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<div id="header">
<h1 class="title">Introduction to gsplot.</h1>
<h4 class="author"><em>Jordan Read, Laura DeCicco, Jordan Walker, Phethala Thongsavanh, Lindsay Carr</em></h4>
<h4 class="date"><em>25 August, 2015</em></h4>
</div>
<div id="TOC">
<ul>
<li><a href="#overview"><span class="toc-section-number">0.1</span> Overview</a></li>
<li><a href="#data-manipulation"><span class="toc-section-number">0.2</span> Data manipulation</a></li>
<li><a href="#simple-timeseries"><span class="toc-section-number">0.3</span> Simple timeseries</a></li>
<li><a href="#simple-timeseries-using-a-log-scale"><span class="toc-section-number">0.4</span> Simple timeseries using a log scale</a></li>
<li><a href="#multiple-plots-in-one-figure"><span class="toc-section-number">0.5</span> Multiple plots in one figure</a></li>
<li><a href="#compare-timeseries-of-different-units"><span class="toc-section-number">0.6</span> Compare timeseries of different units</a></li>
<li><a href="#adding-to-the-plot-retroactively"><span class="toc-section-number">0.7</span> Adding to the plot retroactively</a></li>
</ul>
</div>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(gsplot)
MaumeeDV <-<span class="st"> </span>MaumeeDV
<span class="kw">plot.new</span>()
demoPlot <-<span class="st"> </span><span class="kw">gsplot</span>() %>%
<span class="st"> </span><span class="kw">points</span>(<span class="dt">y=</span><span class="kw">c</span>(<span class="dv">3</span>,<span class="dv">1</span>,<span class="dv">2</span>), <span class="dt">x=</span><span class="dv">1</span>:<span class="dv">3</span>, <span class="dt">xlim=</span><span class="kw">c</span>(<span class="dv">0</span>,<span class="ot">NA</span>),<span class="dt">ylim=</span><span class="kw">c</span>(<span class="dv">0</span>,<span class="ot">NA</span>),
<span class="dt">col=</span><span class="st">"blue"</span>, <span class="dt">pch=</span><span class="dv">18</span>, <span class="dt">legend.name=</span><span class="st">"Points"</span>, <span class="dt">xlab=</span><span class="st">"Index"</span>) %>%
<span class="st"> </span><span class="kw">lines</span>(<span class="kw">c</span>(<span class="dv">3</span>,<span class="dv">4</span>,<span class="dv">3</span>), <span class="kw">c</span>(<span class="dv">2</span>,<span class="dv">4</span>,<span class="dv">6</span>), <span class="dt">legend.name=</span><span class="st">"Lines"</span>, <span class="dt">ylab=</span><span class="st">"Data"</span>) %>%
<span class="st"> </span><span class="kw">abline</span>(<span class="dt">b=</span><span class="dv">1</span>, <span class="dt">a=</span><span class="dv">0</span>, <span class="dt">legend.name=</span><span class="st">"1:1"</span>) %>%
<span class="st"> </span><span class="kw">legend</span>(<span class="dt">location=</span><span class="st">"topleft"</span>,<span class="dt">title=</span><span class="st">"Awesome!"</span>) %>%
<span class="st"> </span><span class="kw">grid</span>() %>%
<span class="st"> </span><span class="kw">error_bar</span>(<span class="dt">x=</span><span class="dv">1</span>:<span class="dv">3</span>, <span class="dt">y=</span><span class="kw">c</span>(<span class="dv">3</span>,<span class="dv">1</span>,<span class="dv">2</span>), <span class="dt">y.high=</span><span class="kw">c</span>(<span class="fl">0.5</span>,<span class="fl">0.25</span>,<span class="dv">1</span>), <span class="dt">y.low=</span><span class="fl">0.1</span>) %>%
<span class="st"> </span><span class="kw">error_bar</span>(<span class="dt">x=</span><span class="dv">1</span>:<span class="dv">3</span>, <span class="dt">y=</span><span class="kw">c</span>(<span class="dv">3</span>,<span class="dv">1</span>,<span class="dv">2</span>), <span class="dt">x.low=</span>.<span class="dv">2</span>, <span class="dt">x.high=</span>.<span class="dv">2</span>, <span class="dt">col=</span><span class="st">"red"</span>,<span class="dt">lwd=</span><span class="dv">3</span>) %>%
<span class="st"> </span><span class="kw">callouts</span>(<span class="dt">x=</span><span class="dv">1</span>, <span class="dt">y=</span><span class="fl">2.8</span>, <span class="dt">lwd=</span><span class="dv">2</span>, <span class="dt">angle=</span><span class="dv">250</span>, <span class="dt">labels=</span><span class="st">"Weird data"</span>) %>%
<span class="st"> </span><span class="kw">title</span>(<span class="st">"Graphing Fun"</span>)
demoPlot</code></pre>
<div class="figure">
<img src="data:image/png;base64,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" alt="Demo workflow" />
<p class="caption">Demo workflow</p>
</div>
<div id="overview" class="section level2">
<h2><span class="header-section-number">0.1</span> Overview</h2>
<p><code>gsplot</code> uses similar plotting graphics to R base graphics, but allows users to execute them in a more intuitive manner. Additionally, as the complexity of the plot features increase, <code>gpslot</code> code is simplistic compared to that of base graphics. <code>gsplot</code> also includes features not present in base graphics that are useful when working with USGS data, such as <code>callouts</code> (combines <code>segments</code> and <code>text</code> into a single call), <code>error_bar</code> (allows an error to be given as <code>y.high</code>, <code>y.low</code>, <code>x.high</code>, and <code>x.low</code> and automatically builds an error bar), and the argument <code>legend.name</code> (an argument within <code>points</code>, <code>lines</code>, etc. which does not require colors, linetypes, and other par information to be redefined within the <code>legend</code> call).</p>
</div>
<div id="data-manipulation" class="section level2">
<h2><span class="header-section-number">0.2</span> Data manipulation</h2>
<p>Data from Maumee River will be used to showcase the workflow and features that <code>gsplot</code> offers. First, the data is manipulated to extract the timeseries as four separate variables - dates (formatted as yyyy-mm-dd), flow (discharge in cubic feet per second), pH, and Wtemp (water temperature in degrees Celcius). Additionally, the USGS site IDs for the sampling stations are identified.</p>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(gsplot)
MaumeeDV <-<span class="st"> </span>MaumeeDV
sites <-<span class="st"> </span><span class="kw">unique</span>(MaumeeDV$site_no)
dates <-<span class="st"> </span><span class="kw">sapply</span>(sites, function(x) MaumeeDV$Date[<span class="kw">which</span>(MaumeeDV$site_no==x)], <span class="dt">USE.NAMES=</span><span class="ot">TRUE</span>)
flow <-<span class="st"> </span><span class="kw">sapply</span>(sites, function(x) MaumeeDV$Flow[<span class="kw">which</span>(MaumeeDV$site_no==x)], <span class="dt">USE.NAMES=</span><span class="ot">TRUE</span>)
pH <-<span class="st"> </span><span class="kw">sapply</span>(sites, function(x) MaumeeDV$pH_Median[<span class="kw">which</span>(MaumeeDV$site_no==x)], <span class="dt">USE.NAMES=</span><span class="ot">TRUE</span>)
Wtemp <-<span class="st"> </span><span class="kw">sapply</span>(sites, function(x) MaumeeDV$Wtemp[<span class="kw">which</span>(MaumeeDV$site_no==x)], <span class="dt">USE.NAMES=</span><span class="ot">TRUE</span>)</code></pre>
</div>
<div id="simple-timeseries" class="section level2">
<h2><span class="header-section-number">0.3</span> Simple timeseries</h2>
<p>First, <code>gsplot</code> is used to create a simple timeseries graph for discharge, and a grid is added to help with data readability.</p>
<pre class="sourceCode r"><code class="sourceCode r">site <-<span class="st"> '04193500'</span>
demoPlot <-<span class="st"> </span><span class="kw">gsplot</span>() %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">lines</span>(dates[[site]], flow[[site]], <span class="dt">col=</span><span class="st">"royalblue"</span>) %>%
<span class="st"> </span><span class="kw">title</span>(<span class="dt">main=</span><span class="kw">paste</span>(<span class="st">"Site"</span>, site), <span class="dt">ylab=</span><span class="st">"Flow, ft3/s"</span>) %>%
<span class="st"> </span><span class="kw">grid</span>()
demoPlot</code></pre>
<div class="figure">
<img src="data:image/png;base64,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" alt="Fig. 1 Simple flow timeseries using gsplot." />
<p class="caption">Fig. 1 Simple flow timeseries using <code>gsplot</code>.</p>
</div>
</div>
<div id="simple-timeseries-using-a-log-scale" class="section level2">
<h2><span class="header-section-number">0.4</span> Simple timeseries using a log scale</h2>
<p>This data may be better represented using a log scale due to the range of flow values. Thus, the yaxis is easily turned into a logged scale by inserting the code <code>log='y'</code>. To make sure that the grid lines correspond to the logged axis, the code <code>equilogs=FALSE</code> is used to let gridlines be drawn at unequal distances from each other.</p>
<pre class="sourceCode r"><code class="sourceCode r">site <-<span class="st"> '04193500'</span>
demoPlot <-<span class="st"> </span><span class="kw">gsplot</span>() %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">lines</span>(dates[[site]], flow[[site]], <span class="dt">col=</span><span class="st">"royalblue"</span>, <span class="dt">log=</span><span class="st">'y'</span>) %>%
<span class="st"> </span><span class="kw">title</span>(<span class="dt">main=</span><span class="kw">paste</span>(<span class="st">"Site"</span>, site), <span class="dt">ylab=</span><span class="st">"Flow, ft3/s"</span>) %>%
<span class="st"> </span><span class="kw">grid</span>(<span class="dt">equilogs=</span><span class="ot">FALSE</span>)
demoPlot</code></pre>
<div class="figure">
<img 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" alt="Fig. 2 Simple flow timeseries with a logged y-axis using gsplot." />
<p class="caption">Fig. 2 Simple flow timeseries with a logged y-axis using <code>gsplot</code>.</p>
</div>
</div>
<div id="multiple-plots-in-one-figure" class="section level2">
<h2><span class="header-section-number">0.5</span> Multiple plots in one figure</h2>
<p>What if you wanted to see if there was any relationship between the pH and water temperature? Consider the following three graphs: pH vs water temperature, pH timeseries, water temperature timeseries. To view these three plots at one time, use <code>layout</code> to “append” the three different plots.</p>
<pre class="sourceCode r"><code class="sourceCode r">site <-<span class="st"> '04193490'</span>
plot1 <-<span class="st"> </span><span class="kw">gsplot</span>() %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">points</span>(Wtemp[[site]], pH[[site]], <span class="dt">col=</span><span class="st">"black"</span>)%>%
<span class="st"> </span><span class="kw">title</span>(<span class="dt">main=</span><span class="kw">paste</span>(<span class="st">"Site"</span>, site), <span class="dt">xlab=</span><span class="st">"Water Temperature (deg C)"</span>, <span class="dt">ylab=</span><span class="st">"pH"</span>)
plot2 <-<span class="st"> </span><span class="kw">gsplot</span>() %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">lines</span>(dates[[site]], pH[[site]], <span class="dt">col=</span><span class="st">"seagreen"</span>)%>%
<span class="st"> </span><span class="kw">title</span>(<span class="dt">main=</span><span class="st">""</span>, <span class="dt">xlab=</span><span class="st">"time"</span>, <span class="dt">ylab=</span><span class="st">"pH"</span>)
plot3 <-<span class="st"> </span><span class="kw">gsplot</span>() %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">lines</span>(dates[[site]], Wtemp[[site]], <span class="dt">col=</span><span class="st">"orangered"</span>)%>%
<span class="st"> </span><span class="kw">title</span>(<span class="dt">main=</span><span class="st">""</span>, <span class="dt">xlab=</span><span class="st">"time"</span>, <span class="dt">ylab=</span><span class="st">"Water Temperature (deg C)"</span>)
<span class="kw">layout</span>(<span class="kw">matrix</span>(<span class="kw">c</span>(<span class="dv">1</span>,<span class="dv">2</span>,<span class="dv">3</span>), <span class="dt">byrow=</span><span class="ot">TRUE</span>, <span class="dt">nrow=</span><span class="dv">3</span>))
plot1
plot2
plot3</code></pre>
<div class="figure">
<img 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" alt="Fig. 3 (a) pH vs water temperature, (b) pH timeseries, (c) water temperature timeseries." />
<p class="caption">Fig. 3 (a) pH vs water temperature, (b) pH timeseries, (c) water temperature timeseries.</p>
</div>
</div>
<div id="compare-timeseries-of-different-units" class="section level2">
<h2><span class="header-section-number">0.6</span> Compare timeseries of different units</h2>
<p>For timeseries, it is sometimes helpful to plot data on the same graph to make comparisons; however, it becomes difficult when the data differ in units. Thus, a second y-axis can easily be added to plot the second timeseries. In this example, we can compare the water temperature and pH timeseries to identify relationships over time. pH is plot using the secondary y-axis by specifying <code>side=4</code>.</p>
<pre class="sourceCode r"><code class="sourceCode r">site <-<span class="st"> '04193490'</span>
demoPlot <-<span class="st"> </span><span class="kw">gsplot</span>(<span class="dt">mar=</span><span class="kw">c</span>(<span class="fl">7.1</span>, <span class="fl">4.1</span>, <span class="fl">4.1</span>, <span class="fl">2.1</span>)) %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">lines</span>(dates[[site]], Wtemp[[site]], <span class="dt">col=</span><span class="st">"orangered"</span>,
<span class="dt">legend.name=</span><span class="st">"Water Temperature"</span>, <span class="dt">ylab=</span><span class="st">'Water Temperature (deg C)'</span>) %>%
<span class="st"> </span><span class="kw">lines</span>(dates[[site]], pH[[site]], <span class="dt">col=</span><span class="st">"seagreen"</span>, <span class="dt">side=</span><span class="dv">4</span>,
<span class="dt">legend.name=</span><span class="st">"pH"</span>, <span class="dt">ylab=</span><span class="st">'pH (pH Units)'</span>) %>%
<span class="st"> </span><span class="kw">title</span>(<span class="dt">main=</span><span class="kw">paste</span>(<span class="st">"Site"</span>, site), <span class="dt">xlab=</span><span class="st">'time'</span>) %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">legend</span>(<span class="dt">location=</span><span class="st">"below"</span>)
demoPlot</code></pre>
<div class="figure">
<img src="data:image/png;base64,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" alt="Fig. 4 Water temperature timeseries on primary y-axis with pH timeseries on secondary y-axis." />
<p class="caption">Fig. 4 Water temperature timeseries on primary y-axis with pH timeseries on secondary y-axis.</p>
</div>
</div>
<div id="adding-to-the-plot-retroactively" class="section level2">
<h2><span class="header-section-number">0.7</span> Adding to the plot retroactively</h2>
<p>Oftentimes, data is plot and observations regarding missing or abnormal data are made afterwards. <code>gsplot</code> makes it easy to add to any plot retroactively by using the plot object (<code>demoPlot</code> in this example) in the call for the plot feature.</p>
<pre class="sourceCode r"><code class="sourceCode r"><span class="co"># initially plot the data</span>
site <-<span class="st"> '04193490'</span>
demoPlot <-<span class="st"> </span><span class="kw">gsplot</span>() %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">lines</span>(dates[[site]], Wtemp[[site]], <span class="dt">col=</span><span class="st">"orangered"</span>) %>%<span class="st"> </span>
<span class="st"> </span><span class="kw">title</span>(<span class="dt">main=</span><span class="kw">paste</span>(<span class="st">"Site"</span>, site), <span class="dt">xlab=</span><span class="st">'time'</span>, <span class="dt">ylab=</span><span class="st">'Water Temperature (deg C)'</span>)
demoPlot</code></pre>
<div class="figure">
<img src="data:image/png;base64,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" alt="Fig. 5 Initial plot of water temperature timeseries." />
<p class="caption">Fig. 5 Initial plot of water temperature timeseries.</p>
</div>
<pre class="sourceCode r"><code class="sourceCode r"><span class="co"># notice the missing data from ~ 1991 through ~2011 and add a callout</span>
demoPlot <-<span class="st"> </span><span class="kw">callouts</span>(demoPlot, <span class="dt">x=</span><span class="kw">as.Date</span>(<span class="st">"2000-01-01"</span>), <span class="dt">y=</span><span class="dv">10</span>,<span class="dt">labels=</span><span class="st">"Missing Data"</span>)
demoPlot</code></pre>
<div class="figure">
<img src="data:image/png;base64,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" alt="Fig. 6 Plot of water temperature timeseries with Missing Data callout retroactively added." />
<p class="caption">Fig. 6 Plot of water temperature timeseries with ‘Missing Data’ callout retroactively added.</p>
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