diff --git a/404.html b/404.html
index 414a5e3..5552930 100644
--- a/404.html
+++ b/404.html
@@ -28,7 +28,7 @@
 
     <a class="navbar-brand me-2" href="http://kylehusmann.com/interlacer/index.html">interlacer</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.1</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/LICENSE.html b/LICENSE.html
index 2b6ae1a..67e8c28 100644
--- a/LICENSE.html
+++ b/LICENSE.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">interlacer</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.1</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/coded-data.html b/articles/coded-data.html
index 4bb395e..29d6eb3 100644
--- a/articles/coded-data.html
+++ b/articles/coded-data.html
@@ -27,7 +27,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">interlacer</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.1</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/extended-column-types.html b/articles/extended-column-types.html
index e59f767..b3c4da3 100644
--- a/articles/extended-column-types.html
+++ b/articles/extended-column-types.html
@@ -27,7 +27,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">interlacer</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.1</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/index.html b/articles/index.html
index cae6446..4615b0e 100644
--- a/articles/index.html
+++ b/articles/index.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">interlacer</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.1</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/interlacer.html b/articles/interlacer.html
index 4111671..772f49d 100644
--- a/articles/interlacer.html
+++ b/articles/interlacer.html
@@ -27,7 +27,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">interlacer</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.1</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/other-approaches.html b/articles/other-approaches.html
index 27f2fa4..3de0fe9 100644
--- a/articles/other-approaches.html
+++ b/articles/other-approaches.html
@@ -27,7 +27,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">interlacer</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.1</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/authors.html b/authors.html
index 332154c..f6265fa 100644
--- a/authors.html
+++ b/authors.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">interlacer</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.1</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -51,13 +51,13 @@ <h2 id="citation">Citation</h2>
 
       <p>Husmann K (2024).
 <em>interlacer: Read Tabular Data With Interlaced Values And Missing Reasons</em>.
-R package version 0.4.0, https://kylehusmann.com/interlacer, <a href="https://github.com/khusmann/interlacer" class="external-link">https://github.com/khusmann/interlacer</a>.
+R package version 0.4.1, https://kylehusmann.com/interlacer, <a href="https://github.com/khusmann/interlacer" class="external-link">https://github.com/khusmann/interlacer</a>.
 </p>
       <pre>@Manual{,
   title = {interlacer: Read Tabular Data With Interlaced Values And Missing Reasons},
   author = {Kyle Husmann},
   year = {2024},
-  note = {R package version 0.4.0, https://kylehusmann.com/interlacer},
+  note = {R package version 0.4.1, https://kylehusmann.com/interlacer},
   url = {https://github.com/khusmann/interlacer},
 }</pre>
     </div>
diff --git a/index.html b/index.html
index 102fe1b..b204290 100644
--- a/index.html
+++ b/index.html
@@ -17,8 +17,8 @@
 <script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet">
 <link href="deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet">
 <script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Read Tabular Data With Interlaced Values And Missing Reasons">
-<meta name="description" content="Textual tabular data sources often encode values and missing reasons into the same texkual column. This packages loads such data and deinterlaces the tabular source into a dataframe with two columns per variable: one for values, and one for missing reasons. This representation provides a tidy and typesafe interface for working with values and missing reasons in R.">
-<meta property="og:description" content="Textual tabular data sources often encode values and missing reasons into the same texkual column. This packages loads such data and deinterlaces the tabular source into a dataframe with two columns per variable: one for values, and one for missing reasons. This representation provides a tidy and typesafe interface for working with values and missing reasons in R.">
+<meta name="description" content="interlacer provides functions for handling tabular data with interlaced missing reasons (e.g., N/A, REFUSED, -99). It introduces a special &quot;interlaced&quot; vector type that enables you to keep the values and missing reasons as separate &quot;channels&quot; of the same column. This allows you to easily work with a variable's values while still preserving its missing reasons for filtering or summary statistics.">
+<meta property="og:description" content="interlacer provides functions for handling tabular data with interlaced missing reasons (e.g., N/A, REFUSED, -99). It introduces a special &quot;interlaced&quot; vector type that enables you to keep the values and missing reasons as separate &quot;channels&quot; of the same column. This allows you to easily work with a variable's values while still preserving its missing reasons for filtering or summary statistics.">
 <meta property="og:image" content="http://kylehusmann.com/interlacer/logo.png">
 <script data-goatcounter="https://interlacer.goatcounter.com/count" async src="//gc.zgo.at/count.js"></script>
 </head>
@@ -30,7 +30,7 @@
 
     <a class="navbar-brand me-2" href="index.html">interlacer</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.1</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/pkgdown.yml b/pkgdown.yml
index a951c84..adf72c6 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -6,7 +6,7 @@ articles:
   extended-column-types: extended-column-types.html
   interlacer: interlacer.html
   other-approaches: other-approaches.html
-last_built: 2024-10-12T00:52Z
+last_built: 2024-10-12T01:41Z
 urls:
   reference: http://kylehusmann.com/interlacer/reference
   article: http://kylehusmann.com/interlacer/articles
diff --git a/reference/across_value_channels.html b/reference/across_value_channels.html
index fbd37cc..09e9846 100644
--- a/reference/across_value_channels.html
+++ b/reference/across_value_channels.html
@@ -11,7 +11,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">interlacer</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.1</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/as.cfactor.html b/reference/as.cfactor.html
index a510635..e773e79 100644
--- a/reference/as.cfactor.html
+++ b/reference/as.cfactor.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">interlacer</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.1</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/as.codes.html b/reference/as.codes.html
index 0beb958..667d174 100644
--- a/reference/as.codes.html
+++ b/reference/as.codes.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">interlacer</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.1</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/as.x_col_spec.html b/reference/as.x_col_spec.html
index 2d0e984..4754757 100644
--- a/reference/as.x_col_spec.html
+++ b/reference/as.x_col_spec.html
@@ -11,7 +11,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">interlacer</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.1</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/as.x_collector.html b/reference/as.x_collector.html
index 094a82a..76622a5 100644
--- a/reference/as.x_collector.html
+++ b/reference/as.x_collector.html
@@ -11,7 +11,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">interlacer</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.1</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/cfactor.html b/reference/cfactor.html
index 4b5f318..e25a285 100644
--- a/reference/cfactor.html
+++ b/reference/cfactor.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">interlacer</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.1</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/codes-set.html b/reference/codes-set.html
index a630974..0db06f5 100644
--- a/reference/codes-set.html
+++ b/reference/codes-set.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">interlacer</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.1</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/codes.html b/reference/codes.html
index eae129a..c92202e 100644
--- a/reference/codes.html
+++ b/reference/codes.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">interlacer</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.1</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/flatten_channels.html b/reference/flatten_channels.html
index 3b0d643..abf521b 100644
--- a/reference/flatten_channels.html
+++ b/reference/flatten_channels.html
@@ -11,7 +11,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">interlacer</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.1</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/grapes-equals-grapes.html b/reference/grapes-equals-grapes.html
index 690de0b..05d1b61 100644
--- a/reference/grapes-equals-grapes.html
+++ b/reference/grapes-equals-grapes.html
@@ -25,7 +25,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">interlacer</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.1</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/index.html b/reference/index.html
index 7cad4ec..5ef044f 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">interlacer</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.1</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/interlaced.html b/reference/interlaced.html
index 7e08d45..c575ca2 100644
--- a/reference/interlaced.html
+++ b/reference/interlaced.html
@@ -13,7 +13,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">interlacer</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.1</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/interlacer_example.html b/reference/interlacer_example.html
index d461413..ec5a8f4 100644
--- a/reference/interlacer_example.html
+++ b/reference/interlacer_example.html
@@ -9,7 +9,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">interlacer</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.1</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/levels-set-.interlacer_interlaced.html b/reference/levels-set-.interlacer_interlaced.html
index 1c9e5fd..0d41926 100644
--- a/reference/levels-set-.interlacer_interlaced.html
+++ b/reference/levels-set-.interlacer_interlaced.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">interlacer</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.1</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/levels.interlacer_interlaced.html b/reference/levels.interlacer_interlaced.html
index f6b9eb2..3e347d6 100644
--- a/reference/levels.interlacer_interlaced.html
+++ b/reference/levels.interlacer_interlaced.html
@@ -13,7 +13,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">interlacer</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.1</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/map_value_channel.html b/reference/map_value_channel.html
index e8b86e1..4806833 100644
--- a/reference/map_value_channel.html
+++ b/reference/map_value_channel.html
@@ -11,7 +11,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">interlacer</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.1</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/na.html b/reference/na.html
index e4eb4a2..951f03a 100644
--- a/reference/na.html
+++ b/reference/na.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">interlacer</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.1</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/na_collectors.html b/reference/na_collectors.html
index e76163f..e90a8fb 100644
--- a/reference/na_collectors.html
+++ b/reference/na_collectors.html
@@ -9,7 +9,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">interlacer</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.1</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/parse_interlaced.html b/reference/parse_interlaced.html
index 4738dc7..86bf702 100644
--- a/reference/parse_interlaced.html
+++ b/reference/parse_interlaced.html
@@ -9,7 +9,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">interlacer</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.1</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/read_interlaced_delim.html b/reference/read_interlaced_delim.html
index 0f9217b..ce92351 100644
--- a/reference/read_interlaced_delim.html
+++ b/reference/read_interlaced_delim.html
@@ -13,7 +13,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">interlacer</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.1</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/reexports.html b/reference/reexports.html
index 6562501..054fcf6 100644
--- a/reference/reexports.html
+++ b/reference/reexports.html
@@ -29,7 +29,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">interlacer</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.1</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/value_channel.html b/reference/value_channel.html
index 6a3dce4..c1a3d00 100644
--- a/reference/value_channel.html
+++ b/reference/value_channel.html
@@ -17,7 +17,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">interlacer</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.1</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/value_collectors.html b/reference/value_collectors.html
index eae6c57..3dfa1ad 100644
--- a/reference/value_collectors.html
+++ b/reference/value_collectors.html
@@ -11,7 +11,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">interlacer</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.1</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/where_value_channel.html b/reference/where_value_channel.html
index dd3d984..3a3c97e 100644
--- a/reference/where_value_channel.html
+++ b/reference/where_value_channel.html
@@ -11,7 +11,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">interlacer</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.1</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/write_interlaced_delim.html b/reference/write_interlaced_delim.html
index d92ae76..d247bc5 100644
--- a/reference/write_interlaced_delim.html
+++ b/reference/write_interlaced_delim.html
@@ -13,7 +13,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">interlacer</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.1</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/x_col.html b/reference/x_col.html
index 436588b..9c2eef0 100644
--- a/reference/x_col.html
+++ b/reference/x_col.html
@@ -11,7 +11,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">interlacer</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.1</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/x_cols.html b/reference/x_cols.html
index 68a352c..be180a8 100644
--- a/reference/x_cols.html
+++ b/reference/x_cols.html
@@ -11,7 +11,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">interlacer</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.1</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/x_spec.html b/reference/x_spec.html
index 041856b..9b9ee0d 100644
--- a/reference/x_spec.html
+++ b/reference/x_spec.html
@@ -9,7 +9,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">interlacer</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.4.1</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/search.json b/search.json
index 010953c..1942f59 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"http://kylehusmann.com/interlacer/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"Apache License","title":"Apache License","text":"Version 2.0, January 2004 <http://www.apache.org/licenses/>","code":""},{"path":[]},{"path":"http://kylehusmann.com/interlacer/LICENSE.html","id":"id_1-definitions","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"1. Definitions","title":"Apache License","text":"“License” shall mean terms conditions use, reproduction, distribution defined Sections 1 9 document. “Licensor” shall mean copyright owner entity authorized copyright owner granting License. “Legal Entity” shall mean union acting entity entities control, controlled , common control entity. purposes definition, “control” means () power, direct indirect, cause direction management entity, whether contract otherwise, (ii) ownership fifty percent (50%) outstanding shares, (iii) beneficial ownership entity. “” (“”) shall mean individual Legal Entity exercising permissions granted License. “Source” form shall mean preferred form making modifications, including limited software source code, documentation source, configuration files. “Object” form shall mean form resulting mechanical transformation translation Source form, including limited compiled object code, generated documentation, conversions media types. “Work” shall mean work authorship, whether Source Object form, made available License, indicated copyright notice included attached work (example provided Appendix ). “Derivative Works” shall mean work, whether Source Object form, based (derived ) Work editorial revisions, annotations, elaborations, modifications represent, whole, original work authorship. purposes License, Derivative Works shall include works remain separable , merely link (bind name) interfaces , Work Derivative Works thereof. “Contribution” shall mean work authorship, including original version Work modifications additions Work Derivative Works thereof, intentionally submitted Licensor inclusion Work copyright owner individual Legal Entity authorized submit behalf copyright owner. purposes definition, “submitted” means form electronic, verbal, written communication sent Licensor representatives, including limited communication electronic mailing lists, source code control systems, issue tracking systems managed , behalf , Licensor purpose discussing improving Work, excluding communication conspicuously marked otherwise designated writing copyright owner “Contribution.” “Contributor” shall mean Licensor individual Legal Entity behalf Contribution received Licensor subsequently incorporated within Work.","code":""},{"path":"http://kylehusmann.com/interlacer/LICENSE.html","id":"id_2-grant-of-copyright-license","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"2. 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Redistribution","title":"Apache License","text":"may reproduce distribute copies Work Derivative Works thereof medium, without modifications, Source Object form, provided meet following conditions: () must give recipients Work Derivative Works copy License; (b) must cause modified files carry prominent notices stating changed files; (c) must retain, Source form Derivative Works distribute, copyright, patent, trademark, attribution notices Source form Work, excluding notices pertain part Derivative Works; (d) Work includes “NOTICE” text file part distribution, Derivative Works distribute must include readable copy attribution notices contained within NOTICE file, excluding notices pertain part Derivative Works, least one following places: within NOTICE text file distributed part Derivative Works; within Source form documentation, provided along Derivative Works; , within display generated Derivative Works, wherever third-party notices normally appear. contents NOTICE file informational purposes modify License. may add attribution notices within Derivative Works distribute, alongside addendum NOTICE text Work, provided additional attribution notices construed modifying License. may add copyright statement modifications may provide additional different license terms conditions use, reproduction, distribution modifications, Derivative Works whole, provided use, reproduction, distribution Work otherwise complies conditions stated License.","code":""},{"path":"http://kylehusmann.com/interlacer/LICENSE.html","id":"id_5-submission-of-contributions","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"5. Submission of Contributions","title":"Apache License","text":"Unless explicitly state otherwise, Contribution intentionally submitted inclusion Work Licensor shall terms conditions License, without additional terms conditions. Notwithstanding , nothing herein shall supersede modify terms separate license agreement may executed Licensor regarding Contributions.","code":""},{"path":"http://kylehusmann.com/interlacer/LICENSE.html","id":"id_6-trademarks","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"6. Trademarks","title":"Apache License","text":"License grant permission use trade names, trademarks, service marks, product names Licensor, except required reasonable customary use describing origin Work reproducing content NOTICE file.","code":""},{"path":"http://kylehusmann.com/interlacer/LICENSE.html","id":"id_7-disclaimer-of-warranty","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"7. Disclaimer of Warranty","title":"Apache License","text":"Unless required applicable law agreed writing, Licensor provides Work (Contributor provides Contributions) “” BASIS, WITHOUT WARRANTIES CONDITIONS KIND, either express implied, including, without limitation, warranties conditions TITLE, NON-INFRINGEMENT, MERCHANTABILITY, FITNESS PARTICULAR PURPOSE. solely responsible determining appropriateness using redistributing Work assume risks associated exercise permissions License.","code":""},{"path":"http://kylehusmann.com/interlacer/LICENSE.html","id":"id_8-limitation-of-liability","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"8. Limitation of Liability","title":"Apache License","text":"event legal theory, whether tort (including negligence), contract, otherwise, unless required applicable law (deliberate grossly negligent acts) agreed writing, shall Contributor liable damages, including direct, indirect, special, incidental, consequential damages character arising result License use inability use Work (including limited damages loss goodwill, work stoppage, computer failure malfunction, commercial damages losses), even Contributor advised possibility damages.","code":""},{"path":"http://kylehusmann.com/interlacer/LICENSE.html","id":"id_9-accepting-warranty-or-additional-liability","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"9. Accepting Warranty or Additional Liability","title":"Apache License","text":"redistributing Work Derivative Works thereof, may choose offer, charge fee , acceptance support, warranty, indemnity, liability obligations /rights consistent License. However, accepting obligations, may act behalf sole responsibility, behalf Contributor, agree indemnify, defend, hold Contributor harmless liability incurred , claims asserted , Contributor reason accepting warranty additional liability. END TERMS CONDITIONS","code":""},{"path":"http://kylehusmann.com/interlacer/LICENSE.html","id":"appendix-how-to-apply-the-apache-license-to-your-work","dir":"","previous_headings":"","what":"APPENDIX: How to apply the Apache License to your work","title":"Apache License","text":"apply Apache License work, attach following boilerplate notice, fields enclosed brackets [] replaced identifying information. (Don’t include brackets!) text enclosed appropriate comment syntax file format. also recommend file class name description purpose included “printed page” copyright notice easier identification within third-party archives.","code":"Copyright [yyyy] [name of copyright owner]  Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at    http://www.apache.org/licenses/LICENSE-2.0  Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."},{"path":"http://kylehusmann.com/interlacer/articles/coded-data.html","id":"warning","dir":"Articles","previous_headings":"","what":"⚠️ ⚠️ ⚠️ WARNING ⚠️ ⚠️ ⚠️","title":"Coded Data","text":"cfactor type highly experimental feature (even compared rest interlacer) thoroughly tested! ’m sharing super pre-alpha, unstable state get feedback invest time polishing implementation.","code":""},{"path":"http://kylehusmann.com/interlacer/articles/coded-data.html","id":"spss-style-codes","dir":"Articles","previous_headings":"","what":"SPSS-style codes","title":"Coded Data","text":"motivating example, consider coded version colors.csv example: missing reasons : -99: N/-98: REFUSED -97: OMITTED colors coded: 1: BLUE 2: RED 3: YELLOW style coding, positive values representing categorical levels negative values representing missing values, common format used SPSS. data can loaded interlaced numeric values follows: representation awkward work codes meaningless obfuscate significance code write results output. wanted select everyone BLUE favorite color, example, write: Similarly, wanted filter OMITTED favorite colors, write: make data ergnomic work , can use interlacer’s v_col_cfactor() na_col_cfactor() collector types load values cfactor instead, allows associate codes human-readable labels: Now human-readable labels, instead magic codes, can used working data: can still convert labels values missing reasons back codes wish, using .codes(). following convert missing reason channel age value channel favorite_color coded representation: recode cfactor channels data frame coded representation can following:","code":"library(readr) library(dplyr, warn.conflicts = FALSE) library(interlacer, warn.conflicts = FALSE)  read_file(   interlacer_example(\"colors_coded.csv\") ) |>   cat() #> person_id,age,favorite_color #> 1,20,1 #> 2,-98,1 #> 3,21,-98 #> 4,30,-97 #> 5,1,-99 #> 6,41,2 #> 7,50,-97 #> 8,30,3 #> 9,-98,-98 #> 10,-97,2 #> 11,10,-98 (df_coded <- read_interlaced_csv(   interlacer_example(\"colors_coded.csv\"),   na = c(-99, -98, -97) )) #> # A tibble: 11 × 3 #>    person_id       age favorite_color #>    <dbl,int> <dbl,int>      <dbl,int> #>  1         1        20              1 #>  2         2     <-98>              1 #>  3         3        21          <-98> #>  4         4        30          <-97> #>  5         5         1          <-99> #>  6         6        41              2 #>  7         7        50          <-97> #>  8         8        30              3 #>  9         9     <-98>          <-98> #> 10        10     <-97>              2 #> 11        11        10          <-98> df_coded |>   filter(favorite_color == 1) #> # A tibble: 2 × 3 #>   person_id       age favorite_color #>   <dbl,int> <dbl,int>      <dbl,int> #> 1         1        20              1 #> 2         2     <-98>              1 df_coded |>   filter(favorite_color == na(-97)) #> # A tibble: 0 × 3 #> # ℹ 3 variables: person_id <dbl,int>, age <dbl,int>, favorite_color <dbl,int> (df_decoded <- read_interlaced_csv(   interlacer_example(\"colors_coded.csv\"),   col_types = x_cols(     favorite_color = v_col_cfactor(codes = c(BLUE = 1, RED = 2, YELLOW = 3)),   ),   na = na_col_cfactor(REFUSED = -99, OMITTED = -98, `N/A` = -97) )) #> # A tibble: 11 × 3 #>     person_id        age favorite_color #>    <dbl,cfct> <dbl,cfct> <cfct,cfct>    #>  1          1         20 BLUE           #>  2          2  <OMITTED> BLUE           #>  3          3         21 <OMITTED>      #>  4          4         30 <N/A>          #>  5          5          1 <REFUSED>      #>  6          6         41 RED            #>  7          7         50 <N/A>          #>  8          8         30 YELLOW         #>  9          9  <OMITTED> <OMITTED>      #> 10         10      <N/A> RED            #> 11         11         10 <OMITTED> df_decoded |>   filter(favorite_color == \"BLUE\") #> # A tibble: 2 × 3 #>    person_id        age favorite_color #>   <dbl,cfct> <dbl,cfct> <cfct,cfct>    #> 1          1         20 BLUE           #> 2          2  <OMITTED> BLUE  df_decoded |>   filter(favorite_color == na(\"OMITTED\")) #> # A tibble: 0 × 3 #> # ℹ 3 variables: person_id <dbl,cfct>, age <dbl,cfct>, #> #   favorite_color <cfct,cfct> df_decoded |>   mutate(     age = map_na_channel(age, as.codes),     favorite_color = map_value_channel(favorite_color, as.codes)   ) #> # A tibble: 11 × 3 #>     person_id       age favorite_color #>    <dbl,cfct> <dbl,int>     <int,cfct> #>  1          1        20              1 #>  2          2     <-98>              1 #>  3          3        21      <OMITTED> #>  4          4        30          <N/A> #>  5          5         1      <REFUSED> #>  6          6        41              2 #>  7          7        50          <N/A> #>  8          8        30              3 #>  9          9     <-98>      <OMITTED> #> 10         10     <-97>              2 #> 11         11        10      <OMITTED> df_decoded |>   mutate(     across_value_channels(where_value_channel(is.cfactor), as.codes),     across_na_channels(where_na_channel(is.cfactor), as.codes),   ) #> # A tibble: 11 × 3 #>    person_id       age favorite_color #>    <dbl,int> <dbl,int>      <int,int> #>  1         1        20              1 #>  2         2     <-98>              1 #>  3         3        21          <-98> #>  4         4        30          <-97> #>  5         5         1          <-99> #>  6         6        41              2 #>  7         7        50          <-97> #>  8         8        30              3 #>  9         9     <-98>          <-98> #> 10        10     <-97>              2 #> 11        11        10          <-98>"},{"path":"http://kylehusmann.com/interlacer/articles/coded-data.html","id":"sas--and-stata-style-codes","dir":"Articles","previous_headings":"","what":"SAS- and Stata-style codes","title":"Coded Data","text":"Like SPSS, SAS Stata encode factor levels numeric values, instead representing missing reasons negative codes, given character codes: example, value coding scheme used favorite_color previous example, except missing reason channels coded follows: “.”: N/“.”: REFUSED “.b”: OMITTED data can easily loaded interlacer cfactor missing reason channel follows:","code":"read_file(   interlacer_example(\"colors_coded_char.csv\") ) |>   cat() #> person_id,age,favorite_color #> 1,20,1 #> 2,.a,1 #> 3,21,.a #> 4,30,.b #> 5,1,. #> 6,41,2 #> 7,50,.b #> 8,30,3 #> 9,.a,.a #> 10,.b,2 #> 11,10,.a read_interlaced_csv(   interlacer_example(\"colors_coded_char.csv\"),   col_types = x_cols(     favorite_color = v_col_cfactor(codes = c(BLUE = 1, RED = 2, YELLOW = 3)),   ),      na = c(`N/A` = \".\", REFUSED = \".a\", OMITTED = \".b\"), ) #> # A tibble: 11 × 3 #>     person_id        age favorite_color #>    <dbl,cfct> <dbl,cfct> <cfct,cfct>    #>  1          1         20 BLUE           #>  2          2  <REFUSED> BLUE           #>  3          3         21 <REFUSED>      #>  4          4         30 <OMITTED>      #>  5          5          1 <N/A>          #>  6          6         41 RED            #>  7          7         50 <OMITTED>      #>  8          8         30 YELLOW         #>  9          9  <REFUSED> <REFUSED>      #> 10         10  <OMITTED> RED            #> 11         11         10 <REFUSED>"},{"path":"http://kylehusmann.com/interlacer/articles/coded-data.html","id":"the-cfactor-type","dir":"Articles","previous_headings":"","what":"The cfactor type","title":"Coded Data","text":"cfactor extension base R’s factor type. created numeric character codes using cfactor() function: cfactor vectors can used wherever regular base R factor types used, fully-compatible factor types: unlike regular factor, cfactor additionally stores codes factor levels. means can convert back coded representation time, desired: IMPORTANT: .numeric() .integer() functions convert cfactor numeric codes coded representation. Instead, order retain full compatibility base R factor type, always returns result coded index level factor: levels changed, cfactor drop codes degrade regular R factor: Finally, base R factor character vector labels, can add codes via .cfactor():","code":"(example_cfactor <- cfactor(   c(10, 20, 30, 10, 20, 30),   codes = c(LEVEL_A = 10, LEVEL_B = 20, LEVEL_C = 30) )) #> <cfactor<int+bd96a>[6]> #> [1] LEVEL_A LEVEL_B LEVEL_C LEVEL_A LEVEL_B LEVEL_C #>  #> Categorical levels: #>    label code #>  LEVEL_A   10 #>  LEVEL_B   20 #>  LEVEL_C   30  (example_cfactor2 <- cfactor(   c(\"a\", \"b\", \"c\", \"a\", \"b\", \"c\"),   codes = c(LEVEL_A = \"a\", LEVEL_B = \"b\", LEVEL_C = \"c\") )) #> <cfactor<chr+99cda>[6]> #> [1] LEVEL_A LEVEL_B LEVEL_C LEVEL_A LEVEL_B LEVEL_C #>  #> Categorical levels: #>    label code #>  LEVEL_A    a #>  LEVEL_B    b #>  LEVEL_C    c is.factor(example_cfactor) #> [1] TRUE levels(example_cfactor) #> [1] \"LEVEL_A\" \"LEVEL_B\" \"LEVEL_C\"  is.factor(example_cfactor2) #> [1] TRUE levels(example_cfactor2) #> [1] \"LEVEL_A\" \"LEVEL_B\" \"LEVEL_C\" codes(example_cfactor) #> LEVEL_A LEVEL_B LEVEL_C  #>      10      20      30 as.codes(example_cfactor) #> [1] 10 20 30 10 20 30  codes(example_cfactor2) #> LEVEL_A LEVEL_B LEVEL_C  #>     \"a\"     \"b\"     \"c\" as.codes(example_cfactor2) #> [1] \"a\" \"b\" \"c\" \"a\" \"b\" \"c\" as.numeric(example_cfactor) #> [1] 1 2 3 1 2 3 as.numeric(example_cfactor2) #> [1] 1 2 3 1 2 3 cfactor_copy <- example_cfactor  # cfactory_copy is a cfactor and a factor is.cfactor(cfactor_copy) #> [1] TRUE is.factor(cfactor_copy) #> [1] TRUE levels(cfactor_copy) #> [1] \"LEVEL_A\" \"LEVEL_B\" \"LEVEL_C\" codes(cfactor_copy) #> LEVEL_A LEVEL_B LEVEL_C  #>      10      20      30  # modify the levels of the cfactor as if it was a regular factor levels(cfactor_copy) <- c(\"C\", \"B\", \"A\")  # now cfactor_copy is just a regular factor is.cfactor(cfactor_copy) #> [1] FALSE is.factor(cfactor_copy) #> [1] TRUE levels(cfactor_copy) #> [1] \"C\" \"B\" \"A\" codes(cfactor_copy) #> NULL as.cfactor(   c(\"LEVEL_A\", \"LEVEL_B\", \"LEVEL_C\", \"LEVEL_A\", \"LEVEL_B\", \"LEVEL_C\"),   codes = c(LEVEL_A = 10, LEVEL_B = 20, LEVEL_C = 30) ) #> <cfactor<int+bd96a>[6]> #> [1] LEVEL_A LEVEL_B LEVEL_C LEVEL_A LEVEL_B LEVEL_C #>  #> Categorical levels: #>    label code #>  LEVEL_A   10 #>  LEVEL_B   20 #>  LEVEL_C   30"},{"path":"http://kylehusmann.com/interlacer/articles/coded-data.html","id":"re-coding-and-writing-an-interlaced-data-frame-","dir":"Articles","previous_headings":"","what":"Re-coding and writing an interlaced data frame.","title":"Coded Data","text":"Re-coding writing interlaced data frame simple calling .codes() cfactor type value missing reason channels, calling one write_interlaced_*() family functions:","code":"df_decoded |>   mutate(     across_value_channels(where_value_channel(is.cfactor), as.codes),     across_na_channels(where_na_channel(is.cfactor), as.codes),   ) |>   write_interlaced_csv(\"output.csv\")"},{"path":"http://kylehusmann.com/interlacer/articles/coded-data.html","id":"haven","dir":"Articles","previous_headings":"","what":"haven","title":"Coded Data","text":"haven package functions loading native SPSS, SAS, Stata native file formats special data frames use column attributes special values keep track value labels missing reasons. complete discussion compares interlacer’s approach, see vignette(\"-approaches\").","code":""},{"path":"http://kylehusmann.com/interlacer/articles/extended-column-types.html","id":"x_cols-extended-cols-specifications","dir":"Articles","previous_headings":"","what":"x_cols: extended cols specifications","title":"Extended Column Types","text":"need fine-grained control value missing reason channel types, can use x_cols() specification, extension readr’s readr::cols() system. x_cols(), can control value channel na channel types columns resulting data frame. useful missing reasons apply particular items opposed file whole. example, say measure following two items: current stress level? Low Moderate High don’t know don’t understand question well feel manage time responsibilities today? Poorly Fairly well Well well apply (Today vacation day) apply (reason) can see, items two selection choices mapped missing reasons. can specified x_cols() follows: Like readr’s readr::cols() function, named x_cols() describes column resulting data frame. Value missing reason channel types declared via calls v_col_*() na_col_*() respectively, assembled x_col(). v_col_*() types mirror readr’s readr::col_* column “collectors”. v_col_double() equivalent readr::col_double(), v_col_character() equivalent readr::col_character(), etc. See vroom’s documentation list available column types. na_col_*() collectors allow declare missing reason channel type loaded column, values interpreted missing reasons. Currently, five options: na_col_default(): Use collector defined na = argument read_interlaced_*() function na_col_none(): Load column without missing reason channel. na_col_factor(): Use factor missing reason channel. Character arguments passed form levels factor. (e.g. na_col_factor(\"REFUSED\", \"OMITTED\", \"N/\")) na_col_integer(): Use integer na channel. Numeric arguments passed values interpreted missing values. (e.g. na_col_integer(-99, -98, -97))) na_col_cfactor(): Use cfactor na channel. (cfactor types covered next vignette,vignette(\"coded-data\")) following example shows collectors action. example use coded version colors.csv example data, demonstrate integer missing reason types:","code":"(df_stress <- read_interlaced_csv(   interlacer_example(\"stress.csv\"),   col_types = x_cols(     person_id = x_col(       v_col_integer(),       na_col_none()     ),     current_stress = x_col(       v_col_factor(levels = c(\"LOW\", \"MODERATE\", \"HIGH\")),       na_col_factor(\"DONT_KNOW\", \"DONT_UNDERSTAND\")     ),     time_management = x_col(       v_col_factor(levels = c(\"POORLY\", \"FAIRLY_WELL\", \"WELL\", \"VERY_WELL\")),       na_col_factor(\"NA_VACATION\", \"NA_OTHER\")     )   ) )) #> # A tibble: 8 × 3 #>   person_id current_stress    time_management #>       <int> <fct,fct>         <fct,fct>       #> 1         1 LOW               VERY_WELL       #> 2         2 MODERATE          POORLY          #> 3         3 <DONT_KNOW>       <NA_OTHER>      #> 4         4 HIGH              POORLY          #> 5         5 <DONT_UNDERSTAND> <NA_OTHER>      #> 6         6 LOW               <NA_VACATION>   #> 7         7 MODERATE          WELL            #> 8         8 <DONT_KNOW>       FAIRLY_WELL read_interlaced_csv(   interlacer_example(\"colors_coded.csv\"),   col_types = x_cols(     person_id = x_col(v_col_integer(), na_col_none()),     age = x_col(v_col_double(), na_col_integer(-99, -98, -97)),     favorite_color = x_col(v_col_integer(), na_col_integer(-99, -98, -97))   ) ) #> # A tibble: 11 × 3 #>    person_id       age favorite_color #>        <int> <dbl,int>      <int,int> #>  1         1        20              1 #>  2         2     <-98>              1 #>  3         3        21          <-98> #>  4         4        30          <-97> #>  5         5         1          <-99> #>  6         6        41              2 #>  7         7        50          <-97> #>  8         8        30              3 #>  9         9     <-98>          <-98> #> 10        10     <-97>              2 #> 11        11        10          <-98>"},{"path":[]},{"path":"http://kylehusmann.com/interlacer/articles/extended-column-types.html","id":"default-collector-types","dir":"Articles","previous_headings":"Shortcuts","what":"Default collector types","title":"Extended Column Types","text":"Like readr’s cols() function, x_cols() function accepts .default argument specifies default value collector. na = argument similarly used specify default missing reason collector used na_col_*() specified, set na_col_default(). taking advantage defaults, specification last example equivalently written :","code":"read_interlaced_csv(   interlacer_example(\"colors_coded.csv\"),   col_types = x_cols(     .default = v_col_integer(),     person_id = x_col(v_col_integer(), na_col_none()),     age = v_col_double(),   ),   na = na_col_integer(-99, -98, -97) ) #> # A tibble: 11 × 3 #>    person_id       age favorite_color #>        <int> <dbl,int>      <int,int> #>  1         1        20              1 #>  2         2     <-98>              1 #>  3         3        21          <-98> #>  4         4        30          <-97> #>  5         5         1          <-99> #>  6         6        41              2 #>  7         7        50          <-97> #>  8         8        30              3 #>  9         9     <-98>          <-98> #> 10        10     <-97>              2 #> 11        11        10          <-98>"},{"path":"http://kylehusmann.com/interlacer/articles/extended-column-types.html","id":"concise-value-and-missing-reason-specifications","dir":"Articles","previous_headings":"Shortcuts","what":"Concise value and missing reason specifications","title":"Extended Column Types","text":"Like readr, value collectors can specified using characters. example, instead v_col_integer(), can use \"\". See vroom’s documentation complete list shortcuts. Similarly, missing reason collectors can specified providing vector missing values; collector type inferred via type vector. conversions follows: na_col_none(): NULL na_col_factor(): character vector, e.g. c(\"REFUSED\", \"OMITTED\", \"N/\") na_col_integer(): numeric vector, e.g. c(-99, -98, -97)) na_col_cfactor(): named character numeric vector, e.g. c(REFUSED = -99, OMITTED = -98, `N/` = -97) Using shortcuts, previous example equivalently written compact form follows:","code":"read_interlaced_csv(   interlacer_example(\"colors_coded.csv\"),   col_types = x_cols(     .default = \"i\",     person_id = x_col(\"i\", NULL),     age = \"d\",   ),   na = c(-99, -98, -97) ) #> # A tibble: 11 × 3 #>    person_id       age favorite_color #>        <int> <dbl,int>      <int,int> #>  1         1        20              1 #>  2         2     <-98>              1 #>  3         3        21          <-98> #>  4         4        30          <-97> #>  5         5         1          <-99> #>  6         6        41              2 #>  7         7        50          <-97> #>  8         8        30              3 #>  9         9     <-98>          <-98> #> 10        10     <-97>              2 #> 11        11        10          <-98>"},{"path":"http://kylehusmann.com/interlacer/articles/extended-column-types.html","id":"next-steps","dir":"Articles","previous_headings":"","what":"Next steps","title":"Extended Column Types","text":"vignette covered column types values missing reasons can explicitly specified using collectors. also illustrated column-level missing values can specified creating extended column type specifications using x_cols(). final examples, used example data set coded values missing reasons. Coded values especially common data sets produced SPSS, SAS, Stata. interlacer provides special column type make working sort data easier: cfactor type. covered next vignette, vignette(\"coded-data\").","code":""},{"path":"http://kylehusmann.com/interlacer/articles/interlacer.html","id":"aggregations-with-missing-reasons","dir":"Articles","previous_headings":"","what":"Aggregations with missing reasons","title":"Introduction to interlacer","text":"Now, interested values source data, functionality need. wanted know values NA? Although information encoded source data, lost missing reasons converted NA values. example, consider favorite_color column. many respondents REFUSED give favorite color? many people just OMITTED answer? question N/respondents (e.g. wasn’t survey form)? mean respondent age groups? current dataframe gets us part way: can see, converted missing reasons single NA, can answer questions missingness general, rather work specific reasons stored source data. Unfortunately, try load data missing reasons intact, lose something else: type information values. Now access missing reasons, columns character vectors. means order anything values, always filter missing reasons, cast remaining values desired type: gives us information want, cumbersome. Notice ’s distinction favorite color values missing reasons! Things start get really complex different columns different sets possible missing reasons. means lot type conversion gymnastics switch value types missing types.","code":"library(dplyr, warn.conflicts = FALSE)  df_simple |>   summarize(     mean_age = mean(age, na.rm = TRUE),     n = n(),     .by = favorite_color   ) |>   arrange(favorite_color) #> # A tibble: 4 × 3 #>   favorite_color mean_age     n #>   <chr>             <dbl> <int> #> 1 BLUE               20       2 #> 2 RED                41       2 #> 3 YELLOW             30       1 #> 4 NA                 22.4     6 (df_with_missing <- read_csv(   interlacer_example(\"colors.csv\"),   col_types = cols(.default = \"c\") )) #> # A tibble: 11 × 3 #>    person_id age     favorite_color #>    <chr>     <chr>   <chr>          #>  1 1         20      BLUE           #>  2 2         REFUSED BLUE           #>  3 3         21      REFUSED        #>  4 4         30      OMITTED        #>  5 5         1       N/A            #>  6 6         41      RED            #>  7 7         50      OMITTED        #>  8 8         30      YELLOW         #>  9 9         REFUSED REFUSED        #> 10 10        OMITTED RED            #> 11 11        10      REFUSED reasons <- c(\"REFUSED\", \"OMITTED\", \"N/A\")  df_with_missing |>   mutate(     age_values = as.numeric(if_else(age %in% reasons, NA, age)),   ) |>   summarize(     mean_age = mean(age_values, na.rm = TRUE),     n = n(),     .by = favorite_color   ) |>   arrange(favorite_color) #> # A tibble: 6 × 3 #>   favorite_color mean_age     n #>   <chr>             <dbl> <int> #> 1 BLUE               20       2 #> 2 N/A                 1       1 #> 3 OMITTED            40       2 #> 4 RED                41       2 #> 5 REFUSED            15.5     3 #> 6 YELLOW             30       1"},{"path":"http://kylehusmann.com/interlacer/articles/interlacer.html","id":"the-interlacer-approach","dir":"Articles","previous_headings":"Aggregations with missing reasons","what":"The interlacer approach","title":"Introduction to interlacer","text":"interlacer built based insight everything becomes much tidy, simple, expressive explicitly work values missing reasons separate channels variable. interlacer introduces new interlaced column type facilitates . read_interlaced_* functions interlacer import data new column type. can see column headers, column loaded composed two channels: value channel, missing reason channel. channel can type. age column, example, double values factor missing reasons: channels can explicitly accessed using value_channel() na_channel() helper functions: helpers rarely needed, however, computations automatically operate interlaced column’s value channel, ignore missing reasons channel. following compute mean age, without missing reasons interfering: (equivalently used value_channel() helper achieve result, albeit verbosity): Although missing reasons excluded computations, still treated unique values. means group age get breakdown unique missing reasons, rather lumped single NA: can see, can generate report , without needing type gymnastics! Also, values neatly distinguished missing reasons.","code":"(df <- read_interlaced_csv(   interlacer_example(\"colors.csv\"),   na = c(\"REFUSED\", \"OMITTED\", \"N/A\") )) #> # A tibble: 11 × 3 #>    person_id       age favorite_color #>    <dbl,fct> <dbl,fct> <chr,fct>      #>  1         1        20 BLUE           #>  2         2 <REFUSED> BLUE           #>  3         3        21 <REFUSED>      #>  4         4        30 <OMITTED>      #>  5         5         1 <N/A>          #>  6         6        41 RED            #>  7         7        50 <OMITTED>      #>  8         8        30 YELLOW         #>  9         9 <REFUSED> <REFUSED>      #> 10        10 <OMITTED> RED            #> 11        11        10 <REFUSED> df$age #> <interlaced<dbl, fct>[11]> #>  [1] 20        <REFUSED> 21        30         1        41        50        #>  [8] 30        <REFUSED> <OMITTED> 10        #> NA levels: REFUSED OMITTED N/A value_channel(df$age) #>  [1] 20 NA 21 30  1 41 50 30 NA NA 10 na_channel(df$age) #>  [1] <NA>    REFUSED <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    REFUSED #> [10] OMITTED <NA>    #> Levels: REFUSED OMITTED N/A mean(df$age, na.rm = TRUE) #> [1] 25.375 mean(value_channel(df$age), na.rm = TRUE) #> [1] 25.375 df |>   summarize(     mean_age = mean(age, na.rm = TRUE),     n = n(),     .by = favorite_color   ) |>   arrange(favorite_color) #> # A tibble: 4 × 3 #>   favorite_color mean_age     n #>   <chr,fct>         <dbl> <int> #> 1 BLUE               20       2 #> 2 RED                41       2 #> 3 YELLOW             30       1 #> 4 <REFUSED>          22.4     6"},{"path":"http://kylehusmann.com/interlacer/articles/interlacer.html","id":"filtering-based-on-missing-reasons","dir":"Articles","previous_headings":"","what":"Filtering based on missing reasons","title":"Introduction to interlacer","text":"interlaced columns also helpful creating samples inclusion / exclusion criteria based missing reasons. example, using example data, say wanted create sample respondents REFUSED give age. indicate value interpreted missing reason, can use na() function value: people REFUSED report age favorite color? ’s also possible combine value conditions missing reason conditions. example, select everyone REFUSED give favorite color, 20 years old:","code":"df |>   filter(age == na(\"REFUSED\")) #> # A tibble: 0 × 3 #> # ℹ 3 variables: person_id <dbl,fct>, age <dbl,fct>, favorite_color <chr,fct>  # na_channel() can also be used to get an equivalent result: df |>   filter(na_channel(age) == \"REFUSED\") #> # A tibble: 2 × 3 #>   person_id       age favorite_color #>   <dbl,fct> <dbl,fct> <chr,fct>      #> 1         2 <REFUSED> BLUE           #> 2         9 <REFUSED> <REFUSED> df |>   filter(age == na(\"REFUSED\") & favorite_color == na(\"REFUSED\")) #> # A tibble: 0 × 3 #> # ℹ 3 variables: person_id <dbl,fct>, age <dbl,fct>, favorite_color <chr,fct> df |>   filter(age > 20 & favorite_color == na(\"REFUSED\")) #> # A tibble: 0 × 3 #> # ℹ 3 variables: person_id <dbl,fct>, age <dbl,fct>, favorite_color <chr,fct>"},{"path":"http://kylehusmann.com/interlacer/articles/interlacer.html","id":"mutations","dir":"Articles","previous_headings":"","what":"Mutations","title":"Introduction to interlacer","text":"might expect, na() function can used values mutations. following pipeline replace favorite color respondents missing value \"REDACTED\" Conditionals also work exactly expect mutations. following replace favorite color respondents age < 18 missing reason \"REDACTED_UNDERAGE\". Respondents missing age replaced \"REDACTED_MISSING_AGE\" following mutation create new column called person_type \"CHILD\" age < 18, \"ADULT\" age >= 18, missing reason \"AGE_UNAVAILABLE\" age missing: Important note: must use dplyr::if_else() interlaced vectors instead R’s base::ifelse() function, base function strips missing reason channel due fundamental limitation base R.","code":"df |>   mutate(     favorite_color = na(\"REDACTED\")   ) #> # A tibble: 11 × 3 #>    person_id       age favorite_color #>    <dbl,fct> <dbl,fct>      <???,fct> #>  1         1        20     <REDACTED> #>  2         2 <REFUSED>     <REDACTED> #>  3         3        21     <REDACTED> #>  4         4        30     <REDACTED> #>  5         5         1     <REDACTED> #>  6         6        41     <REDACTED> #>  7         7        50     <REDACTED> #>  8         8        30     <REDACTED> #>  9         9 <REFUSED>     <REDACTED> #> 10        10 <OMITTED>     <REDACTED> #> 11        11        10     <REDACTED> df |>   mutate(     favorite_color = if_else(       age < 18,       na(\"REDACTED_UNDERAGE\"),       favorite_color,       missing = na(\"REDACTED_MISSING_AGE\")     )   ) #> # A tibble: 11 × 3 #>    person_id       age favorite_color         #>    <dbl,fct> <dbl,fct> <chr,fct>              #>  1         1        20 BLUE                   #>  2         2 <REFUSED> <REDACTED_MISSING_AGE> #>  3         3        21 <REFUSED>              #>  4         4        30 <OMITTED>              #>  5         5         1 <REDACTED_UNDERAGE>    #>  6         6        41 RED                    #>  7         7        50 <OMITTED>              #>  8         8        30 YELLOW                 #>  9         9 <REFUSED> <REDACTED_MISSING_AGE> #> 10        10 <OMITTED> <REDACTED_MISSING_AGE> #> 11        11        10 <REDACTED_UNDERAGE> df |>   mutate(     person_type = if_else(       age < 18,       \"CHILD\",       \"ADULT\",       missing = na(\"AGE_UNAVAILABLE\")     ),   ) #> # A tibble: 11 × 4 #>    person_id       age favorite_color person_type       #>    <dbl,fct> <dbl,fct> <chr,fct>      <chr,fct>         #>  1         1        20 BLUE           ADULT             #>  2         2 <REFUSED> BLUE           <AGE_UNAVAILABLE> #>  3         3        21 <REFUSED>      ADULT             #>  4         4        30 <OMITTED>      ADULT             #>  5         5         1 <N/A>          CHILD             #>  6         6        41 RED            ADULT             #>  7         7        50 <OMITTED>      ADULT             #>  8         8        30 YELLOW         ADULT             #>  9         9 <REFUSED> <REFUSED>      <AGE_UNAVAILABLE> #> 10        10 <OMITTED> RED            <AGE_UNAVAILABLE> #> 11        11        10 <REFUSED>      CHILD"},{"path":"http://kylehusmann.com/interlacer/articles/interlacer.html","id":"multichannel-comparison","dir":"Articles","previous_headings":"","what":"Multichannel comparison","title":"Introduction to interlacer","text":"Comparison operators (e.g. == !=) compare value channels, return NA whenever missing values involved: means missing reasons directly compared: simultaneously compare values missing reason channels, interlacer provides multichannel comparison operators %==% %!=%. operators always return boolean vectors, never return missing values. multichannel comparison, two values considered equal iff: values, values equal missing values, reasons missing values, lack missing reasons way, convert previous example use multichannel comparison, get: Similarly, comparisons values return booleans rather NA, , consequence, regular NA values equivalent interlaced values missing reason: Missing values missing missing reasons called “empty values”, discussed next section.","code":"5 == NA #> [1] NA 5 == na(\"reason\") #> [1] NA na(\"reason1\") == na(\"reason2\") #> [1] NA na(\"reason1\") %==% na(\"reason2\") #> [1] FALSE 5 %==% NA #> [1] FALSE 5 %==% na(\"reason\") #> [1] FALSE NA %==% na() #> [1] TRUE  # Alternatively this can be considered an `NA` missing reason: NA %==% na(NA) #> [1] TRUE"},{"path":"http://kylehusmann.com/interlacer/articles/interlacer.html","id":"empty-values","dir":"Articles","previous_headings":"","what":"Empty values","title":"Introduction to interlacer","text":"Empty values missing values lack missing reasons. values can arise missing actions specified. example, example, empty values created missing = argument if_else() statement specified: can see, empty values displayed <<NA>>. Empty values can detected using multichannel comparsion operator: (Note regular equality work, return NA comparisons!) Empty values often occur result joins, dplyr::*_join() family functions missing = parameter, like dplyr::if_else() . example, say following data frame wanted join sample: ’re missing condition information respondents, show empty values join data frame sample: can remedy replacing empty values join:","code":"df |>   mutate(     favorite_color = if_else(       age < 18,       na(\"REDACTED_UNDERAGE\"),       favorite_color,     )   ) #> # A tibble: 11 × 3 #>    person_id       age favorite_color      #>    <dbl,fct> <dbl,fct> <chr,fct>           #>  1         1        20 BLUE                #>  2         2 <REFUSED> <<NA>>              #>  3         3        21 <REFUSED>           #>  4         4        30 <OMITTED>           #>  5         5         1 <REDACTED_UNDERAGE> #>  6         6        41 RED                 #>  7         7        50 <OMITTED>           #>  8         8        30 YELLOW              #>  9         9 <REFUSED> <<NA>>              #> 10        10 <OMITTED> <<NA>>              #> 11        11        10 <REDACTED_UNDERAGE> df |>   mutate(     favorite_color = if_else(       age < 18,       na(\"REDACTED_UNDERAGE\"),       favorite_color,     )   ) |>   filter(favorite_color %==% na()) #> # A tibble: 3 × 3 #>   person_id       age favorite_color #>   <dbl,fct> <dbl,fct> <chr,fct>      #> 1         2 <REFUSED> <<NA>>         #> 2         9 <REFUSED> <<NA>>         #> 3        10 <OMITTED> <<NA>> conditions <- tribble(   ~person_id, ~condition,   1, \"TREATMENT\",   2, \"CONTROL\",   3, na(\"TECHNICAL_ERROR\"),   6, \"CONTROL\",   8, \"TREATMENT\", ) df |>   left_join(conditions, by = join_by(person_id)) #> # A tibble: 11 × 4 #>    person_id       age favorite_color condition         #>    <dbl,fct> <dbl,fct> <chr,fct>      <chr,fct>         #>  1         1        20 BLUE           TREATMENT         #>  2         2 <REFUSED> BLUE           CONTROL           #>  3         3        21 <REFUSED>      <TECHNICAL_ERROR> #>  4         4        30 <OMITTED>      <<NA>>            #>  5         5         1 <N/A>          <<NA>>            #>  6         6        41 RED            CONTROL           #>  7         7        50 <OMITTED>      <<NA>>            #>  8         8        30 YELLOW         TREATMENT         #>  9         9 <REFUSED> <REFUSED>      <<NA>>            #> 10        10 <OMITTED> RED            <<NA>>            #> 11        11        10 <REFUSED>      <<NA>> df |>   left_join(conditions, by = join_by(person_id)) |>   mutate(     condition = if_else(condition %==% na(), na(\"LEFT_STUDY\"), condition),   ) #> # A tibble: 11 × 4 #>    person_id       age favorite_color condition         #>    <dbl,fct> <dbl,fct> <chr,fct>      <chr,fct>         #>  1         1        20 BLUE           TREATMENT         #>  2         2 <REFUSED> BLUE           CONTROL           #>  3         3        21 <REFUSED>      <TECHNICAL_ERROR> #>  4         4        30 <OMITTED>      <LEFT_STUDY>      #>  5         5         1 <N/A>          <LEFT_STUDY>      #>  6         6        41 RED            CONTROL           #>  7         7        50 <OMITTED>      <LEFT_STUDY>      #>  8         8        30 YELLOW         TREATMENT         #>  9         9 <REFUSED> <REFUSED>      <LEFT_STUDY>      #> 10        10 <OMITTED> RED            <LEFT_STUDY>      #> 11        11        10 <REFUSED>      <LEFT_STUDY>"},{"path":"http://kylehusmann.com/interlacer/articles/interlacer.html","id":"writing-interlaced-files","dir":"Articles","previous_headings":"","what":"Writing interlaced files","title":"Introduction to interlacer","text":"’ve made made changes data, probably want save . interlacer provides write_interlaced_* family functions : combine value missing reasons interlaced character columns, write result csv. Alternatively, want re-interlace columns without writing file control writing process, can use flatten_channels(): value missing reason channels data frames interlaced vectors can similarly accessed using value_channel() na_channel() helper functions:","code":"write_interlaced_csv(df, \"interlaced_output.csv\") flatten_channels(df) #> # A tibble: 11 × 3 #>    person_id age     favorite_color #>        <dbl> <chr>   <chr>          #>  1         1 20      BLUE           #>  2         2 REFUSED BLUE           #>  3         3 21      REFUSED        #>  4         4 30      OMITTED        #>  5         5 1       N/A            #>  6         6 41      RED            #>  7         7 50      OMITTED        #>  8         8 30      YELLOW         #>  9         9 REFUSED REFUSED        #> 10        10 OMITTED RED            #> 11        11 10      REFUSED  # (it works on single vectors as well) flatten_channels(df$age) #>  [1] \"20\"      \"REFUSED\" \"21\"      \"30\"      \"1\"       \"41\"      \"50\"      #>  [8] \"30\"      \"REFUSED\" \"OMITTED\" \"10\" value_channel(df) #> # A tibble: 11 × 3 #>    person_id   age favorite_color #>        <dbl> <dbl> <chr>          #>  1         1    20 BLUE           #>  2         2    NA BLUE           #>  3         3    21 NA             #>  4         4    30 NA             #>  5         5     1 NA             #>  6         6    41 RED            #>  7         7    50 NA             #>  8         8    30 YELLOW         #>  9         9    NA NA             #> 10        10    NA RED            #> 11        11    10 NA na_channel(df) #> # A tibble: 11 × 3 #>    person_id age     favorite_color #>    <fct>     <fct>   <fct>          #>  1 NA        NA      NA             #>  2 NA        REFUSED NA             #>  3 NA        NA      REFUSED        #>  4 NA        NA      OMITTED        #>  5 NA        NA      N/A            #>  6 NA        NA      NA             #>  7 NA        NA      OMITTED        #>  8 NA        NA      NA             #>  9 NA        REFUSED REFUSED        #> 10 NA        OMITTED NA             #> 11 NA        NA      REFUSED"},{"path":"http://kylehusmann.com/interlacer/articles/interlacer.html","id":"next-steps","dir":"Articles","previous_headings":"","what":"Next steps","title":"Introduction to interlacer","text":"far, ’ve covered interlacer’s read_interlaced_* family functions enabled us load interlaced columns contain separate challens value missing reasons. interlaced type enables us create tidy type-aware pipelines can flexibly consider variable’s value missing reasons. examples vignette, column types automatically detected. explicitly specify value missing column types, (specify individual missing reasons specific columns), interlacer extends readr’s collector() system. covered next vignette, vignette(\"na-column-types\").","code":""},{"path":"http://kylehusmann.com/interlacer/articles/other-approaches.html","id":"haven-and-labelled","dir":"Articles","previous_headings":"","what":"haven and labelled","title":"Other Approaches","text":"haven labelled packages rely two functions creating vectors interlace values missing reasons: haven::labelled_spss() haven::tagged_na(). Although create haven_labelled vectors, use different methods representing missing values.","code":""},{"path":"http://kylehusmann.com/interlacer/articles/other-approaches.html","id":"labelled-missing-values-havenlabelled_spss","dir":"Articles","previous_headings":"haven and labelled","what":"“Labelled” missing values (haven::labelled_spss())","title":"Other Approaches","text":"SPSS files loaded haven via haven::read_spss(), values missing reasons loaded single interlaced numeric vector: just numeric vector though, haven::labelled_spss() numeric vector, attributes describing value missing value codes: attributes adjust behavior functions like .na(): makes easy check value missing reason, still filter missing reasons aggregations: ’s little bit improvement working raw coded values, can use .na(), codes get labels, don’t constantly looking codes codebook. still falls short interlacer’s functionality two key reasons: Reason 1: interlacer, value column can whatever type want: numeric, character, factor, etc. labelled missing reasons, values missing reasons need type, usually numeric codes. creates lot type gymnastics potential errors ’re manipulating . Reason 2: Even missing values labelled labelled_spss type, aggregations math operations protected. forget take missing values, get incorrect results / corrupted data:","code":"library(interlacer, warn.conflicts = FALSE) library(haven) library(dplyr) #>  #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': #>  #>     filter, lag #> The following objects are masked from 'package:base': #>  #>     intersect, setdiff, setequal, union  (df_spss <- read_spss(   interlacer_example(\"colors.sav\"), user_na = TRUE )) #> Registered S3 methods overwritten by 'readr': #>   method                    from  #>   as.data.frame.spec_tbl_df vroom #>   as_tibble.spec_tbl_df     vroom #>   format.col_spec           vroom #>   print.col_spec            vroom #>   print.collector           vroom #>   print.date_names          vroom #>   print.locale              vroom #>   str.col_spec              vroom #> # A tibble: 11 × 3 #>    person_id age                favorite_color     #>    <dbl+lbl> <dbl+lbl>          <dbl+lbl>          #>  1  1         20                  1 [BLUE]         #>  2  2        -98 (NA) [REFUSED]   1 [BLUE]         #>  3  3         21                -98 (NA) [REFUSED] #>  4  4         30                -97 (NA) [OMITTED] #>  5  5          1                -99 (NA) [N/A]     #>  6  6         41                  2 [RED]          #>  7  7         50                -97 (NA) [OMITTED] #>  8  8         30                  3 [YELLOW]       #>  9  9        -98 (NA) [REFUSED] -98 (NA) [REFUSED] #> 10 10        -97 (NA) [OMITTED]   2 [RED]          #> 11 11         10                -98 (NA) [REFUSED] attributes(df_spss$favorite_color) #> $label #> [1] \"Favorite color\" #>  #> $na_range #> [1] -Inf    0 #>  #> $class #> [1] \"haven_labelled_spss\" \"haven_labelled\"      \"vctrs_vctr\"          #> [4] \"double\"              #>  #> $format.spss #> [1] \"F8.2\" #>  #> $labels #>    BLUE     RED  YELLOW     N/A REFUSED OMITTED  #>       1       2       3     -99     -98     -97 is.na(df_spss$favorite_color) #>  [1] FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE df_spss |>   mutate(     age_values = if_else(is.na(age), NA, age),     favorite_color_missing_reasons = if_else(       is.na(favorite_color), favorite_color, NA     )   ) |>   summarize(     mean_age = mean(age_values, na.rm = TRUE),     n = n(),     .by = favorite_color_missing_reasons   ) #> # A tibble: 4 × 3 #>   favorite_color_missing_reasons mean_age     n #>   <dbl+lbl>                         <dbl> <int> #> 1  NA                                30.3     5 #> 2 -98 (NA) [REFUSED]                 15.5     3 #> 3 -97 (NA) [OMITTED]                 40       2 #> 4 -99 (NA) [N/A]                      1       1  df_spss |>   mutate(     age_next_year = if_else(is.na(age), NA, age + 1),     .after = person_id   ) #> # A tibble: 11 × 4 #>    person_id age_next_year age                favorite_color     #>    <dbl+lbl>         <dbl> <dbl+lbl>          <dbl+lbl>          #>  1  1                   21  20                  1 [BLUE]         #>  2  2                   NA -98 (NA) [REFUSED]   1 [BLUE]         #>  3  3                   22  21                -98 (NA) [REFUSED] #>  4  4                   31  30                -97 (NA) [OMITTED] #>  5  5                    2   1                -99 (NA) [N/A]     #>  6  6                   42  41                  2 [RED]          #>  7  7                   51  50                -97 (NA) [OMITTED] #>  8  8                   31  30                  3 [YELLOW]       #>  9  9                   NA -98 (NA) [REFUSED] -98 (NA) [REFUSED] #> 10 10                   NA -97 (NA) [OMITTED]   2 [RED]          #> 11 11                   11  10                -98 (NA) [REFUSED] df_spss |>   mutate(     favorite_color_missing_reasons = if_else(       is.na(favorite_color), favorite_color, NA     )   ) |>   summarize(     mean_age = mean(age, na.rm = TRUE),     n = n(),     .by = favorite_color_missing_reasons   ) #> # A tibble: 4 × 3 #>   favorite_color_missing_reasons mean_age     n #>   <dbl+lbl>                         <dbl> <int> #> 1  NA                               -20.8     5 #> 2 -98 (NA) [REFUSED]                -22.3     3 #> 3 -97 (NA) [OMITTED]                 40       2 #> 4 -99 (NA) [N/A]                      1       1  df_spss |>   mutate(     age_next_year = age + 1,     .after = person_id   ) #> # A tibble: 11 × 4 #>    person_id age_next_year age                favorite_color     #>    <dbl+lbl>         <dbl> <dbl+lbl>          <dbl+lbl>          #>  1  1                   21  20                  1 [BLUE]         #>  2  2                  -97 -98 (NA) [REFUSED]   1 [BLUE]         #>  3  3                   22  21                -98 (NA) [REFUSED] #>  4  4                   31  30                -97 (NA) [OMITTED] #>  5  5                    2   1                -99 (NA) [N/A]     #>  6  6                   42  41                  2 [RED]          #>  7  7                   51  50                -97 (NA) [OMITTED] #>  8  8                   31  30                  3 [YELLOW]       #>  9  9                  -97 -98 (NA) [REFUSED] -98 (NA) [REFUSED] #> 10 10                  -96 -97 (NA) [OMITTED]   2 [RED]          #> 11 11                   11  10                -98 (NA) [REFUSED]"},{"path":"http://kylehusmann.com/interlacer/articles/other-approaches.html","id":"tagged-missing-values-haventagged_na","dir":"Articles","previous_headings":"haven and labelled","what":"“Tagged” missing values (haven::tagged_na())","title":"Other Approaches","text":"loading Stata SAS files, haven uses “tagged missingness” approach mirror values handled Stata SAS: approach deviously clever. takes advantage way NaN floating point values stored memory, make possible different “flavors” NA values. (info done, check tagged_na.c source code haven) still act like regular NA values… now can include single character “tag” (usually letter -z). means work .na() include missing reason codes aggregations! Unfortunately, can’t group , dplyr::group_by() tag-aware. :( Another limitation approach requires values types numeric, trick “tagging” NA values depends peculiarities floating point values stored memory.","code":"(df_stata <- read_stata(   interlacer_example(\"colors.dta\") )) #> # A tibble: 11 × 3 #>    person_id age             favorite_color  #>    <dbl+lbl> <dbl+lbl>       <dbl+lbl>       #>  1  1           20               1 [BLUE]    #>  2  2        NA(a) [REFUSED]     1 [BLUE]    #>  3  3           21           NA(a) [REFUSED] #>  4  4           30           NA(b) [OMITTED] #>  5  5            1              NA           #>  6  6           41               2 [RED]     #>  7  7           50           NA(b) [OMITTED] #>  8  8           30               3 [YELLOW]  #>  9  9        NA(a) [REFUSED] NA(a) [REFUSED] #> 10 10        NA(b) [OMITTED]     2 [RED]     #> 11 11           10           NA(a) [REFUSED] is.na(df_stata$age) #>  [1] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  mean(df_stata$age, na.rm = TRUE) #> [1] 25.375 df_stata |>   mutate(     favorite_color_missing_reasons = if_else(       is.na(favorite_color), favorite_color, NA     )   ) |>   summarize(     mean_age = mean(age, na.rm = TRUE),     n = n(),     .by = favorite_color_missing_reasons   ) #> # A tibble: 1 × 3 #>   favorite_color_missing_reasons mean_age     n #>   <dbl+lbl>                         <dbl> <int> #> 1 NA                                 25.4    11"},{"path":"http://kylehusmann.com/interlacer/articles/other-approaches.html","id":"declared","dir":"Articles","previous_headings":"","what":"declared","title":"Other Approaches","text":"declared package uses functiondeclared::declared() constructing interlaced vectors: declared vectors similar haven_labelled_spss vectors, except critical innovation: store actual NA values missing values, keep track missing reasons entirely attributes object: means aggregations work exactly expect!","code":"library(declared)  (dcl <- declared(c(1, 2, 3, -99, -98), na_values = c(-99, -98))) #> <declared<numeric>[5]> #> [1]       1       2       3 NA(-99) NA(-98) #> Missing values: -99, -98 # All the missing reason info is tracked in the attributes attributes(dcl) #> $na_index #> -99 -98  #>   4   5  #>  #> $na_values #> [1] -99 -98 #>  #> $date #> [1] FALSE #>  #> $class #> [1] \"declared\" \"numeric\"  # The data stored has actual NA values, so it works as you would expect # with summary stats like `mean()`, etc. attributes(dcl) <- NULL dcl #> [1]  1  2  3 NA NA dcl <- declared(c(1, 2, 3, -99, -98), na_values = c(-99, -98))  sum(dcl, na.rm = TRUE) #> [1] 6"},{"path":"http://kylehusmann.com/interlacer/articles/other-approaches.html","id":"interlacer","dir":"Articles","previous_headings":"","what":"interlacer","title":"Other Approaches","text":"interlacer builds ideas haven, labelled, declared following goals:","code":""},{"path":"http://kylehusmann.com/interlacer/articles/other-approaches.html","id":"be-fully-generic-add-a-missing-value-channel-to-any-vector-type","dir":"Articles","previous_headings":"interlacer","what":"1. Be fully generic: Add a missing value channel to any vector type","title":"Other Approaches","text":"mentioned , haven::labelled_spss() works numeric character types, haven::tagged_na() works numeric types. declared::declared() supports numeric, character date types. interlaced types, contrast, can imbue vector type missing value channel: Like declared vectors, missing reasons tracked attributes. unlike declared, missing reasons stored entirely separate channel rather tracking indices: data structure drives functional API, described (3) .","code":"interlaced(list(TRUE, FALSE, \"reason\"), na = \"reason\") #> <interlaced<lgl, fct>[3]> #> [1]  TRUE    FALSE    <reason> #> NA levels: reason  interlaced(c(\"2020-01-01\", \"2020-01-02\", \"reason\"), na = \"reason\") |>   map_value_channel(as.Date) #> <interlaced<date, fct>[3]> #> [1] 2020-01-01 2020-01-02 <reason>   #> NA levels: reason   interlaced(c(\"red\", \"green\", \"reason\"), na = \"reason\") |>   map_value_channel(factor) #> <interlaced<fct, fct>[3]> #> [1] red      green    <reason> #> Levels: green red  #> NA levels: reason (int <- interlaced(c(1,2,3, -99, -98), na = c(-99, -98))) #> <interlaced<dbl, int>[5]> #> [1]  1     2     3    <-99> <-98>  attributes(int) #> $na_channel_values #> [1]  NA  NA  NA -99 -98 #>  #> $class #> [1] \"interlacer_interlaced\" \"vctrs_vctr\"            \"numeric\"  attributes(int) <- NULL int #> [1]  1  2  3 NA NA"},{"path":"http://kylehusmann.com/interlacer/articles/other-approaches.html","id":"provide-functions-for-reading-writing-interlaced-csv-files-not-just-spss-sas-stata-files","dir":"Articles","previous_headings":"interlacer","what":"2. Provide functions for reading / writing interlaced CSV files (not just SPSS / SAS / Stata files)","title":"Other Approaches","text":"See interlacer::read_interlaced_csv(), etc.","code":""},{"path":"http://kylehusmann.com/interlacer/articles/other-approaches.html","id":"provide-a-functional-api-that-integrates-well-into-tidy-pipelines","dir":"Articles","previous_headings":"interlacer","what":"3. Provide a functional API that integrates well into tidy pipelines","title":"Other Approaches","text":"interlacer provides functions facilitate working interlaced type Result type, well-understood abstraction functional programming. functions na() map_value_channel() map_na_channel() come influence. na() function creates interlaced type “lifting” value missing reason channel. approach helps create safer separation value missing reason channels, ’s always clear channel ’re making comparisons . example: Similarly, map_value_channel() map_na_channel() allow safely mutate particular channel, without touching values channel. interface especially useful tidy pipelines. Finally, interlaced type based vctrs type system, plays nicely packages tidyverse.","code":"# haven labelled_spss(c(-99, 1, 2), na_values = -99) == 1 # value channel comparison #> [1] FALSE  TRUE FALSE labelled_spss(c(-99, 1, 2), na_values = -99) == -99 # na channel comparison #> [1]  TRUE FALSE FALSE  # declared declared(c(-99, 1, 2), na_values = -99) == 1 # value channel comparison #> [1] FALSE  TRUE FALSE declared(c(-99, 1, 2), na_values = -99) == -99 # na channel comparison #> [1]  TRUE FALSE FALSE  # interlacer  interlaced(c(-99, 1, 2), na = -99) == 1 # value channel comparison #> [1]    NA  TRUE FALSE interlaced(c(-99, 1, 2), na = -99) == na(-99) # na channel comparison #> [1] NA NA NA"},{"path":"http://kylehusmann.com/interlacer/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Kyle Husmann. Author, maintainer.","code":""},{"path":"http://kylehusmann.com/interlacer/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Husmann K (2024). interlacer: Read Tabular Data Interlaced Values Missing Reasons. R package version 0.4.0, https://kylehusmann.com/interlacer, https://github.com/khusmann/interlacer.","code":"@Manual{,   title = {interlacer: Read Tabular Data With Interlaced Values And Missing Reasons},   author = {Kyle Husmann},   year = {2024},   note = {R package version 0.4.0, https://kylehusmann.com/interlacer},   url = {https://github.com/khusmann/interlacer}, }"},{"path":"http://kylehusmann.com/interlacer/index.html","id":"interlacer-","dir":"","previous_headings":"","what":"Read Tabular Data With Interlaced Values And Missing Reasons","title":"Read Tabular Data With Interlaced Values And Missing Reasons","text":"value missing data, sometimes want know missing. Many textual tabular data sources encode missing reasons special values interlaced regular values column (e.g. N/, REFUSED, -99, etc.). Unfortunately, missing reasons lost values converted single NA type. Working missing reasons R traditionally requires loading variables character vectors bunch string comparisons type conversions make sense . interlacer provides functions load variables interlaced data sources special interlaced column type holds values NA reasons separate channels variable. contexts, can treat interlaced columns regular values: take mean interlaced column, example, get mean values, without missing reasons interfering computation. Unlike regular column, however, missing reasons still available. means can still filter data frames variables specific missing reasons, generate summary statistics breakdowns missing reason. words, longer constantly manually include / exclude missing reasons computations filtering awkward string comparisons type conversions… everything just works! addition introduction vignette(\"interlacer\") sure also check : vignette(\"extended-column-types\") see handle variable-level missing reasons vignette(\"coded-data\") recipies working coded data (e.g. data produced SPSS, SAS Stata) vignette(\"-approaches\") deep dive interlacer’s approach compares approaches representing manipulating missing reasons alongside data values","code":""},{"path":"http://kylehusmann.com/interlacer/index.html","id":"id_️-️-️-warning-️-️-️","dir":"","previous_headings":"","what":"⚠️ ⚠️ ⚠️ WARNING ⚠️ ⚠️ ⚠️","title":"Read Tabular Data With Interlaced Values And Missing Reasons","text":"library currently experimental stages, aware interface quite likely change future. meantime, please try let know think!","code":""},{"path":"http://kylehusmann.com/interlacer/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Read Tabular Data With Interlaced Values And Missing Reasons","text":"easiest way get interlacer install via devtools:","code":"install.packages(\"devtools\") # If devtools is not already installed  devtools::install_github(\"khusmann/interlacer\")"},{"path":"http://kylehusmann.com/interlacer/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Read Tabular Data With Interlaced Values And Missing Reasons","text":"use interlacer, load current R session: interlacer supports following file formats read_interlaced_*() functions, extend readr::read_*() family functions: read_interlaced_csv() read_interlaced_tsv() read_interlaced_csv2() read_interlaced_delim() quick demo, consider following example file bundled interlacer: csv file, values interlaced three possible missing reasons: REFUSED, OMITTED, N/. readr, loading data result data frame missing reasons replaced NA: interlacer, missing reasons preserved: can see, printout column defined two types: type values, type missing reasons. age column, example, type double values, type factor missing reasons: Computations automatically operate values: missing reasons still ! indicate value treated missing reason instead regular value, can use na() function. following, example, filter data set individuals REFUSED give favorite color: ’s pipeline compute breakdown mean age respondents favorite color, separate categories missing reason: just scratches surface can done interlacer… check vignette(\"interlacer\") complete overview!","code":"library(interlacer, warn.conflicts = FALSE) library(dplyr, warn.conflicts = FALSE) library(readr)  read_file(interlacer_example(\"colors.csv\")) |>   cat() #> person_id,age,favorite_color #> 1,20,BLUE #> 2,REFUSED,BLUE #> 3,21,REFUSED #> 4,30,OMITTED #> 5,1,N/A #> 6,41,RED #> 7,50,OMITTED #> 8,30,YELLOW #> 9,REFUSED,REFUSED #> 10,OMITTED,RED #> 11,10,REFUSED read_csv(   interlacer_example(\"colors.csv\"),   na = c(\"REFUSED\", \"OMITTED\", \"N/A\") ) #> # A tibble: 11 × 3 #>    person_id   age favorite_color #>        <dbl> <dbl> <chr>          #>  1         1    20 BLUE           #>  2         2    NA BLUE           #>  3         3    21 <NA>           #>  4         4    30 <NA>           #>  5         5     1 <NA>           #>  6         6    41 RED            #>  7         7    50 <NA>           #>  8         8    30 YELLOW         #>  9         9    NA <NA>           #> 10        10    NA RED            #> 11        11    10 <NA> (ex <- read_interlaced_csv(   interlacer_example(\"colors.csv\"),   na = c(\"REFUSED\", \"OMITTED\", \"N/A\") )) #> # A tibble: 11 × 3 #>    person_id       age favorite_color #>    <dbl,fct> <dbl,fct> <chr,fct>      #>  1         1        20 BLUE           #>  2         2 <REFUSED> BLUE           #>  3         3        21 <REFUSED>      #>  4         4        30 <OMITTED>      #>  5         5         1 <N/A>          #>  6         6        41 RED            #>  7         7        50 <OMITTED>      #>  8         8        30 YELLOW         #>  9         9 <REFUSED> <REFUSED>      #> 10        10 <OMITTED> RED            #> 11        11        10 <REFUSED> ex$age #> <interlaced<dbl, fct>[11]> #>  [1] 20        <REFUSED> 21        30        1         41        50        #>  [8] 30        <REFUSED> <OMITTED> 10        #> NA levels: REFUSED OMITTED N/A mean(ex$age, na.rm = TRUE) #> [1] 25.375 ex |>   filter(favorite_color == na(\"REFUSED\")) #> # A tibble: 3 × 3 #>   person_id       age favorite_color #>   <dbl,fct> <dbl,fct> <chr,fct>      #> 1         3        21 <REFUSED>      #> 2         9 <REFUSED> <REFUSED>      #> 3        11        10 <REFUSED> ex |>   summarize(     mean_age = mean(age, na.rm = TRUE),     n = n(),     .by = favorite_color   ) |>   arrange(favorite_color) #> # A tibble: 6 × 3 #>   favorite_color mean_age     n #>   <chr,fct>         <dbl> <int> #> 1 BLUE               20       2 #> 2 RED                41       2 #> 3 YELLOW             30       1 #> 4 <REFUSED>          15.5     3 #> 5 <OMITTED>          40       2 #> 6 <N/A>               1       1"},{"path":"http://kylehusmann.com/interlacer/index.html","id":"known-issues","dir":"","previous_headings":"","what":"Known Issues","title":"Read Tabular Data With Interlaced Values And Missing Reasons","text":"base functions, like base::ifelse(), drop missing reason channel interlaced types, converting regular vectors example: due limitation R. run , use tidyverse equivalent function. Tidyverse functions designed correctly handle type conversions. example, can use dplyr::if_else(): Performance large data sets may notice large datasets interlacer runs significantly slower readr / vroom. Although interlacer uses vroom hood load delimited data, able take advantage many optimizations vroom currently support column-level missing values. soon vroom supports column-level missing values, able remedy !","code":"ex |>   mutate(     favorite_color = ifelse(age < 18, na(\"REDACTED\"), favorite_color)   ) #> # A tibble: 11 × 3 #>    person_id       age favorite_color #>    <dbl,fct> <dbl,fct> <chr>          #>  1         1        20 BLUE           #>  2         2 <REFUSED> <NA>           #>  3         3        21 <NA>           #>  4         4        30 <NA>           #>  5         5         1 <NA>           #>  6         6        41 RED            #>  7         7        50 <NA>           #>  8         8        30 YELLOW         #>  9         9 <REFUSED> <NA>           #> 10        10 <OMITTED> <NA>           #> 11        11        10 <NA> ex |>   mutate(     favorite_color = if_else(       age < 18,       na(\"REDACTED_UNDERAGE\"),       favorite_color,       missing = na(\"REDACTED_MISSING_AGE\")     )   ) #> # A tibble: 11 × 3 #>    person_id       age favorite_color         #>    <dbl,fct> <dbl,fct> <chr,fct>              #>  1         1        20 BLUE                   #>  2         2 <REFUSED> <REDACTED_MISSING_AGE> #>  3         3        21 <REFUSED>              #>  4         4        30 <OMITTED>              #>  5         5         1 <REDACTED_UNDERAGE>    #>  6         6        41 RED                    #>  7         7        50 <OMITTED>              #>  8         8        30 YELLOW                 #>  9         9 <REFUSED> <REDACTED_MISSING_AGE> #> 10        10 <OMITTED> <REDACTED_MISSING_AGE> #> 11        11        10 <REDACTED_UNDERAGE>"},{"path":"http://kylehusmann.com/interlacer/index.html","id":"related-work","dir":"","previous_headings":"","what":"Related work","title":"Read Tabular Data With Interlaced Values And Missing Reasons","text":"interlacer inspired haven, labelled, declared packages. packages provide similar functionality interlacer, focused providing compatibility missing reason data imported SPSS, SAS, Stata. interlacer slightly different aims: fully generic: Add missing value channel vector type. Provide functions reading / writing interlaced CSV files (just SPSS / SAS / Stata files) Provide functional API integrates well tidy pipelines Future versions interlacer provide functions convert packages’ types. detailed discussion, see vignette(\"-approaches\").","code":""},{"path":"http://kylehusmann.com/interlacer/index.html","id":"acknowledgements","dir":"","previous_headings":"","what":"Acknowledgements","title":"Read Tabular Data With Interlaced Values And Missing Reasons","text":"development software supported, whole part, Institute Education Sciences, U.S. Department Education, Grant R305A170047 Pennsylvania State University. opinions expressed authors represent views Institute U.S. Department Education.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/across_value_channels.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply a function across the value or missing reason channels of multiple columns — across_value_channels","title":"Apply a function across the value or missing reason channels of multiple columns — across_value_channels","text":"across_value_channels() across_na_channels() simple wrappers dplyr::across() applies transformations value missing reason channels, respectively.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/across_value_channels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply a function across the value or missing reason channels of multiple columns — across_value_channels","text":"","code":"across_value_channels(.cols, .fns, .names = NULL, .unpack = FALSE)  across_na_channels(.cols, .fns, .names = NULL, .unpack = FALSE)"},{"path":"http://kylehusmann.com/interlacer/reference/across_value_channels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply a function across the value or missing reason channels of multiple columns — across_value_channels","text":".cols <tidy-select> Columns transform. select grouping columns already automatically handled verb (.e. summarise() mutate()). .fns Functions apply selected columns. Possible values : function, e.g. mean. purrr-style lambda, e.g. ~ mean(.x, na.rm = TRUE) named list functions lambdas, e.g. list(mean = mean, n_miss = ~ sum(.na(.x)). function applied column, output named combining function name column name using glue specification .names. Within functions can use cur_column() cur_group() access current column grouping keys respectively. .names glue specification describes name output columns. can use {.col} stand selected column name, {.fn} stand name function applied. default (NULL) equivalent \"{.col}\" single function case \"{.col}_{.fn}\" case list used .fns. .unpack  Optionally unpack data frames returned functions .fns, expands df-columns individual columns, retaining number rows data frame. FALSE, default, unpacking done. TRUE, unpacking done default glue specification \"{outer}_{inner}\". Otherwise, single glue specification can supplied describe name unpacked columns. can use {outer} refer name originally generated .names, {inner} refer names data frame unpacking.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/across_value_channels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply a function across the value or missing reason channels of multiple columns — across_value_channels","text":"like dplyr::across(), across_value_channels() across_na_channels() return tibble one column column .cols function .fns","code":""},{"path":[]},{"path":"http://kylehusmann.com/interlacer/reference/as.cfactor.html","id":null,"dir":"Reference","previous_headings":"","what":"cfactor coercion — as.cfactor","title":"cfactor coercion — as.cfactor","text":"Add codes vector labels","code":""},{"path":"http://kylehusmann.com/interlacer/reference/as.cfactor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"cfactor coercion — as.cfactor","text":"","code":"as.cfactor(x, codes = NULL, ordered = is.ordered(x))  # S3 method for class 'factor' as.cfactor(x, codes = NULL, ordered = is.ordered(x))  as.cordered(x, codes = NULL)"},{"path":"http://kylehusmann.com/interlacer/reference/as.cfactor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"cfactor coercion — as.cfactor","text":"x vector values representing labels factor levels codes named vector unique codes declares mapping labels codes ordered logical flag determine codes regarded ordered (order given).","code":""},{"path":"http://kylehusmann.com/interlacer/reference/as.cfactor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"cfactor coercion — as.cfactor","text":"new cfactor","code":""},{"path":"http://kylehusmann.com/interlacer/reference/as.codes.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert a cfactor vector into a vector of its codes — as.codes","title":"Convert a cfactor vector into a vector of its codes — as.codes","text":"TODO: Write ","code":""},{"path":"http://kylehusmann.com/interlacer/reference/as.codes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert a cfactor vector into a vector of its codes — as.codes","text":"","code":"as.codes(x, ...)  # S3 method for class 'interlacer_interlaced' as.codes(x, ...)  # S3 method for class 'interlacer_cfactor' as.codes(x, ...)"},{"path":"http://kylehusmann.com/interlacer/reference/as.codes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert a cfactor vector into a vector of its codes — as.codes","text":"x cfactor() ... additional arguments (used)","code":""},{"path":"http://kylehusmann.com/interlacer/reference/as.codes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert a cfactor vector into a vector of its codes — as.codes","text":"vector coded values","code":""},{"path":"http://kylehusmann.com/interlacer/reference/as.x_col_spec.html","id":null,"dir":"Reference","previous_headings":"","what":"Extended column specification coercions — as.x_col_spec","title":"Extended column specification coercions — as.x_col_spec","text":"Coerce object column specification. used internally parse col_types argument read_interlaced_*() family functions, can accept readr::cols() specification list().","code":""},{"path":"http://kylehusmann.com/interlacer/reference/as.x_col_spec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extended column specification coercions — as.x_col_spec","text":"","code":"as.x_col_spec(x)"},{"path":"http://kylehusmann.com/interlacer/reference/as.x_col_spec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extended column specification coercions — as.x_col_spec","text":"x value coerce extended column specification","code":""},{"path":"http://kylehusmann.com/interlacer/reference/as.x_col_spec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extended column specification coercions — as.x_col_spec","text":"extended column specification","code":""},{"path":"http://kylehusmann.com/interlacer/reference/as.x_col_spec.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extended column specification coercions — as.x_col_spec","text":"S3 function packages may use col_types argument custom objects.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/as.x_collector.html","id":null,"dir":"Reference","previous_headings":"","what":"Collector shortcuts — as.na_collector","title":"Collector shortcuts — as.na_collector","text":".*_collector functions used internally enable shortcuts defaults specifying extended collectors. See vignette(\"extended-column-types\") full discussion.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/as.x_collector.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Collector shortcuts — as.na_collector","text":"","code":"as.na_collector(x)  as.value_collector(x)  as.x_collector(x)"},{"path":"http://kylehusmann.com/interlacer/reference/as.x_collector.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Collector shortcuts — as.na_collector","text":"x value convert extended collector, value collector, missing reason collector.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/cfactor.html","id":null,"dir":"Reference","previous_headings":"","what":"Coded factors — cfactor","title":"Coded factors — cfactor","text":"TODO: Write ","code":""},{"path":"http://kylehusmann.com/interlacer/reference/cfactor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coded factors — cfactor","text":"","code":"cfactor(x = unspecified(), codes, ordered = FALSE)  cordered(x, codes)  is.cfactor(x)  is.cordered(x)"},{"path":"http://kylehusmann.com/interlacer/reference/cfactor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coded factors — cfactor","text":"x vector character numeric codes codes named vector unique codes declares mapping labels codes ordered logical flag determine codes regarded ordered (order given).","code":""},{"path":"http://kylehusmann.com/interlacer/reference/cfactor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coded factors — cfactor","text":"new cfactor","code":""},{"path":"http://kylehusmann.com/interlacer/reference/codes-set.html","id":null,"dir":"Reference","previous_headings":"","what":"Set the codes for a `cfactor“ — codes<-","title":"Set the codes for a `cfactor“ — codes<-","text":"Set codes cfactor, similar levels<-()","code":""},{"path":"http://kylehusmann.com/interlacer/reference/codes-set.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set the codes for a `cfactor“ — codes<-","text":"","code":"codes(x) <- value"},{"path":"http://kylehusmann.com/interlacer/reference/codes-set.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set the codes for a `cfactor“ — codes<-","text":"value named vector codes cfactor","code":""},{"path":"http://kylehusmann.com/interlacer/reference/codes.html","id":null,"dir":"Reference","previous_headings":"","what":"cfactor attributes — codes","title":"cfactor attributes — codes","text":"Return levels codes cfactor","code":""},{"path":"http://kylehusmann.com/interlacer/reference/codes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"cfactor attributes — codes","text":"","code":"codes(x, ...)  # S3 method for class 'interlacer_cfactor' levels(x)"},{"path":"http://kylehusmann.com/interlacer/reference/codes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"cfactor attributes — codes","text":"x cfactor ... additional arguments (used)","code":""},{"path":"http://kylehusmann.com/interlacer/reference/codes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"cfactor attributes — codes","text":"levels() returns levels cfactor (vector character labels); codes() returns named vector representing codes cfactor","code":""},{"path":"http://kylehusmann.com/interlacer/reference/flatten_channels.html","id":null,"dir":"Reference","previous_headings":"","what":"Flatten a interlaced vector — flatten_channels","title":"Flatten a interlaced vector — flatten_channels","text":"flatten_channels() flattens interlaced vector single channel. useful step right writing interlaced vector file, example.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/flatten_channels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Flatten a interlaced vector — flatten_channels","text":"","code":"flatten_channels(x, ...)"},{"path":"http://kylehusmann.com/interlacer/reference/flatten_channels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Flatten a interlaced vector — flatten_channels","text":"x interlaced vector ... Additional arguments, used","code":""},{"path":"http://kylehusmann.com/interlacer/reference/flatten_channels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Flatten a interlaced vector — flatten_channels","text":"vector, flattened","code":""},{"path":"http://kylehusmann.com/interlacer/reference/grapes-equals-grapes.html","id":null,"dir":"Reference","previous_headings":"","what":"Multichannel comparison — %==%","title":"Multichannel comparison — %==%","text":"Regular comparison operators (.e. == !=) apply value channel; used missing values always result NA. contrast, multichannel comparison operators %==% %!=% compare value missing reason channels variable. Multichannel equality %==% operates identically regular == operands values, return FALSE instead NA one missing. values compared %==% missing values, missing reasons compared. Multichannel comparison operators always return boolean values, never NA","code":""},{"path":"http://kylehusmann.com/interlacer/reference/grapes-equals-grapes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multichannel comparison — %==%","text":"","code":"x %==% y  x %!=% y"},{"path":"http://kylehusmann.com/interlacer/reference/grapes-equals-grapes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multichannel comparison — %==%","text":"x vector y vector compare","code":""},{"path":"http://kylehusmann.com/interlacer/reference/grapes-equals-grapes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Multichannel comparison — %==%","text":"boolean vector indicating result comparison","code":""},{"path":"http://kylehusmann.com/interlacer/reference/interlaced.html","id":null,"dir":"Reference","previous_headings":"","what":"Construct an interlaced vector — interlaced","title":"Construct an interlaced vector — interlaced","text":"interlaced type extends vectors adding \"missing reason\" channel can used distinguish different types missingness. interlaced() function constructs new interlaced vector vector list values.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/interlaced.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Construct an interlaced vector — interlaced","text":"","code":"interlaced(x, na = NULL)  as.interlaced(x, na = NULL, ...)  # Default S3 method as.interlaced(x, na = NULL, ...)  # S3 method for class 'interlacer_interlaced' as.interlaced(x, ...)  # S3 method for class 'data.frame' as.interlaced(x, ...)  is.interlaced(x)"},{"path":"http://kylehusmann.com/interlacer/reference/interlaced.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Construct an interlaced vector — interlaced","text":"x vector list values na vector values interpret missing values ... Additional arguments, used","code":""},{"path":"http://kylehusmann.com/interlacer/reference/interlaced.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Construct an interlaced vector — interlaced","text":"interlaced vector","code":""},{"path":"http://kylehusmann.com/interlacer/reference/interlacer_example.html","id":null,"dir":"Reference","previous_headings":"","what":"Get a path to one of interlacer's example data sets — interlacer_example","title":"Get a path to one of interlacer's example data sets — interlacer_example","text":"interlacer comes bundled number sample files inst/extdata directory. function make easy access","code":""},{"path":"http://kylehusmann.com/interlacer/reference/interlacer_example.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get a path to one of interlacer's example data sets — interlacer_example","text":"","code":"interlacer_example(file = NULL)"},{"path":"http://kylehusmann.com/interlacer/reference/interlacer_example.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get a path to one of interlacer's example data sets — interlacer_example","text":"file Name file. NULL, example files listed.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/interlacer_example.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get a path to one of interlacer's example data sets — interlacer_example","text":"","code":"interlacer_example() #> [1] \"colors.csv\"            \"colors.dta\"            \"colors.sav\"            #> [4] \"colors_coded.csv\"      \"colors_coded_char.csv\" \"stress.csv\"            interlacer_example(\"colors.csv\") #> [1] \"/home/runner/work/_temp/Library/interlacer/extdata/colors.csv\""},{"path":"http://kylehusmann.com/interlacer/reference/levels-set-.interlacer_interlaced.html","id":null,"dir":"Reference","previous_headings":"","what":"Set the factor level attributes of interlaced vectors — levels<-.interlacer_interlaced","title":"Set the factor level attributes of interlaced vectors — levels<-.interlacer_interlaced","text":"Set factor level attributes interlaced vectors","code":""},{"path":"http://kylehusmann.com/interlacer/reference/levels-set-.interlacer_interlaced.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set the factor level attributes of interlaced vectors — levels<-.interlacer_interlaced","text":"","code":"# S3 method for class 'interlacer_interlaced' levels(x) <- value  na_levels(x) <- value"},{"path":"http://kylehusmann.com/interlacer/reference/levels-set-.interlacer_interlaced.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set the factor level attributes of interlaced vectors — levels<-.interlacer_interlaced","text":"value vector new levels set","code":""},{"path":"http://kylehusmann.com/interlacer/reference/levels.interlacer_interlaced.html","id":null,"dir":"Reference","previous_headings":"","what":"Factor level attributes of interlaced vectors — levels.interlacer_interlaced","title":"Factor level attributes of interlaced vectors — levels.interlacer_interlaced","text":"base S3 levels() function overloaded interlaced vectors, value channel factor type, levels() return levels. Similarly na_levels() return levels missing reason channel.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/levels.interlacer_interlaced.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Factor level attributes of interlaced vectors — levels.interlacer_interlaced","text":"","code":"# S3 method for class 'interlacer_interlaced' levels(x)  na_levels(x)"},{"path":"http://kylehusmann.com/interlacer/reference/levels.interlacer_interlaced.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Factor level attributes of interlaced vectors — levels.interlacer_interlaced","text":"x interlaced vector","code":""},{"path":"http://kylehusmann.com/interlacer/reference/levels.interlacer_interlaced.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Factor level attributes of interlaced vectors — levels.interlacer_interlaced","text":"levels values missing reason channel","code":""},{"path":"http://kylehusmann.com/interlacer/reference/map_value_channel.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply a function to one of the channels of an interlaced vector — map_value_channel","title":"Apply a function to one of the channels of an interlaced vector — map_value_channel","text":"map_value_channel() modifies values interlaced vector. map_na_channel() modifies missing reason channel interlaced vector.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/map_value_channel.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply a function to one of the channels of an interlaced vector — map_value_channel","text":"","code":"map_value_channel(x, fn)  map_na_channel(x, fn)"},{"path":"http://kylehusmann.com/interlacer/reference/map_value_channel.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply a function to one of the channels of an interlaced vector — map_value_channel","text":"x interlaced vector fn function maps values missing reasons new values","code":""},{"path":"http://kylehusmann.com/interlacer/reference/map_value_channel.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply a function to one of the channels of an interlaced vector — map_value_channel","text":"new interlaced vector, modified according supplied function","code":""},{"path":"http://kylehusmann.com/interlacer/reference/na.html","id":null,"dir":"Reference","previous_headings":"","what":"Interpret a value as a missing reason — na","title":"Interpret a value as a missing reason — na","text":"na() lifts value interlaced missing reason channel.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interpret a value as a missing reason — na","text":"","code":"na(x = NA)"},{"path":"http://kylehusmann.com/interlacer/reference/na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interpret a value as a missing reason — na","text":"x character numeric value","code":""},{"path":"http://kylehusmann.com/interlacer/reference/na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interpret a value as a missing reason — na","text":"interlaced value","code":""},{"path":"http://kylehusmann.com/interlacer/reference/na_collectors.html","id":null,"dir":"Reference","previous_headings":"","what":"Missing reason collectors — na_collectors","title":"Missing reason collectors — na_collectors","text":"Missing reason collectors used extended column specifications specify type column's missing reason channel.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/na_collectors.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Missing reason collectors — na_collectors","text":"","code":"na_col_default()  na_col_none()  na_col_integer(...)  na_col_factor(...)  na_col_cfactor(...)"},{"path":"http://kylehusmann.com/interlacer/reference/na_collectors.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Missing reason collectors — na_collectors","text":"... values interpret missing values. case na_col_cfactor(), arguments must named.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/na_collectors.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Missing reason collectors — na_collectors","text":"new missing reason collector object","code":""},{"path":"http://kylehusmann.com/interlacer/reference/na_collectors.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Missing reason collectors — na_collectors","text":"na_col_default() used signal missing reason type inherit specification provided na =  argument calling read_interlaced_*() function","code":""},{"path":[]},{"path":"http://kylehusmann.com/interlacer/reference/parse_interlaced.html","id":null,"dir":"Reference","previous_headings":"","what":"Parse a character vector into an interlaced vector type — parse_interlaced","title":"Parse a character vector into an interlaced vector type — parse_interlaced","text":"parse_interlaced converts character vector interlaced vector parsing readr collector type.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/parse_interlaced.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parse a character vector into an interlaced vector type — parse_interlaced","text":"","code":"parse_interlaced(x, na, .default = v_col_guess())"},{"path":"http://kylehusmann.com/interlacer/reference/parse_interlaced.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Parse a character vector into an interlaced vector type — parse_interlaced","text":"x character vector na missing reason collector (e.g. na_col_integer()), one shortcuts (e.g. list missing values) .default value collector parse character values (e.g. v_col_double(), v_col_integer(), etc.)","code":""},{"path":"http://kylehusmann.com/interlacer/reference/parse_interlaced.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Parse a character vector into an interlaced vector type — parse_interlaced","text":"interlaced vector","code":""},{"path":"http://kylehusmann.com/interlacer/reference/read_interlaced_delim.html","id":null,"dir":"Reference","previous_headings":"","what":"Read an delimited file with interlaced missing reasons into a tibble — read_interlaced_delim","title":"Read an delimited file with interlaced missing reasons into a tibble — read_interlaced_delim","text":"read_interlaced_*(), family functions extend readr's read_delim(), read_csv, etc. functions use data sources values interlaced missing reasons. functions return tibble interlaced columns.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/read_interlaced_delim.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read an delimited file with interlaced missing reasons into a tibble — read_interlaced_delim","text":"","code":"read_interlaced_delim(   file,   delim = NULL,   quote = \"\\\"\",   escape_backslash = FALSE,   escape_double = TRUE,   col_names = TRUE,   col_types = NULL,   col_select = NULL,   id = NULL,   locale = readr::default_locale(),   na = na_col_none(),   comment = \"\",   trim_ws = FALSE,   skip = 0,   n_max = Inf,   guess_max = min(1000, n_max),   name_repair = \"unique\",   progress = readr::show_progress(),   show_col_types = readr::should_show_types(),   skip_empty_rows = TRUE )  read_interlaced_csv(   file,   col_names = TRUE,   col_types = NULL,   col_select = NULL,   id = NULL,   locale = readr::default_locale(),   na = na_col_none(),   quote = \"\\\"\",   comment = \"\",   trim_ws = TRUE,   skip = 0,   n_max = Inf,   guess_max = min(1000, n_max),   name_repair = \"unique\",   progress = readr::show_progress(),   show_col_types = readr::should_show_types(),   skip_empty_rows = TRUE )  read_interlaced_csv2(   file,   col_names = TRUE,   col_types = NULL,   col_select = NULL,   id = NULL,   locale = readr::default_locale(),   na = na_col_none(),   quote = \"\\\"\",   comment = \"\",   trim_ws = TRUE,   skip = 0,   n_max = Inf,   guess_max = min(1000, n_max),   name_repair = \"unique\",   progress = readr::show_progress(),   show_col_types = readr::should_show_types(),   skip_empty_rows = TRUE )  read_interlaced_tsv(   file,   col_names = TRUE,   col_types = NULL,   col_select = NULL,   id = NULL,   locale = readr::default_locale(),   na = na_col_none(),   quote = \"\\\"\",   comment = \"\",   trim_ws = TRUE,   skip = 0,   n_max = Inf,   guess_max = min(1000, n_max),   name_repair = \"unique\",   progress = readr::show_progress(),   show_col_types = readr::should_show_types(),   skip_empty_rows = TRUE )  interlaced_vroom(   file,   delim = NULL,   col_names = TRUE,   col_types = NULL,   col_select = NULL,   id = NULL,   skip = 0,   n_max = Inf,   na = na_col_none(),   quote = \"\\\"\",   comment = \"\",   skip_empty_rows = TRUE,   trim_ws = TRUE,   escape_double = TRUE,   escape_backslash = FALSE,   locale = vroom::default_locale(),   guess_max = 100,   progress = vroom::vroom_progress(),   show_col_types = NULL,   .name_repair = \"unique\" )"},{"path":"http://kylehusmann.com/interlacer/reference/read_interlaced_delim.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read an delimited file with interlaced missing reasons into a tibble — read_interlaced_delim","text":"file Either path file, connection, literal data (either single string raw vector). Files ending .gz, .bz2, .xz, .zip automatically uncompressed. Files starting http://, https://, ftp://, ftps:// automatically downloaded. Remote gz files can also automatically downloaded decompressed. Literal data useful examples tests. recognised literal data, input must either wrapped (), string containing least one new line, vector containing least one string new line. Using value clipboard() read system clipboard. delim Single character used separate fields within record. quote Single character used quote strings. escape_backslash file use backslashes escape special characters? general escape_double backslashes can used escape delimiter character, quote character, add special characters like \\\\n. escape_double file escape quotes doubling ? .e. option TRUE, value \"\"\"\" represents single quote, \\\". col_names Either TRUE, FALSE character vector column names. TRUE, first row input used column names, included data frame. FALSE, column names generated automatically: X1, X2, X3 etc. col_names character vector, values used names columns, first row input read first row output data frame. Missing (NA) column names generate warning, filled dummy names ...1, ...2 etc. Duplicate column names generate warning made unique, see name_repair control done. col_types One NULL, cols() specification, string. See vignette(\"readr\") details. NULL, column types inferred guess_max rows input, interspersed throughout file. convenient (fast), robust. guessed types wrong, need increase guess_max supply correct types . Column specifications created list() cols() must contain one column specification column. want read subset columns, use cols_only(). Alternatively, can use compact string representation character represents one column: c = character = integer n = number d = double l = logical f = factor D = date T = date time t = time ? = guess _ - = skip default, reading file without column specification print message showing readr guessed . remove message, set show_col_types = FALSE set options(readr.show_col_types = FALSE). col_select Columns include results. can use mini-language dplyr::select() refer columns name. Use c() use one selection expression. Although usage less common, col_select also accepts numeric column index. See ?tidyselect::language full details selection language. id name column store file path. useful reading multiple input files data file paths, data collection date. NULL (default) extra column created. locale locale controls defaults vary place place. default locale US-centric (like R), can use locale() create locale controls things like default time zone, encoding, decimal mark, big mark, day/month names. na NA col spec defined na_cols() character numeric vector values interpret missing values. comment string used identify comments. text comment characters silently ignored. trim_ws leading trailing whitespace (ASCII spaces tabs) trimmed field parsing ? skip Number lines skip reading data. comment supplied commented lines ignored skipping. n_max Maximum number lines read. guess_max Maximum number lines use guessing column types. never use number lines read. See vignette(\"column-types\", package = \"readr\") details. name_repair, .name_repair Handling column names. default behaviour ensure column names \"unique\". Various repair strategies supported: \"minimal\": name repair checks, beyond basic existence names. \"unique\" (default value): Make sure names unique empty. \"check_unique\": name repair, check unique. \"unique_quiet\": Repair unique strategy, quietly. \"universal\": Make names unique syntactic. \"universal_quiet\": Repair universal strategy, quietly. function: Apply custom name repair (e.g., name_repair = make.names names style base R). purrr-style anonymous function, see rlang::as_function(). argument passed repair vctrs::vec_as_names(). See details terms strategies used enforce . progress Display progress bar? default display interactive session knitting document. automatic progress bar can disabled setting option readr.show_progress FALSE. show_col_types FALSE, show guessed column types. TRUE always show column types, even supplied. NULL (default) show column types explicitly supplied col_types argument. skip_empty_rows blank rows ignored altogether? .e. option TRUE blank rows represented .  FALSE represented NA values columns.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/read_interlaced_delim.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read an delimited file with interlaced missing reasons into a tibble — read_interlaced_delim","text":"tibble(), interlaced columns.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/read_interlaced_delim.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read an delimited file with interlaced missing reasons into a tibble — read_interlaced_delim","text":"","code":"# Beep boop"},{"path":"http://kylehusmann.com/interlacer/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. generics .factor, .ordered vctrs vec_c","code":""},{"path":"http://kylehusmann.com/interlacer/reference/value_channel.html","id":null,"dir":"Reference","previous_headings":"","what":"Access the channels of an interlaced vector — value_channel","title":"Access the channels of an interlaced vector — value_channel","text":"value_channel() returns value channel interlaced vector na_channel() returns missing reason channel interlaced vector","code":""},{"path":"http://kylehusmann.com/interlacer/reference/value_channel.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access the channels of an interlaced vector — value_channel","text":"","code":"value_channel(x, ...)  na_channel(x, ...)"},{"path":"http://kylehusmann.com/interlacer/reference/value_channel.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access the channels of an interlaced vector — value_channel","text":"x interlaced vector ... Additional arguments, used","code":""},{"path":"http://kylehusmann.com/interlacer/reference/value_channel.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access the channels of an interlaced vector — value_channel","text":"value missing reasons channel","code":""},{"path":"http://kylehusmann.com/interlacer/reference/value_collectors.html","id":null,"dir":"Reference","previous_headings":"","what":"Value collectors — value_collectors","title":"Value collectors — value_collectors","text":"Value collectors used extended column specifications specify value type column. think wrappers around readr's col_*() collector types.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/value_collectors.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Value collectors — value_collectors","text":"","code":"v_col_guess()  v_col_cfactor(codes, ordered = FALSE)  v_col_character()  v_col_date(format = \"\")  v_col_datetime(format = \"\")  v_col_double()  v_col_factor(levels = NULL, ordered = FALSE)  v_col_integer()  v_col_big_integer()  v_col_logical()  v_col_number()  v_col_skip()  v_col_time(format = \"\")"},{"path":"http://kylehusmann.com/interlacer/reference/value_collectors.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Value collectors — value_collectors","text":"codes named vector unique codes declares mapping labels codes. ordered ordered factor? format format specification, described readr::col_datetime() levels Character vector allowed levels. levels = NULL (default), levels discovered unique values x, order appear x.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/value_collectors.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Value collectors — value_collectors","text":"new value collector object","code":""},{"path":"http://kylehusmann.com/interlacer/reference/value_collectors.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Value collectors — value_collectors","text":"addition column types supported readr, interlacer additionally can load cfactor() types via v_col_cfactor()","code":""},{"path":[]},{"path":"http://kylehusmann.com/interlacer/reference/where_value_channel.html","id":null,"dir":"Reference","previous_headings":"","what":"Select variables with a function applied on value or missing reason channels — where_value_channel","title":"Select variables with a function applied on value or missing reason channels — where_value_channel","text":"where_value_channel() where_na_channel() simple wrappers tidyselect::() apply selection function value missing reason channel columns, respectively.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/where_value_channel.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Select variables with a function applied on value or missing reason channels — where_value_channel","text":"","code":"where_value_channel(fn)  where_na_channel(fn)"},{"path":"http://kylehusmann.com/interlacer/reference/where_value_channel.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Select variables with a function applied on value or missing reason channels — where_value_channel","text":"fn function returns TRUE FALSE (technically, predicate function). Can also purrr-like formula.","code":""},{"path":[]},{"path":"http://kylehusmann.com/interlacer/reference/write_interlaced_delim.html","id":null,"dir":"Reference","previous_headings":"","what":"Interlace a deinterlaced data frame and write it to a file — write_interlaced_delim","title":"Interlace a deinterlaced data frame and write it to a file — write_interlaced_delim","text":"write_interlaced_*() family functions take data frame interlaced columns, flatten interlaced columns, write file. Non-interlaced columns just pass . behavior functions match similarly named counterparts readr.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/write_interlaced_delim.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interlace a deinterlaced data frame and write it to a file — write_interlaced_delim","text":"","code":"write_interlaced_delim(   x,   file,   delim = \" \",   empty = \"NA\",   append = FALSE,   col_names = !append,   quote = c(\"needed\", \"all\", \"none\"),   escape = c(\"double\", \"backslash\", \"none\"),   eol = \"\\n\",   num_threads = readr::readr_threads(),   progress = readr::show_progress() )  write_interlaced_csv(   x,   file,   empty = \"NA\",   append = FALSE,   col_names = !append,   quote = c(\"needed\", \"all\", \"none\"),   escape = c(\"double\", \"backslash\", \"none\"),   eol = \"\\n\",   num_threads = readr::readr_threads(),   progress = readr::show_progress() )  write_interlaced_csv2(   x,   file,   empty = \"NA\",   append = FALSE,   col_names = !append,   quote = c(\"needed\", \"all\", \"none\"),   escape = c(\"double\", \"backslash\", \"none\"),   eol = \"\\n\",   num_threads = readr::readr_threads(),   progress = readr::show_progress() )  write_interlaced_excel_csv(   x,   file,   empty = \"NA\",   append = FALSE,   col_names = !append,   quote = c(\"needed\", \"all\", \"none\"),   escape = c(\"double\", \"backslash\", \"none\"),   eol = \"\\n\",   num_threads = readr::readr_threads(),   progress = readr::show_progress() )  write_interlaced_excel_csv2(   x,   file,   empty = \"NA\",   append = FALSE,   col_names = !append,   quote = c(\"needed\", \"all\", \"none\"),   escape = c(\"double\", \"backslash\", \"none\"),   eol = \"\\n\",   num_threads = readr::readr_threads(),   progress = readr::show_progress() )  write_interlaced_tsv(   x,   file,   empty = \"NA\",   append = FALSE,   col_names = !append,   quote = c(\"needed\", \"all\", \"none\"),   escape = c(\"double\", \"backslash\", \"none\"),   eol = \"\\n\",   num_threads = readr::readr_threads(),   progress = readr::show_progress() )"},{"path":"http://kylehusmann.com/interlacer/reference/write_interlaced_delim.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interlace a deinterlaced data frame and write it to a file — write_interlaced_delim","text":"x data frame tibble write disk. file File connection write . delim Delimiter used separate values. Defaults \" \" write_delim(), \",\" write_excel_csv() \";\" write_excel_csv2(). Must single character. empty String used empty values (NA values non-interlaced columns). Defaults NA. append FALSE, overwrite existing file. TRUE, append existing file. cases, file exist new file created. col_names FALSE, column names included top file. TRUE, column names included. specified, col_names take opposite value given append. quote handle fields contain characters need quoted. needed - Values quoted needed: contain delimiter, quote, newline. - Quote fields. none - Never quote fields. escape type escape use quotes data. double - quotes escaped doubling . backslash - quotes escaped preceding backslash. none - quotes escaped. eol end line character use. commonly either \"\\n\" Unix style newlines, \"\\r\\n\" Windows style newlines. num_threads Number threads use reading materializing vectors. data contains newlines within fields parser automatically forced use single thread . progress Display progress bar? default display interactive session knitting document. display updated every 50,000 values display estimated reading time 5 seconds . automatic progress bar can disabled setting option readr.show_progress FALSE.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/write_interlaced_delim.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interlace a deinterlaced data frame and write it to a file — write_interlaced_delim","text":"write_interlaced_* returns input x invisibly","code":""},{"path":"http://kylehusmann.com/interlacer/reference/x_col.html","id":null,"dir":"Reference","previous_headings":"","what":"Construct an extended collector for an extended column specification — x_col","title":"Construct an extended collector for an extended column specification — x_col","text":"Extended collectors used x_cols() column specifications indicate value missing reason channel types used loading data read_interlaced_*().","code":""},{"path":"http://kylehusmann.com/interlacer/reference/x_col.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Construct an extended collector for an extended column specification — x_col","text":"","code":"x_col(value_collector, na_collector = na_col_default())"},{"path":"http://kylehusmann.com/interlacer/reference/x_col.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Construct an extended collector for an extended column specification — x_col","text":"value_collector value collector na_collector missing reason collector","code":""},{"path":"http://kylehusmann.com/interlacer/reference/x_col.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Construct an extended collector for an extended column specification — x_col","text":"new extended collector object","code":""},{"path":[]},{"path":"http://kylehusmann.com/interlacer/reference/x_cols.html","id":null,"dir":"Reference","previous_headings":"","what":"Construct an extended column specification — x_cols","title":"Construct an extended column specification — x_cols","text":"Extended column specifications used read_interlaced_*() family functions col_types argument specify value missing reason channel types.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/x_cols.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Construct an extended column specification — x_cols","text":"","code":"x_cols(..., .default = v_col_guess())  x_cols_only(...)"},{"path":"http://kylehusmann.com/interlacer/reference/x_cols.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Construct an extended column specification — x_cols","text":"... named argument list extended collectors value collectors. .default default value collector","code":""},{"path":"http://kylehusmann.com/interlacer/reference/x_cols.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Construct an extended column specification — x_cols","text":"new extended column specification","code":""},{"path":"http://kylehusmann.com/interlacer/reference/x_cols.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Construct an extended column specification — x_cols","text":"Like readr::cols(), x_cols() includes columns input data, guessing column types default, creating missing reason channels according na =  argument read function. x_cols_only() includes columns specify, like readr::cols_only(). general, can substitute list() x_cols() without changing behavior.","code":""},{"path":[]},{"path":"http://kylehusmann.com/interlacer/reference/x_spec.html","id":null,"dir":"Reference","previous_headings":"","what":"Examine the extended column specification for a data frame — x_spec","title":"Examine the extended column specification for a data frame — x_spec","text":"x_spec() extracts full extended column specification tibble created read_interlaced_*() family functions.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/x_spec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Examine the extended column specification for a data frame — x_spec","text":"","code":"x_spec(x)"},{"path":"http://kylehusmann.com/interlacer/reference/x_spec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Examine the extended column specification for a data frame — x_spec","text":"x data frame loaded read_interlaced_*()","code":""},{"path":"http://kylehusmann.com/interlacer/reference/x_spec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Examine the extended column specification for a data frame — x_spec","text":"extended column specification object","code":""},{"path":[]}]
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See the License for the specific language governing permissions and limitations under the License."},{"path":"http://kylehusmann.com/interlacer/articles/coded-data.html","id":"warning","dir":"Articles","previous_headings":"","what":"⚠️ ⚠️ ⚠️ WARNING ⚠️ ⚠️ ⚠️","title":"Coded Data","text":"cfactor type highly experimental feature (even compared rest interlacer) thoroughly tested! ’m sharing super pre-alpha, unstable state get feedback invest time polishing implementation.","code":""},{"path":"http://kylehusmann.com/interlacer/articles/coded-data.html","id":"spss-style-codes","dir":"Articles","previous_headings":"","what":"SPSS-style codes","title":"Coded Data","text":"motivating example, consider coded version colors.csv example: missing reasons : -99: N/-98: REFUSED -97: OMITTED colors coded: 1: BLUE 2: RED 3: YELLOW style coding, positive values representing categorical levels negative values representing missing values, common format used SPSS. data can loaded interlaced numeric values follows: representation awkward work codes meaningless obfuscate significance code write results output. wanted select everyone BLUE favorite color, example, write: Similarly, wanted filter OMITTED favorite colors, write: make data ergnomic work , can use interlacer’s v_col_cfactor() na_col_cfactor() collector types load values cfactor instead, allows associate codes human-readable labels: Now human-readable labels, instead magic codes, can used working data: can still convert labels values missing reasons back codes wish, using .codes(). following convert missing reason channel age value channel favorite_color coded representation: recode cfactor channels data frame coded representation can following:","code":"library(readr) library(dplyr, warn.conflicts = FALSE) library(interlacer, warn.conflicts = FALSE)  read_file(   interlacer_example(\"colors_coded.csv\") ) |>   cat() #> person_id,age,favorite_color #> 1,20,1 #> 2,-98,1 #> 3,21,-98 #> 4,30,-97 #> 5,1,-99 #> 6,41,2 #> 7,50,-97 #> 8,30,3 #> 9,-98,-98 #> 10,-97,2 #> 11,10,-98 (df_coded <- read_interlaced_csv(   interlacer_example(\"colors_coded.csv\"),   na = c(-99, -98, -97) )) #> # A tibble: 11 × 3 #>    person_id       age favorite_color #>    <dbl,int> <dbl,int>      <dbl,int> #>  1         1        20              1 #>  2         2     <-98>              1 #>  3         3        21          <-98> #>  4         4        30          <-97> #>  5         5         1          <-99> #>  6         6        41              2 #>  7         7        50          <-97> #>  8         8        30              3 #>  9         9     <-98>          <-98> #> 10        10     <-97>              2 #> 11        11        10          <-98> df_coded |>   filter(favorite_color == 1) #> # A tibble: 2 × 3 #>   person_id       age favorite_color #>   <dbl,int> <dbl,int>      <dbl,int> #> 1         1        20              1 #> 2         2     <-98>              1 df_coded |>   filter(favorite_color == na(-97)) #> # A tibble: 0 × 3 #> # ℹ 3 variables: person_id <dbl,int>, age <dbl,int>, favorite_color <dbl,int> (df_decoded <- read_interlaced_csv(   interlacer_example(\"colors_coded.csv\"),   col_types = x_cols(     favorite_color = v_col_cfactor(codes = c(BLUE = 1, RED = 2, YELLOW = 3)),   ),   na = na_col_cfactor(REFUSED = -99, OMITTED = -98, `N/A` = -97) )) #> # A tibble: 11 × 3 #>     person_id        age favorite_color #>    <dbl,cfct> <dbl,cfct> <cfct,cfct>    #>  1          1         20 BLUE           #>  2          2  <OMITTED> BLUE           #>  3          3         21 <OMITTED>      #>  4          4         30 <N/A>          #>  5          5          1 <REFUSED>      #>  6          6         41 RED            #>  7          7         50 <N/A>          #>  8          8         30 YELLOW         #>  9          9  <OMITTED> <OMITTED>      #> 10         10      <N/A> RED            #> 11         11         10 <OMITTED> df_decoded |>   filter(favorite_color == \"BLUE\") #> # A tibble: 2 × 3 #>    person_id        age favorite_color #>   <dbl,cfct> <dbl,cfct> <cfct,cfct>    #> 1          1         20 BLUE           #> 2          2  <OMITTED> BLUE  df_decoded |>   filter(favorite_color == na(\"OMITTED\")) #> # A tibble: 0 × 3 #> # ℹ 3 variables: person_id <dbl,cfct>, age <dbl,cfct>, #> #   favorite_color <cfct,cfct> df_decoded |>   mutate(     age = map_na_channel(age, as.codes),     favorite_color = map_value_channel(favorite_color, as.codes)   ) #> # A tibble: 11 × 3 #>     person_id       age favorite_color #>    <dbl,cfct> <dbl,int>     <int,cfct> #>  1          1        20              1 #>  2          2     <-98>              1 #>  3          3        21      <OMITTED> #>  4          4        30          <N/A> #>  5          5         1      <REFUSED> #>  6          6        41              2 #>  7          7        50          <N/A> #>  8          8        30              3 #>  9          9     <-98>      <OMITTED> #> 10         10     <-97>              2 #> 11         11        10      <OMITTED> df_decoded |>   mutate(     across_value_channels(where_value_channel(is.cfactor), as.codes),     across_na_channels(where_na_channel(is.cfactor), as.codes),   ) #> # A tibble: 11 × 3 #>    person_id       age favorite_color #>    <dbl,int> <dbl,int>      <int,int> #>  1         1        20              1 #>  2         2     <-98>              1 #>  3         3        21          <-98> #>  4         4        30          <-97> #>  5         5         1          <-99> #>  6         6        41              2 #>  7         7        50          <-97> #>  8         8        30              3 #>  9         9     <-98>          <-98> #> 10        10     <-97>              2 #> 11        11        10          <-98>"},{"path":"http://kylehusmann.com/interlacer/articles/coded-data.html","id":"sas--and-stata-style-codes","dir":"Articles","previous_headings":"","what":"SAS- and Stata-style codes","title":"Coded Data","text":"Like SPSS, SAS Stata encode factor levels numeric values, instead representing missing reasons negative codes, given character codes: example, value coding scheme used favorite_color previous example, except missing reason channels coded follows: “.”: N/“.”: REFUSED “.b”: OMITTED data can easily loaded interlacer cfactor missing reason channel follows:","code":"read_file(   interlacer_example(\"colors_coded_char.csv\") ) |>   cat() #> person_id,age,favorite_color #> 1,20,1 #> 2,.a,1 #> 3,21,.a #> 4,30,.b #> 5,1,. #> 6,41,2 #> 7,50,.b #> 8,30,3 #> 9,.a,.a #> 10,.b,2 #> 11,10,.a read_interlaced_csv(   interlacer_example(\"colors_coded_char.csv\"),   col_types = x_cols(     favorite_color = v_col_cfactor(codes = c(BLUE = 1, RED = 2, YELLOW = 3)),   ),      na = c(`N/A` = \".\", REFUSED = \".a\", OMITTED = \".b\"), ) #> # A tibble: 11 × 3 #>     person_id        age favorite_color #>    <dbl,cfct> <dbl,cfct> <cfct,cfct>    #>  1          1         20 BLUE           #>  2          2  <REFUSED> BLUE           #>  3          3         21 <REFUSED>      #>  4          4         30 <OMITTED>      #>  5          5          1 <N/A>          #>  6          6         41 RED            #>  7          7         50 <OMITTED>      #>  8          8         30 YELLOW         #>  9          9  <REFUSED> <REFUSED>      #> 10         10  <OMITTED> RED            #> 11         11         10 <REFUSED>"},{"path":"http://kylehusmann.com/interlacer/articles/coded-data.html","id":"the-cfactor-type","dir":"Articles","previous_headings":"","what":"The cfactor type","title":"Coded Data","text":"cfactor extension base R’s factor type. created numeric character codes using cfactor() function: cfactor vectors can used wherever regular base R factor types used, fully-compatible factor types: unlike regular factor, cfactor additionally stores codes factor levels. means can convert back coded representation time, desired: IMPORTANT: .numeric() .integer() functions convert cfactor numeric codes coded representation. Instead, order retain full compatibility base R factor type, always returns result coded index level factor: levels changed, cfactor drop codes degrade regular R factor: Finally, base R factor character vector labels, can add codes via .cfactor():","code":"(example_cfactor <- cfactor(   c(10, 20, 30, 10, 20, 30),   codes = c(LEVEL_A = 10, LEVEL_B = 20, LEVEL_C = 30) )) #> <cfactor<int+bd96a>[6]> #> [1] LEVEL_A LEVEL_B LEVEL_C LEVEL_A LEVEL_B LEVEL_C #>  #> Categorical levels: #>    label code #>  LEVEL_A   10 #>  LEVEL_B   20 #>  LEVEL_C   30  (example_cfactor2 <- cfactor(   c(\"a\", \"b\", \"c\", \"a\", \"b\", \"c\"),   codes = c(LEVEL_A = \"a\", LEVEL_B = \"b\", LEVEL_C = \"c\") )) #> <cfactor<chr+99cda>[6]> #> [1] LEVEL_A LEVEL_B LEVEL_C LEVEL_A LEVEL_B LEVEL_C #>  #> Categorical levels: #>    label code #>  LEVEL_A    a #>  LEVEL_B    b #>  LEVEL_C    c is.factor(example_cfactor) #> [1] TRUE levels(example_cfactor) #> [1] \"LEVEL_A\" \"LEVEL_B\" \"LEVEL_C\"  is.factor(example_cfactor2) #> [1] TRUE levels(example_cfactor2) #> [1] \"LEVEL_A\" \"LEVEL_B\" \"LEVEL_C\" codes(example_cfactor) #> LEVEL_A LEVEL_B LEVEL_C  #>      10      20      30 as.codes(example_cfactor) #> [1] 10 20 30 10 20 30  codes(example_cfactor2) #> LEVEL_A LEVEL_B LEVEL_C  #>     \"a\"     \"b\"     \"c\" as.codes(example_cfactor2) #> [1] \"a\" \"b\" \"c\" \"a\" \"b\" \"c\" as.numeric(example_cfactor) #> [1] 1 2 3 1 2 3 as.numeric(example_cfactor2) #> [1] 1 2 3 1 2 3 cfactor_copy <- example_cfactor  # cfactory_copy is a cfactor and a factor is.cfactor(cfactor_copy) #> [1] TRUE is.factor(cfactor_copy) #> [1] TRUE levels(cfactor_copy) #> [1] \"LEVEL_A\" \"LEVEL_B\" \"LEVEL_C\" codes(cfactor_copy) #> LEVEL_A LEVEL_B LEVEL_C  #>      10      20      30  # modify the levels of the cfactor as if it was a regular factor levels(cfactor_copy) <- c(\"C\", \"B\", \"A\")  # now cfactor_copy is just a regular factor is.cfactor(cfactor_copy) #> [1] FALSE is.factor(cfactor_copy) #> [1] TRUE levels(cfactor_copy) #> [1] \"C\" \"B\" \"A\" codes(cfactor_copy) #> NULL as.cfactor(   c(\"LEVEL_A\", \"LEVEL_B\", \"LEVEL_C\", \"LEVEL_A\", \"LEVEL_B\", \"LEVEL_C\"),   codes = c(LEVEL_A = 10, LEVEL_B = 20, LEVEL_C = 30) ) #> <cfactor<int+bd96a>[6]> #> [1] LEVEL_A LEVEL_B LEVEL_C LEVEL_A LEVEL_B LEVEL_C #>  #> Categorical levels: #>    label code #>  LEVEL_A   10 #>  LEVEL_B   20 #>  LEVEL_C   30"},{"path":"http://kylehusmann.com/interlacer/articles/coded-data.html","id":"re-coding-and-writing-an-interlaced-data-frame-","dir":"Articles","previous_headings":"","what":"Re-coding and writing an interlaced data frame.","title":"Coded Data","text":"Re-coding writing interlaced data frame simple calling .codes() cfactor type value missing reason channels, calling one write_interlaced_*() family functions:","code":"df_decoded |>   mutate(     across_value_channels(where_value_channel(is.cfactor), as.codes),     across_na_channels(where_na_channel(is.cfactor), as.codes),   ) |>   write_interlaced_csv(\"output.csv\")"},{"path":"http://kylehusmann.com/interlacer/articles/coded-data.html","id":"haven","dir":"Articles","previous_headings":"","what":"haven","title":"Coded Data","text":"haven package functions loading native SPSS, SAS, Stata native file formats special data frames use column attributes special values keep track value labels missing reasons. complete discussion compares interlacer’s approach, see vignette(\"-approaches\").","code":""},{"path":"http://kylehusmann.com/interlacer/articles/extended-column-types.html","id":"x_cols-extended-cols-specifications","dir":"Articles","previous_headings":"","what":"x_cols: extended cols specifications","title":"Extended Column Types","text":"need fine-grained control value missing reason channel types, can use x_cols() specification, extension readr’s readr::cols() system. x_cols(), can control value channel na channel types columns resulting data frame. useful missing reasons apply particular items opposed file whole. example, say measure following two items: current stress level? Low Moderate High don’t know don’t understand question well feel manage time responsibilities today? Poorly Fairly well Well well apply (Today vacation day) apply (reason) can see, items two selection choices mapped missing reasons. can specified x_cols() follows: Like readr’s readr::cols() function, named x_cols() describes column resulting data frame. Value missing reason channel types declared via calls v_col_*() na_col_*() respectively, assembled x_col(). v_col_*() types mirror readr’s readr::col_* column “collectors”. v_col_double() equivalent readr::col_double(), v_col_character() equivalent readr::col_character(), etc. See vroom’s documentation list available column types. na_col_*() collectors allow declare missing reason channel type loaded column, values interpreted missing reasons. Currently, five options: na_col_default(): Use collector defined na = argument read_interlaced_*() function na_col_none(): Load column without missing reason channel. na_col_factor(): Use factor missing reason channel. Character arguments passed form levels factor. (e.g. na_col_factor(\"REFUSED\", \"OMITTED\", \"N/\")) na_col_integer(): Use integer na channel. Numeric arguments passed values interpreted missing values. (e.g. na_col_integer(-99, -98, -97))) na_col_cfactor(): Use cfactor na channel. (cfactor types covered next vignette,vignette(\"coded-data\")) following example shows collectors action. example use coded version colors.csv example data, demonstrate integer missing reason types:","code":"(df_stress <- read_interlaced_csv(   interlacer_example(\"stress.csv\"),   col_types = x_cols(     person_id = x_col(       v_col_integer(),       na_col_none()     ),     current_stress = x_col(       v_col_factor(levels = c(\"LOW\", \"MODERATE\", \"HIGH\")),       na_col_factor(\"DONT_KNOW\", \"DONT_UNDERSTAND\")     ),     time_management = x_col(       v_col_factor(levels = c(\"POORLY\", \"FAIRLY_WELL\", \"WELL\", \"VERY_WELL\")),       na_col_factor(\"NA_VACATION\", \"NA_OTHER\")     )   ) )) #> # A tibble: 8 × 3 #>   person_id current_stress    time_management #>       <int> <fct,fct>         <fct,fct>       #> 1         1 LOW               VERY_WELL       #> 2         2 MODERATE          POORLY          #> 3         3 <DONT_KNOW>       <NA_OTHER>      #> 4         4 HIGH              POORLY          #> 5         5 <DONT_UNDERSTAND> <NA_OTHER>      #> 6         6 LOW               <NA_VACATION>   #> 7         7 MODERATE          WELL            #> 8         8 <DONT_KNOW>       FAIRLY_WELL read_interlaced_csv(   interlacer_example(\"colors_coded.csv\"),   col_types = x_cols(     person_id = x_col(v_col_integer(), na_col_none()),     age = x_col(v_col_double(), na_col_integer(-99, -98, -97)),     favorite_color = x_col(v_col_integer(), na_col_integer(-99, -98, -97))   ) ) #> # A tibble: 11 × 3 #>    person_id       age favorite_color #>        <int> <dbl,int>      <int,int> #>  1         1        20              1 #>  2         2     <-98>              1 #>  3         3        21          <-98> #>  4         4        30          <-97> #>  5         5         1          <-99> #>  6         6        41              2 #>  7         7        50          <-97> #>  8         8        30              3 #>  9         9     <-98>          <-98> #> 10        10     <-97>              2 #> 11        11        10          <-98>"},{"path":[]},{"path":"http://kylehusmann.com/interlacer/articles/extended-column-types.html","id":"default-collector-types","dir":"Articles","previous_headings":"Shortcuts","what":"Default collector types","title":"Extended Column Types","text":"Like readr’s cols() function, x_cols() function accepts .default argument specifies default value collector. na = argument similarly used specify default missing reason collector used na_col_*() specified, set na_col_default(). taking advantage defaults, specification last example equivalently written :","code":"read_interlaced_csv(   interlacer_example(\"colors_coded.csv\"),   col_types = x_cols(     .default = v_col_integer(),     person_id = x_col(v_col_integer(), na_col_none()),     age = v_col_double(),   ),   na = na_col_integer(-99, -98, -97) ) #> # A tibble: 11 × 3 #>    person_id       age favorite_color #>        <int> <dbl,int>      <int,int> #>  1         1        20              1 #>  2         2     <-98>              1 #>  3         3        21          <-98> #>  4         4        30          <-97> #>  5         5         1          <-99> #>  6         6        41              2 #>  7         7        50          <-97> #>  8         8        30              3 #>  9         9     <-98>          <-98> #> 10        10     <-97>              2 #> 11        11        10          <-98>"},{"path":"http://kylehusmann.com/interlacer/articles/extended-column-types.html","id":"concise-value-and-missing-reason-specifications","dir":"Articles","previous_headings":"Shortcuts","what":"Concise value and missing reason specifications","title":"Extended Column Types","text":"Like readr, value collectors can specified using characters. example, instead v_col_integer(), can use \"\". See vroom’s documentation complete list shortcuts. Similarly, missing reason collectors can specified providing vector missing values; collector type inferred via type vector. conversions follows: na_col_none(): NULL na_col_factor(): character vector, e.g. c(\"REFUSED\", \"OMITTED\", \"N/\") na_col_integer(): numeric vector, e.g. c(-99, -98, -97)) na_col_cfactor(): named character numeric vector, e.g. c(REFUSED = -99, OMITTED = -98, `N/` = -97) Using shortcuts, previous example equivalently written compact form follows:","code":"read_interlaced_csv(   interlacer_example(\"colors_coded.csv\"),   col_types = x_cols(     .default = \"i\",     person_id = x_col(\"i\", NULL),     age = \"d\",   ),   na = c(-99, -98, -97) ) #> # A tibble: 11 × 3 #>    person_id       age favorite_color #>        <int> <dbl,int>      <int,int> #>  1         1        20              1 #>  2         2     <-98>              1 #>  3         3        21          <-98> #>  4         4        30          <-97> #>  5         5         1          <-99> #>  6         6        41              2 #>  7         7        50          <-97> #>  8         8        30              3 #>  9         9     <-98>          <-98> #> 10        10     <-97>              2 #> 11        11        10          <-98>"},{"path":"http://kylehusmann.com/interlacer/articles/extended-column-types.html","id":"next-steps","dir":"Articles","previous_headings":"","what":"Next steps","title":"Extended Column Types","text":"vignette covered column types values missing reasons can explicitly specified using collectors. also illustrated column-level missing values can specified creating extended column type specifications using x_cols(). final examples, used example data set coded values missing reasons. Coded values especially common data sets produced SPSS, SAS, Stata. interlacer provides special column type make working sort data easier: cfactor type. covered next vignette, vignette(\"coded-data\").","code":""},{"path":"http://kylehusmann.com/interlacer/articles/interlacer.html","id":"aggregations-with-missing-reasons","dir":"Articles","previous_headings":"","what":"Aggregations with missing reasons","title":"Introduction to interlacer","text":"Now, interested values source data, functionality need. wanted know values NA? Although information encoded source data, lost missing reasons converted NA values. example, consider favorite_color column. many respondents REFUSED give favorite color? many people just OMITTED answer? question N/respondents (e.g. wasn’t survey form)? mean respondent age groups? current dataframe gets us part way: can see, converted missing reasons single NA, can answer questions missingness general, rather work specific reasons stored source data. Unfortunately, try load data missing reasons intact, lose something else: type information values. Now access missing reasons, columns character vectors. means order anything values, always filter missing reasons, cast remaining values desired type: gives us information want, cumbersome. Notice ’s distinction favorite color values missing reasons! Things start get really complex different columns different sets possible missing reasons. means lot type conversion gymnastics switch value types missing types.","code":"library(dplyr, warn.conflicts = FALSE)  df_simple |>   summarize(     mean_age = mean(age, na.rm = TRUE),     n = n(),     .by = favorite_color   ) |>   arrange(favorite_color) #> # A tibble: 4 × 3 #>   favorite_color mean_age     n #>   <chr>             <dbl> <int> #> 1 BLUE               20       2 #> 2 RED                41       2 #> 3 YELLOW             30       1 #> 4 NA                 22.4     6 (df_with_missing <- read_csv(   interlacer_example(\"colors.csv\"),   col_types = cols(.default = \"c\") )) #> # A tibble: 11 × 3 #>    person_id age     favorite_color #>    <chr>     <chr>   <chr>          #>  1 1         20      BLUE           #>  2 2         REFUSED BLUE           #>  3 3         21      REFUSED        #>  4 4         30      OMITTED        #>  5 5         1       N/A            #>  6 6         41      RED            #>  7 7         50      OMITTED        #>  8 8         30      YELLOW         #>  9 9         REFUSED REFUSED        #> 10 10        OMITTED RED            #> 11 11        10      REFUSED reasons <- c(\"REFUSED\", \"OMITTED\", \"N/A\")  df_with_missing |>   mutate(     age_values = as.numeric(if_else(age %in% reasons, NA, age)),   ) |>   summarize(     mean_age = mean(age_values, na.rm = TRUE),     n = n(),     .by = favorite_color   ) |>   arrange(favorite_color) #> # A tibble: 6 × 3 #>   favorite_color mean_age     n #>   <chr>             <dbl> <int> #> 1 BLUE               20       2 #> 2 N/A                 1       1 #> 3 OMITTED            40       2 #> 4 RED                41       2 #> 5 REFUSED            15.5     3 #> 6 YELLOW             30       1"},{"path":"http://kylehusmann.com/interlacer/articles/interlacer.html","id":"the-interlacer-approach","dir":"Articles","previous_headings":"Aggregations with missing reasons","what":"The interlacer approach","title":"Introduction to interlacer","text":"interlacer built based insight everything becomes much tidy, simple, expressive explicitly work values missing reasons separate channels variable. interlacer introduces new interlaced column type facilitates . read_interlaced_* functions interlacer import data new column type. can see column headers, column loaded composed two channels: value channel, missing reason channel. channel can type. age column, example, double values factor missing reasons: channels can explicitly accessed using value_channel() na_channel() helper functions: helpers rarely needed, however, computations automatically operate interlaced column’s value channel, ignore missing reasons channel. following compute mean age, without missing reasons interfering: (equivalently used value_channel() helper achieve result, albeit verbosity): Although missing reasons excluded computations, still treated unique values. means group age get breakdown unique missing reasons, rather lumped single NA: can see, can generate report , without needing type gymnastics! Also, values neatly distinguished missing reasons.","code":"(df <- read_interlaced_csv(   interlacer_example(\"colors.csv\"),   na = c(\"REFUSED\", \"OMITTED\", \"N/A\") )) #> # A tibble: 11 × 3 #>    person_id       age favorite_color #>    <dbl,fct> <dbl,fct> <chr,fct>      #>  1         1        20 BLUE           #>  2         2 <REFUSED> BLUE           #>  3         3        21 <REFUSED>      #>  4         4        30 <OMITTED>      #>  5         5         1 <N/A>          #>  6         6        41 RED            #>  7         7        50 <OMITTED>      #>  8         8        30 YELLOW         #>  9         9 <REFUSED> <REFUSED>      #> 10        10 <OMITTED> RED            #> 11        11        10 <REFUSED> df$age #> <interlaced<dbl, fct>[11]> #>  [1] 20        <REFUSED> 21        30         1        41        50        #>  [8] 30        <REFUSED> <OMITTED> 10        #> NA levels: REFUSED OMITTED N/A value_channel(df$age) #>  [1] 20 NA 21 30  1 41 50 30 NA NA 10 na_channel(df$age) #>  [1] <NA>    REFUSED <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    REFUSED #> [10] OMITTED <NA>    #> Levels: REFUSED OMITTED N/A mean(df$age, na.rm = TRUE) #> [1] 25.375 mean(value_channel(df$age), na.rm = TRUE) #> [1] 25.375 df |>   summarize(     mean_age = mean(age, na.rm = TRUE),     n = n(),     .by = favorite_color   ) |>   arrange(favorite_color) #> # A tibble: 4 × 3 #>   favorite_color mean_age     n #>   <chr,fct>         <dbl> <int> #> 1 BLUE               20       2 #> 2 RED                41       2 #> 3 YELLOW             30       1 #> 4 <REFUSED>          22.4     6"},{"path":"http://kylehusmann.com/interlacer/articles/interlacer.html","id":"filtering-based-on-missing-reasons","dir":"Articles","previous_headings":"","what":"Filtering based on missing reasons","title":"Introduction to interlacer","text":"interlaced columns also helpful creating samples inclusion / exclusion criteria based missing reasons. example, using example data, say wanted create sample respondents REFUSED give age. indicate value interpreted missing reason, can use na() function value: people REFUSED report age favorite color? ’s also possible combine value conditions missing reason conditions. example, select everyone REFUSED give favorite color, 20 years old:","code":"df |>   filter(age == na(\"REFUSED\")) #> # A tibble: 0 × 3 #> # ℹ 3 variables: person_id <dbl,fct>, age <dbl,fct>, favorite_color <chr,fct>  # na_channel() can also be used to get an equivalent result: df |>   filter(na_channel(age) == \"REFUSED\") #> # A tibble: 2 × 3 #>   person_id       age favorite_color #>   <dbl,fct> <dbl,fct> <chr,fct>      #> 1         2 <REFUSED> BLUE           #> 2         9 <REFUSED> <REFUSED> df |>   filter(age == na(\"REFUSED\") & favorite_color == na(\"REFUSED\")) #> # A tibble: 0 × 3 #> # ℹ 3 variables: person_id <dbl,fct>, age <dbl,fct>, favorite_color <chr,fct> df |>   filter(age > 20 & favorite_color == na(\"REFUSED\")) #> # A tibble: 0 × 3 #> # ℹ 3 variables: person_id <dbl,fct>, age <dbl,fct>, favorite_color <chr,fct>"},{"path":"http://kylehusmann.com/interlacer/articles/interlacer.html","id":"mutations","dir":"Articles","previous_headings":"","what":"Mutations","title":"Introduction to interlacer","text":"might expect, na() function can used values mutations. following pipeline replace favorite color respondents missing value \"REDACTED\" Conditionals also work exactly expect mutations. following replace favorite color respondents age < 18 missing reason \"REDACTED_UNDERAGE\". Respondents missing age replaced \"REDACTED_MISSING_AGE\" following mutation create new column called person_type \"CHILD\" age < 18, \"ADULT\" age >= 18, missing reason \"AGE_UNAVAILABLE\" age missing: Important note: must use dplyr::if_else() interlaced vectors instead R’s base::ifelse() function, base function strips missing reason channel due fundamental limitation base R.","code":"df |>   mutate(     favorite_color = na(\"REDACTED\")   ) #> # A tibble: 11 × 3 #>    person_id       age favorite_color #>    <dbl,fct> <dbl,fct>      <???,fct> #>  1         1        20     <REDACTED> #>  2         2 <REFUSED>     <REDACTED> #>  3         3        21     <REDACTED> #>  4         4        30     <REDACTED> #>  5         5         1     <REDACTED> #>  6         6        41     <REDACTED> #>  7         7        50     <REDACTED> #>  8         8        30     <REDACTED> #>  9         9 <REFUSED>     <REDACTED> #> 10        10 <OMITTED>     <REDACTED> #> 11        11        10     <REDACTED> df |>   mutate(     favorite_color = if_else(       age < 18,       na(\"REDACTED_UNDERAGE\"),       favorite_color,       missing = na(\"REDACTED_MISSING_AGE\")     )   ) #> # A tibble: 11 × 3 #>    person_id       age favorite_color         #>    <dbl,fct> <dbl,fct> <chr,fct>              #>  1         1        20 BLUE                   #>  2         2 <REFUSED> <REDACTED_MISSING_AGE> #>  3         3        21 <REFUSED>              #>  4         4        30 <OMITTED>              #>  5         5         1 <REDACTED_UNDERAGE>    #>  6         6        41 RED                    #>  7         7        50 <OMITTED>              #>  8         8        30 YELLOW                 #>  9         9 <REFUSED> <REDACTED_MISSING_AGE> #> 10        10 <OMITTED> <REDACTED_MISSING_AGE> #> 11        11        10 <REDACTED_UNDERAGE> df |>   mutate(     person_type = if_else(       age < 18,       \"CHILD\",       \"ADULT\",       missing = na(\"AGE_UNAVAILABLE\")     ),   ) #> # A tibble: 11 × 4 #>    person_id       age favorite_color person_type       #>    <dbl,fct> <dbl,fct> <chr,fct>      <chr,fct>         #>  1         1        20 BLUE           ADULT             #>  2         2 <REFUSED> BLUE           <AGE_UNAVAILABLE> #>  3         3        21 <REFUSED>      ADULT             #>  4         4        30 <OMITTED>      ADULT             #>  5         5         1 <N/A>          CHILD             #>  6         6        41 RED            ADULT             #>  7         7        50 <OMITTED>      ADULT             #>  8         8        30 YELLOW         ADULT             #>  9         9 <REFUSED> <REFUSED>      <AGE_UNAVAILABLE> #> 10        10 <OMITTED> RED            <AGE_UNAVAILABLE> #> 11        11        10 <REFUSED>      CHILD"},{"path":"http://kylehusmann.com/interlacer/articles/interlacer.html","id":"multichannel-comparison","dir":"Articles","previous_headings":"","what":"Multichannel comparison","title":"Introduction to interlacer","text":"Comparison operators (e.g. == !=) compare value channels, return NA whenever missing values involved: means missing reasons directly compared: simultaneously compare values missing reason channels, interlacer provides multichannel comparison operators %==% %!=%. operators always return boolean vectors, never return missing values. multichannel comparison, two values considered equal iff: values, values equal missing values, reasons missing values, lack missing reasons way, convert previous example use multichannel comparison, get: Similarly, comparisons values return booleans rather NA, , consequence, regular NA values equivalent interlaced values missing reason: Missing values missing missing reasons called “empty values”, discussed next section.","code":"5 == NA #> [1] NA 5 == na(\"reason\") #> [1] NA na(\"reason1\") == na(\"reason2\") #> [1] NA na(\"reason1\") %==% na(\"reason2\") #> [1] FALSE 5 %==% NA #> [1] FALSE 5 %==% na(\"reason\") #> [1] FALSE NA %==% na() #> [1] TRUE  # Alternatively this can be considered an `NA` missing reason: NA %==% na(NA) #> [1] TRUE"},{"path":"http://kylehusmann.com/interlacer/articles/interlacer.html","id":"empty-values","dir":"Articles","previous_headings":"","what":"Empty values","title":"Introduction to interlacer","text":"Empty values missing values lack missing reasons. values can arise missing actions specified. example, example, empty values created missing = argument if_else() statement specified: can see, empty values displayed <<NA>>. Empty values can detected using multichannel comparsion operator: (Note regular equality work, return NA comparisons!) Empty values often occur result joins, dplyr::*_join() family functions missing = parameter, like dplyr::if_else() . example, say following data frame wanted join sample: ’re missing condition information respondents, show empty values join data frame sample: can remedy replacing empty values join:","code":"df |>   mutate(     favorite_color = if_else(       age < 18,       na(\"REDACTED_UNDERAGE\"),       favorite_color,     )   ) #> # A tibble: 11 × 3 #>    person_id       age favorite_color      #>    <dbl,fct> <dbl,fct> <chr,fct>           #>  1         1        20 BLUE                #>  2         2 <REFUSED> <<NA>>              #>  3         3        21 <REFUSED>           #>  4         4        30 <OMITTED>           #>  5         5         1 <REDACTED_UNDERAGE> #>  6         6        41 RED                 #>  7         7        50 <OMITTED>           #>  8         8        30 YELLOW              #>  9         9 <REFUSED> <<NA>>              #> 10        10 <OMITTED> <<NA>>              #> 11        11        10 <REDACTED_UNDERAGE> df |>   mutate(     favorite_color = if_else(       age < 18,       na(\"REDACTED_UNDERAGE\"),       favorite_color,     )   ) |>   filter(favorite_color %==% na()) #> # A tibble: 3 × 3 #>   person_id       age favorite_color #>   <dbl,fct> <dbl,fct> <chr,fct>      #> 1         2 <REFUSED> <<NA>>         #> 2         9 <REFUSED> <<NA>>         #> 3        10 <OMITTED> <<NA>> conditions <- tribble(   ~person_id, ~condition,   1, \"TREATMENT\",   2, \"CONTROL\",   3, na(\"TECHNICAL_ERROR\"),   6, \"CONTROL\",   8, \"TREATMENT\", ) df |>   left_join(conditions, by = join_by(person_id)) #> # A tibble: 11 × 4 #>    person_id       age favorite_color condition         #>    <dbl,fct> <dbl,fct> <chr,fct>      <chr,fct>         #>  1         1        20 BLUE           TREATMENT         #>  2         2 <REFUSED> BLUE           CONTROL           #>  3         3        21 <REFUSED>      <TECHNICAL_ERROR> #>  4         4        30 <OMITTED>      <<NA>>            #>  5         5         1 <N/A>          <<NA>>            #>  6         6        41 RED            CONTROL           #>  7         7        50 <OMITTED>      <<NA>>            #>  8         8        30 YELLOW         TREATMENT         #>  9         9 <REFUSED> <REFUSED>      <<NA>>            #> 10        10 <OMITTED> RED            <<NA>>            #> 11        11        10 <REFUSED>      <<NA>> df |>   left_join(conditions, by = join_by(person_id)) |>   mutate(     condition = if_else(condition %==% na(), na(\"LEFT_STUDY\"), condition),   ) #> # A tibble: 11 × 4 #>    person_id       age favorite_color condition         #>    <dbl,fct> <dbl,fct> <chr,fct>      <chr,fct>         #>  1         1        20 BLUE           TREATMENT         #>  2         2 <REFUSED> BLUE           CONTROL           #>  3         3        21 <REFUSED>      <TECHNICAL_ERROR> #>  4         4        30 <OMITTED>      <LEFT_STUDY>      #>  5         5         1 <N/A>          <LEFT_STUDY>      #>  6         6        41 RED            CONTROL           #>  7         7        50 <OMITTED>      <LEFT_STUDY>      #>  8         8        30 YELLOW         TREATMENT         #>  9         9 <REFUSED> <REFUSED>      <LEFT_STUDY>      #> 10        10 <OMITTED> RED            <LEFT_STUDY>      #> 11        11        10 <REFUSED>      <LEFT_STUDY>"},{"path":"http://kylehusmann.com/interlacer/articles/interlacer.html","id":"writing-interlaced-files","dir":"Articles","previous_headings":"","what":"Writing interlaced files","title":"Introduction to interlacer","text":"’ve made made changes data, probably want save . interlacer provides write_interlaced_* family functions : combine value missing reasons interlaced character columns, write result csv. Alternatively, want re-interlace columns without writing file control writing process, can use flatten_channels(): value missing reason channels data frames interlaced vectors can similarly accessed using value_channel() na_channel() helper functions:","code":"write_interlaced_csv(df, \"interlaced_output.csv\") flatten_channels(df) #> # A tibble: 11 × 3 #>    person_id age     favorite_color #>        <dbl> <chr>   <chr>          #>  1         1 20      BLUE           #>  2         2 REFUSED BLUE           #>  3         3 21      REFUSED        #>  4         4 30      OMITTED        #>  5         5 1       N/A            #>  6         6 41      RED            #>  7         7 50      OMITTED        #>  8         8 30      YELLOW         #>  9         9 REFUSED REFUSED        #> 10        10 OMITTED RED            #> 11        11 10      REFUSED  # (it works on single vectors as well) flatten_channels(df$age) #>  [1] \"20\"      \"REFUSED\" \"21\"      \"30\"      \"1\"       \"41\"      \"50\"      #>  [8] \"30\"      \"REFUSED\" \"OMITTED\" \"10\" value_channel(df) #> # A tibble: 11 × 3 #>    person_id   age favorite_color #>        <dbl> <dbl> <chr>          #>  1         1    20 BLUE           #>  2         2    NA BLUE           #>  3         3    21 NA             #>  4         4    30 NA             #>  5         5     1 NA             #>  6         6    41 RED            #>  7         7    50 NA             #>  8         8    30 YELLOW         #>  9         9    NA NA             #> 10        10    NA RED            #> 11        11    10 NA na_channel(df) #> # A tibble: 11 × 3 #>    person_id age     favorite_color #>    <fct>     <fct>   <fct>          #>  1 NA        NA      NA             #>  2 NA        REFUSED NA             #>  3 NA        NA      REFUSED        #>  4 NA        NA      OMITTED        #>  5 NA        NA      N/A            #>  6 NA        NA      NA             #>  7 NA        NA      OMITTED        #>  8 NA        NA      NA             #>  9 NA        REFUSED REFUSED        #> 10 NA        OMITTED NA             #> 11 NA        NA      REFUSED"},{"path":"http://kylehusmann.com/interlacer/articles/interlacer.html","id":"next-steps","dir":"Articles","previous_headings":"","what":"Next steps","title":"Introduction to interlacer","text":"far, ’ve covered interlacer’s read_interlaced_* family functions enabled us load interlaced columns contain separate challens value missing reasons. interlaced type enables us create tidy type-aware pipelines can flexibly consider variable’s value missing reasons. examples vignette, column types automatically detected. explicitly specify value missing column types, (specify individual missing reasons specific columns), interlacer extends readr’s collector() system. covered next vignette, vignette(\"na-column-types\").","code":""},{"path":"http://kylehusmann.com/interlacer/articles/other-approaches.html","id":"haven-and-labelled","dir":"Articles","previous_headings":"","what":"haven and labelled","title":"Other Approaches","text":"haven labelled packages rely two functions creating vectors interlace values missing reasons: haven::labelled_spss() haven::tagged_na(). Although create haven_labelled vectors, use different methods representing missing values.","code":""},{"path":"http://kylehusmann.com/interlacer/articles/other-approaches.html","id":"labelled-missing-values-havenlabelled_spss","dir":"Articles","previous_headings":"haven and labelled","what":"“Labelled” missing values (haven::labelled_spss())","title":"Other Approaches","text":"SPSS files loaded haven via haven::read_spss(), values missing reasons loaded single interlaced numeric vector: just numeric vector though, haven::labelled_spss() numeric vector, attributes describing value missing value codes: attributes adjust behavior functions like .na(): makes easy check value missing reason, still filter missing reasons aggregations: ’s little bit improvement working raw coded values, can use .na(), codes get labels, don’t constantly looking codes codebook. still falls short interlacer’s functionality two key reasons: Reason 1: interlacer, value column can whatever type want: numeric, character, factor, etc. labelled missing reasons, values missing reasons need type, usually numeric codes. creates lot type gymnastics potential errors ’re manipulating . Reason 2: Even missing values labelled labelled_spss type, aggregations math operations protected. forget take missing values, get incorrect results / corrupted data:","code":"library(interlacer, warn.conflicts = FALSE) library(haven) library(dplyr) #>  #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': #>  #>     filter, lag #> The following objects are masked from 'package:base': #>  #>     intersect, setdiff, setequal, union  (df_spss <- read_spss(   interlacer_example(\"colors.sav\"), user_na = TRUE )) #> Registered S3 methods overwritten by 'readr': #>   method                    from  #>   as.data.frame.spec_tbl_df vroom #>   as_tibble.spec_tbl_df     vroom #>   format.col_spec           vroom #>   print.col_spec            vroom #>   print.collector           vroom #>   print.date_names          vroom #>   print.locale              vroom #>   str.col_spec              vroom #> # A tibble: 11 × 3 #>    person_id age                favorite_color     #>    <dbl+lbl> <dbl+lbl>          <dbl+lbl>          #>  1  1         20                  1 [BLUE]         #>  2  2        -98 (NA) [REFUSED]   1 [BLUE]         #>  3  3         21                -98 (NA) [REFUSED] #>  4  4         30                -97 (NA) [OMITTED] #>  5  5          1                -99 (NA) [N/A]     #>  6  6         41                  2 [RED]          #>  7  7         50                -97 (NA) [OMITTED] #>  8  8         30                  3 [YELLOW]       #>  9  9        -98 (NA) [REFUSED] -98 (NA) [REFUSED] #> 10 10        -97 (NA) [OMITTED]   2 [RED]          #> 11 11         10                -98 (NA) [REFUSED] attributes(df_spss$favorite_color) #> $label #> [1] \"Favorite color\" #>  #> $na_range #> [1] -Inf    0 #>  #> $class #> [1] \"haven_labelled_spss\" \"haven_labelled\"      \"vctrs_vctr\"          #> [4] \"double\"              #>  #> $format.spss #> [1] \"F8.2\" #>  #> $labels #>    BLUE     RED  YELLOW     N/A REFUSED OMITTED  #>       1       2       3     -99     -98     -97 is.na(df_spss$favorite_color) #>  [1] FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE df_spss |>   mutate(     age_values = if_else(is.na(age), NA, age),     favorite_color_missing_reasons = if_else(       is.na(favorite_color), favorite_color, NA     )   ) |>   summarize(     mean_age = mean(age_values, na.rm = TRUE),     n = n(),     .by = favorite_color_missing_reasons   ) #> # A tibble: 4 × 3 #>   favorite_color_missing_reasons mean_age     n #>   <dbl+lbl>                         <dbl> <int> #> 1  NA                                30.3     5 #> 2 -98 (NA) [REFUSED]                 15.5     3 #> 3 -97 (NA) [OMITTED]                 40       2 #> 4 -99 (NA) [N/A]                      1       1  df_spss |>   mutate(     age_next_year = if_else(is.na(age), NA, age + 1),     .after = person_id   ) #> # A tibble: 11 × 4 #>    person_id age_next_year age                favorite_color     #>    <dbl+lbl>         <dbl> <dbl+lbl>          <dbl+lbl>          #>  1  1                   21  20                  1 [BLUE]         #>  2  2                   NA -98 (NA) [REFUSED]   1 [BLUE]         #>  3  3                   22  21                -98 (NA) [REFUSED] #>  4  4                   31  30                -97 (NA) [OMITTED] #>  5  5                    2   1                -99 (NA) [N/A]     #>  6  6                   42  41                  2 [RED]          #>  7  7                   51  50                -97 (NA) [OMITTED] #>  8  8                   31  30                  3 [YELLOW]       #>  9  9                   NA -98 (NA) [REFUSED] -98 (NA) [REFUSED] #> 10 10                   NA -97 (NA) [OMITTED]   2 [RED]          #> 11 11                   11  10                -98 (NA) [REFUSED] df_spss |>   mutate(     favorite_color_missing_reasons = if_else(       is.na(favorite_color), favorite_color, NA     )   ) |>   summarize(     mean_age = mean(age, na.rm = TRUE),     n = n(),     .by = favorite_color_missing_reasons   ) #> # A tibble: 4 × 3 #>   favorite_color_missing_reasons mean_age     n #>   <dbl+lbl>                         <dbl> <int> #> 1  NA                               -20.8     5 #> 2 -98 (NA) [REFUSED]                -22.3     3 #> 3 -97 (NA) [OMITTED]                 40       2 #> 4 -99 (NA) [N/A]                      1       1  df_spss |>   mutate(     age_next_year = age + 1,     .after = person_id   ) #> # A tibble: 11 × 4 #>    person_id age_next_year age                favorite_color     #>    <dbl+lbl>         <dbl> <dbl+lbl>          <dbl+lbl>          #>  1  1                   21  20                  1 [BLUE]         #>  2  2                  -97 -98 (NA) [REFUSED]   1 [BLUE]         #>  3  3                   22  21                -98 (NA) [REFUSED] #>  4  4                   31  30                -97 (NA) [OMITTED] #>  5  5                    2   1                -99 (NA) [N/A]     #>  6  6                   42  41                  2 [RED]          #>  7  7                   51  50                -97 (NA) [OMITTED] #>  8  8                   31  30                  3 [YELLOW]       #>  9  9                  -97 -98 (NA) [REFUSED] -98 (NA) [REFUSED] #> 10 10                  -96 -97 (NA) [OMITTED]   2 [RED]          #> 11 11                   11  10                -98 (NA) [REFUSED]"},{"path":"http://kylehusmann.com/interlacer/articles/other-approaches.html","id":"tagged-missing-values-haventagged_na","dir":"Articles","previous_headings":"haven and labelled","what":"“Tagged” missing values (haven::tagged_na())","title":"Other Approaches","text":"loading Stata SAS files, haven uses “tagged missingness” approach mirror values handled Stata SAS: approach deviously clever. takes advantage way NaN floating point values stored memory, make possible different “flavors” NA values. (info done, check tagged_na.c source code haven) still act like regular NA values… now can include single character “tag” (usually letter -z). means work .na() include missing reason codes aggregations! Unfortunately, can’t group , dplyr::group_by() tag-aware. :( Another limitation approach requires values types numeric, trick “tagging” NA values depends peculiarities floating point values stored memory.","code":"(df_stata <- read_stata(   interlacer_example(\"colors.dta\") )) #> # A tibble: 11 × 3 #>    person_id age             favorite_color  #>    <dbl+lbl> <dbl+lbl>       <dbl+lbl>       #>  1  1           20               1 [BLUE]    #>  2  2        NA(a) [REFUSED]     1 [BLUE]    #>  3  3           21           NA(a) [REFUSED] #>  4  4           30           NA(b) [OMITTED] #>  5  5            1              NA           #>  6  6           41               2 [RED]     #>  7  7           50           NA(b) [OMITTED] #>  8  8           30               3 [YELLOW]  #>  9  9        NA(a) [REFUSED] NA(a) [REFUSED] #> 10 10        NA(b) [OMITTED]     2 [RED]     #> 11 11           10           NA(a) [REFUSED] is.na(df_stata$age) #>  [1] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  mean(df_stata$age, na.rm = TRUE) #> [1] 25.375 df_stata |>   mutate(     favorite_color_missing_reasons = if_else(       is.na(favorite_color), favorite_color, NA     )   ) |>   summarize(     mean_age = mean(age, na.rm = TRUE),     n = n(),     .by = favorite_color_missing_reasons   ) #> # A tibble: 1 × 3 #>   favorite_color_missing_reasons mean_age     n #>   <dbl+lbl>                         <dbl> <int> #> 1 NA                                 25.4    11"},{"path":"http://kylehusmann.com/interlacer/articles/other-approaches.html","id":"declared","dir":"Articles","previous_headings":"","what":"declared","title":"Other Approaches","text":"declared package uses functiondeclared::declared() constructing interlaced vectors: declared vectors similar haven_labelled_spss vectors, except critical innovation: store actual NA values missing values, keep track missing reasons entirely attributes object: means aggregations work exactly expect!","code":"library(declared)  (dcl <- declared(c(1, 2, 3, -99, -98), na_values = c(-99, -98))) #> <declared<numeric>[5]> #> [1]       1       2       3 NA(-99) NA(-98) #> Missing values: -99, -98 # All the missing reason info is tracked in the attributes attributes(dcl) #> $na_index #> -99 -98  #>   4   5  #>  #> $na_values #> [1] -99 -98 #>  #> $date #> [1] FALSE #>  #> $class #> [1] \"declared\" \"numeric\"  # The data stored has actual NA values, so it works as you would expect # with summary stats like `mean()`, etc. attributes(dcl) <- NULL dcl #> [1]  1  2  3 NA NA dcl <- declared(c(1, 2, 3, -99, -98), na_values = c(-99, -98))  sum(dcl, na.rm = TRUE) #> [1] 6"},{"path":"http://kylehusmann.com/interlacer/articles/other-approaches.html","id":"interlacer","dir":"Articles","previous_headings":"","what":"interlacer","title":"Other Approaches","text":"interlacer builds ideas haven, labelled, declared following goals:","code":""},{"path":"http://kylehusmann.com/interlacer/articles/other-approaches.html","id":"be-fully-generic-add-a-missing-value-channel-to-any-vector-type","dir":"Articles","previous_headings":"interlacer","what":"1. Be fully generic: Add a missing value channel to any vector type","title":"Other Approaches","text":"mentioned , haven::labelled_spss() works numeric character types, haven::tagged_na() works numeric types. declared::declared() supports numeric, character date types. interlaced types, contrast, can imbue vector type missing value channel: Like declared vectors, missing reasons tracked attributes. unlike declared, missing reasons stored entirely separate channel rather tracking indices: data structure drives functional API, described (3) .","code":"interlaced(list(TRUE, FALSE, \"reason\"), na = \"reason\") #> <interlaced<lgl, fct>[3]> #> [1]  TRUE    FALSE    <reason> #> NA levels: reason  interlaced(c(\"2020-01-01\", \"2020-01-02\", \"reason\"), na = \"reason\") |>   map_value_channel(as.Date) #> <interlaced<date, fct>[3]> #> [1] 2020-01-01 2020-01-02 <reason>   #> NA levels: reason   interlaced(c(\"red\", \"green\", \"reason\"), na = \"reason\") |>   map_value_channel(factor) #> <interlaced<fct, fct>[3]> #> [1] red      green    <reason> #> Levels: green red  #> NA levels: reason (int <- interlaced(c(1,2,3, -99, -98), na = c(-99, -98))) #> <interlaced<dbl, int>[5]> #> [1]  1     2     3    <-99> <-98>  attributes(int) #> $na_channel_values #> [1]  NA  NA  NA -99 -98 #>  #> $class #> [1] \"interlacer_interlaced\" \"vctrs_vctr\"            \"numeric\"  attributes(int) <- NULL int #> [1]  1  2  3 NA NA"},{"path":"http://kylehusmann.com/interlacer/articles/other-approaches.html","id":"provide-functions-for-reading-writing-interlaced-csv-files-not-just-spss-sas-stata-files","dir":"Articles","previous_headings":"interlacer","what":"2. Provide functions for reading / writing interlaced CSV files (not just SPSS / SAS / Stata files)","title":"Other Approaches","text":"See interlacer::read_interlaced_csv(), etc.","code":""},{"path":"http://kylehusmann.com/interlacer/articles/other-approaches.html","id":"provide-a-functional-api-that-integrates-well-into-tidy-pipelines","dir":"Articles","previous_headings":"interlacer","what":"3. Provide a functional API that integrates well into tidy pipelines","title":"Other Approaches","text":"interlacer provides functions facilitate working interlaced type Result type, well-understood abstraction functional programming. functions na() map_value_channel() map_na_channel() come influence. na() function creates interlaced type “lifting” value missing reason channel. approach helps create safer separation value missing reason channels, ’s always clear channel ’re making comparisons . example: Similarly, map_value_channel() map_na_channel() allow safely mutate particular channel, without touching values channel. interface especially useful tidy pipelines. Finally, interlaced type based vctrs type system, plays nicely packages tidyverse.","code":"# haven labelled_spss(c(-99, 1, 2), na_values = -99) == 1 # value channel comparison #> [1] FALSE  TRUE FALSE labelled_spss(c(-99, 1, 2), na_values = -99) == -99 # na channel comparison #> [1]  TRUE FALSE FALSE  # declared declared(c(-99, 1, 2), na_values = -99) == 1 # value channel comparison #> [1] FALSE  TRUE FALSE declared(c(-99, 1, 2), na_values = -99) == -99 # na channel comparison #> [1]  TRUE FALSE FALSE  # interlacer  interlaced(c(-99, 1, 2), na = -99) == 1 # value channel comparison #> [1]    NA  TRUE FALSE interlaced(c(-99, 1, 2), na = -99) == na(-99) # na channel comparison #> [1] NA NA NA"},{"path":"http://kylehusmann.com/interlacer/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Kyle Husmann. Author, maintainer.","code":""},{"path":"http://kylehusmann.com/interlacer/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Husmann K (2024). interlacer: Read Tabular Data Interlaced Values Missing Reasons. R package version 0.4.1, https://kylehusmann.com/interlacer, https://github.com/khusmann/interlacer.","code":"@Manual{,   title = {interlacer: Read Tabular Data With Interlaced Values And Missing Reasons},   author = {Kyle Husmann},   year = {2024},   note = {R package version 0.4.1, https://kylehusmann.com/interlacer},   url = {https://github.com/khusmann/interlacer}, }"},{"path":"http://kylehusmann.com/interlacer/index.html","id":"interlacer-","dir":"","previous_headings":"","what":"Read Tabular Data With Interlaced Values And Missing Reasons","title":"Read Tabular Data With Interlaced Values And Missing Reasons","text":"value missing data, sometimes want know missing. Many textual tabular data sources encode missing reasons special values interlaced regular values column (e.g. N/, REFUSED, -99, etc.). Unfortunately, missing reasons lost values converted single NA type. Working missing reasons R traditionally requires loading variables character vectors bunch string comparisons type conversions make sense . interlacer provides functions load variables interlaced data sources special interlaced column type holds values NA reasons separate channels variable. contexts, can treat interlaced columns regular values: take mean interlaced column, example, get mean values, without missing reasons interfering computation. Unlike regular column, however, missing reasons still available. means can still filter data frames variables specific missing reasons, generate summary statistics breakdowns missing reason. words, longer constantly manually include / exclude missing reasons computations filtering awkward string comparisons type conversions… everything just works! addition introduction vignette(\"interlacer\") sure also check : vignette(\"extended-column-types\") see handle variable-level missing reasons vignette(\"coded-data\") recipies working coded data (e.g. data produced SPSS, SAS Stata) vignette(\"-approaches\") deep dive interlacer’s approach compares approaches representing manipulating missing reasons alongside data values","code":""},{"path":"http://kylehusmann.com/interlacer/index.html","id":"id_️-️-️-warning-️-️-️","dir":"","previous_headings":"","what":"⚠️ ⚠️ ⚠️ WARNING ⚠️ ⚠️ ⚠️","title":"Read Tabular Data With Interlaced Values And Missing Reasons","text":"library currently experimental stages, aware interface quite likely change future. meantime, please try let know think!","code":""},{"path":"http://kylehusmann.com/interlacer/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Read Tabular Data With Interlaced Values And Missing Reasons","text":"easiest way get interlacer install via devtools:","code":"install.packages(\"devtools\") # If devtools is not already installed  devtools::install_github(\"khusmann/interlacer\")"},{"path":"http://kylehusmann.com/interlacer/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Read Tabular Data With Interlaced Values And Missing Reasons","text":"use interlacer, load current R session: interlacer supports following file formats read_interlaced_*() functions, extend readr::read_*() family functions: read_interlaced_csv() read_interlaced_tsv() read_interlaced_csv2() read_interlaced_delim() quick demo, consider following example file bundled interlacer: csv file, values interlaced three possible missing reasons: REFUSED, OMITTED, N/. readr, loading data result data frame missing reasons replaced NA: interlacer, missing reasons preserved: can see, printout column defined two types: type values, type missing reasons. age column, example, type double values, type factor missing reasons: Computations automatically operate values: missing reasons still ! indicate value treated missing reason instead regular value, can use na() function. following, example, filter data set individuals REFUSED give favorite color: ’s pipeline compute breakdown mean age respondents favorite color, separate categories missing reason: just scratches surface can done interlacer… check vignette(\"interlacer\") complete overview!","code":"library(interlacer, warn.conflicts = FALSE) library(dplyr, warn.conflicts = FALSE) library(readr)  read_file(interlacer_example(\"colors.csv\")) |>   cat() #> person_id,age,favorite_color #> 1,20,BLUE #> 2,REFUSED,BLUE #> 3,21,REFUSED #> 4,30,OMITTED #> 5,1,N/A #> 6,41,RED #> 7,50,OMITTED #> 8,30,YELLOW #> 9,REFUSED,REFUSED #> 10,OMITTED,RED #> 11,10,REFUSED read_csv(   interlacer_example(\"colors.csv\"),   na = c(\"REFUSED\", \"OMITTED\", \"N/A\") ) #> # A tibble: 11 × 3 #>    person_id   age favorite_color #>        <dbl> <dbl> <chr>          #>  1         1    20 BLUE           #>  2         2    NA BLUE           #>  3         3    21 <NA>           #>  4         4    30 <NA>           #>  5         5     1 <NA>           #>  6         6    41 RED            #>  7         7    50 <NA>           #>  8         8    30 YELLOW         #>  9         9    NA <NA>           #> 10        10    NA RED            #> 11        11    10 <NA> (ex <- read_interlaced_csv(   interlacer_example(\"colors.csv\"),   na = c(\"REFUSED\", \"OMITTED\", \"N/A\") )) #> # A tibble: 11 × 3 #>    person_id       age favorite_color #>    <dbl,fct> <dbl,fct> <chr,fct>      #>  1         1        20 BLUE           #>  2         2 <REFUSED> BLUE           #>  3         3        21 <REFUSED>      #>  4         4        30 <OMITTED>      #>  5         5         1 <N/A>          #>  6         6        41 RED            #>  7         7        50 <OMITTED>      #>  8         8        30 YELLOW         #>  9         9 <REFUSED> <REFUSED>      #> 10        10 <OMITTED> RED            #> 11        11        10 <REFUSED> ex$age #> <interlaced<dbl, fct>[11]> #>  [1] 20        <REFUSED> 21        30        1         41        50        #>  [8] 30        <REFUSED> <OMITTED> 10        #> NA levels: REFUSED OMITTED N/A mean(ex$age, na.rm = TRUE) #> [1] 25.375 ex |>   filter(favorite_color == na(\"REFUSED\")) #> # A tibble: 3 × 3 #>   person_id       age favorite_color #>   <dbl,fct> <dbl,fct> <chr,fct>      #> 1         3        21 <REFUSED>      #> 2         9 <REFUSED> <REFUSED>      #> 3        11        10 <REFUSED> ex |>   summarize(     mean_age = mean(age, na.rm = TRUE),     n = n(),     .by = favorite_color   ) |>   arrange(favorite_color) #> # A tibble: 6 × 3 #>   favorite_color mean_age     n #>   <chr,fct>         <dbl> <int> #> 1 BLUE               20       2 #> 2 RED                41       2 #> 3 YELLOW             30       1 #> 4 <REFUSED>          15.5     3 #> 5 <OMITTED>          40       2 #> 6 <N/A>               1       1"},{"path":"http://kylehusmann.com/interlacer/index.html","id":"known-issues","dir":"","previous_headings":"","what":"Known Issues","title":"Read Tabular Data With Interlaced Values And Missing Reasons","text":"base functions, like base::ifelse(), drop missing reason channel interlaced types, converting regular vectors example: due limitation R. run , use tidyverse equivalent function. Tidyverse functions designed correctly handle type conversions. example, can use dplyr::if_else(): Performance large data sets may notice large datasets interlacer runs significantly slower readr / vroom. Although interlacer uses vroom hood load delimited data, able take advantage many optimizations vroom currently support column-level missing values. soon vroom supports column-level missing values, able remedy !","code":"ex |>   mutate(     favorite_color = ifelse(age < 18, na(\"REDACTED\"), favorite_color)   ) #> # A tibble: 11 × 3 #>    person_id       age favorite_color #>    <dbl,fct> <dbl,fct> <chr>          #>  1         1        20 BLUE           #>  2         2 <REFUSED> <NA>           #>  3         3        21 <NA>           #>  4         4        30 <NA>           #>  5         5         1 <NA>           #>  6         6        41 RED            #>  7         7        50 <NA>           #>  8         8        30 YELLOW         #>  9         9 <REFUSED> <NA>           #> 10        10 <OMITTED> <NA>           #> 11        11        10 <NA> ex |>   mutate(     favorite_color = if_else(       age < 18,       na(\"REDACTED_UNDERAGE\"),       favorite_color,       missing = na(\"REDACTED_MISSING_AGE\")     )   ) #> # A tibble: 11 × 3 #>    person_id       age favorite_color         #>    <dbl,fct> <dbl,fct> <chr,fct>              #>  1         1        20 BLUE                   #>  2         2 <REFUSED> <REDACTED_MISSING_AGE> #>  3         3        21 <REFUSED>              #>  4         4        30 <OMITTED>              #>  5         5         1 <REDACTED_UNDERAGE>    #>  6         6        41 RED                    #>  7         7        50 <OMITTED>              #>  8         8        30 YELLOW                 #>  9         9 <REFUSED> <REDACTED_MISSING_AGE> #> 10        10 <OMITTED> <REDACTED_MISSING_AGE> #> 11        11        10 <REDACTED_UNDERAGE>"},{"path":"http://kylehusmann.com/interlacer/index.html","id":"related-work","dir":"","previous_headings":"","what":"Related work","title":"Read Tabular Data With Interlaced Values And Missing Reasons","text":"interlacer inspired haven, labelled, declared packages. packages provide similar functionality interlacer, focused providing compatibility missing reason data imported SPSS, SAS, Stata. interlacer slightly different aims: fully generic: Add missing value channel vector type. Provide functions reading / writing interlaced CSV files (just SPSS / SAS / Stata files) Provide functional API integrates well tidy pipelines Future versions interlacer provide functions convert packages’ types. detailed discussion, see vignette(\"-approaches\").","code":""},{"path":"http://kylehusmann.com/interlacer/index.html","id":"acknowledgements","dir":"","previous_headings":"","what":"Acknowledgements","title":"Read Tabular Data With Interlaced Values And Missing Reasons","text":"development software supported, whole part, Institute Education Sciences, U.S. Department Education, Grant R305A170047 Pennsylvania State University. opinions expressed authors represent views Institute U.S. Department Education.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/across_value_channels.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply a function across the value or missing reason channels of multiple columns — across_value_channels","title":"Apply a function across the value or missing reason channels of multiple columns — across_value_channels","text":"across_value_channels() across_na_channels() simple wrappers dplyr::across() applies transformations value missing reason channels, respectively.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/across_value_channels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply a function across the value or missing reason channels of multiple columns — across_value_channels","text":"","code":"across_value_channels(.cols, .fns, .names = NULL, .unpack = FALSE)  across_na_channels(.cols, .fns, .names = NULL, .unpack = FALSE)"},{"path":"http://kylehusmann.com/interlacer/reference/across_value_channels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply a function across the value or missing reason channels of multiple columns — across_value_channels","text":".cols <tidy-select> Columns transform. select grouping columns already automatically handled verb (.e. summarise() mutate()). .fns Functions apply selected columns. Possible values : function, e.g. mean. purrr-style lambda, e.g. ~ mean(.x, na.rm = TRUE) named list functions lambdas, e.g. list(mean = mean, n_miss = ~ sum(.na(.x)). function applied column, output named combining function name column name using glue specification .names. Within functions can use cur_column() cur_group() access current column grouping keys respectively. .names glue specification describes name output columns. can use {.col} stand selected column name, {.fn} stand name function applied. default (NULL) equivalent \"{.col}\" single function case \"{.col}_{.fn}\" case list used .fns. .unpack  Optionally unpack data frames returned functions .fns, expands df-columns individual columns, retaining number rows data frame. FALSE, default, unpacking done. TRUE, unpacking done default glue specification \"{outer}_{inner}\". Otherwise, single glue specification can supplied describe name unpacked columns. can use {outer} refer name originally generated .names, {inner} refer names data frame unpacking.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/across_value_channels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply a function across the value or missing reason channels of multiple columns — across_value_channels","text":"like dplyr::across(), across_value_channels() across_na_channels() return tibble one column column .cols function .fns","code":""},{"path":[]},{"path":"http://kylehusmann.com/interlacer/reference/as.cfactor.html","id":null,"dir":"Reference","previous_headings":"","what":"cfactor coercion — as.cfactor","title":"cfactor coercion — as.cfactor","text":"Add codes vector labels","code":""},{"path":"http://kylehusmann.com/interlacer/reference/as.cfactor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"cfactor coercion — as.cfactor","text":"","code":"as.cfactor(x, codes = NULL, ordered = is.ordered(x))  # S3 method for class 'factor' as.cfactor(x, codes = NULL, ordered = is.ordered(x))  as.cordered(x, codes = NULL)"},{"path":"http://kylehusmann.com/interlacer/reference/as.cfactor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"cfactor coercion — as.cfactor","text":"x vector values representing labels factor levels codes named vector unique codes declares mapping labels codes ordered logical flag determine codes regarded ordered (order given).","code":""},{"path":"http://kylehusmann.com/interlacer/reference/as.cfactor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"cfactor coercion — as.cfactor","text":"new cfactor","code":""},{"path":"http://kylehusmann.com/interlacer/reference/as.codes.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert a cfactor vector into a vector of its codes — as.codes","title":"Convert a cfactor vector into a vector of its codes — as.codes","text":"TODO: Write ","code":""},{"path":"http://kylehusmann.com/interlacer/reference/as.codes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert a cfactor vector into a vector of its codes — as.codes","text":"","code":"as.codes(x, ...)  # S3 method for class 'interlacer_interlaced' as.codes(x, ...)  # S3 method for class 'interlacer_cfactor' as.codes(x, ...)"},{"path":"http://kylehusmann.com/interlacer/reference/as.codes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert a cfactor vector into a vector of its codes — as.codes","text":"x cfactor() ... additional arguments (used)","code":""},{"path":"http://kylehusmann.com/interlacer/reference/as.codes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert a cfactor vector into a vector of its codes — as.codes","text":"vector coded values","code":""},{"path":"http://kylehusmann.com/interlacer/reference/as.x_col_spec.html","id":null,"dir":"Reference","previous_headings":"","what":"Extended column specification coercions — as.x_col_spec","title":"Extended column specification coercions — as.x_col_spec","text":"Coerce object column specification. used internally parse col_types argument read_interlaced_*() family functions, can accept readr::cols() specification list().","code":""},{"path":"http://kylehusmann.com/interlacer/reference/as.x_col_spec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extended column specification coercions — as.x_col_spec","text":"","code":"as.x_col_spec(x)"},{"path":"http://kylehusmann.com/interlacer/reference/as.x_col_spec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extended column specification coercions — as.x_col_spec","text":"x value coerce extended column specification","code":""},{"path":"http://kylehusmann.com/interlacer/reference/as.x_col_spec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extended column specification coercions — as.x_col_spec","text":"extended column specification","code":""},{"path":"http://kylehusmann.com/interlacer/reference/as.x_col_spec.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extended column specification coercions — as.x_col_spec","text":"S3 function packages may use col_types argument custom objects.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/as.x_collector.html","id":null,"dir":"Reference","previous_headings":"","what":"Collector shortcuts — as.na_collector","title":"Collector shortcuts — as.na_collector","text":".*_collector functions used internally enable shortcuts defaults specifying extended collectors. See vignette(\"extended-column-types\") full discussion.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/as.x_collector.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Collector shortcuts — as.na_collector","text":"","code":"as.na_collector(x)  as.value_collector(x)  as.x_collector(x)"},{"path":"http://kylehusmann.com/interlacer/reference/as.x_collector.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Collector shortcuts — as.na_collector","text":"x value convert extended collector, value collector, missing reason collector.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/cfactor.html","id":null,"dir":"Reference","previous_headings":"","what":"Coded factors — cfactor","title":"Coded factors — cfactor","text":"TODO: Write ","code":""},{"path":"http://kylehusmann.com/interlacer/reference/cfactor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coded factors — cfactor","text":"","code":"cfactor(x = unspecified(), codes, ordered = FALSE)  cordered(x, codes)  is.cfactor(x)  is.cordered(x)"},{"path":"http://kylehusmann.com/interlacer/reference/cfactor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coded factors — cfactor","text":"x vector character numeric codes codes named vector unique codes declares mapping labels codes ordered logical flag determine codes regarded ordered (order given).","code":""},{"path":"http://kylehusmann.com/interlacer/reference/cfactor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coded factors — cfactor","text":"new cfactor","code":""},{"path":"http://kylehusmann.com/interlacer/reference/codes-set.html","id":null,"dir":"Reference","previous_headings":"","what":"Set the codes for a `cfactor“ — codes<-","title":"Set the codes for a `cfactor“ — codes<-","text":"Set codes cfactor, similar levels<-()","code":""},{"path":"http://kylehusmann.com/interlacer/reference/codes-set.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set the codes for a `cfactor“ — codes<-","text":"","code":"codes(x) <- value"},{"path":"http://kylehusmann.com/interlacer/reference/codes-set.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set the codes for a `cfactor“ — codes<-","text":"value named vector codes cfactor","code":""},{"path":"http://kylehusmann.com/interlacer/reference/codes.html","id":null,"dir":"Reference","previous_headings":"","what":"cfactor attributes — codes","title":"cfactor attributes — codes","text":"Return levels codes cfactor","code":""},{"path":"http://kylehusmann.com/interlacer/reference/codes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"cfactor attributes — codes","text":"","code":"codes(x, ...)  # S3 method for class 'interlacer_cfactor' levels(x)"},{"path":"http://kylehusmann.com/interlacer/reference/codes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"cfactor attributes — codes","text":"x cfactor ... additional arguments (used)","code":""},{"path":"http://kylehusmann.com/interlacer/reference/codes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"cfactor attributes — codes","text":"levels() returns levels cfactor (vector character labels); codes() returns named vector representing codes cfactor","code":""},{"path":"http://kylehusmann.com/interlacer/reference/flatten_channels.html","id":null,"dir":"Reference","previous_headings":"","what":"Flatten a interlaced vector — flatten_channels","title":"Flatten a interlaced vector — flatten_channels","text":"flatten_channels() flattens interlaced vector single channel. useful step right writing interlaced vector file, example.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/flatten_channels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Flatten a interlaced vector — flatten_channels","text":"","code":"flatten_channels(x, ...)"},{"path":"http://kylehusmann.com/interlacer/reference/flatten_channels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Flatten a interlaced vector — flatten_channels","text":"x interlaced vector ... Additional arguments, used","code":""},{"path":"http://kylehusmann.com/interlacer/reference/flatten_channels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Flatten a interlaced vector — flatten_channels","text":"vector, flattened","code":""},{"path":"http://kylehusmann.com/interlacer/reference/grapes-equals-grapes.html","id":null,"dir":"Reference","previous_headings":"","what":"Multichannel comparison — %==%","title":"Multichannel comparison — %==%","text":"Regular comparison operators (.e. == !=) apply value channel; used missing values always result NA. contrast, multichannel comparison operators %==% %!=% compare value missing reason channels variable. Multichannel equality %==% operates identically regular == operands values, return FALSE instead NA one missing. values compared %==% missing values, missing reasons compared. Multichannel comparison operators always return boolean values, never NA","code":""},{"path":"http://kylehusmann.com/interlacer/reference/grapes-equals-grapes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multichannel comparison — %==%","text":"","code":"x %==% y  x %!=% y"},{"path":"http://kylehusmann.com/interlacer/reference/grapes-equals-grapes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multichannel comparison — %==%","text":"x vector y vector compare","code":""},{"path":"http://kylehusmann.com/interlacer/reference/grapes-equals-grapes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Multichannel comparison — %==%","text":"boolean vector indicating result comparison","code":""},{"path":"http://kylehusmann.com/interlacer/reference/interlaced.html","id":null,"dir":"Reference","previous_headings":"","what":"Construct an interlaced vector — interlaced","title":"Construct an interlaced vector — interlaced","text":"interlaced type extends vectors adding \"missing reason\" channel can used distinguish different types missingness. interlaced() function constructs new interlaced vector vector list values.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/interlaced.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Construct an interlaced vector — interlaced","text":"","code":"interlaced(x, na = NULL)  as.interlaced(x, na = NULL, ...)  # Default S3 method as.interlaced(x, na = NULL, ...)  # S3 method for class 'interlacer_interlaced' as.interlaced(x, ...)  # S3 method for class 'data.frame' as.interlaced(x, ...)  is.interlaced(x)"},{"path":"http://kylehusmann.com/interlacer/reference/interlaced.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Construct an interlaced vector — interlaced","text":"x vector list values na vector values interpret missing values ... Additional arguments, used","code":""},{"path":"http://kylehusmann.com/interlacer/reference/interlaced.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Construct an interlaced vector — interlaced","text":"interlaced vector","code":""},{"path":"http://kylehusmann.com/interlacer/reference/interlacer_example.html","id":null,"dir":"Reference","previous_headings":"","what":"Get a path to one of interlacer's example data sets — interlacer_example","title":"Get a path to one of interlacer's example data sets — interlacer_example","text":"interlacer comes bundled number sample files inst/extdata directory. function make easy access","code":""},{"path":"http://kylehusmann.com/interlacer/reference/interlacer_example.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get a path to one of interlacer's example data sets — interlacer_example","text":"","code":"interlacer_example(file = NULL)"},{"path":"http://kylehusmann.com/interlacer/reference/interlacer_example.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get a path to one of interlacer's example data sets — interlacer_example","text":"file Name file. NULL, example files listed.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/interlacer_example.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get a path to one of interlacer's example data sets — interlacer_example","text":"","code":"interlacer_example() #> [1] \"colors.csv\"            \"colors.dta\"            \"colors.sav\"            #> [4] \"colors_coded.csv\"      \"colors_coded_char.csv\" \"stress.csv\"            interlacer_example(\"colors.csv\") #> [1] \"/home/runner/work/_temp/Library/interlacer/extdata/colors.csv\""},{"path":"http://kylehusmann.com/interlacer/reference/levels-set-.interlacer_interlaced.html","id":null,"dir":"Reference","previous_headings":"","what":"Set the factor level attributes of interlaced vectors — levels<-.interlacer_interlaced","title":"Set the factor level attributes of interlaced vectors — levels<-.interlacer_interlaced","text":"Set factor level attributes interlaced vectors","code":""},{"path":"http://kylehusmann.com/interlacer/reference/levels-set-.interlacer_interlaced.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set the factor level attributes of interlaced vectors — levels<-.interlacer_interlaced","text":"","code":"# S3 method for class 'interlacer_interlaced' levels(x) <- value  na_levels(x) <- value"},{"path":"http://kylehusmann.com/interlacer/reference/levels-set-.interlacer_interlaced.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set the factor level attributes of interlaced vectors — levels<-.interlacer_interlaced","text":"value vector new levels set","code":""},{"path":"http://kylehusmann.com/interlacer/reference/levels.interlacer_interlaced.html","id":null,"dir":"Reference","previous_headings":"","what":"Factor level attributes of interlaced vectors — levels.interlacer_interlaced","title":"Factor level attributes of interlaced vectors — levels.interlacer_interlaced","text":"base S3 levels() function overloaded interlaced vectors, value channel factor type, levels() return levels. Similarly na_levels() return levels missing reason channel.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/levels.interlacer_interlaced.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Factor level attributes of interlaced vectors — levels.interlacer_interlaced","text":"","code":"# S3 method for class 'interlacer_interlaced' levels(x)  na_levels(x)"},{"path":"http://kylehusmann.com/interlacer/reference/levels.interlacer_interlaced.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Factor level attributes of interlaced vectors — levels.interlacer_interlaced","text":"x interlaced vector","code":""},{"path":"http://kylehusmann.com/interlacer/reference/levels.interlacer_interlaced.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Factor level attributes of interlaced vectors — levels.interlacer_interlaced","text":"levels values missing reason channel","code":""},{"path":"http://kylehusmann.com/interlacer/reference/map_value_channel.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply a function to one of the channels of an interlaced vector — map_value_channel","title":"Apply a function to one of the channels of an interlaced vector — map_value_channel","text":"map_value_channel() modifies values interlaced vector. map_na_channel() modifies missing reason channel interlaced vector.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/map_value_channel.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply a function to one of the channels of an interlaced vector — map_value_channel","text":"","code":"map_value_channel(x, fn)  map_na_channel(x, fn)"},{"path":"http://kylehusmann.com/interlacer/reference/map_value_channel.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply a function to one of the channels of an interlaced vector — map_value_channel","text":"x interlaced vector fn function maps values missing reasons new values","code":""},{"path":"http://kylehusmann.com/interlacer/reference/map_value_channel.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply a function to one of the channels of an interlaced vector — map_value_channel","text":"new interlaced vector, modified according supplied function","code":""},{"path":"http://kylehusmann.com/interlacer/reference/na.html","id":null,"dir":"Reference","previous_headings":"","what":"Interpret a value as a missing reason — na","title":"Interpret a value as a missing reason — na","text":"na() lifts value interlaced missing reason channel.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interpret a value as a missing reason — na","text":"","code":"na(x = NA)"},{"path":"http://kylehusmann.com/interlacer/reference/na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interpret a value as a missing reason — na","text":"x character numeric value","code":""},{"path":"http://kylehusmann.com/interlacer/reference/na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interpret a value as a missing reason — na","text":"interlaced value","code":""},{"path":"http://kylehusmann.com/interlacer/reference/na_collectors.html","id":null,"dir":"Reference","previous_headings":"","what":"Missing reason collectors — na_collectors","title":"Missing reason collectors — na_collectors","text":"Missing reason collectors used extended column specifications specify type column's missing reason channel.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/na_collectors.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Missing reason collectors — na_collectors","text":"","code":"na_col_default()  na_col_none()  na_col_integer(...)  na_col_factor(...)  na_col_cfactor(...)"},{"path":"http://kylehusmann.com/interlacer/reference/na_collectors.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Missing reason collectors — na_collectors","text":"... values interpret missing values. case na_col_cfactor(), arguments must named.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/na_collectors.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Missing reason collectors — na_collectors","text":"new missing reason collector object","code":""},{"path":"http://kylehusmann.com/interlacer/reference/na_collectors.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Missing reason collectors — na_collectors","text":"na_col_default() used signal missing reason type inherit specification provided na =  argument calling read_interlaced_*() function","code":""},{"path":[]},{"path":"http://kylehusmann.com/interlacer/reference/parse_interlaced.html","id":null,"dir":"Reference","previous_headings":"","what":"Parse a character vector into an interlaced vector type — parse_interlaced","title":"Parse a character vector into an interlaced vector type — parse_interlaced","text":"parse_interlaced converts character vector interlaced vector parsing readr collector type.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/parse_interlaced.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parse a character vector into an interlaced vector type — parse_interlaced","text":"","code":"parse_interlaced(x, na, .default = v_col_guess())"},{"path":"http://kylehusmann.com/interlacer/reference/parse_interlaced.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Parse a character vector into an interlaced vector type — parse_interlaced","text":"x character vector na missing reason collector (e.g. na_col_integer()), one shortcuts (e.g. list missing values) .default value collector parse character values (e.g. v_col_double(), v_col_integer(), etc.)","code":""},{"path":"http://kylehusmann.com/interlacer/reference/parse_interlaced.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Parse a character vector into an interlaced vector type — parse_interlaced","text":"interlaced vector","code":""},{"path":"http://kylehusmann.com/interlacer/reference/read_interlaced_delim.html","id":null,"dir":"Reference","previous_headings":"","what":"Read an delimited file with interlaced missing reasons into a tibble — read_interlaced_delim","title":"Read an delimited file with interlaced missing reasons into a tibble — read_interlaced_delim","text":"read_interlaced_*(), family functions extend readr's read_delim(), read_csv, etc. functions use data sources values interlaced missing reasons. functions return tibble interlaced columns.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/read_interlaced_delim.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read an delimited file with interlaced missing reasons into a tibble — read_interlaced_delim","text":"","code":"read_interlaced_delim(   file,   delim = NULL,   quote = \"\\\"\",   escape_backslash = FALSE,   escape_double = TRUE,   col_names = TRUE,   col_types = NULL,   col_select = NULL,   id = NULL,   locale = readr::default_locale(),   na = na_col_none(),   comment = \"\",   trim_ws = FALSE,   skip = 0,   n_max = Inf,   guess_max = min(1000, n_max),   name_repair = \"unique\",   progress = readr::show_progress(),   show_col_types = readr::should_show_types(),   skip_empty_rows = TRUE )  read_interlaced_csv(   file,   col_names = TRUE,   col_types = NULL,   col_select = NULL,   id = NULL,   locale = readr::default_locale(),   na = na_col_none(),   quote = \"\\\"\",   comment = \"\",   trim_ws = TRUE,   skip = 0,   n_max = Inf,   guess_max = min(1000, n_max),   name_repair = \"unique\",   progress = readr::show_progress(),   show_col_types = readr::should_show_types(),   skip_empty_rows = TRUE )  read_interlaced_csv2(   file,   col_names = TRUE,   col_types = NULL,   col_select = NULL,   id = NULL,   locale = readr::default_locale(),   na = na_col_none(),   quote = \"\\\"\",   comment = \"\",   trim_ws = TRUE,   skip = 0,   n_max = Inf,   guess_max = min(1000, n_max),   name_repair = \"unique\",   progress = readr::show_progress(),   show_col_types = readr::should_show_types(),   skip_empty_rows = TRUE )  read_interlaced_tsv(   file,   col_names = TRUE,   col_types = NULL,   col_select = NULL,   id = NULL,   locale = readr::default_locale(),   na = na_col_none(),   quote = \"\\\"\",   comment = \"\",   trim_ws = TRUE,   skip = 0,   n_max = Inf,   guess_max = min(1000, n_max),   name_repair = \"unique\",   progress = readr::show_progress(),   show_col_types = readr::should_show_types(),   skip_empty_rows = TRUE )  interlaced_vroom(   file,   delim = NULL,   col_names = TRUE,   col_types = NULL,   col_select = NULL,   id = NULL,   skip = 0,   n_max = Inf,   na = na_col_none(),   quote = \"\\\"\",   comment = \"\",   skip_empty_rows = TRUE,   trim_ws = TRUE,   escape_double = TRUE,   escape_backslash = FALSE,   locale = vroom::default_locale(),   guess_max = 100,   progress = vroom::vroom_progress(),   show_col_types = NULL,   .name_repair = \"unique\" )"},{"path":"http://kylehusmann.com/interlacer/reference/read_interlaced_delim.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read an delimited file with interlaced missing reasons into a tibble — read_interlaced_delim","text":"file Either path file, connection, literal data (either single string raw vector). Files ending .gz, .bz2, .xz, .zip automatically uncompressed. Files starting http://, https://, ftp://, ftps:// automatically downloaded. Remote gz files can also automatically downloaded decompressed. Literal data useful examples tests. recognised literal data, input must either wrapped (), string containing least one new line, vector containing least one string new line. Using value clipboard() read system clipboard. delim Single character used separate fields within record. quote Single character used quote strings. escape_backslash file use backslashes escape special characters? general escape_double backslashes can used escape delimiter character, quote character, add special characters like \\\\n. escape_double file escape quotes doubling ? .e. option TRUE, value \"\"\"\" represents single quote, \\\". col_names Either TRUE, FALSE character vector column names. TRUE, first row input used column names, included data frame. FALSE, column names generated automatically: X1, X2, X3 etc. col_names character vector, values used names columns, first row input read first row output data frame. Missing (NA) column names generate warning, filled dummy names ...1, ...2 etc. Duplicate column names generate warning made unique, see name_repair control done. col_types One NULL, cols() specification, string. See vignette(\"readr\") details. NULL, column types inferred guess_max rows input, interspersed throughout file. convenient (fast), robust. guessed types wrong, need increase guess_max supply correct types . Column specifications created list() cols() must contain one column specification column. want read subset columns, use cols_only(). Alternatively, can use compact string representation character represents one column: c = character = integer n = number d = double l = logical f = factor D = date T = date time t = time ? = guess _ - = skip default, reading file without column specification print message showing readr guessed . remove message, set show_col_types = FALSE set options(readr.show_col_types = FALSE). col_select Columns include results. can use mini-language dplyr::select() refer columns name. Use c() use one selection expression. Although usage less common, col_select also accepts numeric column index. See ?tidyselect::language full details selection language. id name column store file path. useful reading multiple input files data file paths, data collection date. NULL (default) extra column created. locale locale controls defaults vary place place. default locale US-centric (like R), can use locale() create locale controls things like default time zone, encoding, decimal mark, big mark, day/month names. na NA col spec defined na_cols() character numeric vector values interpret missing values. comment string used identify comments. text comment characters silently ignored. trim_ws leading trailing whitespace (ASCII spaces tabs) trimmed field parsing ? skip Number lines skip reading data. comment supplied commented lines ignored skipping. n_max Maximum number lines read. guess_max Maximum number lines use guessing column types. never use number lines read. See vignette(\"column-types\", package = \"readr\") details. name_repair, .name_repair Handling column names. default behaviour ensure column names \"unique\". Various repair strategies supported: \"minimal\": name repair checks, beyond basic existence names. \"unique\" (default value): Make sure names unique empty. \"check_unique\": name repair, check unique. \"unique_quiet\": Repair unique strategy, quietly. \"universal\": Make names unique syntactic. \"universal_quiet\": Repair universal strategy, quietly. function: Apply custom name repair (e.g., name_repair = make.names names style base R). purrr-style anonymous function, see rlang::as_function(). argument passed repair vctrs::vec_as_names(). See details terms strategies used enforce . progress Display progress bar? default display interactive session knitting document. automatic progress bar can disabled setting option readr.show_progress FALSE. show_col_types FALSE, show guessed column types. TRUE always show column types, even supplied. NULL (default) show column types explicitly supplied col_types argument. skip_empty_rows blank rows ignored altogether? .e. option TRUE blank rows represented .  FALSE represented NA values columns.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/read_interlaced_delim.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read an delimited file with interlaced missing reasons into a tibble — read_interlaced_delim","text":"tibble(), interlaced columns.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/read_interlaced_delim.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read an delimited file with interlaced missing reasons into a tibble — read_interlaced_delim","text":"","code":"# Beep boop"},{"path":"http://kylehusmann.com/interlacer/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. generics .factor, .ordered vctrs vec_c","code":""},{"path":"http://kylehusmann.com/interlacer/reference/value_channel.html","id":null,"dir":"Reference","previous_headings":"","what":"Access the channels of an interlaced vector — value_channel","title":"Access the channels of an interlaced vector — value_channel","text":"value_channel() returns value channel interlaced vector na_channel() returns missing reason channel interlaced vector","code":""},{"path":"http://kylehusmann.com/interlacer/reference/value_channel.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access the channels of an interlaced vector — value_channel","text":"","code":"value_channel(x, ...)  na_channel(x, ...)"},{"path":"http://kylehusmann.com/interlacer/reference/value_channel.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access the channels of an interlaced vector — value_channel","text":"x interlaced vector ... Additional arguments, used","code":""},{"path":"http://kylehusmann.com/interlacer/reference/value_channel.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access the channels of an interlaced vector — value_channel","text":"value missing reasons channel","code":""},{"path":"http://kylehusmann.com/interlacer/reference/value_collectors.html","id":null,"dir":"Reference","previous_headings":"","what":"Value collectors — value_collectors","title":"Value collectors — value_collectors","text":"Value collectors used extended column specifications specify value type column. think wrappers around readr's col_*() collector types.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/value_collectors.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Value collectors — value_collectors","text":"","code":"v_col_guess()  v_col_cfactor(codes, ordered = FALSE)  v_col_character()  v_col_date(format = \"\")  v_col_datetime(format = \"\")  v_col_double()  v_col_factor(levels = NULL, ordered = FALSE)  v_col_integer()  v_col_big_integer()  v_col_logical()  v_col_number()  v_col_skip()  v_col_time(format = \"\")"},{"path":"http://kylehusmann.com/interlacer/reference/value_collectors.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Value collectors — value_collectors","text":"codes named vector unique codes declares mapping labels codes. ordered ordered factor? format format specification, described readr::col_datetime() levels Character vector allowed levels. levels = NULL (default), levels discovered unique values x, order appear x.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/value_collectors.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Value collectors — value_collectors","text":"new value collector object","code":""},{"path":"http://kylehusmann.com/interlacer/reference/value_collectors.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Value collectors — value_collectors","text":"addition column types supported readr, interlacer additionally can load cfactor() types via v_col_cfactor()","code":""},{"path":[]},{"path":"http://kylehusmann.com/interlacer/reference/where_value_channel.html","id":null,"dir":"Reference","previous_headings":"","what":"Select variables with a function applied on value or missing reason channels — where_value_channel","title":"Select variables with a function applied on value or missing reason channels — where_value_channel","text":"where_value_channel() where_na_channel() simple wrappers tidyselect::() apply selection function value missing reason channel columns, respectively.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/where_value_channel.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Select variables with a function applied on value or missing reason channels — where_value_channel","text":"","code":"where_value_channel(fn)  where_na_channel(fn)"},{"path":"http://kylehusmann.com/interlacer/reference/where_value_channel.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Select variables with a function applied on value or missing reason channels — where_value_channel","text":"fn function returns TRUE FALSE (technically, predicate function). Can also purrr-like formula.","code":""},{"path":[]},{"path":"http://kylehusmann.com/interlacer/reference/write_interlaced_delim.html","id":null,"dir":"Reference","previous_headings":"","what":"Interlace a deinterlaced data frame and write it to a file — write_interlaced_delim","title":"Interlace a deinterlaced data frame and write it to a file — write_interlaced_delim","text":"write_interlaced_*() family functions take data frame interlaced columns, flatten interlaced columns, write file. Non-interlaced columns just pass . behavior functions match similarly named counterparts readr.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/write_interlaced_delim.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interlace a deinterlaced data frame and write it to a file — write_interlaced_delim","text":"","code":"write_interlaced_delim(   x,   file,   delim = \" \",   empty = \"NA\",   append = FALSE,   col_names = !append,   quote = c(\"needed\", \"all\", \"none\"),   escape = c(\"double\", \"backslash\", \"none\"),   eol = \"\\n\",   num_threads = readr::readr_threads(),   progress = readr::show_progress() )  write_interlaced_csv(   x,   file,   empty = \"NA\",   append = FALSE,   col_names = !append,   quote = c(\"needed\", \"all\", \"none\"),   escape = c(\"double\", \"backslash\", \"none\"),   eol = \"\\n\",   num_threads = readr::readr_threads(),   progress = readr::show_progress() )  write_interlaced_csv2(   x,   file,   empty = \"NA\",   append = FALSE,   col_names = !append,   quote = c(\"needed\", \"all\", \"none\"),   escape = c(\"double\", \"backslash\", \"none\"),   eol = \"\\n\",   num_threads = readr::readr_threads(),   progress = readr::show_progress() )  write_interlaced_excel_csv(   x,   file,   empty = \"NA\",   append = FALSE,   col_names = !append,   quote = c(\"needed\", \"all\", \"none\"),   escape = c(\"double\", \"backslash\", \"none\"),   eol = \"\\n\",   num_threads = readr::readr_threads(),   progress = readr::show_progress() )  write_interlaced_excel_csv2(   x,   file,   empty = \"NA\",   append = FALSE,   col_names = !append,   quote = c(\"needed\", \"all\", \"none\"),   escape = c(\"double\", \"backslash\", \"none\"),   eol = \"\\n\",   num_threads = readr::readr_threads(),   progress = readr::show_progress() )  write_interlaced_tsv(   x,   file,   empty = \"NA\",   append = FALSE,   col_names = !append,   quote = c(\"needed\", \"all\", \"none\"),   escape = c(\"double\", \"backslash\", \"none\"),   eol = \"\\n\",   num_threads = readr::readr_threads(),   progress = readr::show_progress() )"},{"path":"http://kylehusmann.com/interlacer/reference/write_interlaced_delim.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interlace a deinterlaced data frame and write it to a file — write_interlaced_delim","text":"x data frame tibble write disk. file File connection write . delim Delimiter used separate values. Defaults \" \" write_delim(), \",\" write_excel_csv() \";\" write_excel_csv2(). Must single character. empty String used empty values (NA values non-interlaced columns). Defaults NA. append FALSE, overwrite existing file. TRUE, append existing file. cases, file exist new file created. col_names FALSE, column names included top file. TRUE, column names included. specified, col_names take opposite value given append. quote handle fields contain characters need quoted. needed - Values quoted needed: contain delimiter, quote, newline. - Quote fields. none - Never quote fields. escape type escape use quotes data. double - quotes escaped doubling . backslash - quotes escaped preceding backslash. none - quotes escaped. eol end line character use. commonly either \"\\n\" Unix style newlines, \"\\r\\n\" Windows style newlines. num_threads Number threads use reading materializing vectors. data contains newlines within fields parser automatically forced use single thread . progress Display progress bar? default display interactive session knitting document. display updated every 50,000 values display estimated reading time 5 seconds . automatic progress bar can disabled setting option readr.show_progress FALSE.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/write_interlaced_delim.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interlace a deinterlaced data frame and write it to a file — write_interlaced_delim","text":"write_interlaced_* returns input x invisibly","code":""},{"path":"http://kylehusmann.com/interlacer/reference/x_col.html","id":null,"dir":"Reference","previous_headings":"","what":"Construct an extended collector for an extended column specification — x_col","title":"Construct an extended collector for an extended column specification — x_col","text":"Extended collectors used x_cols() column specifications indicate value missing reason channel types used loading data read_interlaced_*().","code":""},{"path":"http://kylehusmann.com/interlacer/reference/x_col.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Construct an extended collector for an extended column specification — x_col","text":"","code":"x_col(value_collector, na_collector = na_col_default())"},{"path":"http://kylehusmann.com/interlacer/reference/x_col.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Construct an extended collector for an extended column specification — x_col","text":"value_collector value collector na_collector missing reason collector","code":""},{"path":"http://kylehusmann.com/interlacer/reference/x_col.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Construct an extended collector for an extended column specification — x_col","text":"new extended collector object","code":""},{"path":[]},{"path":"http://kylehusmann.com/interlacer/reference/x_cols.html","id":null,"dir":"Reference","previous_headings":"","what":"Construct an extended column specification — x_cols","title":"Construct an extended column specification — x_cols","text":"Extended column specifications used read_interlaced_*() family functions col_types argument specify value missing reason channel types.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/x_cols.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Construct an extended column specification — x_cols","text":"","code":"x_cols(..., .default = v_col_guess())  x_cols_only(...)"},{"path":"http://kylehusmann.com/interlacer/reference/x_cols.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Construct an extended column specification — x_cols","text":"... named argument list extended collectors value collectors. .default default value collector","code":""},{"path":"http://kylehusmann.com/interlacer/reference/x_cols.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Construct an extended column specification — x_cols","text":"new extended column specification","code":""},{"path":"http://kylehusmann.com/interlacer/reference/x_cols.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Construct an extended column specification — x_cols","text":"Like readr::cols(), x_cols() includes columns input data, guessing column types default, creating missing reason channels according na =  argument read function. x_cols_only() includes columns specify, like readr::cols_only(). general, can substitute list() x_cols() without changing behavior.","code":""},{"path":[]},{"path":"http://kylehusmann.com/interlacer/reference/x_spec.html","id":null,"dir":"Reference","previous_headings":"","what":"Examine the extended column specification for a data frame — x_spec","title":"Examine the extended column specification for a data frame — x_spec","text":"x_spec() extracts full extended column specification tibble created read_interlaced_*() family functions.","code":""},{"path":"http://kylehusmann.com/interlacer/reference/x_spec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Examine the extended column specification for a data frame — x_spec","text":"","code":"x_spec(x)"},{"path":"http://kylehusmann.com/interlacer/reference/x_spec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Examine the extended column specification for a data frame — x_spec","text":"x data frame loaded read_interlaced_*()","code":""},{"path":"http://kylehusmann.com/interlacer/reference/x_spec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Examine the extended column specification for a data frame — x_spec","text":"extended column specification object","code":""},{"path":[]}]