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DataSimulation.html
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<title>Data Simulation</title>
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) 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<h1 class="title toc-ignore">Data Simulation</h1>
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<div id="TOC" class="toc">
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<ul>
<li><a href="#helper-functions">Helper functions</a></li>
<li><a href="#simulating-one-variable">Simulating one variable</a><ul>
<li><a href="#categorical-variables">Categorical variables</a></li>
<li><a href="#numerical-variables">Numerical variables</a></li>
<li><a href="#distributions">Distributions</a></li>
<li><a href="#putting-variables-together">Putting variables together</a></li>
</ul></li>
<li><a href="#simulating-dependent-variables">Simulating dependent variables</a><ul>
<li><a href="#rule-based">Rule based</a></li>
<li><a href="#correlation-based">Correlation based</a></li>
</ul></li>
<li><a href="#exporting">Exporting</a></li>
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<section id="main-content">
<p>The purpose of this training is to enable the participants to simulate dummy data which they can use for their analyses or visualizations.</p>
<div id="helper-functions" class="section level2">
<h2>Helper functions</h2>
<p>We will need some functions from the dplyr package. If <code>library(dplyr)</code> gives you an error, you might have to install the package first, by typing into your console: <code>install.packages("dplyr")</code>.</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1"></a><span class="kw">set.seed</span>(<span class="dv">64</span>) </span>
<span id="cb1-2"><a href="#cb1-2"></a></span>
<span id="cb1-3"><a href="#cb1-3"></a><span class="kw">library</span>(dplyr)</span></code></pre></div>
</div>
<div id="simulating-one-variable" class="section level2">
<h2>Simulating one variable</h2>
<p>Let’s start with the most simple but most time-consuming way. Type everything manually and save it in a vector:</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1"></a>client_gen <-<span class="st"> </span><span class="kw">c</span>(<span class="st">"Millenial"</span>,<span class="st">"Gen X"</span>,<span class="st">"Millenial"</span>,</span>
<span id="cb2-2"><a href="#cb2-2"></a> <span class="st">"Baby Boomer"</span>,<span class="st">"Gen X"</span>,<span class="st">"Millenial"</span>,<span class="st">"Gen X"</span>)</span>
<span id="cb2-3"><a href="#cb2-3"></a></span>
<span id="cb2-4"><a href="#cb2-4"></a><span class="kw">data.frame</span>(<span class="dt">id=</span><span class="dv">1</span><span class="op">:</span><span class="dv">7</span>,client_gen)</span></code></pre></div>
<pre><code>## id client_gen
## 1 1 Millenial
## 2 2 Gen X
## 3 3 Millenial
## 4 4 Baby Boomer
## 5 5 Gen X
## 6 6 Millenial
## 7 7 Gen X</code></pre>
<div id="categorical-variables" class="section level3">
<h3>Categorical variables</h3>
<p>For categorical variables, we can save some time using the <code>sample</code> function. You specify first the possible values and then how many of these values you would like to pick. If you want to allow values to be picked more than once, make sure to set <code>replace=TRUE</code>.</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1"></a>client_gen <-<span class="st"> </span><span class="kw">sample</span>(<span class="kw">c</span>(<span class="st">"Millenial"</span>,<span class="st">"Gen X"</span>,<span class="st">"Baby Boomer"</span>),<span class="dv">7</span>,<span class="dt">replace=</span>T)</span>
<span id="cb4-2"><a href="#cb4-2"></a></span>
<span id="cb4-3"><a href="#cb4-3"></a><span class="kw">data.frame</span>(<span class="dt">id=</span><span class="dv">1</span><span class="op">:</span><span class="dv">7</span>,client_gen)</span></code></pre></div>
<pre><code>## id client_gen
## 1 1 Baby Boomer
## 2 2 Millenial
## 3 3 Millenial
## 4 4 Millenial
## 5 5 Gen X
## 6 6 Millenial
## 7 7 Gen X</code></pre>
</div>
<div id="numerical-variables" class="section level3">
<h3>Numerical variables</h3>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1"></a>client_age <-<span class="st"> </span><span class="kw">sample</span>(<span class="dv">1</span><span class="op">:</span><span class="dv">100</span>,<span class="dt">size=</span><span class="dv">7</span>,<span class="dt">replace=</span>T)</span>
<span id="cb6-2"><a href="#cb6-2"></a></span>
<span id="cb6-3"><a href="#cb6-3"></a><span class="kw">data.frame</span>(<span class="dt">id=</span><span class="dv">1</span><span class="op">:</span><span class="dv">7</span>,client_age)</span></code></pre></div>
<pre><code>## id client_age
## 1 1 81
## 2 2 89
## 3 3 39
## 4 4 30
## 5 5 92
## 6 6 78
## 7 7 88</code></pre>
</div>
<div id="distributions" class="section level3">
<h3>Distributions</h3>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1"></a>client_age <-<span class="st"> </span><span class="kw">runif</span>(<span class="dv">7</span>,<span class="dt">min=</span><span class="dv">1</span>,<span class="dt">max=</span><span class="dv">100</span>)</span>
<span id="cb8-2"><a href="#cb8-2"></a></span>
<span id="cb8-3"><a href="#cb8-3"></a><span class="kw">data.frame</span>(<span class="dt">id=</span><span class="dv">1</span><span class="op">:</span><span class="dv">7</span>,client_age)</span></code></pre></div>
<pre><code>## id client_age
## 1 1 96.795935
## 2 2 67.709852
## 3 3 78.775870
## 4 4 7.020656
## 5 5 29.639363
## 6 6 22.111168
## 7 7 70.647920</code></pre>
<p>We can use the <code>round()</code> function to round each value to their next integer. But uniformly distributed variables are not always what we want. Imagine that we simulate 10000 clients and distributes their ages uniformly. Then there are as many 99 year old clients as there are 50 year old clients:</p>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1"></a><span class="kw">runif</span>(<span class="dv">10000</span>,<span class="dv">1</span>,<span class="dv">100</span>) <span class="op">%>%</span><span class="st"> </span><span class="kw">hist</span>()</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p>But we can easily access a whole list of other distribution functions, like the famous Normal distribution (with mean and standard deviation as parameters).</p>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1"></a><span class="kw">rnorm</span>(<span class="dv">10000</span>,<span class="dt">mean=</span><span class="dv">50</span>,<span class="dt">sd=</span><span class="dv">20</span>) <span class="op">%>%</span><span class="st"> </span><span class="kw">hist</span>()</span></code></pre></div>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAMAAABNUi8GAAAAq1BMVEUAAAAAADoAAGYAOjoAOmYAOpAAZpAAZrY6AAA6ADo6AGY6OgA6Ojo6OmY6OpA6ZpA6ZrY6kLY6kNtmAABmADpmOgBmOjpmkLZmkNtmtttmtv+QOgCQZgCQZjqQkGaQkLaQtpCQttuQtv+Q2/+2ZgC2Zjq2ZpC2kGa229u22/+2///T09PbkDrbkGbbtmbbtpDb27bb29vb////tmb/25D/27b//7b//9v///9nY54hAAAACXBIWXMAAA7DAAAOwwHHb6hkAAARXklEQVR4nO3da0PbyMGGYbENhTY9QrPthp7xts3Weygm2P//l1VHM2Dk0eCHmUfKfX3YN292Vkieu0IajFXtAGNV6R0AjiFQWCNQWCNQWCNQWCNQWCNQWCNQWCNQWCNQWCNQWCNQWCNQWCNQWCNQWCNQWCNQWCNQWCNQWCNQWCNQWCNQWCNQWCNQWCNQWCNQWCNQWCNQWCNQWCNQWCNQWCNQWCPQuO1NdX7X/d+L/f8T/Ot/faf+gt9eVtXvxBudKQKNOx7o5w9fqQPdVLUr8UZnikDjngR6YFXJA11XZ7fiTc4Wgca9dAb9/n1Vnf32U9NnrUn0x6+r6md/bE+u22+r6t1tW27d2t8/VF992v34oR7367q7h+vq4sf31dk3u+8v61GPX2a/gXaTJNoh0LgXAl1XVR/mEGj/N83AekhT2PvHvz6/23T/us6uDrTzfki787gBAg0QaFwXXKsPtDkL3u1+aC8Uu2/x95d9t1ftJeTF3XY1dHv+afe/+r/6+V0z6KoN9Kr5b6tv+vGtcAN8i39EoHGHgdY1vftP/2+7QPsr0VV3Um3+XIfYBfqxH/jTv983G6j/vi6x+2dbbLiZ3XBdQKA9Ao07DLT7m+6Ks01quApo0hr+/KS17V+GDTRn313/z8dAww0QaIBA4164Bv38obuk/GffYddb+9394/DnMNCm6Hd/++l6NNBwAwQaINC4F9dBP//1/XBLM+EM2pX4MB4oZ9ARBBo3tlC//fPjFef4NWjbWnti3N8kvRAo16AjCDTuMNC6t9+33+fbG++mpvG7+OEMen73+Wb8GnTkLv7wB6tfGgKNe+EM+u1+WbP9ueSxddD9NejRm6RwHZRAAwQa99K3+B+aK9Bff9r1Pzb61P8g6E/df1D/1W8+HdzFv/vHuv6rsUCDDRDoIwJ9M6svvS0JAtVbte+Vq8+OL7y1BIkIVK//uTu34goE+gY+f33Zv9cJpyJQWCNQWCNQWCNQWCNQWCNQWCNQWCNQWCNQWCNQWCNQWEsLdHjfrfzDXoCXJQW6Ht5eu+GjrZBHSqDbm8ffT+C9uMgiJdCH6+FDMnYbvskjC86gsJZ4DdqfQrkGRSZpd/HDRwdy/kQmrIPCGoHCGgv1sMZCPayxzARrLNTDGmdQWGOhHtZYqIc11kFhTRPo/jEt9A4t8UI9gUJLvFBPoNASLzMRKLTEC/UECi3OoLAmXqgnUGiJF+oJFFrioggUWgQKa2lFNc+TbC9D19zFj6riSu/ijKTdJJ3d1pehzfPTCHRc9d8YXqXp0peZtjfNY6MJdBSBKr1moX51fkeg4whU6VUL9asLAh03IVCuUidLuwbts3y4Hns/Ey/spEA5x06WehfffZPf3hDoKAJVYh1UjkCVCFSOQJUIVI5AlQhUjkCVCFSOQJUIVI5AlQhUjkCVCFSOQJUIVI5AlQhUjkCVCFSOQJUIVI5AlQhUjkCVCFSOQJUIVI5AlQhUjkCVCFSOQJUIVI5AlQhUjkCVCFSOQJUIVI5AlQhUjkCVCFSOQJUIVI5AlQhUjkCVCFSOQJUIVI5AlQhUjkCVCFSOQJUIVI5AlQhUjkCVCFSOQJUIVI5AlQhUjkCVCFSOQJUIVI5AlQhUjkCVCFSOQJUIVI5AldJeie1N9xy0kackEWiDQJXSnjRX9c9C3Ax/OGlzC0WgSimvxP5ZnXWq53cnb26pCFQp5ZUYnnZc2/AoxFEEqsQZVI5AlRKvQftTKNegRxCoUtor8XDd3cWPnD8JtCEJlCfK91gHlZMEGh9R+jAzIVA5AlVioV6OQJVYqJcjUCWWmeQIVImFejkCVeIMKkegSizUyxGoEgv1cgSqxDqoHIEqaY7zi/sB3DEEqpR2F99ceW5YqD+OQJWSA23v34MFp9dvbqkIVCk10D5NlpnGEahSaqD3l22gLNSPI1AlzqByBKqUFmhzn36xG26XTtzcUhGoUuJx1o2e3Y7/IIlAdwSqxUK9HIEqEWiaCb8sRKBKBJomXo4mPwLtEWgaAs0s/S7+6G8lLf5lI9DMko5zezP6Q/jXbG6OCDSz1N/qvFBuboYINLPE49xUI+8Sed3m5odAM+MmKQ2BZkagaQg0MwJNQ6CZEWgaAs2MQNMQaGYEmoZAMyPQNASaGYGmIdDMCDQNgWZGoGkINDMCTUOgmRFoGgLNjEDTEGhmBJqGQDMj0DQEmhmBpiHQzAg0DYFmRqBpCDQzAk1DoJkRaBoCzYxA0xBoZgSahkAzI9A0BJoZgaYh0MwINA2BZrY/zofrKvKxNkmbWyoCzSw4znVVjX609ys2t0wEmtnT4zy50cW/bASa2fPjXB970GH65haHQDN7cpzNYzg/7rY3o0/bTtvcEhFoZo/H2Xx+clfm2GPkkja3UASaWXAXf3Yr3NxSEWhmrIOmIdDMguNc1d/gx58hl7y5ZSLQzB6Pc9VegD5cn7Rcv/iXjUAzC65Bu0+fP+EOaUegBKq2P87tTffNfU2gxxBoZo/HuW4f4HF/yU+SjiHQzILjvL+squrEtabFv2wEmhnLTGkINLPUJ80dfVIngRKo2uNxRuNrLlP7C9TR5dLFv2wEmlmwDhq9fR9u9GvrkfeTLP5lI9DMgnXQ6O37sFS6G18tXfzLRqCZHSzUH8EZlECzCxbq4z/jXA/POuYalEAzeTzO2JO2G817Rhuj72he/MtGoJmFv9UZvYtP2NxSEWhmLNSnIdDMWKhPQ6CZBcdZf5M/v1sdW2xioZ5Acwtuks5u1+d3x5ZDWWYi0OyevB+0ye7I+0FZqCfQ7J4s1DeBHnlHPWdQAs3u4Ay6OvKpDSzUE2huz69B10eX61moJ9DMnt7F8476GALNTHOc1Z5kc8YINDMW6tMQaGZJP4tnoZ5Ac3t+nMc+t4FlJgLN7uA4V+NvC2WhnkCzOzhOFuqPItDMDo7z2EffsFBPoLk9P87jn27HQj2BZnZwF//6z6d/srmlItDMeEd9qIqzCTSu9KspQaAhRTm5Ao2PKP1qShwu1J/0e3Mzf1EI1M7zXzs+9m6mCQ3P/EUhUDvB+0G7MsdWOLsxsXPrzF8UArXz+C3+D90b7Y5+Rn3040dm/qIQqJ2DM+ixd9THP35k5i8Kgdp58o76+p/r096xPPMXhUDtPH9H/Wnr9ASqiksxovSrKcE6aIhA7RBoiEDtJH30TdLm5ohA7aR89E3S5maJQO2kfPRNyubmiUDtpHz0Tcrm5olA7SR99E3C5uaJQO2kffRNwuZmiUDt8NE3IQK1wzpoiEDtPLkGFW5ungjUTsqT5lI2N08Eaie4STppfelgc7NEoHZ4kFeIQO1wkxQiUDsEGiJQO91RaO6QdgQqi0sxovSrKREEqlhomvmLQqB2CDREoHYINESgdgg0RKB2CDREoHYINESgdoZAFR9ttyNQWVyKEaVfTQkW6kMEaodAQwRqh0BDBGqHQEMEaodAQwRqh0BDBGqHQEMEaodAQwRqh0BDBGqHQEMEaodAQwRqh0BDBGqHQEMEaodAQwRqh0BDBGqHQEMEaodAQwRqh0BDBGon7Si2N5FfC5n5i0KgdpKOYl31v1a3qUZ+v27mLwqB2kk5iuDXPtcjzwKZ+YtCoHZSjiL4iLGxT7ud+YtCoHY4g4YI1E7iNWh/CuUa9K3jUowo/WpKpB3F8AEPo0+jm/mLQqB2WAcNEagdAg0RqB0W6kMEaoeF+hCB2mGZKUSgdjQL9Y8f3ijbsSII1A5n0BCB2mGhPkSgdlioDxGoHdZBQwRqh0BDBGon7S6+ufLcsFCfIS7FiNKvpkRyoO39++jDZ2f+ohCondRA+zRnucxUxQm6IFCp1EDvL9tAZ/mO+jxdEKjUF3UGzdIFgUqlBdp8F7zYDbdLJ24uOwKdocSjqBs9ux3/QRKBijYiGRFXekYm+JLWQb+0QOMjSs/IBASqnnWf/OIjSs/IBASqnnWf/OIjSs/IBASqnnWf/OIjSs/IBASqnnWf/OIjSs/IBASqnnWf/OIjSs/IBASqnnWf/OIjSs/IBASqnnWf/OIjSs/IBASqnnWf/OIjSs/IBASqnnWf/OIjSs/IBASqnnWf/OIjSs/IBASqnnWf/OIjSs/IBASqnnWf/OIjSs/IBASqnnWf/OIjSs/IBASqnnWf/OIjSs/IBASqnnWf/OIjSs/IBASqnnWf/OIjSs/IBASqnnWf/OIjSs/IBASqnnWf/OIjSs/IBASqnnWf/OIjSs/IBASqnnWf/OIjSs/IBASqnnWf/OIjSs/IBASqnnWf/OIjSs/IBASqnnWf/OIjSs/IBASqnnWf/OIjSs/IBASqnnWf/OIjSs/IBASqnnWf/OIjSs/IBASqnnWf/OIjSs/IBASqnnWf/OIjSs/IBASqnnWf/OIjSs/IBASqnnWf/OIjSs/IBASqnnWf/OIjSs/IBASqnnWf/OIjSs/IBASqnnWf/OIjSs/IBASqnnWf/OIjSs/IBASqnnWf/OIjSs/IBMsJNM9jugg0swUFajLrPvnFR5SbrckIVD3CJ7/4iBk8qIZA1SN88lOMKDefw7Raby7pS5vMqU9cihHl5nOYVuvNJX1pkzn1iUsxotx8DtNqvbmkL20ypz5xKUaUm89hWq03l/SlTebUJy7FiHLzOUyr9eaSvrTJnPrEpRhRbj6HabXeXNKXNplTn7gUI8rN5zCt1ptL+tImc+oTl2JEufkcptV6c0lf2mROfeJSjCg3n8O0Wm8u6UubzKlPXIoR5eZzmFbrzSV9aZM59YlLMaLcfA7Tar25pC9tMqc+cSlGlJvPYVqtN5f0pU3m1CcuxYhy8zlMq/Xmkr60yZz6xKUYUW4+h2m13lywYZO3IxNoZrMJ1GXGoiNsdoRA335zwYZdZiw6wmZHJCOKv6eZQNUjbHYk1+G+1ZT3E580envT/Y/mq+8km0vgMh8EejDkraa8n/iUwevqqvvDZvjDSZtL4TIfBHow5K2mvJ/4hLHbm32W6/O7kzcX/mezuUcn0IMhb3uVmvIfP1x/HP64efpN/vi+TLnSxoK9ss0unoSxE86ggFbiNWh/Ch29BgW00k6/D9fdOZvzJzIp/6MC4AgChTUChTUChTUChTUChTUChTUChbXlBFr2x81LVXpWlxRo6R2Ybj67Wn5Py++ByoyOZD67Wn5Py++ByoyOZD67Wn5Py++ByoyOZD67Wn5Py++ByoyOZD67Wn5Py++ByoyOZD67Wn5Py++ByoyOZD67Wn5Py++ByoyOZD67Wn5Py++ByoyOZD67Wn5Py+8BcASBwhqBwhqBwhqBwhqBwhqBwhqBwhqBwhqBwhqBwhqBwhqBwtpCAt1U1dlt6Z2I6j5e9WLnvr/3v2w/4H2/kyX3dhmBburXb2M84737X/S76L2/D9ftEwj2O1l0bxcRaPfh+auL0vsRMzx6wnt/N91zsPY7WXZvFxHo/WXz2fnr0ceLuVj3k2y9v5vqqv0f0n4ny+7tMgJtv3VuPCc8sPpVfQl65b+/XaDDTpbd20UE2l0g+V7U9R6um4dPrK7s97eNcb+TZfeWQHOrZ999fwlUzP1b5hP1JZ37/vItXsz6puO5er7d95ebJDHvZZu9bqbr2Xff3w3LTGLeC9977STXN0nu+7thoV5tbf2jw71VVXWPO/Xe3/56c7+TJfd2IYFiqQgU1ggU1ggU1ggU1ggU1ggU1ggU1ggU1ggU1ggU1ggU1ggU1ggU1ggU1ggU1ggU1ggU1ggU1ggU1ggU1ggU1ggU1ggU1ggU1ggU1ggU1ggU1ggU1ggU1ggU1ggU1ggU1ggU1ggU1ggU1ggU1ggU1gj0rW1vbB/YNQcECmsECmsECmsECmsECmsECmsECmsECmsECmsECmsECmsECmsECmsECmsECmsECmsECmsECmsECmsECmsECmsECmsECmsECmv/B+si9wr1JRSAAAAAAElFTkSuQmCC" /><!-- --></p>
<p>If we want to limit the values to not be smaller than 0 or larger than 100, we can use pmin and pmax.</p>
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1"></a><span class="kw">rnorm</span>(<span class="dv">10000</span>,<span class="dt">mean=</span><span class="dv">50</span>,<span class="dt">sd=</span><span class="dv">20</span>) <span class="op">%>%</span><span class="st"> </span><span class="kw">pmax</span>(<span class="dv">0</span>) <span class="op">%>%</span><span class="st"> </span><span class="kw">pmin</span>(<span class="dv">100</span>) <span class="op">%>%</span><span class="st"> </span><span class="kw">hist</span>()</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p>For many applications (like balance distribution or any data that contains outliers) I like to use the Exponential distribution (with parameter rate and expectation 1/rate).</p>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1"></a><span class="kw">rexp</span>(<span class="dv">10000</span>,<span class="dt">rate=</span><span class="fl">0.01</span>) <span class="op">%>%</span><span class="st"> </span><span class="kw">hist</span>()</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p>If you want to explore further probability distributions check out this <a href="https://www.stat.umn.edu/geyer/old/5101/rlook.html">link</a>.</p>
</div>
<div id="putting-variables-together" class="section level3">
<h3>Putting variables together</h3>
<p>To create our first simulated dataframe, we can start by simulating the variables separately and then putting them together.</p>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1"></a><span class="kw">set.seed</span>(<span class="dv">61</span>)</span>
<span id="cb14-2"><a href="#cb14-2"></a></span>
<span id="cb14-3"><a href="#cb14-3"></a>k <-<span class="st"> </span><span class="dv">7</span></span>
<span id="cb14-4"><a href="#cb14-4"></a></span>
<span id="cb14-5"><a href="#cb14-5"></a>id <-<span class="st"> </span><span class="dv">1</span><span class="op">:</span>k</span>
<span id="cb14-6"><a href="#cb14-6"></a>name <-<span class="st"> </span><span class="kw">c</span>(<span class="st">"Frank"</span>,<span class="st">"Dorian"</span>,<span class="st">"Eva"</span>,<span class="st">"Elena"</span>,<span class="st">"Andy"</span>,<span class="st">"Barbara"</span>,<span class="st">"Yvonne"</span>)</span>
<span id="cb14-7"><a href="#cb14-7"></a>age <-<span class="st"> </span><span class="kw">rnorm</span>(k,<span class="dt">mean=</span><span class="dv">30</span>,<span class="dt">sd=</span><span class="dv">10</span>) <span class="op">%>%</span><span class="st"> </span><span class="kw">pmax</span>(<span class="dv">18</span>) <span class="op">%>%</span><span class="st"> </span><span class="kw">round</span>()</span>
<span id="cb14-8"><a href="#cb14-8"></a>ocupation <-<span class="st"> </span><span class="kw">sample</span>(<span class="kw">c</span>(<span class="st">"analyst"</span>,<span class="st">"manager"</span>,<span class="st">"sr analyst"</span>),k,<span class="dt">replace=</span>T,<span class="dt">prob=</span><span class="kw">c</span>(<span class="dv">10</span>,<span class="dv">2</span>,<span class="dv">3</span>))</span>
<span id="cb14-9"><a href="#cb14-9"></a>balance <-<span class="st"> </span><span class="kw">rexp</span>(k,<span class="dt">rate=</span><span class="fl">0.001</span>) <span class="op">%>%</span><span class="st"> </span><span class="kw">round</span>(<span class="dv">2</span>)</span>
<span id="cb14-10"><a href="#cb14-10"></a>married <-<span class="st"> </span><span class="kw">sample</span>(<span class="kw">c</span>(<span class="st">"Yes"</span>,<span class="st">"No"</span>),k,<span class="dt">replace=</span>T,<span class="dt">prob=</span><span class="kw">c</span>(<span class="fl">0.6</span>,<span class="fl">0.4</span>))</span>
<span id="cb14-11"><a href="#cb14-11"></a></span>
<span id="cb14-12"><a href="#cb14-12"></a>data <-<span class="st"> </span><span class="kw">data.frame</span>(<span class="dt">client_id=</span>id,name,age,ocupation,balance,<span class="dt">married_flg=</span>married)</span>
<span id="cb14-13"><a href="#cb14-13"></a>data</span></code></pre></div>
<pre><code>## client_id name age ocupation balance married_flg
## 1 1 Frank 26 sr analyst 245.96 No
## 2 2 Dorian 26 analyst 2273.39 No
## 3 3 Eva 18 manager 2270.47 No
## 4 4 Elena 34 analyst 373.45 No
## 5 5 Andy 18 analyst 961.21 Yes
## 6 6 Barbara 28 analyst 69.32 Yes
## 7 7 Yvonne 37 analyst 3218.13 Yes</code></pre>
<p>Great! We just simulated a dataset which we can use now for visualization or modelling purposes.</p>
</div>
</div>
<div id="simulating-dependent-variables" class="section level2">
<h2>Simulating dependent variables</h2>
<p>If we want to have a dataset that “makes sense” from a real world perspective, it would be great if managers in general had higher balances than analysts? Or if 18 years old clients are less likely to be married than 30 years olds? In this section we are going to have a look at techniques to create dependence between variables.</p>
<div id="rule-based" class="section level3">
<h3>Rule based</h3>
<p>We can use <code>ifelse()</code> and <code>case_when()</code> to create new variables that depend on others.</p>
<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1"></a>k <-<span class="st"> </span><span class="dv">7</span></span>
<span id="cb16-2"><a href="#cb16-2"></a>married <-<span class="st"> </span><span class="kw">sample</span>(<span class="kw">c</span>(<span class="st">"Y"</span>,<span class="st">"N"</span>),k,<span class="dt">replace=</span>T)</span>
<span id="cb16-3"><a href="#cb16-3"></a></span>
<span id="cb16-4"><a href="#cb16-4"></a>data <-<span class="st"> </span><span class="kw">data.frame</span>(<span class="dt">id=</span><span class="dv">1</span><span class="op">:</span>k,married)</span>
<span id="cb16-5"><a href="#cb16-5"></a></span>
<span id="cb16-6"><a href="#cb16-6"></a>data <span class="op">%>%</span><span class="st"> </span><span class="kw">mutate</span>(</span>
<span id="cb16-7"><a href="#cb16-7"></a> <span class="dt">age=</span><span class="kw">ifelse</span>(married<span class="op">==</span><span class="st">"Y"</span>,<span class="kw">rnorm</span>(k,<span class="dv">45</span>,<span class="dv">10</span>),<span class="kw">rnorm</span>(k,<span class="dv">30</span>,<span class="dv">10</span>)) <span class="op">%>%</span><span class="st"> </span><span class="kw">pmax</span>(<span class="dv">18</span>) <span class="op">%>%</span><span class="st"> </span><span class="kw">round</span>()</span>
<span id="cb16-8"><a href="#cb16-8"></a>)</span></code></pre></div>
<pre><code>## id married age
## 1 1 N 18
## 2 2 N 28
## 3 3 N 18
## 4 4 Y 55
## 5 5 Y 28
## 6 6 Y 51
## 7 7 Y 31</code></pre>
<p>In this small example we will not see the effect, but when we simulate 1000 clients and take a look at their average age, we can see that there is a difference between the two groups.</p>
<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1"></a>k <-<span class="st"> </span><span class="dv">1000</span></span>
<span id="cb18-2"><a href="#cb18-2"></a>married <-<span class="st"> </span><span class="kw">sample</span>(<span class="kw">c</span>(<span class="st">"Y"</span>,<span class="st">"N"</span>),k,<span class="dt">replace=</span>T)</span>
<span id="cb18-3"><a href="#cb18-3"></a></span>
<span id="cb18-4"><a href="#cb18-4"></a>data <-<span class="st"> </span><span class="kw">data.frame</span>(<span class="dt">id=</span><span class="dv">1</span><span class="op">:</span>k,married)</span>
<span id="cb18-5"><a href="#cb18-5"></a></span>
<span id="cb18-6"><a href="#cb18-6"></a>data <span class="op">%>%</span><span class="st"> </span><span class="kw">mutate</span>(</span>
<span id="cb18-7"><a href="#cb18-7"></a> <span class="dt">age=</span><span class="kw">ifelse</span>(married<span class="op">==</span><span class="st">"Y"</span>,<span class="kw">rnorm</span>(k,<span class="dv">45</span>,<span class="dv">10</span>),<span class="kw">rnorm</span>(k,<span class="dv">30</span>,<span class="dv">10</span>)) <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb18-8"><a href="#cb18-8"></a><span class="st"> </span><span class="kw">pmax</span>(<span class="dv">18</span>) <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb18-9"><a href="#cb18-9"></a><span class="st"> </span><span class="kw">round</span>()</span>
<span id="cb18-10"><a href="#cb18-10"></a> ) <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb18-11"><a href="#cb18-11"></a><span class="st"> </span><span class="kw">group_by</span>(married) <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb18-12"><a href="#cb18-12"></a><span class="st"> </span><span class="kw">summarise</span>(<span class="dt">avg_age=</span><span class="kw">mean</span>(age))</span></code></pre></div>
<pre><code>## # A tibble: 2 x 2
## married avg_age
## <chr> <dbl>
## 1 N 30.4
## 2 Y 44.6</code></pre>
<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1"></a>k <-<span class="st"> </span><span class="dv">1000</span></span>
<span id="cb20-2"><a href="#cb20-2"></a></span>
<span id="cb20-3"><a href="#cb20-3"></a>ocupation <-<span class="st"> </span><span class="kw">sample</span>(<span class="kw">c</span>(<span class="st">"analyst"</span>,<span class="st">"manager"</span>,<span class="st">"sr analyst"</span>),k,<span class="dt">replace=</span>T,<span class="dt">prob=</span><span class="kw">c</span>(<span class="dv">10</span>,<span class="dv">2</span>,<span class="dv">3</span>))</span>
<span id="cb20-4"><a href="#cb20-4"></a></span>
<span id="cb20-5"><a href="#cb20-5"></a>data <-<span class="st"> </span><span class="kw">data.frame</span>(<span class="dt">id=</span><span class="dv">1</span><span class="op">:</span>k,ocupation)</span>
<span id="cb20-6"><a href="#cb20-6"></a></span>
<span id="cb20-7"><a href="#cb20-7"></a>data <-<span class="st"> </span>data <span class="op">%>%</span><span class="st"> </span><span class="kw">mutate</span>(<span class="dt">balance=</span><span class="kw">case_when</span>(</span>
<span id="cb20-8"><a href="#cb20-8"></a> ocupation<span class="op">==</span><span class="st">"analyst"</span> <span class="op">~</span><span class="st"> </span><span class="kw">rexp</span>(k,<span class="fl">0.01</span>),</span>
<span id="cb20-9"><a href="#cb20-9"></a> ocupation<span class="op">==</span><span class="st">"sr analyst"</span> <span class="op">~</span><span class="st"> </span><span class="kw">rexp</span>(k,<span class="fl">0.005</span>),</span>
<span id="cb20-10"><a href="#cb20-10"></a> <span class="ot">TRUE</span> <span class="op">~</span><span class="st"> </span><span class="kw">rexp</span>(k,<span class="fl">0.001</span>) <span class="co">#this is the else case</span></span>
<span id="cb20-11"><a href="#cb20-11"></a>))</span>
<span id="cb20-12"><a href="#cb20-12"></a></span>
<span id="cb20-13"><a href="#cb20-13"></a><span class="co">#Check the average balance per group</span></span>
<span id="cb20-14"><a href="#cb20-14"></a>data <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb20-15"><a href="#cb20-15"></a><span class="st"> </span><span class="kw">group_by</span>(ocupation) <span class="op">%>%</span><span class="st"> </span></span>
<span id="cb20-16"><a href="#cb20-16"></a><span class="st"> </span><span class="kw">summarise</span>(<span class="dt">avg_balance=</span><span class="kw">mean</span>(balance))</span></code></pre></div>
<pre><code>## # A tibble: 3 x 2
## ocupation avg_balance
## <chr> <dbl>
## 1 analyst 102.
## 2 manager 1109.
## 3 sr analyst 201.</code></pre>
</div>
<div id="correlation-based" class="section level3">
<h3>Correlation based</h3>
<p>If we just deal with numeric variables and want to have a slightly more complex connection between the different variables, we can also try this approach, for which we specify a correlation matrix beforehand and reorder our variables afterwards so that they match the desired correlation.</p>
<p>Of course, we need to find reasonable correlation values, for example between age and number of kids (slightly positively correlated) or between savings and number of kids (slightly negatively correlated).</p>
<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1"></a><span class="kw">set.seed</span>(<span class="dv">64</span>)</span>
<span id="cb22-2"><a href="#cb22-2"></a></span>
<span id="cb22-3"><a href="#cb22-3"></a>k <-<span class="st"> </span><span class="dv">2000</span></span>
<span id="cb22-4"><a href="#cb22-4"></a></span>
<span id="cb22-5"><a href="#cb22-5"></a>age <-<span class="st"> </span><span class="kw">rnorm</span>(k,<span class="dt">mean=</span><span class="dv">35</span>,<span class="dt">sd=</span><span class="dv">10</span>) <span class="op">%>%</span><span class="st"> </span><span class="kw">pmax</span>(<span class="dv">18</span>) <span class="op">%>%</span><span class="st"> </span><span class="kw">round</span>()</span>
<span id="cb22-6"><a href="#cb22-6"></a>balance <-<span class="st"> </span><span class="kw">rexp</span>(k,<span class="dt">rate=</span><span class="fl">0.001</span>) <span class="op">%>%</span><span class="st"> </span><span class="kw">round</span>(<span class="dv">2</span>)</span>
<span id="cb22-7"><a href="#cb22-7"></a>tenure <-<span class="st"> </span><span class="kw">rnorm</span>(k,<span class="dt">mean=</span><span class="dv">15</span>,<span class="dt">sd=</span><span class="dv">5</span>) <span class="op">%>%</span><span class="st"> </span><span class="kw">pmax</span>(<span class="dv">1</span>) <span class="op">%>%</span><span class="st"> </span><span class="kw">round</span>()</span>
<span id="cb22-8"><a href="#cb22-8"></a>kids_cnt <-<span class="st"> </span><span class="kw">sample</span>(<span class="dv">0</span><span class="op">:</span><span class="dv">5</span>,k,<span class="dt">replace=</span>T,<span class="dt">prob=</span><span class="kw">c</span>(<span class="dv">100</span>,<span class="dv">120</span>,<span class="dv">80</span>,<span class="dv">30</span>,<span class="dv">5</span>,<span class="dv">1</span>))</span>
<span id="cb22-9"><a href="#cb22-9"></a></span>
<span id="cb22-10"><a href="#cb22-10"></a></span>
<span id="cb22-11"><a href="#cb22-11"></a>data <-<span class="st"> </span><span class="kw">data.frame</span>(age,balance,kids_cnt,tenure)</span>
<span id="cb22-12"><a href="#cb22-12"></a>data <span class="op">%>%</span><span class="st"> </span><span class="kw">head</span>(<span class="dv">7</span>)</span></code></pre></div>
<pre><code>## age balance kids_cnt tenure
## 1 18 3665.34 2 10
## 2 18 268.55 2 8
## 3 22 1628.59 0 22
## 4 50 1995.58 1 12
## 5 35 1510.58 0 20
## 6 32 58.58 0 5
## 7 45 945.11 0 21</code></pre>
<p>We directly see that there are things that don’t make sense, like the 22-years-old with a tenure of 22 years.</p>
<p>To improve this, we want to reshuffle the rows and get a distribution close to a desired one. First we simulate a helping dataset of same size, where every entry is random normal distributed.</p>
<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1"></a>nvars <-<span class="st"> </span><span class="kw">ncol</span>(data)</span>
<span id="cb24-2"><a href="#cb24-2"></a>numobs <-<span class="st"> </span><span class="kw">nrow</span>(data)</span>
<span id="cb24-3"><a href="#cb24-3"></a></span>
<span id="cb24-4"><a href="#cb24-4"></a><span class="kw">set.seed</span>(<span class="dv">3</span>)</span>
<span id="cb24-5"><a href="#cb24-5"></a>rnorm_helper <-<span class="st"> </span><span class="kw">matrix</span>(<span class="kw">rnorm</span>(nvars<span class="op">*</span>numobs,<span class="dv">0</span>,<span class="dv">1</span>),<span class="dt">nrow=</span>nvars)</span></code></pre></div>
<p>The correlation of this matrix should be close to the identity matrix.</p>
<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1"></a><span class="kw">cor</span>(<span class="kw">t</span>(rnorm_helper))</span></code></pre></div>
<pre><code>## [,1] [,2] [,3] [,4]
## [1,] 1.000000000 -0.00574905 0.009783835 -0.023569599
## [2,] -0.005749050 1.00000000 0.049500977 0.010347672
## [3,] 0.009783835 0.04950098 1.000000000 0.005859748
## [4,] -0.023569599 0.01034767 0.005859748 1.000000000</code></pre>
<p>Next, we specify our desired correlation matrix:</p>
<div class="sourceCode" id="cb27"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1"></a>Q <-<span class="st"> </span><span class="kw">matrix</span>(<span class="kw">c</span>(<span class="dv">1</span>,<span class="fl">0.3</span>,<span class="fl">0.4</span>,<span class="fl">0.2</span>, <span class="fl">0.3</span>,<span class="dv">1</span>,<span class="dv">0</span>,<span class="fl">0.3</span>, <span class="fl">0.4</span>,<span class="dv">0</span>,<span class="dv">1</span>,<span class="op">-</span><span class="fl">0.3</span>, <span class="fl">0.2</span>,<span class="fl">0.3</span>,<span class="op">-</span><span class="fl">0.3</span>,<span class="dv">1</span>),<span class="dt">ncol=</span>nvars)</span>
<span id="cb27-2"><a href="#cb27-2"></a></span>
<span id="cb27-3"><a href="#cb27-3"></a>Q</span></code></pre></div>
<pre><code>## [,1] [,2] [,3] [,4]
## [1,] 1.0 0.3 0.4 0.2
## [2,] 0.3 1.0 0.0 0.3
## [3,] 0.4 0.0 1.0 -0.3
## [4,] 0.2 0.3 -0.3 1.0</code></pre>
<p>We can now multiply the <code>rnorm_helper</code> matrix with the Cholesky decomposition of our desired correlation matrix <code>Q</code>. Why this works, is explained in the following comment. If you are not interested in mathematical details, you can skip this part.</p>
<p><img src="data:image/png;base64,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" /></p>
<p>(Explanation found <a href="https://math.stackexchange.com/q/163472">here</a>)</p>
<div class="sourceCode" id="cb29"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1"></a>L <-<span class="st"> </span><span class="kw">t</span>(<span class="kw">chol</span>(Q))</span>
<span id="cb29-2"><a href="#cb29-2"></a>Z <-<span class="st"> </span>L <span class="op">%*%</span><span class="st"> </span>rnorm_helper</span></code></pre></div>
<p>Good, now we convert this new data to a data frame and give it the name of our original data. The correlation of this dataset is close to our desired outcome.</p>
<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1"></a>raw <-<span class="st"> </span><span class="kw">as.data.frame</span>(<span class="kw">t</span>(Z),<span class="dt">row.names=</span><span class="ot">NULL</span>,<span class="dt">optional=</span><span class="ot">FALSE</span>)</span>
<span id="cb30-2"><a href="#cb30-2"></a><span class="kw">names</span>(raw) <-<span class="st"> </span><span class="kw">names</span>(data)</span>
<span id="cb30-3"><a href="#cb30-3"></a><span class="kw">head</span>(raw,<span class="dv">7</span>,<span class="dt">addrownums=</span><span class="ot">FALSE</span>)</span></code></pre></div>
<pre><code>## age balance kids_cnt tenure
## 1 -0.9619334 -0.56763178 -0.1130367 -1.3627041
## 2 0.1957828 0.08747126 0.1520695 0.9806283
## 3 -1.2188574 0.84333548 -1.3231141 -0.6184486
## 4 -0.7163585 0.02610745 -0.1802922 -0.4043929
## 5 -0.9530173 -0.90428943 0.8118207 -0.6505000
## 6 -0.5784837 -1.07244273 -0.2978104 -1.7172915
## 7 -0.4844551 -0.85227479 0.9530955 0.1474831</code></pre>
<div class="sourceCode" id="cb32"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb32-1"><a href="#cb32-1"></a><span class="kw">cor</span>(raw)</span></code></pre></div>
<pre><code>## age balance kids_cnt tenure
## age 1.0000000 0.29376273 0.39741190 0.1708577
## balance 0.2937627 1.00000000 0.04302845 0.2777607
## kids_cnt 0.3974119 0.04302845 1.00000000 -0.3046079
## tenure 0.1708577 0.27776070 -0.30460794 1.0000000</code></pre>
<p>However, this dataset <code>raw</code> does not have anything to do with our original data. It is still our transformed random normal data. But as we know that this dataset has the correct correlation, we can use this to reorder the rows of our other dataset.</p>
<p>And then we just replace the largest value of the random normal dataset with the largest value in our dataset, the second largest with the second largest etc. We go column by column and repeat this procedure.</p>
<div class="sourceCode" id="cb34"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb34-1"><a href="#cb34-1"></a><span class="cf">for</span>(name <span class="cf">in</span> <span class="kw">names</span>(raw)) {</span>
<span id="cb34-2"><a href="#cb34-2"></a> raw <-<span class="st"> </span>raw[<span class="kw">order</span>(raw[,name]),]</span>
<span id="cb34-3"><a href="#cb34-3"></a> data <-<span class="st"> </span>data[<span class="kw">order</span>(data[,name]),]</span>
<span id="cb34-4"><a href="#cb34-4"></a> raw[,name] <-<span class="st"> </span>data[,name]</span>
<span id="cb34-5"><a href="#cb34-5"></a>}</span></code></pre></div>
<p>Let’s check the correlation of this new dataset. It is not exactly what we wished, but close enough. The reason for this is that our variables take less values than a random normal distributed variable (e.g. kids count just takes values between 0 and 5).</p>
<div class="sourceCode" id="cb35"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb35-1"><a href="#cb35-1"></a><span class="kw">cor</span>(raw)</span></code></pre></div>
<pre><code>## age balance kids_cnt tenure
## age 1.0000000 0.24231169 0.36996781 0.1734406
## balance 0.2423117 1.00000000 0.01880674 0.2358795
## kids_cnt 0.3699678 0.01880674 1.00000000 -0.3038222
## tenure 0.1734406 0.23587953 -0.30382217 1.0000000</code></pre>
<p>We can also take a look at the reshuffled dataset.</p>
<div class="sourceCode" id="cb37"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb37-1"><a href="#cb37-1"></a><span class="kw">head</span>(raw,<span class="dv">7</span>,<span class="dt">addrownums=</span><span class="ot">FALSE</span>)</span></code></pre></div>
<pre><code>## age balance kids_cnt tenure
## 1934 34 36.36 3 1
## 733 37 52.44 1 1
## 123 22 290.91 2 1
## 1032 26 130.01 2 1
## 1463 32 88.87 2 1
## 448 43 26.54 4 1
## 1804 35 911.63 2 2</code></pre>
</div>
</div>
<div id="exporting" class="section level2">
<h2>Exporting</h2>
<p>To export this dataset, we can use the base R function.</p>
<div class="sourceCode" id="cb39"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb39-1"><a href="#cb39-1"></a><span class="kw">write.csv</span>(raw,<span class="st">"data.csv"</span>)</span></code></pre></div>
<p>Alternatively, if this is not fast enough, we can also use the <code>fwrite</code> function from the data.table package which is much faster.</p>
<div class="sourceCode" id="cb40"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb40-1"><a href="#cb40-1"></a><span class="kw">library</span>(data.table)</span>
<span id="cb40-2"><a href="#cb40-2"></a><span class="kw">fwrite</span>(raw,<span class="st">"data.csv"</span>)</span></code></pre></div>
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