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fixed url in tidyr section
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@jules32
jules32 authored Mar 12, 2018
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93 changes: 47 additions & 46 deletions docs/tidyr.html
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<meta http-equiv="X-UA-Compatible" content="IE=edge">
<title>Introduction to Open Data Science</title>
<meta name="description" content="This is official open data science training for the Ocean Health Index.">
<meta name="generator" content="bookdown 0.5 and GitBook 2.6.7">
<meta name="generator" content="bookdown 0.7 and GitBook 2.6.7">

<meta property="og:title" content="Introduction to Open Data Science" />
<meta property="og:type" content="book" />
Expand Down Expand Up @@ -393,19 +393,19 @@ <h2><span class="header-section-number">7.2</span> <code>tidyr</code> basics</h2
<p>In the <em>long</em> format, you usually have 1 column for the observed variable and the other columns are ID variables. The <code>mpg</code> dataset is an example of a <em>long</em> dataset with each row representing a single car and each column representing a variable of that car such as <code>manufacturer</code> and <code>year</code>.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">mpg</code></pre></div>
<pre><code>## # A tibble: 234 x 11
## manufacturer model displ year cyl trans drv cty hwy
## &lt;chr&gt; &lt;chr&gt; &lt;dbl&gt; &lt;int&gt; &lt;int&gt; &lt;chr&gt; &lt;chr&gt; &lt;int&gt; &lt;int&gt;
## 1 audi a4 1.8 1999 4 auto(l5) f 18 29
## 2 audi a4 1.8 1999 4 manual(m5) f 21 29
## 3 audi a4 2.0 2008 4 manual(m6) f 20 31
## 4 audi a4 2.0 2008 4 auto(av) f 21 30
## 5 audi a4 2.8 1999 6 auto(l5) f 16 26
## 6 audi a4 2.8 1999 6 manual(m5) f 18 26
## 7 audi a4 3.1 2008 6 auto(av) f 18 27
## 8 audi a4 quattro 1.8 1999 4 manual(m5) 4 18 26
## 9 audi a4 quattro 1.8 1999 4 auto(l5) 4 16 25
## 10 audi a4 quattro 2.0 2008 4 manual(m6) 4 20 28
## # ... with 224 more rows, and 2 more variables: fl &lt;chr&gt;, class &lt;chr&gt;</code></pre>
## manufacturer model displ year cyl trans drv cty hwy fl
## &lt;chr&gt; &lt;chr&gt; &lt;dbl&gt; &lt;int&gt; &lt;int&gt; &lt;chr&gt; &lt;chr&gt; &lt;int&gt; &lt;int&gt; &lt;chr&gt;
## 1 audi a4 1.80 1999 4 auto(l… f 18 29 p
## 2 audi a4 1.80 1999 4 manual… f 21 29 p
## 3 audi a4 2.00 2008 4 manual… f 20 31 p
## 4 audi a4 2.00 2008 4 auto(a… f 21 30 p
## 5 audi a4 2.80 1999 6 auto(l… f 16 26 p
## 6 audi a4 2.80 1999 6 manual… f 18 26 p
## 7 audi a4 3.10 2008 6 auto(a… f 18 27 p
## 8 audi a4 quat… 1.80 1999 4 manual… 4 18 26 p
## 9 audi a4 quat… 1.80 1999 4 auto(l… 4 16 25 p
## 10 audi a4 quat… 2.00 2008 4 manual… 4 20 28 p
## # ... with 224 more rows, and 1 more variable: class &lt;chr&gt;</code></pre>
<p><br></p>
<p>These different data formats mainly affect readability. For humans, the wide format is often more intuitive since we can often see more of the data on the screen due to it’s shape. However, the long format is more machine readable and is closer to the formatting of databases. The ID variables in our dataframes are similar to the fields in a database and observed variables are like the database values.</p>
<p><strong>Note:</strong> Generally, mathematical operations are better in long format, although some plotting functions actually work better with wide format.</p>
Expand Down Expand Up @@ -488,23 +488,23 @@ <h2><span class="header-section-number">7.4</span> <code>gather()</code> data fr
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">head</span>(gap_long)</code></pre></div>
<pre><code>## # A tibble: 6 x 2
## obstype_year obs_values
## &lt;chr&gt; &lt;chr&gt;
## 1 continent Africa
## 2 continent Africa
## 3 continent Africa
## 4 continent Africa
## 5 continent Africa
## 6 continent Africa</code></pre>
## &lt;chr&gt; &lt;chr&gt;
## 1 continent Africa
## 2 continent Africa
## 3 continent Africa
## 4 continent Africa
## 5 continent Africa
## 6 continent Africa</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">tail</span>(gap_long)</code></pre></div>
<pre><code>## # A tibble: 6 x 2
## obstype_year obs_values
## &lt;chr&gt; &lt;chr&gt;
## 1 pop_2007 9031088
## 2 pop_2007 7554661
## 3 pop_2007 71158647
## 4 pop_2007 60776238
## 5 pop_2007 20434176
## 6 pop_2007 4115771</code></pre>
## &lt;chr&gt; &lt;chr&gt;
## 1 pop_2007 9031088
## 2 pop_2007 7554661
## 3 pop_2007 71158647
## 4 pop_2007 60776238
## 5 pop_2007 20434176
## 6 pop_2007 4115771</code></pre>
<p>We have reshaped our dataframe but this new format isn’t really what we wanted.</p>
<p>What went wrong? Notice that it didn’t know that we wanted to keep <code>continent</code> and <code>country</code> untouched; we need to give it more information about which columns we want reshaped. We can do this in several ways.</p>
<p>One way is to identify the columns is by name. Listing them explicitly can be a good approach if there are just a few. But in our case we have 30 columns. I’m not going to list them out here since there is way too much potential for error if I tried to list <code>gdpPercap_1952</code>, <code>gdpPercap_1957</code>, <code>gdpPercap_1962</code> and so on. But we could use some of <code>dplyr</code>’s awesome helper functions — because we expect that there is a better way to do this!</p>
Expand Down Expand Up @@ -549,24 +549,24 @@ <h2><span class="header-section-number">7.4</span> <code>gather()</code> data fr
## $ obs_values: num 2449 3521 1063 851 543 ...</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">head</span>(gap_long)</code></pre></div>
<pre><code>## # A tibble: 6 x 5
## continent country obs_type year obs_values
## &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;int&gt; &lt;dbl&gt;
## 1 Africa Algeria gdpPercap 1952 2449
## 2 Africa Angola gdpPercap 1952 3521
## 3 Africa Benin gdpPercap 1952 1063
## 4 Africa Botswana gdpPercap 1952 851
## 5 Africa Burkina Faso gdpPercap 1952 543
## 6 Africa Burundi gdpPercap 1952 339</code></pre>
## continent country obs_type year obs_values
## &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;int&gt; &lt;dbl&gt;
## 1 Africa Algeria gdpPercap 1952 2449.
## 2 Africa Angola gdpPercap 1952 3521.
## 3 Africa Benin gdpPercap 1952 1063.
## 4 Africa Botswana gdpPercap 1952 851.
## 5 Africa Burkina Faso gdpPercap 1952 543.
## 6 Africa Burundi gdpPercap 1952 339.</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">tail</span>(gap_long)</code></pre></div>
<pre><code>## # A tibble: 6 x 5
## continent country obs_type year obs_values
## &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;int&gt; &lt;dbl&gt;
## 1 Europe Sweden pop 2007 9031088
## 2 Europe Switzerland pop 2007 7554661
## 3 Europe Turkey pop 2007 71158647
## 4 Europe United Kingdom pop 2007 60776238
## 5 Oceania Australia pop 2007 20434176
## 6 Oceania New Zealand pop 2007 4115771</code></pre>
## continent country obs_type year obs_values
## &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;int&gt; &lt;dbl&gt;
## 1 Europe Sweden pop 2007 9031088.
## 2 Europe Switzerland pop 2007 7554661.
## 3 Europe Turkey pop 2007 71158647.
## 4 Europe United Kingdom pop 2007 60776238.
## 5 Oceania Australia pop 2007 20434176.
## 6 Oceania New Zealand pop 2007 4115771.</code></pre>
<p>Excellent. This is long format: every row is a unique observation. Yay!</p>
</div>
<div id="plot-long-format-data" class="section level2">
Expand Down Expand Up @@ -713,7 +713,7 @@ <h2><span class="header-section-number">7.7</span> clean up and save your .Rmd</
<span class="kw">str</span>(gap_wide_new)</code></pre></div>
<div id="complete" class="section level3">
<h3><span class="header-section-number">7.7.1</span> <code>complete()</code></h3>
<p>One of the coolest functions in <code>tidyr</code> is the function <code>complete()</code>. Jarrett Byrnes has written up a <a href="(http://www.imachordata.com/you-complete-me/)">great blog piece</a> showcasing the utility of this function so I’m going to use that example here.</p>
<p>One of the coolest functions in <code>tidyr</code> is the function <code>complete()</code>. Jarrett Byrnes has written up a <a href="http://www.imachordata.com/you-complete-me/">great blog piece</a> showcasing the utility of this function so I’m going to use that example here.</p>
<p>We’ll start with an example dataframe where the data recorder enters the Abundance of two species of kelp, <em>Saccharina</em> and <em>Agarum</em> in the years 1999, 2000 and 2004.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">kelpdf &lt;-<span class="st"> </span><span class="kw">data.frame</span>(
<span class="dt">Year =</span> <span class="kw">c</span>(<span class="dv">1999</span>, <span class="dv">2000</span>, <span class="dv">2004</span>, <span class="dv">1999</span>, <span class="dv">2004</span>),
Expand Down Expand Up @@ -771,10 +771,11 @@ <h2><span class="header-section-number">7.8</span> Other links</h2>
"facebook": true,
"twitter": true,
"google": false,
"linkedin": false,
"weibo": false,
"instapper": false,
"vk": false,
"all": ["facebook", "google", "twitter", "weibo", "instapaper"]
"all": ["facebook", "google", "twitter", "linkedin", "weibo", "instapaper"]
},
"fontsettings": {
"theme": "white",
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2 changes: 1 addition & 1 deletion tidyr.Rmd
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### `complete()`

One of the coolest functions in `tidyr` is the function `complete()`. Jarrett Byrnes has written up a [great blog piece]((http://www.imachordata.com/you-complete-me/)) showcasing the utility of this function so I'm going to use that example here.
One of the coolest functions in `tidyr` is the function `complete()`. Jarrett Byrnes has written up a [great blog piece](http://www.imachordata.com/you-complete-me/) showcasing the utility of this function so I'm going to use that example here.

We'll start with an example dataframe where the data recorder enters the Abundance of two species of kelp, *Saccharina* and *Agarum* in the years 1999, 2000 and 2004.
```{r, eval=F}
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