edit reshape r melt

Author: hauselin

_posts/2020-06-27-reshape-r-dataframes-from-wide-to-long-with-melt/reshape-r-dataframes-from-wide-to-long-with-melt.Rmd

@@ -36,17 +36,17 @@ How do you reshape a dataframe from wide to long form in R? How does the `melt()
 
 ![Summary of how melt() works](./main_wide2long.png)
 
-Common terms for this transformation are melt, pivot-long, unpivot, gather, stack. Many functions have been written to convert data from wide to long form, but I believe `melt()` from the `data.table` library is the best. See `melt()` documentation [here](https://cran.r-project.org/web/packages/data.table/vignettes/datatable-reshape.html). 
+Common terms for this transformation are melt, pivot-long, unpivot, gather, stack, and reshape. Many functions have been written to convert data from wide to long form, but I believe `melt()` from the `data.table` library is the best. See `melt()` documentation [here](https://cran.r-project.org/web/packages/data.table/vignettes/datatable-reshape.html). 
 
-* other functions like `gather` and `pivot_longer` are often just wrapper functions for `melt()`—these other functions simplify `melt` and often can't deal with more complex transformations
+* Python's [pandas](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.melt.html) library also has the equivalent `melt` function/method that works the same way (see my pandas melt tutorial [here](https://towardsdatascience.com/reshape-pandas-dataframe-with-melt-in-python-tutorial-and-visualization-29ec1450bb02))
+* other functions like `gather` and `pivot_longer` are often just wrapper functions for `melt()` or `reshape()`—these other functions simplify `melt` and often can't deal with more complex transformations
 * `melt` is more powerful but isn't any more complicated than the other functions...
 * `melt` alone is often enough for all your wide-to-long transformations; don't need to learn `gather`, `pivot_longer`, and `melt`; `melt` alone is enough!
-* Python's [pandas](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.melt.html) library also has the equivalent `melt` function/method that works the same way (see my pandas melt tutorial [here](https://towardsdatascience.com/reshape-pandas-dataframe-with-melt-in-python-tutorial-and-visualization-29ec1450bb02))
 * `data.table` package's implementation of `melt`, which is extremely powerful—much more efficient and powerful than the reshape library's `melt` function. According to the [documentation](https://cran.r-project.org/web/packages/data.table/vignettes/datatable-reshape.html): 
 
 > The melt and dcast functions for data.tables are for reshaping wide-to-long and long-to-wide, respectively; the implementations are specifically designed with large in-memory data (e.g. 10Gb) in mind.
 
-Reminder again: We're using `melt` from the `data.table` library, not `reshape` library! Compare the documentation of the `melt` functions from the two libraries to the differences: `?data.table::melt` and `?reshape::melt`
+Reminder: We're using `melt` from the `data.table` library, not `reshape` library! Compare the documentation of the `melt` functions from the two libraries to the differences: `?data.table::melt` and `?reshape::melt`
 
 ```{r}
 # load data.table library so we use its melt implementation

_posts/2020-06-27-reshape-r-dataframes-from-wide-to-long-with-melt/reshape-r-dataframes-from-wide-to-long-with-melt.html

@@ -985,18 +985,18 @@ <h2 id="consider-being-a-patron-and-supporting-my-work">Consider being a patron
 <figure>
 <img src="main_wide2long.png" alt="" /><figcaption>Summary of how melt() works</figcaption>
 </figure>
-<p>Common terms for this transformation are melt, pivot-long, unpivot, gather, stack. Many functions have been written to convert data from wide to long form, but I believe <code>melt()</code> from the <code>data.table</code> library is the best. See <code>melt()</code> documentation <a href="https://cran.r-project.org/web/packages/data.table/vignettes/datatable-reshape.html">here</a>.</p>
+<p>Common terms for this transformation are melt, pivot-long, unpivot, gather, stack, and reshape. Many functions have been written to convert data from wide to long form, but I believe <code>melt()</code> from the <code>data.table</code> library is the best. See <code>melt()</code> documentation <a href="https://cran.r-project.org/web/packages/data.table/vignettes/datatable-reshape.html">here</a>.</p>
 <ul>
-<li>other functions like <code>gather</code> and <code>pivot_longer</code> are often just wrapper functions for <code>melt()</code>—these other functions simplify <code>melt</code> and often can’t deal with more complex transformations</li>
+<li>Python’s <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.melt.html">pandas</a> library also has the equivalent <code>melt</code> function/method that works the same way (see my pandas melt tutorial <a href="https://towardsdatascience.com/reshape-pandas-dataframe-with-melt-in-python-tutorial-and-visualization-29ec1450bb02">here</a>)</li>
+<li>other functions like <code>gather</code> and <code>pivot_longer</code> are often just wrapper functions for <code>melt()</code> or <code>reshape()</code>—these other functions simplify <code>melt</code> and often can’t deal with more complex transformations</li>
 <li><code>melt</code> is more powerful but isn’t any more complicated than the other functions…</li>
 <li><code>melt</code> alone is often enough for all your wide-to-long transformations; don’t need to learn <code>gather</code>, <code>pivot_longer</code>, and <code>melt</code>; <code>melt</code> alone is enough!</li>
-<li>Python’s <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.melt.html">pandas</a> library also has the equivalent <code>melt</code> function/method that works the same way (see my pandas melt tutorial <a href="https://towardsdatascience.com/reshape-pandas-dataframe-with-melt-in-python-tutorial-and-visualization-29ec1450bb02">here</a>)</li>
 <li><code>data.table</code> package’s implementation of <code>melt</code>, which is extremely powerful—much more efficient and powerful than the reshape library’s <code>melt</code> function. According to the <a href="https://cran.r-project.org/web/packages/data.table/vignettes/datatable-reshape.html">documentation</a>:</li>
 </ul>
 <blockquote>
 <p>The melt and dcast functions for data.tables are for reshaping wide-to-long and long-to-wide, respectively; the implementations are specifically designed with large in-memory data (e.g. 10Gb) in mind.</p>
 </blockquote>
-<p>Reminder again: We’re using <code>melt</code> from the <code>data.table</code> library, not <code>reshape</code> library! Compare the documentation of the <code>melt</code> functions from the two libraries to the differences: <code>?data.table::melt</code> and <code>?reshape::melt</code></p>
+<p>Reminder: We’re using <code>melt</code> from the <code>data.table</code> library, not <code>reshape</code> library! Compare the documentation of the <code>melt</code> functions from the two libraries to the differences: <code>?data.table::melt</code> and <code>?reshape::melt</code></p>
 <div class="layout-chunk" data-layout="l-body">
 <pre class="r"><code>
 # load data.table library so we use its melt implementation

docs/posts/2020-06-27-reshape-r-dataframes-from-wide-to-long-with-melt/index.html

@@ -1398,18 +1398,18 @@ <h2 id="consider-being-a-patron-and-supporting-my-work">Consider being a patron
 <figure>
 <img src="main_wide2long.png" alt="" /><figcaption>Summary of how melt() works</figcaption>
 </figure>
-<p>Common terms for this transformation are melt, pivot-long, unpivot, gather, stack. Many functions have been written to convert data from wide to long form, but I believe <code>melt()</code> from the <code>data.table</code> library is the best. See <code>melt()</code> documentation <a href="https://cran.r-project.org/web/packages/data.table/vignettes/datatable-reshape.html">here</a>.</p>
+<p>Common terms for this transformation are melt, pivot-long, unpivot, gather, stack, and reshape. Many functions have been written to convert data from wide to long form, but I believe <code>melt()</code> from the <code>data.table</code> library is the best. See <code>melt()</code> documentation <a href="https://cran.r-project.org/web/packages/data.table/vignettes/datatable-reshape.html">here</a>.</p>
 <ul>
-<li>other functions like <code>gather</code> and <code>pivot_longer</code> are often just wrapper functions for <code>melt()</code>—these other functions simplify <code>melt</code> and often can’t deal with more complex transformations</li>
+<li>Python’s <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.melt.html">pandas</a> library also has the equivalent <code>melt</code> function/method that works the same way (see my pandas melt tutorial <a href="https://towardsdatascience.com/reshape-pandas-dataframe-with-melt-in-python-tutorial-and-visualization-29ec1450bb02">here</a>)</li>
+<li>other functions like <code>gather</code> and <code>pivot_longer</code> are often just wrapper functions for <code>melt()</code> or <code>reshape()</code>—these other functions simplify <code>melt</code> and often can’t deal with more complex transformations</li>
 <li><code>melt</code> is more powerful but isn’t any more complicated than the other functions…</li>
 <li><code>melt</code> alone is often enough for all your wide-to-long transformations; don’t need to learn <code>gather</code>, <code>pivot_longer</code>, and <code>melt</code>; <code>melt</code> alone is enough!</li>
-<li>Python’s <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.melt.html">pandas</a> library also has the equivalent <code>melt</code> function/method that works the same way (see my pandas melt tutorial <a href="https://towardsdatascience.com/reshape-pandas-dataframe-with-melt-in-python-tutorial-and-visualization-29ec1450bb02">here</a>)</li>
 <li><code>data.table</code> package’s implementation of <code>melt</code>, which is extremely powerful—much more efficient and powerful than the reshape library’s <code>melt</code> function. According to the <a href="https://cran.r-project.org/web/packages/data.table/vignettes/datatable-reshape.html">documentation</a>:</li>
 </ul>
 <blockquote>
 <p>The melt and dcast functions for data.tables are for reshaping wide-to-long and long-to-wide, respectively; the implementations are specifically designed with large in-memory data (e.g. 10Gb) in mind.</p>
 </blockquote>
-<p>Reminder again: We’re using <code>melt</code> from the <code>data.table</code> library, not <code>reshape</code> library! Compare the documentation of the <code>melt</code> functions from the two libraries to the differences: <code>?data.table::melt</code> and <code>?reshape::melt</code></p>
+<p>Reminder: We’re using <code>melt</code> from the <code>data.table</code> library, not <code>reshape</code> library! Compare the documentation of the <code>melt</code> functions from the two libraries to the differences: <code>?data.table::melt</code> and <code>?reshape::melt</code></p>
 <div class="layout-chunk" data-layout="l-body">
 <pre class="r"><code>
 # load data.table library so we use its melt implementation

docs/posts/posts.json

@@ -16,7 +16,7 @@
       "data.table"
     ],
     "preview": "posts/2020-06-27-reshape-r-dataframes-from-wide-to-long-with-melt/./main_wide2long.png",
-    "last_modified": "2020-06-28T07:19:17-04:00",
+    "last_modified": "2020-06-29T10:42:43-04:00",
     "preview_width": 1169,
     "preview_height": 905
   },

docs/sitemap.xml

@@ -34,7 +34,7 @@
   </url>
   <url>
     <loc>https://hausetutorials.netlify.com/posts/2020-06-27-reshape-r-dataframes-from-wide-to-long-with-melt/</loc>
-    <lastmod>2020-06-28T07:19:17-04:00</lastmod>
+    <lastmod>2020-06-29T10:42:43-04:00</lastmod>
   </url>
   <url>
     <loc>https://hausetutorials.netlify.com/posts/2020-05-17-reshape-python-pandas-dataframe-from-long-to-wide-with-pivottable/</loc>