lvls.R
-f1c61b38debb5db82e40cb1654c50709 *tests/testthat/test-match.R
-210ce289487d4a5d01b378b7597fa701 *tests/testthat/test-na.R
-f6a7caf23ea42136e7f039a4e0661533 *tests/testthat/test-other.R
-97cf819454725614cf97d0e6f23ac029 *tests/testthat/test-recode.R
-71237f456927f53d2aeb1ba3f03e52f0 *tests/testthat/test-relabel.R
-4c2c10190e8496a878055c547986cd36 *tests/testthat/test-relevel.R
-22a140307f414469f6672661d068ed55 *tests/testthat/test-reorder.R
-d9ff4cd69cfe07632abc902f83c86e65 *tests/testthat/test-rev.R
-f6174b12a8ed4bf8d1ea0df40158dd5a *tests/testthat/test-shift.R
-a79cc6f56ea23f5614ac7b02dbc982e5 *tests/testthat/test-shuffle.R
-8b6ae7bfc489dac59c69ca0012f1601c *tests/testthat/test-unique.R
-bbdb91afad85f5260e20a9da8256afac *tests/testthat/test-utils.R
-46f3fdb2904c70a13a191d3a6c4286cb *vignettes/forcats.Rmd
diff --git a/NEWS.md b/NEWS.md
index 2915494..b138b7a 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,5 @@
+# forcats (development version)
+
 # forcats 1.0.0
 
 ## New features
diff --git a/build/vignette.rds b/build/vignette.rds
index 84db1b6..43173dc 100644
Binary files a/build/vignette.rds and b/build/vignette.rds differ
diff --git a/debian/changelog b/debian/changelog
index 0caed07..97cd49e 100644
--- a/debian/changelog
+++ b/debian/changelog
@@ -1,3 +1,9 @@
+r-cran-forcats (1.0.0+git20230130.1.4a8525a-1) UNRELEASED; urgency=low
+
+  * New upstream snapshot.
+
+ -- Debian Janitor <janitor@jelmer.uk>  Wed, 01 Feb 2023 19:04:35 -0000
+
 r-cran-forcats (1.0.0-1) unstable; urgency=medium
 
   * New upstream release
diff --git a/inst/doc/forcats.html b/inst/doc/forcats.html
index 47839dd..184da05 100644
--- a/inst/doc/forcats.html
+++ b/inst/doc/forcats.html
@@ -30,23 +30,23 @@ document.addEventListener('DOMContentLoaded', function(e) {
 </script>
 
 <style type="text/css">
-code{white-space: pre-wrap;}
-span.smallcaps{font-variant: small-caps;}
-span.underline{text-decoration: underline;}
-div.column{display: inline-block; vertical-align: top; width: 50%;}
-div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
-ul.task-list{list-style: none;}
-</style>
+  code{white-space: pre-wrap;}
+  span.smallcaps{font-variant: small-caps;}
+  span.underline{text-decoration: underline;}
+  div.column{display: inline-block; vertical-align: top; width: 50%;}
+  div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
+  ul.task-list{list-style: none;}
+    </style>
 
 
 
 <style type="text/css">
-code {
-white-space: pre;
-}
-.sourceCode {
-overflow: visible;
-}
+  code {
+    white-space: pre;
+  }
+  .sourceCode {
+    overflow: visible;
+  }
 </style>
 <style type="text/css" data-origin="pandoc">
 pre > code.sourceCode { white-space: pre; position: relative; }
@@ -64,54 +64,55 @@ pre > code.sourceCode { white-space: pre-wrap; }
 pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
 }
 pre.numberSource code
-{ counter-reset: source-line 0; }
+  { counter-reset: source-line 0; }
 pre.numberSource code > span
-{ position: relative; left: -4em; counter-increment: source-line; }
+  { position: relative; left: -4em; counter-increment: source-line; }
 pre.numberSource code > span > a:first-child::before
-{ content: counter(source-line);
-position: relative; left: -1em; text-align: right; vertical-align: baseline;
-border: none; display: inline-block;
--webkit-touch-callout: none; -webkit-user-select: none;
--khtml-user-select: none; -moz-user-select: none;
--ms-user-select: none; user-select: none;
-padding: 0 4px; width: 4em;
-color: #aaaaaa;
-}
-pre.numberSource { margin-left: 3em; border-left: 1px solid #aaaaaa; padding-left: 4px; }
+  { content: counter(source-line);
+    position: relative; left: -1em; text-align: right; vertical-align: baseline;
+    border: none; display: inline-block;
+    -webkit-touch-callout: none; -webkit-user-select: none;
+    -khtml-user-select: none; -moz-user-select: none;
+    -ms-user-select: none; user-select: none;
+    padding: 0 4px; width: 4em;
+    color: #aaaaaa;
+  }
+pre.numberSource { margin-left: 3em; border-left: 1px solid #aaaaaa;  padding-left: 4px; }
 div.sourceCode
-{ }
+  {   }
 @media screen {
 pre > code.sourceCode > span > a:first-child::before { text-decoration: underline; }
 }
-code span.al { color: #ff0000; font-weight: bold; } 
-code span.an { color: #60a0b0; font-weight: bold; font-style: italic; } 
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-code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } 
+code span.al { color: #ff0000; font-weight: bold; } /* Alert */
+code span.an { color: #60a0b0; font-weight: bold; font-style: italic; } /* Annotation */
+code span.at { color: #7d9029; } /* Attribute */
+code span.bn { color: #40a070; } /* BaseN */
+code span.bu { color: #008000; } /* BuiltIn */
+code span.cf { color: #007020; font-weight: bold; } /* ControlFlow */
+code span.ch { color: #4070a0; } /* Char */
+code span.cn { color: #880000; } /* Constant */
+code span.co { color: #60a0b0; font-style: italic; } /* Comment */
+code span.cv { color: #60a0b0; font-weight: bold; font-style: italic; } /* CommentVar */
+code span.do { color: #ba2121; font-style: italic; } /* Documentation */
+code span.dt { color: #902000; } /* DataType */
+code span.dv { color: #40a070; } /* DecVal */
+code span.er { color: #ff0000; font-weight: bold; } /* Error */
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+code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } /* Warning */
+
 </style>
 <script>
 // apply pandoc div.sourceCode style to pre.sourceCode instead
@@ -359,14 +360,14 @@ colors of star wars characters?” Let’s start off by making a bar
 plot:</p>
 <div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(starwars, <span class="fu">aes</span>(<span class="at">y =</span> hair_color)) <span class="sc">+</span> </span>
 <span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_bar</span>()</span></code></pre></div>
-<p><img 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" alt="A bar chart of hair color of starwars characters. The bars are alphabetically ordered, making it hard to see general patterns." /></p>
+<p><img src="data:image/png;base64,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" alt="A bar chart of hair color of starwars characters. The bars are alphabetically ordered, making it hard to see general patterns." /></p>
 <p>That’s okay, but it would be more helpful the graph was ordered by
 count. This is a case of an <strong>unordered</strong> categorical
 variable where we want it ordered by its frequency. To do so, we can use
 the function <code>fct_infreq()</code>:</p>
 <div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(starwars, <span class="fu">aes</span>(<span class="at">y =</span> <span class="fu">fct_infreq</span>(hair_color))) <span class="sc">+</span> </span>
 <span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_bar</span>()</span></code></pre></div>
-<p><img 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" alt="The bar chart of hair color, now ordered so that the least frequent colours come first and the most frequent colors come last. This makes it easy to see that the most common hair color is none (~35), followed by brown (~18), then black (~12). Surprisingly, NAs are at the top of the graph, even though there are ~5 NAs and other colors have smaller values." /></p>
+<p><img src="data:image/png;base64,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" alt="The bar chart of hair color, now ordered so that the least frequent colours come first and the most frequent colors come last. This makes it easy to see that the most common hair color is none (~35), followed by brown (~18), then black (~12). Surprisingly, NAs are at the top of the graph, even though there are ~5 NAs and other colors have smaller values." /></p>
 <p>Note that <code>fct_infreq()</code> it automatically puts NA at the
 top, even though that doesn’t have the smallest number of entries.</p>
 <p>It’s a little surprising that the <code>NA</code> bar isn’t ordered
@@ -408,7 +409,7 @@ expect:</p>
 <div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(starwars, <span class="fu">aes</span>(<span class="at">y =</span> <span class="fu">fct_infreq</span>(<span class="fu">fct_na_value_to_level</span>(hair_color)))) <span class="sc">+</span> </span>
 <span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_bar</span>() <span class="sc">+</span> </span>
 <span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">y =</span> <span class="st">&quot;Hair color&quot;</span>)</span></code></pre></div>
-<p><img 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" alt="The bar chart of hair color, now ordered so that NAs are ordered where you&#39;d expect: in between white (4) and black (12)." /></p>
+<p><img src="data:image/png;base64,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" alt="The bar chart of hair color, now ordered so that NAs are ordered where you&#39;d expect: in between white (4) and black (12)." /></p>
 <p>(If you need the opposite operation, you can use
 <code>fct_na_level_to_value()</code>.)</p>
 </div>
@@ -417,7 +418,7 @@ expect:</p>
 <p>Let’s take a look at skin color now:</p>
 <div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>starwars <span class="sc">%&gt;%</span></span>
 <span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">count</span>(skin_color, <span class="at">sort =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; # A tibble: 31 × 2</span></span>
+<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; # A tibble: 31 x 2</span></span>
 <span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;    skin_color     n</span></span>
 <span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;    &lt;chr&gt;      &lt;int&gt;</span></span>
 <span id="cb7-6"><a href="#cb7-6" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;  1 fair          17</span></span>
@@ -430,7 +431,7 @@ expect:</p>
 <span id="cb7-13"><a href="#cb7-13" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;  8 blue           2</span></span>
 <span id="cb7-14"><a href="#cb7-14" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;  9 blue, grey     2</span></span>
 <span id="cb7-15"><a href="#cb7-15" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 10 orange         2</span></span>
-<span id="cb7-16"><a href="#cb7-16" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; # … with 21 more rows</span></span></code></pre></div>
+<span id="cb7-16"><a href="#cb7-16" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; # ... with 21 more rows</span></span></code></pre></div>
 <p>We see that there’s 31 different skin colors - if we want to make a
 plot this would be way too many to display! Let’s reduce it to only be
 the top 5. We can use <code>fct_lump()</code> to “lump” all the
@@ -439,7 +440,7 @@ is the number of levels we want to keep.</p>
 <div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>starwars <span class="sc">%&gt;%</span></span>
 <span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">skin_color =</span> <span class="fu">fct_lump</span>(skin_color, <span class="at">n =</span> <span class="dv">5</span>)) <span class="sc">%&gt;%</span></span>
 <span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">count</span>(skin_color, <span class="at">sort =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; # A tibble: 6 × 2</span></span>
+<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; # A tibble: 6 x 2</span></span>
 <span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;   skin_color     n</span></span>
 <span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;   &lt;fct&gt;      &lt;int&gt;</span></span>
 <span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 1 Other         41</span></span>
@@ -455,7 +456,7 @@ have:</p>
 <div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>starwars <span class="sc">%&gt;%</span></span>
 <span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">skin_color =</span> <span class="fu">fct_lump</span>(skin_color, <span class="at">prop =</span> .<span class="dv">1</span>)) <span class="sc">%&gt;%</span></span>
 <span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">count</span>(skin_color, <span class="at">sort =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; # A tibble: 3 × 2</span></span>
+<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; # A tibble: 3 x 2</span></span>
 <span id="cb9-5"><a href="#cb9-5" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;   skin_color     n</span></span>
 <span id="cb9-6"><a href="#cb9-6" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;   &lt;fct&gt;      &lt;int&gt;</span></span>
 <span id="cb9-7"><a href="#cb9-7" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 1 Other         59</span></span>
@@ -467,7 +468,7 @@ with the argument <code>other_level</code>:</p>
 <div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>starwars <span class="sc">%&gt;%</span></span>
 <span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">skin_color =</span> <span class="fu">fct_lump</span>(skin_color, <span class="at">prop =</span> .<span class="dv">1</span>, <span class="at">other_level =</span> <span class="st">&quot;extra&quot;</span>)) <span class="sc">%&gt;%</span></span>
 <span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">count</span>(skin_color, <span class="at">sort =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; # A tibble: 3 × 2</span></span>
+<span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; # A tibble: 3 x 2</span></span>
 <span id="cb10-5"><a href="#cb10-5" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;   skin_color     n</span></span>
 <span id="cb10-6"><a href="#cb10-6" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;   &lt;fct&gt;      &lt;int&gt;</span></span>
 <span id="cb10-7"><a href="#cb10-7" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 1 extra         59</span></span>
@@ -482,7 +483,7 @@ We’ll only look at the 6 most popular eye colors and remove
 <span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">mean_mass =</span> <span class="fu">mean</span>(mass, <span class="at">na.rm =</span> <span class="cn">TRUE</span>))</span>
 <span id="cb11-5"><a href="#cb11-5" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb11-6"><a href="#cb11-6" aria-hidden="true" tabindex="-1"></a>avg_mass_eye_color</span>
-<span id="cb11-7"><a href="#cb11-7" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; # A tibble: 7 × 2</span></span>
+<span id="cb11-7"><a href="#cb11-7" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; # A tibble: 7 x 2</span></span>
 <span id="cb11-8"><a href="#cb11-8" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;   eye_color mean_mass</span></span>
 <span id="cb11-9"><a href="#cb11-9" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;   &lt;fct&gt;         &lt;dbl&gt;</span></span>
 <span id="cb11-10"><a href="#cb11-10" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 1 black          76.3</span></span>
@@ -503,7 +504,7 @@ was ordered by <code>mean_mass</code>. We can do this with
 <span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">eye_color =</span> <span class="fu">fct_reorder</span>(eye_color, mean_mass)) <span class="sc">%&gt;%</span></span>
 <span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>(<span class="fu">aes</span>(<span class="at">x =</span> eye_color, <span class="at">y =</span> mean_mass)) <span class="sc">+</span> </span>
 <span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>()</span></code></pre></div>
-<p><img 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" alt="A column chart with eye color on the x-axis and mean mass on the y-axis. The bars are ordered by mean_mass, so that the tallest bar (orange eye color with mean mass of ~275) is at the far right." /></p>
+<p><img src="data:image/png;base64,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" alt="A column chart with eye color on the x-axis and mean mass on the y-axis. The bars are ordered by mean_mass, so that the tallest bar (orange eye color with mean mass of ~275) is at the far right." /></p>
 </div>
 <div id="manually-reordering" class="section level2">
 <h2>Manually reordering</h2>
@@ -512,7 +513,7 @@ general social survey. What is the income distribution among the
 respondents?</p>
 <div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a>gss_cat <span class="sc">%&gt;%</span></span>
 <span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">count</span>(rincome)</span>
-<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; # A tibble: 16 × 2</span></span>
+<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; # A tibble: 16 x 2</span></span>
 <span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;    rincome            n</span></span>
 <span id="cb13-5"><a href="#cb13-5" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;    &lt;fct&gt;          &lt;int&gt;</span></span>
 <span id="cb13-6"><a href="#cb13-6" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;  1 No answer        183</span></span>
@@ -549,10 +550,10 @@ by reordering the levels of <code>rincome</code> randomly with
 <span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">fct_shuffle</span>()</span>
 <span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a><span class="fu">levels</span>(reshuffled_income)</span>
-<span id="cb15-5"><a href="#cb15-5" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;  [1] &quot;Refused&quot;        &quot;$1000 to 2999&quot;  &quot;$7000 to 7999&quot;  &quot;$10000 - 14999&quot;</span></span>
-<span id="cb15-6"><a href="#cb15-6" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;  [5] &quot;$25000 or more&quot; &quot;$15000 - 19999&quot; &quot;Lt $1000&quot;       &quot;$3000 to 3999&quot; </span></span>
-<span id="cb15-7"><a href="#cb15-7" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;  [9] &quot;$6000 to 6999&quot;  &quot;$5000 to 5999&quot;  &quot;$8000 to 9999&quot;  &quot;No answer&quot;     </span></span>
-<span id="cb15-8"><a href="#cb15-8" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; [13] &quot;Not applicable&quot; &quot;$20000 - 24999&quot; &quot;Don&#39;t know&quot;     &quot;$4000 to 4999&quot;</span></span></code></pre></div>
+<span id="cb15-5"><a href="#cb15-5" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;  [1] &quot;$4000 to 4999&quot;  &quot;$1000 to 2999&quot;  &quot;Lt $1000&quot;       &quot;$10000 - 14999&quot;</span></span>
+<span id="cb15-6"><a href="#cb15-6" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;  [5] &quot;Don&#39;t know&quot;     &quot;$6000 to 6999&quot;  &quot;Refused&quot;        &quot;No answer&quot;     </span></span>
+<span id="cb15-7"><a href="#cb15-7" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;  [9] &quot;$25000 or more&quot; &quot;Not applicable&quot; &quot;$20000 - 24999&quot; &quot;$3000 to 3999&quot; </span></span>
+<span id="cb15-8"><a href="#cb15-8" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; [13] &quot;$5000 to 5999&quot;  &quot;$8000 to 9999&quot;  &quot;$15000 - 19999&quot; &quot;$7000 to 7999&quot;</span></span></code></pre></div>
 <p>Now if we plotted it, it would show in this order, which is all over
 the place! How can we fix this and put it in the right order?</p>
 <p>We can use the function <code>fct_relevel()</code> when we need to
@@ -566,17 +567,17 @@ move it to the end, you set <code>after</code> equal to
 <code>$1000 to 2999</code> to the front. We would write:</p>
 <div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="fu">fct_relevel</span>(reshuffled_income, <span class="fu">c</span>(<span class="st">&quot;Lt $1000&quot;</span>, <span class="st">&quot;$1000 to 2999&quot;</span>)) <span class="sc">%&gt;%</span></span>
 <span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">levels</span>()</span>
-<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;  [1] &quot;Lt $1000&quot;       &quot;$1000 to 2999&quot;  &quot;Refused&quot;        &quot;$7000 to 7999&quot; </span></span>
-<span id="cb16-4"><a href="#cb16-4" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;  [5] &quot;$10000 - 14999&quot; &quot;$25000 or more&quot; &quot;$15000 - 19999&quot; &quot;$3000 to 3999&quot; </span></span>
-<span id="cb16-5"><a href="#cb16-5" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;  [9] &quot;$6000 to 6999&quot;  &quot;$5000 to 5999&quot;  &quot;$8000 to 9999&quot;  &quot;No answer&quot;     </span></span>
-<span id="cb16-6"><a href="#cb16-6" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; [13] &quot;Not applicable&quot; &quot;$20000 - 24999&quot; &quot;Don&#39;t know&quot;     &quot;$4000 to 4999&quot;</span></span></code></pre></div>
+<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;  [1] &quot;Lt $1000&quot;       &quot;$1000 to 2999&quot;  &quot;$4000 to 4999&quot;  &quot;$10000 - 14999&quot;</span></span>
+<span id="cb16-4"><a href="#cb16-4" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;  [5] &quot;Don&#39;t know&quot;     &quot;$6000 to 6999&quot;  &quot;Refused&quot;        &quot;No answer&quot;     </span></span>
+<span id="cb16-5"><a href="#cb16-5" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;  [9] &quot;$25000 or more&quot; &quot;Not applicable&quot; &quot;$20000 - 24999&quot; &quot;$3000 to 3999&quot; </span></span>
+<span id="cb16-6"><a href="#cb16-6" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; [13] &quot;$5000 to 5999&quot;  &quot;$8000 to 9999&quot;  &quot;$15000 - 19999&quot; &quot;$7000 to 7999&quot;</span></span></code></pre></div>
 <p>What if we want to move them to the second and third place?</p>
 <div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="fu">fct_relevel</span>(reshuffled_income, <span class="fu">c</span>(<span class="st">&quot;Lt $1000&quot;</span>, <span class="st">&quot;$1000 to 2999&quot;</span>), <span class="at">after =</span> <span class="dv">1</span>) <span class="sc">%&gt;%</span></span>
 <span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">levels</span>()</span>
-<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;  [1] &quot;Refused&quot;        &quot;Lt $1000&quot;       &quot;$1000 to 2999&quot;  &quot;$7000 to 7999&quot; </span></span>
-<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;  [5] &quot;$10000 - 14999&quot; &quot;$25000 or more&quot; &quot;$15000 - 19999&quot; &quot;$3000 to 3999&quot; </span></span>
-<span id="cb17-5"><a href="#cb17-5" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;  [9] &quot;$6000 to 6999&quot;  &quot;$5000 to 5999&quot;  &quot;$8000 to 9999&quot;  &quot;No answer&quot;     </span></span>
-<span id="cb17-6"><a href="#cb17-6" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; [13] &quot;Not applicable&quot; &quot;$20000 - 24999&quot; &quot;Don&#39;t know&quot;     &quot;$4000 to 4999&quot;</span></span></code></pre></div>
+<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;  [1] &quot;$4000 to 4999&quot;  &quot;Lt $1000&quot;       &quot;$1000 to 2999&quot;  &quot;$10000 - 14999&quot;</span></span>
+<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;  [5] &quot;Don&#39;t know&quot;     &quot;$6000 to 6999&quot;  &quot;Refused&quot;        &quot;No answer&quot;     </span></span>
+<span id="cb17-5"><a href="#cb17-5" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;  [9] &quot;$25000 or more&quot; &quot;Not applicable&quot; &quot;$20000 - 24999&quot; &quot;$3000 to 3999&quot; </span></span>
+<span id="cb17-6"><a href="#cb17-6" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; [13] &quot;$5000 to 5999&quot;  &quot;$8000 to 9999&quot;  &quot;$15000 - 19999&quot; &quot;$7000 to 7999&quot;</span></span></code></pre></div>
 </div>
 
 

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