update tasklist

Author: Adam M. Wilson

Tasklist.Rmd

@@ -6,7 +6,7 @@ output:
     toc_depth: 1
 ---
 
-2```{r, echo=FALSE, message=FALSE, results='hide', purl=FALSE}
+```{r, echo=FALSE, message=FALSE, results='hide', purl=FALSE}
 library(dplyr)
 source("functions.R")
 yamls=yaml_dir()

Tasklist.md

@@ -6,12 +6,7 @@ output:
     toc_depth: 1
 ---
 
-2```{r, echo=FALSE, message=FALSE, results='hide', purl=FALSE}
-library(dplyr)
-source("functions.R")
-yamls=yaml_dir()
-yamls=yamls[order(sapply(yamls,function(x) x$date))]
-```
+
 
 Below are a set of tasks that we will work on in class (either alone or in small groups).
 

docs/Tasklist.html

@@ -474,7 +474,6 @@ <h1 class="title toc-ignore">Tasklist</h1>
 </div>
 
 
-<p>2<code>{r, echo=FALSE, message=FALSE, results='hide', purl=FALSE} library(dplyr) source(&quot;functions.R&quot;) yamls=yaml_dir() yamls=yamls[order(sapply(yamls,function(x) x$date))]</code></p>
 <p>Below are a set of tasks that we will work on in class (either alone or in small groups).</p>
 <hr />
 <div id="cs_01-create-a-simple-functioning-script" class="section level1">

docs/presentations/day_07.Rmd

@@ -4,7 +4,13 @@ title: "Joining / Merging Data"
 
 # Relational Data
 
-## Defining Terms
+## Relational Data
+
+![](http://r4ds.had.co.nz/diagrams/relational-nycflights.png)
+
+## Visualizing Relational Data
+
+![](http://r4ds.had.co.nz/diagrams/join-setup.png)
 
 * **Primary key**: uniquely identifies an observation in its own table. For example, `planes$tailnum` is a primary key because it uniquely identifies each plane in the planes table.
 * **Foreign key**: uniquely identifies an observation in another table. For example, the `flights$tailnum` is a foreign key because it appears in the flights table where it matches each flight to a unique plane.
@@ -15,13 +21,7 @@ title: "Joining / Merging Data"
 * **Filtering joins**: filter observations from one data frame based on whether or not they match an observation in the other table.
 * **Set operations**: treat observations as if they were set elements
 
-## Relational Data
-
-![](http://r4ds.had.co.nz/diagrams/relational-nycflights.png)
 
-## Visualizing Relational Data
-
-![](http://r4ds.had.co.nz/diagrams/join-setup.png)
 
 ## Inner Join
 ![](http://r4ds.had.co.nz/diagrams/join-inner.png)
@@ -62,7 +62,9 @@ Matches pairs of observations whenever their keys are equal:
 
 Anti-joins are useful for diagnosing join mismatches.
 
-## Other Cases (`merge()`)
+# Compare with other functions
+
+## `merge()`
 
 `base::merge()` can perform all four types of joins:
 
@@ -76,7 +78,7 @@ dplyr              | merge
 * specific dplyr verbs more clearly convey the intent of your code: they are concealed in the arguments of <span class="bullet_code">merge()</span>.
 * dplyr's joins are considerably faster and don't mess with the order of the rows.
 
-## Other Cases (SQL)
+## SQL
 
 SQL is the inspiration for dplyr's conventions, so the translation is straightforward:
 

docs/presentations/day_07.html

@@ -454,8 +454,12 @@
     <h1 class="title">Joining / Merging Data</h1>
 </section>
 
-<section><section id="relational-data-and-r" class="title-slide slide level1"><h1>Relational Data and R</h1></section><section id="defining-terms" class="slide level2">
-<h2>Defining Terms</h2>
+<section><section id="relational-data" class="title-slide slide level1"><h1>Relational Data</h1></section><section id="relational-data-1" class="slide level2">
+<h2>Relational Data</h2>
+<p><img data-src="http://r4ds.had.co.nz/diagrams/relational-nycflights.png" /></p>
+</section><section id="visualizing-relational-data" class="slide level2">
+<h2>Visualizing Relational Data</h2>
+<p><img data-src="http://r4ds.had.co.nz/diagrams/join-setup.png" /></p>
 <ul>
 <li class="fragment"><strong>Primary key</strong>: uniquely identifies an observation in its own table. For example, <code>planes$tailnum</code> is a primary key because it uniquely identifies each plane in the planes table.</li>
 <li class="fragment"><strong>Foreign key</strong>: uniquely identifies an observation in another table. For example, the <code>flights$tailnum</code> is a foreign key because it appears in the flights table where it matches each flight to a unique plane.</li>
@@ -467,12 +471,6 @@ <h2>3 families of verbs designed to work with relational data</h2>
 <li class="fragment"><strong>Filtering joins</strong>: filter observations from one data frame based on whether or not they match an observation in the other table.</li>
 <li class="fragment"><strong>Set operations</strong>: treat observations as if they were set elements</li>
 </ul>
-</section><section id="relational-data" class="slide level2">
-<h2>Relational Data</h2>
-<p><img data-src="http://r4ds.had.co.nz/diagrams/relational-nycflights.png" /></p>
-</section><section id="visualizing-relational-data" class="slide level2">
-<h2>Visualizing Relational Data</h2>
-<p><img data-src="http://r4ds.had.co.nz/diagrams/join-setup.png" /></p>
 </section><section id="inner-join" class="slide level2">
 <h2>Inner Join</h2>
 <p><img data-src="http://r4ds.had.co.nz/diagrams/join-inner.png" /></p>
@@ -507,8 +505,9 @@ <h3 id="semi_joinx-y-keeps-all-observations-in-x-that-have-a-match-in-y."><code>
 <h2><code>anti_join(x, y)</code> drops all observations in x that have a match in y.</h2>
 <p><img data-src="http://r4ds.had.co.nz/diagrams/join-anti.png" /></p>
 <p>Anti-joins are useful for diagnosing join mismatches.</p>
-</section><section id="other-cases-merge" class="slide level2">
-<h2>Other Cases (<code>merge()</code>)</h2>
+</section></section>
+<section><section id="compare-with-other-functions" class="title-slide slide level1"><h1>Compare with other functions</h1></section><section id="merge" class="slide level2">
+<h2><code>merge()</code></h2>
 <p><code>base::merge()</code> can perform all four types of joins:</p>
 <table>
 <thead>
@@ -540,8 +539,8 @@ <h2>Other Cases (<code>merge()</code>)</h2>
 <li class="fragment">specific dplyr verbs more clearly convey the intent of your code: they are concealed in the arguments of <span class="bullet_code">merge()</span>.</li>
 <li class="fragment">dplyr’s joins are considerably faster and don’t mess with the order of the rows.</li>
 </ul>
-</section><section id="other-cases-sql" class="slide level2">
-<h2>Other Cases (SQL)</h2>
+</section><section id="sql" class="slide level2">
+<h2>SQL</h2>
 <p>SQL is the inspiration for dplyr’s conventions, so the translation is straightforward:</p>
 <table>
 <thead>

docs/presentations/day_07.md

@@ -2,9 +2,15 @@
 title: "Joining / Merging Data"
 ---
 
-# Relational Data and R
+# Relational Data
 
-## Defining Terms
+## Relational Data
+
+![](http://r4ds.had.co.nz/diagrams/relational-nycflights.png)
+
+## Visualizing Relational Data
+
+![](http://r4ds.had.co.nz/diagrams/join-setup.png)
 
 * **Primary key**: uniquely identifies an observation in its own table. For example, `planes$tailnum` is a primary key because it uniquely identifies each plane in the planes table.
 * **Foreign key**: uniquely identifies an observation in another table. For example, the `flights$tailnum` is a foreign key because it appears in the flights table where it matches each flight to a unique plane.
@@ -15,13 +21,7 @@ title: "Joining / Merging Data"
 * **Filtering joins**: filter observations from one data frame based on whether or not they match an observation in the other table.
 * **Set operations**: treat observations as if they were set elements
 
-## Relational Data
-
-![](http://r4ds.had.co.nz/diagrams/relational-nycflights.png)
 
-## Visualizing Relational Data
-
-![](http://r4ds.had.co.nz/diagrams/join-setup.png)
 
 ## Inner Join
 ![](http://r4ds.had.co.nz/diagrams/join-inner.png)
@@ -62,7 +62,9 @@ Matches pairs of observations whenever their keys are equal:
 
 Anti-joins are useful for diagnosing join mismatches.
 
-## Other Cases (`merge()`)
+# Compare with other functions
+
+## `merge()`
 
 `base::merge()` can perform all four types of joins:
 
@@ -76,7 +78,7 @@ dplyr              | merge
 * specific dplyr verbs more clearly convey the intent of your code: they are concealed in the arguments of <span class="bullet_code">merge()</span>.
 * dplyr's joins are considerably faster and don't mess with the order of the rows.
 
-## Other Cases (SQL)
+## SQL
 
 SQL is the inspiration for dplyr's conventions, so the translation is straightforward: