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<meta name="author" content="Norwin Roosen" />
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<meta name="date" content="2018-05-26" />
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<title>Visualising the History of openSenseMap.org</title>
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rel="stylesheet" type="text/css" />
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</head>
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<body>
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<h1 class="title toc-ignore">Visualising the History of openSenseMap.org</h1>
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<h4 class="author"><em>Norwin Roosen</em></h4>
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<h4 class="date"><em>2018-05-26</em></h4>
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<div id="TOC">
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<ul>
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<li><a href="#plot-count-of-boxes-by-time">Plot count of boxes by time</a><ul>
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<li><a href="#and-exposure">…and exposure</a></li>
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<li><a href="#and-grouptag">…and grouptag</a></li>
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</ul></li>
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<li><a href="#plot-rate-of-growth-and-inactivity-per-week">Plot rate of growth and inactivity per week</a></li>
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<li><a href="#plot-duration-of-boxes-being-active">Plot duration of boxes being active</a><ul>
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<li><a href="#by-exposure">…by exposure</a></li>
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<li><a href="#by-grouptag">…by grouptag</a></li>
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<li><a href="#by-year-of-registration">…by year of registration</a></li>
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</ul></li>
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<li><a href="#more-visualisations">More Visualisations</a></li>
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</ul>
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</div>
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<blockquote>
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<p>This vignette serves as an example on data wrangling & visualization with <code>opensensmapr</code>, <code>dplyr</code> and <code>ggplot2</code>.</p>
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</blockquote>
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<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># required packages:</span>
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<span class="kw">library</span>(opensensmapr) <span class="co"># data download</span>
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<span class="kw">library</span>(dplyr) <span class="co"># data wrangling</span>
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<span class="kw">library</span>(ggplot2) <span class="co"># plotting</span>
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<span class="kw">library</span>(lubridate) <span class="co"># date arithmetic</span>
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<span class="kw">library</span>(zoo) <span class="co"># rollmean()</span></code></pre></div>
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<p>openSenseMap.org has grown quite a bit in the last years; it would be interesting to see how we got to the current 1781 sensor stations, split up by various attributes of the boxes.</p>
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<p>While <code>opensensmapr</code> provides extensive methods of filtering boxes by attributes on the server, we do the filtering within R to save time and gain flexibility. So the first step is to retrieve <em>all the boxes</em>:</p>
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<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># if you want to see results for a specific subset of boxes,</span>
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<span class="co"># just specify a filter such as grouptag='ifgi' here</span>
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boxes =<span class="st"> </span><span class="kw">osem_boxes</span>()</code></pre></div>
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<div id="plot-count-of-boxes-by-time" class="section level1 tabset">
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<h1>Plot count of boxes by time</h1>
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<p>By looking at the <code>createdAt</code> attribute of each box we know the exact time a box was registered. With this approach we have no information about boxes that were deleted in the meantime, but that’s okay for now.</p>
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<div id="and-exposure" class="section level2">
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<h2>…and exposure</h2>
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<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">exposure_counts =<span class="st"> </span>boxes %>%
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<span class="st"> </span><span class="kw">group_by</span>(exposure) %>%
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<span class="st"> </span><span class="kw">mutate</span>(<span class="dt">count =</span> <span class="kw">row_number</span>(createdAt))
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exposure_colors =<span class="st"> </span><span class="kw">c</span>(<span class="dt">indoor =</span> <span class="st">'red'</span>, <span class="dt">outdoor =</span> <span class="st">'lightgreen'</span>, <span class="dt">mobile =</span> <span class="st">'blue'</span>, <span class="dt">unknown =</span> <span class="st">'darkgrey'</span>)
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<span class="kw">ggplot</span>(exposure_counts, <span class="kw">aes</span>(<span class="dt">x =</span> createdAt, <span class="dt">y =</span> count, <span class="dt">colour =</span> exposure)) +
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<span class="st"> </span><span class="kw">geom_line</span>() +
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<span class="st"> </span><span class="kw">scale_colour_manual</span>(<span class="dt">values =</span> exposure_colors) +
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<span class="st"> </span><span class="kw">xlab</span>(<span class="st">'Registration Date'</span>) +<span class="st"> </span><span class="kw">ylab</span>(<span class="st">'senseBox count'</span>)</code></pre></div>
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<p><img 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" /><!-- --></p>
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<p>Outdoor boxes are growing <em>fast</em>! We can also see the introduction of <code>mobile</code> sensor “stations” in 2017. While mobile boxes are still few, we can expect a quick rise in 2018 once the new <a href="https://sensebox.de/blog/2018-03-06-senseBox_MCU">senseBox MCU with GPS support is released</a>.</p>
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<p>Let’s have a quick summary:</p>
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<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">exposure_counts %>%
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<span class="st"> </span><span class="kw">summarise</span>(
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<span class="dt">oldest =</span> <span class="kw">min</span>(createdAt),
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<span class="dt">newest =</span> <span class="kw">max</span>(createdAt),
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<span class="dt">count =</span> <span class="kw">max</span>(count)
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) %>%
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<span class="st"> </span><span class="kw">arrange</span>(<span class="kw">desc</span>(count))</code></pre></div>
|
|
|
<div class="kable-table">
|
|
|
<table>
|
|
|
<thead>
|
|
|
<tr class="header">
|
|
|
<th align="left">exposure</th>
|
|
|
<th align="left">oldest</th>
|
|
|
<th align="left">newest</th>
|
|
|
<th align="right">count</th>
|
|
|
</tr>
|
|
|
</thead>
|
|
|
<tbody>
|
|
|
<tr class="odd">
|
|
|
<td align="left">outdoor</td>
|
|
|
<td align="left">2015-02-18 16:53:41</td>
|
|
|
<td align="left">2018-05-26 08:39:12</td>
|
|
|
<td align="right">1416</td>
|
|
|
</tr>
|
|
|
<tr class="even">
|
|
|
<td align="left">indoor</td>
|
|
|
<td align="left">2015-02-08 17:36:40</td>
|
|
|
<td align="left">2018-05-26 10:29:27</td>
|
|
|
<td align="right">290</td>
|
|
|
</tr>
|
|
|
<tr class="odd">
|
|
|
<td align="left">mobile</td>
|
|
|
<td align="left">2017-05-24 08:16:36</td>
|
|
|
<td align="left">2018-05-24 07:08:32</td>
|
|
|
<td align="right">55</td>
|
|
|
</tr>
|
|
|
<tr class="even">
|
|
|
<td align="left">unknown</td>
|
|
|
<td align="left">2014-05-28 15:36:14</td>
|
|
|
<td align="left">2016-06-25 15:11:11</td>
|
|
|
<td align="right">20</td>
|
|
|
</tr>
|
|
|
</tbody>
|
|
|
</table>
|
|
|
</div>
|
|
|
</div>
|
|
|
<div id="and-grouptag" class="section level2">
|
|
|
<h2>…and grouptag</h2>
|
|
|
<p>We can try to find out where the increases in growth came from, by analysing the box count by grouptag.</p>
|
|
|
<p>Caveats: Only a small subset of boxes has a grouptag, and we should assume that these groups are actually bigger. Also, we can see that grouptag naming is inconsistent (<code>Luftdaten</code>, <code>luftdaten.info</code>, …)</p>
|
|
|
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">grouptag_counts =<span class="st"> </span>boxes %>%
|
|
|
<span class="st"> </span><span class="kw">group_by</span>(grouptag) %>%
|
|
|
<span class="st"> </span><span class="co"># only include grouptags with 8 or more members</span>
|
|
|
<span class="st"> </span><span class="kw">filter</span>(<span class="kw">length</span>(grouptag) >=<span class="st"> </span><span class="dv">8</span> &&<span class="st"> </span>!<span class="kw">is.na</span>(grouptag)) %>%
|
|
|
<span class="st"> </span><span class="kw">mutate</span>(<span class="dt">count =</span> <span class="kw">row_number</span>(createdAt))
|
|
|
|
|
|
<span class="co"># helper for sorting the grouptags by boxcount</span>
|
|
|
sortLvls =<span class="st"> </span>function(oldFactor, <span class="dt">ascending =</span> <span class="ot">TRUE</span>) {
|
|
|
lvls =<span class="st"> </span><span class="kw">table</span>(oldFactor) %>%<span class="st"> </span><span class="kw">sort</span>(., <span class="dt">decreasing =</span> !ascending) %>%<span class="st"> </span><span class="kw">names</span>()
|
|
|
<span class="kw">factor</span>(oldFactor, <span class="dt">levels =</span> lvls)
|
|
|
}
|
|
|
grouptag_counts$grouptag =<span class="st"> </span><span class="kw">sortLvls</span>(grouptag_counts$grouptag, <span class="dt">ascending =</span> <span class="ot">FALSE</span>)
|
|
|
|
|
|
<span class="kw">ggplot</span>(grouptag_counts, <span class="kw">aes</span>(<span class="dt">x =</span> createdAt, <span class="dt">y =</span> count, <span class="dt">colour =</span> grouptag)) +
|
|
|
<span class="st"> </span><span class="kw">geom_line</span>(<span class="kw">aes</span>(<span class="dt">group =</span> grouptag)) +
|
|
|
<span class="st"> </span><span class="kw">xlab</span>(<span class="st">'Registration Date'</span>) +<span class="st"> </span><span class="kw">ylab</span>(<span class="st">'senseBox count'</span>)</code></pre></div>
|
|
|
<p><img src="data:image/png;base64,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" /><!-- --></p>
|
|
|
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">grouptag_counts %>%
|
|
|
<span class="st"> </span><span class="kw">summarise</span>(
|
|
|
<span class="dt">oldest =</span> <span class="kw">min</span>(createdAt),
|
|
|
<span class="dt">newest =</span> <span class="kw">max</span>(createdAt),
|
|
|
<span class="dt">count =</span> <span class="kw">max</span>(count)
|
|
|
) %>%
|
|
|
<span class="st"> </span><span class="kw">arrange</span>(<span class="kw">desc</span>(count))</code></pre></div>
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|
|
<div class="kable-table">
|
|
|
<table>
|
|
|
<thead>
|
|
|
<tr class="header">
|
|
|
<th align="left">grouptag</th>
|
|
|
<th align="left">oldest</th>
|
|
|
<th align="left">newest</th>
|
|
|
<th align="right">count</th>
|
|
|
</tr>
|
|
|
</thead>
|
|
|
<tbody>
|
|
|
<tr class="odd">
|
|
|
<td align="left">Luftdaten</td>
|
|
|
<td align="left">2017-03-14 17:01:16</td>
|
|
|
<td align="left">2018-05-21 02:20:50</td>
|
|
|
<td align="right">109</td>
|
|
|
</tr>
|
|
|
<tr class="even">
|
|
|
<td align="left">ifgi</td>
|
|
|
<td align="left">2016-06-17 08:04:54</td>
|
|
|
<td align="left">2018-05-15 10:27:02</td>
|
|
|
<td align="right">35</td>
|
|
|
</tr>
|
|
|
<tr class="odd">
|
|
|
<td align="left">MakeLight</td>
|
|
|
<td align="left">2015-02-18 16:53:41</td>
|
|
|
<td align="left">2018-02-02 13:50:21</td>
|
|
|
<td align="right">15</td>
|
|
|
</tr>
|
|
|
<tr class="even">
|
|
|
<td align="left">Bad_Hersfeld</td>
|
|
|
<td align="left">2017-07-18 13:32:03</td>
|
|
|
<td align="left">2018-03-22 09:10:07</td>
|
|
|
<td align="right">13</td>
|
|
|
</tr>
|
|
|
<tr class="odd">
|
|
|
<td align="left">luftdaten.info</td>
|
|
|
<td align="left">2017-05-01 10:15:44</td>
|
|
|
<td align="left">2018-05-17 11:47:21</td>
|
|
|
<td align="right">12</td>
|
|
|
</tr>
|
|
|
<tr class="even">
|
|
|
<td align="left">dwih-sp</td>
|
|
|
<td align="left">2016-08-09 08:06:02</td>
|
|
|
<td align="left">2016-11-23 10:16:04</td>
|
|
|
<td align="right">11</td>
|
|
|
</tr>
|
|
|
<tr class="odd">
|
|
|
<td align="left">Che Aria Tira?</td>
|
|
|
<td align="left">2018-03-11 10:50:42</td>
|
|
|
<td align="left">2018-03-11 23:11:20</td>
|
|
|
<td align="right">10</td>
|
|
|
</tr>
|
|
|
<tr class="even">
|
|
|
<td align="left">Luftdaten.info</td>
|
|
|
<td align="left">2017-04-03 14:10:20</td>
|
|
|
<td align="left">2018-04-16 16:31:24</td>
|
|
|
<td align="right">10</td>
|
|
|
</tr>
|
|
|
<tr class="odd">
|
|
|
<td align="left">Feinstaub</td>
|
|
|
<td align="left">2017-04-08 06:38:25</td>
|
|
|
<td align="left">2018-03-29 17:27:55</td>
|
|
|
<td align="right">9</td>
|
|
|
</tr>
|
|
|
<tr class="even">
|
|
|
<td align="left">PGKN</td>
|
|
|
<td align="left">2018-04-08 07:01:57</td>
|
|
|
<td align="left">2018-04-27 18:38:51</td>
|
|
|
<td align="right">9</td>
|
|
|
</tr>
|
|
|
<tr class="odd">
|
|
|
<td align="left">Raumanmeri</td>
|
|
|
<td align="left">2017-03-13 11:35:39</td>
|
|
|
<td align="left">2017-04-27 05:36:20</td>
|
|
|
<td align="right">9</td>
|
|
|
</tr>
|
|
|
<tr class="even">
|
|
|
<td align="left">Sofia</td>
|
|
|
<td align="left">2017-04-11 04:40:11</td>
|
|
|
<td align="left">2018-03-15 13:26:56</td>
|
|
|
<td align="right">9</td>
|
|
|
</tr>
|
|
|
<tr class="odd">
|
|
|
<td align="left">IKG</td>
|
|
|
<td align="left">2017-03-21 19:02:11</td>
|
|
|
<td align="left">2017-12-18 14:30:21</td>
|
|
|
<td align="right">8</td>
|
|
|
</tr>
|
|
|
</tbody>
|
|
|
</table>
|
|
|
</div>
|
|
|
</div>
|
|
|
</div>
|
|
|
<div id="plot-rate-of-growth-and-inactivity-per-week" class="section level1">
|
|
|
<h1>Plot rate of growth and inactivity per week</h1>
|
|
|
<p>First we group the boxes by <code>createdAt</code> into bins of one week:</p>
|
|
|
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">bins =<span class="st"> 'week'</span>
|
|
|
mvavg_bins =<span class="st"> </span><span class="dv">6</span>
|
|
|
|
|
|
growth =<span class="st"> </span>boxes %>%
|
|
|
<span class="st"> </span><span class="kw">mutate</span>(<span class="dt">week =</span> <span class="kw">cut</span>(<span class="kw">as.Date</span>(createdAt), <span class="dt">breaks =</span> bins)) %>%
|
|
|
<span class="st"> </span><span class="kw">group_by</span>(week) %>%
|
|
|
<span class="st"> </span><span class="kw">summarize</span>(<span class="dt">count =</span> <span class="kw">length</span>(week)) %>%
|
|
|
<span class="st"> </span><span class="kw">mutate</span>(<span class="dt">event =</span> <span class="st">'registered'</span>)</code></pre></div>
|
|
|
<p>We can do the same for <code>updatedAt</code>, which informs us about the last change to a box, including uploaded measurements. This method of determining inactive boxes is fairly inaccurate and should be considered an approximation, because we have no information about intermediate inactive phases. Also deleted boxes would probably have a big impact here.</p>
|
|
|
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">inactive =<span class="st"> </span>boxes %>%
|
|
|
<span class="st"> </span><span class="co"># remove boxes that were updated in the last two days,</span>
|
|
|
<span class="st"> </span><span class="co"># b/c any box becomes inactive at some point by definition of updatedAt</span>
|
|
|
<span class="st"> </span><span class="kw">filter</span>(updatedAt <<span class="st"> </span><span class="kw">now</span>() -<span class="st"> </span><span class="kw">days</span>(<span class="dv">2</span>)) %>%
|
|
|
<span class="st"> </span><span class="kw">mutate</span>(<span class="dt">week =</span> <span class="kw">cut</span>(<span class="kw">as.Date</span>(updatedAt), <span class="dt">breaks =</span> bins)) %>%
|
|
|
<span class="st"> </span><span class="kw">group_by</span>(week) %>%
|
|
|
<span class="st"> </span><span class="kw">summarize</span>(<span class="dt">count =</span> <span class="kw">length</span>(week)) %>%
|
|
|
<span class="st"> </span><span class="kw">mutate</span>(<span class="dt">event =</span> <span class="st">'inactive'</span>)</code></pre></div>
|
|
|
<p>Now we can combine both datasets for plotting:</p>
|
|
|
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">boxes_by_date =<span class="st"> </span><span class="kw">bind_rows</span>(growth, inactive) %>%<span class="st"> </span><span class="kw">group_by</span>(event)
|
|
|
|
|
|
<span class="kw">ggplot</span>(boxes_by_date, <span class="kw">aes</span>(<span class="dt">x =</span> <span class="kw">as.Date</span>(week), <span class="dt">colour =</span> event)) +
|
|
|
<span class="st"> </span><span class="kw">xlab</span>(<span class="st">'Time'</span>) +<span class="st"> </span><span class="kw">ylab</span>(<span class="kw">paste</span>(<span class="st">'rate per '</span>, bins)) +
|
|
|
<span class="st"> </span><span class="kw">scale_x_date</span>(<span class="dt">date_breaks=</span><span class="st">"years"</span>, <span class="dt">date_labels=</span><span class="st">"%Y"</span>) +
|
|
|
<span class="st"> </span><span class="kw">scale_colour_manual</span>(<span class="dt">values =</span> <span class="kw">c</span>(<span class="dt">registered =</span> <span class="st">'lightgreen'</span>, <span class="dt">inactive =</span> <span class="st">'grey'</span>)) +
|
|
|
<span class="st"> </span><span class="kw">geom_point</span>(<span class="kw">aes</span>(<span class="dt">y =</span> count), <span class="dt">size =</span> <span class="fl">0.5</span>) +
|
|
|
<span class="st"> </span><span class="co"># moving average, make first and last value NA (to ensure identical length of vectors)</span>
|
|
|
<span class="st"> </span><span class="kw">geom_line</span>(<span class="kw">aes</span>(<span class="dt">y =</span> <span class="kw">rollmean</span>(count, mvavg_bins, <span class="dt">fill =</span> <span class="kw">list</span>(<span class="ot">NA</span>, <span class="ot">NULL</span>, <span class="ot">NA</span>))))</code></pre></div>
|
|
|
<p><img src="data:image/png;base64,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" /><!-- --></p>
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<p>We see a sudden rise in early 2017, which lines up with the fast growing grouptag <code>Luftdaten</code>. This was enabled by an integration of openSenseMap.org into the firmware of the air quality monitoring project <a href="https://luftdaten.info">luftdaten.info</a>. The dips in mid 2017 and early 2018 could possibly be explained by production/delivery issues of the senseBox hardware, but I have no data on the exact time frames to verify.</p>
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</div>
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<div id="plot-duration-of-boxes-being-active" class="section level1 tabset">
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<h1>Plot duration of boxes being active</h1>
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<p>While we are looking at <code>createdAt</code> and <code>updatedAt</code>, we can also extract the duration of activity of each box, and look at metrics by exposure and grouptag once more:</p>
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<div id="by-exposure" class="section level2">
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<h2>…by exposure</h2>
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<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">duration =<span class="st"> </span>boxes %>%
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<span class="st"> </span><span class="kw">group_by</span>(exposure) %>%
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<span class="st"> </span><span class="kw">filter</span>(!<span class="kw">is.na</span>(updatedAt)) %>%
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<span class="st"> </span><span class="kw">mutate</span>(<span class="dt">duration =</span> <span class="kw">difftime</span>(updatedAt, createdAt, <span class="dt">units=</span><span class="st">'days'</span>))
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<span class="kw">ggplot</span>(duration, <span class="kw">aes</span>(<span class="dt">x =</span> exposure, <span class="dt">y =</span> duration)) +
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<span class="st"> </span><span class="kw">geom_boxplot</span>() +
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<span class="st"> </span><span class="kw">coord_flip</span>() +<span class="st"> </span><span class="kw">ylab</span>(<span class="st">'Duration active in Days'</span>)</code></pre></div>
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<p><img src="data:image/png;base64,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" /><!-- --></p>
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<p>The time of activity averages at only 152 days, though there are boxes with 759 days of activity, spanning a large chunk of openSenseMap’s existence.</p>
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</div>
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<div id="by-grouptag" class="section level2">
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<h2>…by grouptag</h2>
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<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">duration =<span class="st"> </span>boxes %>%
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<span class="st"> </span><span class="kw">group_by</span>(grouptag) %>%
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<span class="st"> </span><span class="co"># only include grouptags with 8 or more members</span>
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<span class="st"> </span><span class="kw">filter</span>(<span class="kw">length</span>(grouptag) >=<span class="st"> </span><span class="dv">8</span> &&<span class="st"> </span>!<span class="kw">is.na</span>(grouptag) &&<span class="st"> </span>!<span class="kw">is.na</span>(updatedAt)) %>%
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<span class="st"> </span><span class="kw">mutate</span>(<span class="dt">duration =</span> <span class="kw">difftime</span>(updatedAt, createdAt, <span class="dt">units=</span><span class="st">'days'</span>))
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<span class="kw">ggplot</span>(duration, <span class="kw">aes</span>(<span class="dt">x =</span> grouptag, <span class="dt">y =</span> duration)) +
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<span class="st"> </span><span class="kw">geom_boxplot</span>() +
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<span class="st"> </span><span class="kw">coord_flip</span>() +<span class="st"> </span><span class="kw">ylab</span>(<span class="st">'Duration active in Days'</span>)</code></pre></div>
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<p><img src="data:image/png;base64,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" /><!-- --></p>
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<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">duration %>%
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<span class="st"> </span><span class="kw">summarize</span>(
|
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<span class="dt">duration_avg =</span> <span class="kw">round</span>(<span class="kw">mean</span>(duration)),
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<span class="dt">duration_min =</span> <span class="kw">round</span>(<span class="kw">min</span>(duration)),
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<span class="dt">duration_max =</span> <span class="kw">round</span>(<span class="kw">max</span>(duration)),
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<span class="dt">oldest_box =</span> <span class="kw">round</span>(<span class="kw">max</span>(<span class="kw">difftime</span>(<span class="kw">now</span>(), createdAt, <span class="dt">units=</span><span class="st">'days'</span>)))
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) %>%
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<span class="st"> </span><span class="kw">arrange</span>(<span class="kw">desc</span>(duration_avg))</code></pre></div>
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<div class="kable-table">
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|
|
<table>
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|
|
<thead>
|
|
|
<tr class="header">
|
|
|
<th align="left">grouptag</th>
|
|
|
<th align="left">duration_avg</th>
|
|
|
<th align="left">duration_min</th>
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|
|
<th align="left">duration_max</th>
|
|
|
<th align="left">oldest_box</th>
|
|
|
</tr>
|
|
|
</thead>
|
|
|
<tbody>
|
|
|
<tr class="odd">
|
|
|
<td align="left">dwih-sp</td>
|
|
|
<td align="left">627 days</td>
|
|
|
<td align="left">549 days</td>
|
|
|
<td align="left">655 days</td>
|
|
|
<td align="left">655 days</td>
|
|
|
</tr>
|
|
|
<tr class="even">
|
|
|
<td align="left">Feinstaub</td>
|
|
|
<td align="left">219 days</td>
|
|
|
<td align="left">4 days</td>
|
|
|
<td align="left">413 days</td>
|
|
|
<td align="left">413 days</td>
|
|
|
</tr>
|
|
|
<tr class="odd">
|
|
|
<td align="left">ifgi</td>
|
|
|
<td align="left">207 days</td>
|
|
|
<td align="left">0 days</td>
|
|
|
<td align="left">622 days</td>
|
|
|
<td align="left">708 days</td>
|
|
|
</tr>
|
|
|
<tr class="even">
|
|
|
<td align="left">Sofia</td>
|
|
|
<td align="left">200 days</td>
|
|
|
<td align="left">15 days</td>
|
|
|
<td align="left">410 days</td>
|
|
|
<td align="left">410 days</td>
|
|
|
</tr>
|
|
|
<tr class="odd">
|
|
|
<td align="left">Bad_Hersfeld</td>
|
|
|
<td align="left">197 days</td>
|
|
|
<td align="left">65 days</td>
|
|
|
<td align="left">312 days</td>
|
|
|
<td align="left">312 days</td>
|
|
|
</tr>
|
|
|
<tr class="even">
|
|
|
<td align="left">Luftdaten</td>
|
|
|
<td align="left">187 days</td>
|
|
|
<td align="left">0 days</td>
|
|
|
<td align="left">424 days</td>
|
|
|
<td align="left">438 days</td>
|
|
|
</tr>
|
|
|
<tr class="odd">
|
|
|
<td align="left">luftdaten.info</td>
|
|
|
<td align="left">183 days</td>
|
|
|
<td align="left">9 days</td>
|
|
|
<td align="left">360 days</td>
|
|
|
<td align="left">390 days</td>
|
|
|
</tr>
|
|
|
<tr class="even">
|
|
|
<td align="left">IKG</td>
|
|
|
<td align="left">163 days</td>
|
|
|
<td align="left">70 days</td>
|
|
|
<td align="left">260 days</td>
|
|
|
<td align="left">431 days</td>
|
|
|
</tr>
|
|
|
<tr class="odd">
|
|
|
<td align="left">Luftdaten.info</td>
|
|
|
<td align="left">86 days</td>
|
|
|
<td align="left">5 days</td>
|
|
|
<td align="left">376 days</td>
|
|
|
<td align="left">418 days</td>
|
|
|
</tr>
|
|
|
<tr class="even">
|
|
|
<td align="left">Che Aria Tira?</td>
|
|
|
<td align="left">75 days</td>
|
|
|
<td align="left">71 days</td>
|
|
|
<td align="left">76 days</td>
|
|
|
<td align="left">76 days</td>
|
|
|
</tr>
|
|
|
<tr class="odd">
|
|
|
<td align="left">Raumanmeri</td>
|
|
|
<td align="left">45 days</td>
|
|
|
<td align="left">7 days</td>
|
|
|
<td align="left">318 days</td>
|
|
|
<td align="left">439 days</td>
|
|
|
</tr>
|
|
|
<tr class="even">
|
|
|
<td align="left">PGKN</td>
|
|
|
<td align="left">35 days</td>
|
|
|
<td align="left">29 days</td>
|
|
|
<td align="left">48 days</td>
|
|
|
<td align="left">48 days</td>
|
|
|
</tr>
|
|
|
</tbody>
|
|
|
</table>
|
|
|
</div>
|
|
|
<p>The time of activity averages at only 191 days, though there are boxes with 655 days of activity, spanning a large chunk of openSenseMap’s existence.</p>
|
|
|
</div>
|
|
|
<div id="by-year-of-registration" class="section level2">
|
|
|
<h2>…by year of registration</h2>
|
|
|
<p>This is less useful, as older boxes are active for a longer time by definition. If you have an idea how to compensate for that, please send a <a href="https://github.com/sensebox/opensensmapr/pulls">Pull Request</a>!</p>
|
|
|
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># NOTE: boxes older than 2016 missing due to missing updatedAt in database</span>
|
|
|
duration =<span class="st"> </span>boxes %>%
|
|
|
<span class="st"> </span><span class="kw">mutate</span>(<span class="dt">year =</span> <span class="kw">cut</span>(<span class="kw">as.Date</span>(createdAt), <span class="dt">breaks =</span> <span class="st">'year'</span>)) %>%
|
|
|
<span class="st"> </span><span class="kw">group_by</span>(year) %>%
|
|
|
<span class="st"> </span><span class="kw">filter</span>(!<span class="kw">is.na</span>(updatedAt)) %>%
|
|
|
<span class="st"> </span><span class="kw">mutate</span>(<span class="dt">duration =</span> <span class="kw">difftime</span>(updatedAt, createdAt, <span class="dt">units=</span><span class="st">'days'</span>))
|
|
|
|
|
|
<span class="kw">ggplot</span>(duration, <span class="kw">aes</span>(<span class="dt">x =</span> <span class="kw">substr</span>(<span class="kw">as.character</span>(year), <span class="dv">0</span>, <span class="dv">4</span>), <span class="dt">y =</span> duration)) +
|
|
|
<span class="st"> </span><span class="kw">geom_boxplot</span>() +
|
|
|
<span class="st"> </span><span class="kw">coord_flip</span>() +<span class="st"> </span><span class="kw">ylab</span>(<span class="st">'Duration active in Days'</span>) +<span class="st"> </span><span class="kw">xlab</span>(<span class="st">'Year of Registration'</span>)</code></pre></div>
|
|
|
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAMAAABNUi8GAAAC31BMVEUAAAABAQECAgIDAwMEBAQFBQUGBgYHBwcICAgJCQkKCgoLCwsMDAwNDQ0ODg4PDw8QEBASEhITExMUFBQVFRUWFhYXFxcYGBgZGRkaGhobGxscHBwdHR0eHh4fHx8gICAhISEiIiIjIyMkJCQlJSUoKCgpKSkqKiorKyssLCwtLS0uLi4vLy8wMDAxMTEyMjIzMzM0NDQ1NTU2NjY3Nzc4ODg5OTk6Ojo7Ozs8PDw9PT0+Pj4/Pz9AQEBBQUFCQkJDQ0NERERFRUVGRkZHR0dJSUlKSkpLS0tMTExNTU1OTk5PT09QUFBRUVFSUlJTU1NUVFRVVVVWVlZXV1dYWFhaWlpbW1tcXFxdXV1eXl5fX19gYGBhYWFiYmJjY2NkZGRlZWVnZ2doaGhpaWlqampra2tsbGxtbW1ubm5vb29wcHBxcXFycnJzc3N0dHR1dXV2dnZ3d3d4eHh5eXl6enp7e3t8fHx9fX1+fn5/f3+AgICBgYGCgoKEhISFhYWGhoaHh4eIiIiJiYmLi4uMjIyNjY2Ojo6QkJCRkZGSkpKTk5OUlJSVlZWWlpaXl5eYmJiZmZmampqbm5ucnJydnZ2enp6fn5+goKChoaGioqKjo6OkpKSlpaWmpqanp6eoqKipqamqqqqrq6usrKytra2urq6vr6+wsLCysrKzs7O0tLS1tbW2tra3t7e4uLi5ubm6urq7u7u8vLy9vb2+vr6/v7/AwMDBwcHCwsLDw8PExMTFxcXGxsbHx8fJycnKysrLy8vMzMzNzc3Ozs7Pz8/Q0NDR0dHS0tLT09PU1NTV1dXW1tbX19fY2NjZ2dna2trb29vc3Nzd3d3e3t7f39/g4ODh4eHi4uLj4+Pk5OTl5eXm5ubn5+fo6Ojp6enq6urr6+vs7Ozt7e3u7u7v7+/w8PDx8fHy8vLz8/P09PT19fX29vb39/f4+Pj5+fn6+vr7+/v8/Pz9/f3+/v7////ha5a3AAAACXBIWXMAAA7DAAAOwwHHb6hkAAAZBUlEQVR4nO3d+39U5Z3A8ZG0RltWKIi7q6t1cXVbt9WxaMtNpG5tK2As5SZUKLp4aVVou6JF1AJSqyLEWm3A4gUMLVLWymKQSgiCYLGhVWiWmxAugUC4hIQ8f8CeSc4TzgnMd+Z8cyZ5KJ/PDwlJzjzz5Hner8wlw0nCEDlcor0nQCQFUHI6gJLTAZScDqDkdAAlp2s10JoD6aqpS/+1SB2NZ5iauoPxDBTTfA45N594xjkYz3ziArpvd7r2C1+LVG08w1SbqngGimk+B8zeeAaKaT4HTTzj7D0ayzAAVQdQMYAqA6gcQLUBVA6gAA0FUDGAKgOoHEC1AVQOoAANBVAxgCoDqBxAtQFUDqAADQVQMYAqA6gcQLUBVA6gAA0FUDGAKgOoHEC1AVQOoAANBVAxgCoDqBxAtQFUDqAADQVQMYAqA6gcQLUBVA6gAA0FUDGAKgOoHEC1AVQOoAANBVAxgCoDqBxAtQFUDqCtBfrOW9reT10coHIAbSXQsqS6r6cuD1A5gLYS6LvJt7elbU4y+Yu0X/z59anLA1QOoK0G+mH6y7+RTL6Y9ovPAzSLAArQUAAVA6gygMoBFKChACoGUHUAFQOoMoDKARSgoQAqBlB1ABUDqDKAygEUoKEAKgZQdQAVA6gygMoBFKChACoGUHUAFQOoMoDKARSgoQAqBlB1ABUDqDKAygEUoKEAKgZQdQAVA6gygMoBFKChACoGUHUAFQOoMoDKARSgoQAqBlB1ABUDqDKAygEUoKEAKgZQdQAVA6gygMoBFKChACoGUHUAFQOoMoDKARSgoQAqBlB1ABUDqDKAygEUoKEAKgZQdQAVA6gygMoBFKChACoGUHUAFQOoMoDKARSgoQAqBlB1ABUDqDKAyp2eQN+6Y9RD241ZM270U7Xeh/Vjq723i0bfNrkKoK0MoGLZAd1WsLNuznhzaFhF/ePFxiwef7MHdNvgyvoZMwDaygAqlh3QdYXGbBlulk8yZu39xny4qsADunvo9tqnXvC+WltdXV21J137j26taGyJHmjf1OV3V8htTTuFUNVmX3YHZqo2nmEOmr3xDBTTfGpMPONUHY1lmOyAphROKzQLvJ+X20amPhqcuomff/N3xxzx3hclk8kN6S85M2lTA+2RzKJxGb8HOu2q9d9nBFo69vn6JqC3pT5MAf3zvVuPvDrV+6BiyZIlOw6k69DGRQsbm6UH2jt1+TcXyv0x7RRCHTY12R2Yqbp4hjlsDsYzUEzzOWLiGefgsThG2Z8d0IaZE7Z475Y/4t3a32eBzi0yZv/ghqYjuA+qjfugYtkBXXNPfepdzfAdDU8UW6Bld++sff0nBqCtC6Bi2QGdPaCgoGCEMavvvevpWgu0oXj0iEmVAG1lABXLDmjmAKoNoGIAVQdQMYAqA6gcQAEaCqBiAFUHUDGAKgOoHEABGgqgYgBVB1AxgCoDqBxAARoKoGIAVQdQMYAqA6gcQAEaCqBiAFUHUDGAKgOoHEABGgqgYgBVB1AxgCoDqBxAARoKoGIAVQdQMYAqA6gcQAEaCqBiAFUHUDGAKgOoHEABGgqgYgBVB1AxgCoDqBxAARoKoGIAVQdQMYAqA6gcQAEaCqBiAFUHUDGAKgOoHEABGgqgYgBVB1AxgCoDqBxAARoKoGIAVQdQMYAqA6gcQAEaCqBiAFUHUDGAKgOoHEABGgqgYgBVB1AxgCoDqNyZC3RF8qYBabuxR4/+ab/YD6BZBNBWAt32s4e1vZC6PEDlANpKoK0NoHIABWgogIoBVBlA5QCqDaByAAVoKICKAVQZQOUAqg2gcgAFaCiAigFUGUDlAKoNoHIABWgogIoBVBlA5QCqDaByAAVoKICKAVQZQOUAqg2gcgAFaCiAigFUGUDlAKoNoHJnDNDDHzcG0AwBVCxnQOflJxoDaIYAKpYzoJcO+UvwKwBNF0DFcga0s3B6BYAGAqhYzoDeMg+gWQVQsZwBLf3S5MUlXgDNEEDFcgY03w+gGQKoGM+DKgOonPtAj5fMembpcYBmCqBiOQO686oOl3bPu7oSoBkCqFjOgA756mZjNvcYBtAMAVQsZ0C7LUu9LbkAoBkCqFjOgJ7fBLRbWwDd+vLs7HstwrFCc4tfimegpvl80NodAKjYyUCHXLfFmC3XDm0LoK8mT/t+2NodAKjYyUB3JvO6d89LtsmDpLnJyup2qEdyQFxDjb23tTsAULGTgZrjywoLS9rmaaa5ycMRryeWeiQHxTXUfwE0TbkBOqvUlPhF2yiAagOomL/OFmjigTb9VSdAAZohf50t0Iq9yo0CqDaAivnrHLgP2r8+9XZHxC0EqDaAivnr3Ax0ypTEY1O8RneNtlEA1QZQMX+dm4H27Zvo09frhjnRNgqg2gAq5q9z4CY+WafZKIBqA6iYv84nvR50db9oGwVQbQAV89c5APT9a7p4nd0z2kYBVBtAxfx1DgDt1eulbi8+23VjtI0CqDaAivnrHAD62aXme4vNY9+JtlEA1QZQMX+dA0A7LjEzJpp3u0TbKIBqA6iYv87BJ+r7fPTuFXunt8nrQQEK0Az56xwAWn7V1Lpen048HG2jAKoNoGL+OoeeZjpuDi0qaYi2UQDVBlAxf50DQAe+o9kogGoDqJi/zgGgN/xIs1EA1QZQMX+dg+dmunLiG7xgOdsAmq6/i3MzARSgGfLXuZ3OzQRQgGbIX2desKwMoOniBcv6ACrkOFBesBwtgKaLFyzrA6iQ80DNio1m0aCf1kbbKIBqA6iYv84BoNMTrx/63MCLH4i2UQDVBlAxf50DQC+a1vDrKxrm/Uu0jQKoNoCK+escfKK+zAy7z7x3drSNAqg2gIr56xwAeuUDmz67yjxxabSNAqg2gIr56xwAuuBTid7mscTPo20UQLUBVMxf5+Cj+IolNWbZ2xE3CqDaACrmrzOnX1QG0HRx+kV9ABVyGyinX4wYQNOVs/ug6xsr3xPtJOAA1QZQMX+dgydu6JDwyuvwmTv3R9gogGoDqJi/zgGgy7v/+qONv/nC2nVf+36EjQKoNoCK+escAHrVgtTb3/Y0f7oowkYBVBtAxfx1DgDttDL1tvQ8s/XcCBsFUG0AFfPXOQD0m9+uNqb62zc2TP1KhI0CqDaAivnrHAC65bJ/6NnrvEs2FZ+zJMJGAVQbQMX8dQ7+qvPYvEkTXz5qduyIslEA1QZQMX+dg0CrFj5ZtSHiC+oBqg6gYv46B89u161bh4qRl/0t2kYBVBtAxfx1DgD9xi21nSqqbgpv4Vt3jHpouzFrxo1+KvWztX6s90DqdwVeA/3fjQJUG0DF/HUO/iZppelUYd7uHNyGbQU76+aMN4eGVdQ/XmzM4vE3Vzd9oewX/hEA1QZQMX+dg/8nqSQF9M0Lg9uwrtB7dD/cLJ9kzNr7jflwVUET0MMPHvHeHti2bdueqnQdqP7Ln9M16/QHekfaby7LPtpW3tohUu2sOpZ2CyJ1yMQzzr7aOEaxL14KAB3Xc1enivIvtvw9Z+20QrNghvfDdGTqo8FNQJ9bkXpblEwmN6Tfw5lJodMeqPTNtWXr4/qO3Mo+WA8ArRmUl+iUuLHFK0VKxz5f3wT0ttSHTUC3/aDxPMzby8rKKven6+DWZW+na9rpD3RU2m8uy5aXlbR2iFTb99el3YJIHTbxjHPgWByj7PPXOXR2u/J5r6wLnwG8YeaELd675Y94t/b3pT7RBPTlRc1HcB9UG/dBxfx1znD6xTX3NJ7yrmb4joYnilP/agTacGdV8xEA1QZQMX+dm4G+97PntpqjHy549kvBbZg9oKCgYIQxq++96+nGewWNQD++88QRANUGUDF/nS3QV8/6TKd/Xt41kejYI9pGAVQbQMX8dbZAr/xubcNjHW7dFfGP0ABUH0DF/HW2QPPfNGZPYln0jQKoNoCK+evc/N+OVxpTl3gv+kYBVBtAxfx1BqgygKYLoPoAKuQ40Gv69++X+Ep/r2gbBVBtABXz19kCHX6iaBsFUG0AFfPXmT/kpQyg6QKoPoAKATQQQAGaIX+dAaoMoOkCqD6ACrkNtM8Ocy1/aS5CAE1XboBe+K2Jif+e2Fi0jQKoNoCK+etsgb4z+FuJm77VWLSNAqg2gIr563zS34uPGkC1AVTMX+fgg6TjJbOeWRrtBOAA1QdQMX+dA0B3XtXh0u55V1dG2yiAagOomL/OAaBDvrrZmM09hkXbKIBqA6iYv84BoN0aX09fckG0jQKoNoCK+escAHp+E9Bu0TYKoNoAKuavc/Am/rotxmy5dmi0jQKoNoCK+escfJCUzOvePS/Jg6SsAmi6cvg007LCwhKeZsougKaLF4voA6gQQAMBFKAZ8tcZoMoAmi6A6gOokPNAB76j2SiAagOomL/OAaA3/EizUQDVBlAxf50DQEuvnPhGiVe0jQKoNoCK+escAJrvF22jAKoNoGL+OvMgSRlA05VjoKv7RdsogGoDqJi/zgGg71/TxevsntE2CqDaACrmr3MAaK9eL3V78dmuG6NtFEC1AVTMX+fg3+pcar632Dz2nWgbBVBtABXz1zkAtOMSM2OiebdLtI0CqDaAivnrHPxvx30+eveKvdN5RX1WATRdOQNaftXUul6fTjwcbaMAqg2gYv46h55mOm4OLSqJ+JeSAKoNoGL+OgeBVi18smpD7al2Qwig2gAq5q9z8Ca+W7cOFSMv+1u0jQKoNoCK+escAPqNW2o7VVTdFHELAaoNoGL+OgefB11pOlWYtztH2ygd0JeT1/drh3r0uDa2oX7Q2h0AqNjJQC8qSQF988K2ALpp5rPZ90KEY4WeK5oVz0BN83m3tTsAULGTgY7ruatTRfkXv98WQCMV0wZUm6p4BoppPgAVOxlozaC8RKfEjfsBmiGAiuUGaGHq7LXl815ZF/UPxgNUG0DFWgBNXLMqokyAtjKAirUAuuIrZ925F6BZBVCxHN0HPT7nwq4vRD0xE0BbEUDFWgL1HiRNOvfLt3oBNEMAFcsd0IfyvzjQC6AZAqhYjoA2vHrRec8o/hANQLUBVKwF0HV9EkM+ic4ToPoAKtYCaIfuf9DwBKg+gIq1APrTIzqfAFUHULEWQNUBVBtAxQCqDqBiAFUGUDmAagOoHEABGgqgYgBVBlA5gGoDqBxAARoKoGIAVQZQOYBqA6gcQAEaCqBiAFUGUDmAagOoHEABGgqgYgBVBlA5gGoDqBxAARoKoGIAVQZQOYBqA6gcQE8DoJW/buvzg7a654qinCRVKKb5/LIo8zGzKzNvBUBP1Zpk77jOnfx3Uc8ePfrGP2rv5IeZtwKgp2p18r3Wfid/V01LJsviH/WPAD1VAI0eQG0AdTKA2gDqZAC1AdTJAGoDqJMB1AZQJwOoDaBOBlAbQJ0MoDaAOhlAbQB1MoDaAOpkALUB1MkAagOokwHUBlAnA6gNoE4GUBtAnQygNoA6GUBtAHUygNoA6mQAtQHUyQBqA6iTAdQGUCcDqA2gTgZQG0CdDKA2gDoZQG0AdTKA2gDqZAC1AdTJAGoDqJMB1AZQJwOoDaBOBlAbQJ0MoDaAOhlAbQB1MoDaAOpkALUB1MkAagOokwHUBlAnA6gNoE4GUBtAnQygNoA6GUBtAHUygNoA6mQAtQHUyc4YoG/dMeqh7casGTf6qVrvw/qx1d7bzRNGPrjHPwCgTnamAN1WsLNuznhzaFhF/ePFxiwef7MHtH7M2uO/mu4fAVAnO1OAris0Zstws3ySMWvvN+bDVQUe0NU/NuZYlX8EQJ3sTAHqVTut0CyY4f0wHZn6aLAHdNHkR0dP/sT7YHtZWVnl/nQd3Lrs7VgqzeKYVwAaKldAX8+8FSUrst7ZjWnx7N/nX2VGoKVjn69vAnpb6sMU0Pkjyo+8NsH7oCiZTG5If9GZybYMoMFyBTTeitNfVa3/PgPQhpkTtnjvlj/i3drfl/pECujSKcZU39pgzJ7y8vLd+9J1cNfqslj6UxbH/A6goXIFdHHmrXjvg6x39v/S4tln70JmALrmnvrUu5rhOxqeaOSeAlp1+6a64gf9I7gP6mRnyn3Q2QMKCgpGeI+L7r3r6cYfuimgpvTOEZMq/SMA6mRnCtDMAdTJAGoDqJMB1AZQJwOoDaBOBlAbQJ0MoDaAOhlAbQB1MoDaAOpkALUB1MkAagOokwHUBlAnA6gNoE4GUBtAnQygNoA6GUBtAHUygNoA6mQAtQHUyQBqA6iTAdQGUCcDqA2gTgZQG0CdDKA2gDoZQG0AdTKA2gDqZAC1AdTJAGoDqJMB1AZQJwOoDaBOBlAbQJ0MoDaAOhlAbQB1MoDaAOpkALUB1MkAagOokwHUBlAnA6gNoE4GUBtAnQygNoA6GUBtAHUygNoA6mQAtQHUyQBqA6iTAdQGUCcDqA2gTgZQG0CdDKA2gDoZQG2OAF2bpLaoPPNWAPRU7Xp9dqbmFr+U8Zisei2eYeYWz4lnoJjm83Jx5mMW7Mq8FQBVVm2q4hkopvkcMHvjGSim+Rw08YwDUGUAlQOoNoDKARSgoQAqBlBlAJUDqDaAygEUoKEAKgZQZQCVA6g2gMoBFKChACoGUGUAlQOoNoDKARSgoQAqBlBlAJUDqDaAygEUoKEAKgZQZQCVA6g2gMoBFKChACoGUGUAlQOoNoDKARSgoQAqBlBlAJUDqDaAygEUoKEAKgZQZQCVA6g2gMoBFKChACp22gD9ZP2OWGa6+3A8w2x3bD5b11fGM1BM89m8Poszg2XRnkOxDBMX0PR9kNyQu8EVrUhube8phPpDcl97TyFUcbKuvadwigDabgE0mwDabgE0m3IItLJob+4GV7St6EB7TyHU34qOtvcUQv216Hh7T+EU5RAoUesDKDld7oCuGTf6qdqcjR6ht+4Y9dD25vm4MK3V3zcOzWfzhJEP7nFoPuFyBvTQsIr6x4tzNXqEthXsrJsz3s7HhWlV3j7GuDOf+jFrj/9qujvzaVHOgC6fZMza+3M1eoTWFRqzZbidjwPTOjZhyRjjznxW/9ibUpU782lRzoAumOH98BqZq9GjVTut0M7HgWnNXLh1jHFnPosmPzp68ifuzKdFuQV6W65Gj1Tp2Ofr7Xzaf1olUxuagbown/kjyo+8NsGd+bQodzfxj3i3rvflavQINcycsMU0z6f9p/XkqDGjBo6pcmY+S6cYU31rgzPzaVHOgNYM39HwhAv3ttfcU59658/HiWmlfoI6M5+q2zfVFT/oznxalLunmVbfe9fTLjxfMXtAQUHBiOb5uDCtFFB35lN654hJlQ7NJxxP1JPTAZScDqDkdAAlpwMoOR1AyekASk4HUHI6gGZuYCKROOerzzUIh1QkZmU52KmPbPnZLK7yDAmgmRt4/cqVv/9RXtrfUPedYvY+UJrNSGmPbPnZTFd55gTQzA28NfV2boe/pvm6xy7Lsj4y01WeOQE0c01a6i+ZaA4m1hvzcWK3yV/59YFmw43ndez9vkkmEv1NfompHNrtgqE7jckvvaXzv85vuqh/iNkxuMsFdx3xjxww0PvKjC61NXdf3PEm/79me5cPXu7EVdohJvT2PvHovzf89kvnXDy9rVegHQNo5pq0mJuHBoBeOWGZubrPkv/tdY2p6/NYvQfs+DXXliy77qrjJv8/Xl0/JP9w42X8Q+q/8J8r5pz/3/6Rvzmnxpg+d5tBX1taOuiCqsYDU0ADlztxlXaIVWftMA2XT6n41P1lUxMr2n4R2iuAZs7XMqZvAKj3o+341I+MealL0w13fsmyvM3GbO7gQZtszF8SH6cuYg9ZeO5eY345yj/yQH6x2X7Wqg15u4ypPX9h4+D5ocsFrtIO0XDJLPP+WVuWJDaZhtc3tscytE8AzZz9cTYsAPQN7xN1K569vVsz0MLuqYMunWXyFxuz24fmHzIl2TRS05HmlhHm6csb5ic6eXV4svErKaCBy524yuZrGd/fjO9nanp+dnjRobb87ts5gGbOv0P4+YebgH6QAlpizKHeV0z8/QstgHZ/pvFrPjR7yKM9mkbygb7a+Vjvn5lXOlekOnETX9ISaOoqm6+lLG/XhS96nyz74eVdft/GK9COATRz/kPqvI89oO8Y8ysf6KJzjxrzbDPQkrytxmzJWxqEZg+Z33G/dzvdzwI9eG6Rd39gXaLcmH0jyxuv45RAU1fZfC0Nl4w8p9ose8z718BB7bMQ7RFAM5d6UnLxT/Ie8Gic/421//NlH2hJYvqKx88/e7W5YfSO1IOkq69b1vQg6QQ0e0jtZd8snX/xPf6Rxgz+3A3eYF+/8q2Sb3ZvOkFTS6DNV9l8LWZ8YqgxSxPTV8+75KfttRRtH0Azl/q1Tv61half6/zh8o7Xr/eBNkzs+rlbN/bsZ+Z0HZj6xM4h3bo1Ps10AlrzIZsHdP7Huw/ZI838RJH31X2j/6nTgE1N19ESaPNVNg9hViS8O6lm+ufPvuh+t846ltMAetr04gUunh4x1wH0NGnfm/92Bt2wnwigp0nrOw860t5zaI8ASk4HUHI6gJLTAZScDqDkdAAlpwMoOR1Ayen+H1XWwivltVqdAAAAAElFTkSuQmCC" /><!-- --></p>
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</div>
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</div>
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<div id="more-visualisations" class="section level1">
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<h1>More Visualisations</h1>
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<p>Other visualisations come to mind, and are left as an exercise to the reader. If you implemented some, feel free to add them to this vignette via a <a href="https://github.com/sensebox/opensensmapr/pulls">Pull Request</a>.</p>
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<ul>
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<li>growth by phenomenon</li>
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<li>growth by location -> (interactive) map</li>
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<li>set inactive rate in relation to total box count</li>
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<li>filter timespans with big dips in growth rate, and extrapolate the amount of senseBoxes that could be on the platform today, assuming there were no production issues ;)</li>
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</ul>
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</div>
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