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<h1 class="title toc-ignore">Introduction to the dataRetrieval package</h1>
<h4 class="author"><em>Laura A. DeCicco and Robert M. Hirsch</em></h4>
<h4 class="date"><em>19 May, 2017</em></h4>


<div id="TOC">
<ul>
<li><a href="#usgs-web-retrievals"><span class="toc-section-number">1</span> USGS Web Retrievals</a><ul>
<li><a href="#site-information"><span class="toc-section-number">1.1</span> Site Information</a><ul>
<li><a href="#readnwissite"><span class="toc-section-number">1.1.1</span> readNWISsite</a></li>
<li><a href="#whatnwisdata"><span class="toc-section-number">1.1.2</span> whatNWISdata</a></li>
</ul></li>
<li><a href="#parameter-information"><span class="toc-section-number">1.2</span> Parameter Information</a></li>
<li><a href="#daily-data"><span class="toc-section-number">1.3</span> Daily Data</a></li>
<li><a href="#unit-data"><span class="toc-section-number">1.4</span> Unit Data</a></li>
<li><a href="#water-quality-data"><span class="toc-section-number">1.5</span> Water Quality Data</a></li>
<li><a href="#groundwater-level-data"><span class="toc-section-number">1.6</span> Groundwater Level Data</a></li>
<li><a href="#peak-flow-data"><span class="toc-section-number">1.7</span> Peak Flow Data</a></li>
<li><a href="#rating-curve-data"><span class="toc-section-number">1.8</span> Rating Curve Data</a></li>
<li><a href="#surface-water-measurement-data"><span class="toc-section-number">1.9</span> Surface-Water Measurement Data</a></li>
<li><a href="#water-use-data"><span class="toc-section-number">1.10</span> Water Use Data</a></li>
<li><a href="#statistics-data"><span class="toc-section-number">1.11</span> Statistics Data</a></li>
</ul></li>
<li><a href="#water-quality-portal-web-retrievals"><span class="toc-section-number">2</span> Water Quality Portal Web Retrievals</a></li>
<li><a href="#generalized-retrievals"><span class="toc-section-number">3</span> Generalized Retrievals</a><ul>
<li><a href="#nwis"><span class="toc-section-number">3.1</span> NWIS</a><ul>
<li><a href="#sites-whatnwissites"><span class="toc-section-number">3.1.1</span> Sites: whatNWISsites</a></li>
<li><a href="#data-readnwisdata"><span class="toc-section-number">3.1.2</span> Data: readNWISdata</a></li>
</ul></li>
<li><a href="#wqp"><span class="toc-section-number">3.2</span> WQP</a><ul>
<li><a href="#sites-whatwqpsites"><span class="toc-section-number">3.2.1</span> Sites: whatWQPsites</a></li>
<li><a href="#data-readwqpdata"><span class="toc-section-number">3.2.2</span> Data: readWQPdata</a></li>
<li><a href="#availability-whatwqpdata"><span class="toc-section-number">3.2.3</span> Availability: whatWQPdata</a></li>
<li><a href="#samples-whatwqpsamples"><span class="toc-section-number">3.2.4</span> Samples: whatWQPsamples</a></li>
<li><a href="#metrics-whatwqpmetrics"><span class="toc-section-number">3.2.5</span> Metrics: whatWQPmetrics</a></li>
</ul></li>
</ul></li>
<li><a href="#embedded-metadata"><span class="toc-section-number">4</span> Embedded Metadata</a></li>
<li><a href="#getting-started-in-r"><span class="toc-section-number">5</span> Getting Started in R</a><ul>
<li><a href="#new-to-r"><span class="toc-section-number">5.1</span> New to R?</a></li>
<li><a href="#r-user-installing-dataretrieval"><span class="toc-section-number">5.2</span> R User: Installing dataRetrieval</a></li>
</ul></li>
<li><a href="#creating-tables-in-microsoft-software-from-r"><span class="toc-section-number">6</span> Creating Tables in Microsoft® Software from R</a></li>
<li><a href="#disclaimer"><span class="toc-section-number">7</span> Disclaimer</a></li>
</ul>
</div>

<p>The <code>dataRetrieval</code> package was created to simplify the process of loading hydrologic data into the R environment. It is designed to retrieve the major data types of U.S. Geological Survey (USGS) hydrologic data that are available on the Web, as well as data from the Water Quality Portal (WQP), which currently houses water quality data from the Environmental Protection Agency (EPA), U.S. Department of Agriculture (USDA), and USGS. Direct USGS data is obtained from a service called the National Water Information System (NWIS).</p>
<p>For information on getting started in R and installing the package, see <a href="#getting-started-in-r">Getting Started</a>. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.</p>
<p>A quick workflow for USGS <code>dataRetrieval</code> functions:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(dataRetrieval)
<span class="co"># Choptank River near Greensboro, MD</span>
siteNumber &lt;-<span class="st"> &quot;01491000&quot;</span>
ChoptankInfo &lt;-<span class="st"> </span><span class="kw">readNWISsite</span>(siteNumber)
parameterCd &lt;-<span class="st"> &quot;00060&quot;</span>

<span class="co"># Raw daily data:</span>
rawDailyData &lt;-<span class="st"> </span><span class="kw">readNWISdv</span>(siteNumber, parameterCd, <span class="st">&quot;1980-01-01&quot;</span>, 
    <span class="st">&quot;2010-01-01&quot;</span>)

<span class="co"># Sample data Nitrate:</span>
parameterCd &lt;-<span class="st"> &quot;00618&quot;</span>
qwData &lt;-<span class="st"> </span><span class="kw">readNWISqw</span>(siteNumber, parameterCd, <span class="st">&quot;1980-01-01&quot;</span>, <span class="st">&quot;2010-01-01&quot;</span>)

pCode &lt;-<span class="st"> </span><span class="kw">readNWISpCode</span>(parameterCd)</code></pre></div>
<p>USGS data are made available through the National Water Information System (NWIS).</p>
<p>Table 1 describes the functions available in the <code>dataRetrieval</code> package.</p>
<table class='gmisc_table' style='border-collapse: collapse; margin-top: 1em; margin-bottom: 1em;' >
<thead>
<tr><td colspan='3' style='text-align: left;'>
Table 1: dataRetrieval functions</td></tr>
<tr>
<th style='border-bottom: 1px solid grey; border-top: 2px solid grey; text-align: center;'>Function.Name</th>
<th style='border-bottom: 1px solid grey; border-top: 2px solid grey; text-align: center;'>Arguments</th>
<th style='border-bottom: 1px solid grey; border-top: 2px solid grey; text-align: center;'>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>readNWISdata</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>...</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>NWIS data using user-specified queries</td>
</tr>
<tr style='background-color: #f7f7f7;'>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'></td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>service</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'></td>
</tr>
<tr>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>readNWISdv</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>siteNumber</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>NWIS daily data</td>
</tr>
<tr style='background-color: #f7f7f7;'>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'></td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>parameterCd</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'></td>
</tr>
<tr>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'></td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>startDate</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'></td>
</tr>
<tr style='background-color: #f7f7f7;'>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'></td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>endDate</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'></td>
</tr>
<tr>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'></td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>statCd</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'></td>
</tr>
<tr style='background-color: #f7f7f7;'>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>readNWISqw</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>siteNumber</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>NWIS water quality data</td>
</tr>
<tr>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'></td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>parameterCd</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'></td>
</tr>
<tr style='background-color: #f7f7f7;'>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'></td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>startDate</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'></td>
</tr>
<tr>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'></td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>endDate</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'></td>
</tr>
<tr style='background-color: #f7f7f7;'>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'></td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>expanded</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'></td>
</tr>
<tr>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>readNWISuv</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>siteNumber</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>NWIS instantaneous value data</td>
</tr>
<tr style='background-color: #f7f7f7;'>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'></td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>parameterCd</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'></td>
</tr>
<tr>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'></td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>startDate</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'></td>
</tr>
<tr style='background-color: #f7f7f7;'>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'></td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>endDate</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'></td>
</tr>
<tr>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>readNWISrating</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>siteNumber</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>NWIS rating table for active streamgage</td>
</tr>
<tr style='background-color: #f7f7f7;'>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'></td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>type</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'></td>
</tr>
<tr>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>readNWISmeas</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>siteNumber</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>NWIS surface-water measurements</td>
</tr>
<tr style='background-color: #f7f7f7;'>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'></td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>startDate</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'></td>
</tr>
<tr>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'></td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>endDate</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'></td>
</tr>
<tr style='background-color: #f7f7f7;'>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>readNWISpeak</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>siteNumber</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>NWIS peak flow data</td>
</tr>
<tr>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'></td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>startDate</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'></td>
</tr>
<tr style='background-color: #f7f7f7;'>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'></td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>endDate</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'></td>
</tr>
<tr>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>readNWISgwl</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>siteNumber</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>NWIS groundwater level measurements</td>
</tr>
<tr style='background-color: #f7f7f7;'>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'></td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>startDate</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'></td>
</tr>
<tr>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'></td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>endDate</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'></td>
</tr>
<tr style='background-color: #f7f7f7;'>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>readNWISuse</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>stateCd</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>NWIS water use</td>
</tr>
<tr>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'></td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>countyCd</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'></td>
</tr>
<tr style='background-color: #f7f7f7;'>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'></td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>years</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'></td>
</tr>
<tr>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'></td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>categories</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'></td>
</tr>
<tr style='background-color: #f7f7f7;'>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>readNWISstat</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>siteNumbers</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>NWIS statistical service</td>
</tr>
<tr>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'></td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>parameterCd</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'></td>
</tr>
<tr style='background-color: #f7f7f7;'>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'></td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>startDate</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'></td>
</tr>
<tr>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'></td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>endDate</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'></td>
</tr>
<tr style='background-color: #f7f7f7;'>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'></td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>statReportType</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'></td>
</tr>
<tr>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'></td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>statType</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'></td>
</tr>
<tr style='background-color: #f7f7f7;'>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>readNWISpCode</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>parameterCd</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>NWIS parameter code information</td>
</tr>
<tr>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>readNWISsite</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>siteNumber</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>NWIS site information</td>
</tr>
<tr style='background-color: #f7f7f7;'>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>whatNWISsites</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>...</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>NWIS site search using user-specified queries</td>
</tr>
<tr>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>whatNWISdata</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>siteNumber</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>NWIS data availability, including period of record and count</td>
</tr>
<tr style='background-color: #f7f7f7;'>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'></td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>service</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'></td>
</tr>
<tr>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>readWQPdata</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>...</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>WQP data using user-specified queries</td>
</tr>
<tr style='background-color: #f7f7f7;'>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>readWQPqw</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>siteNumber</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>WQP data</td>
</tr>
<tr>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'></td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>parameterCd</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'></td>
</tr>
<tr style='background-color: #f7f7f7;'>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'></td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>startDate</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'></td>
</tr>
<tr>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'></td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>endDate</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'></td>
</tr>
<tr style='background-color: #f7f7f7;'>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; border-bottom: 2px solid grey; text-align: left;'>whatWQPsites</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; border-bottom: 2px solid grey; text-align: left;'>...</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; border-bottom: 2px solid grey; text-align: left;'>WQP site search using user-specified queries</td>
</tr>
</tbody>
</table>
<div id="usgs-web-retrievals" class="section level1">
<h1><span class="header-section-number">1</span> USGS Web Retrievals</h1>
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<p>In this section, examples of National Water Information System (NWIS) retrievals show how to get raw data into R. This data includes <a href="#site-information">site information</a>, measured <a href="#parameter-information">parameter information</a>, historical <a href="#daily-data">daily values</a>, <a href="#unit-data">unit values</a> (which include real-time data but can also include other sensor data stored at regular time intervals), <a href="#water-quality-data">water quality data</a>, <a href="#groundwater-level-data">groundwater level data</a>, <a href="#peak-flow-data">peak flow data</a>, <a href="#rating-curve-data">rating curve data</a>, <a href="#surface-water-measurement-data">surface-water measurement data</a>, <a href="#water-use-data">water use data</a>, and <a href="#statistics-data">statistics data</a>. The section <a href="#embedded-metadata">Embedded Metadata</a> shows instructions for getting metadata that is attached to each returned data frame.</p>
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<p>The USGS organizes hydrologic data in a standard structure. Streamgages are located throughout the United States, and each streamgage has a unique ID (referred in this document and throughout the <code>dataRetrieval</code> package as <code>siteNumber</code>). Often (but not always), these ID’s are 8 digits for surface-water sites and 15 digits for groundwater sites. The first step to finding data is discovering this <code>siteNumber</code>. There are many ways to do this, one is the <a href="https://maps.waterdata.usgs.gov/mapper/index.html">National Water Information System: Mapper</a>.</p>
<p>Once the <code>siteNumber</code> is known, the next required input for USGS data retrievals is the “parameter code”. This is a 5-digit code that specifies the measured parameter being requested. For example, parameter code 00631 represents “Nitrate plus nitrite, water, filtered, milligrams per liter as nitrogen”, with units of “mg/l as N”.</p>
<p>Not every station will measure all parameters. A short list of commonly measured parameters is shown in Table 2.</p>
<table class='gmisc_table' style='border-collapse: collapse; margin-top: 1em; margin-bottom: 1em;' >
<thead>
<tr><td colspan='2' style='text-align: left;'>
Table 2: Common USGS Parameter Codes</td></tr>
<tr>
<th style='border-bottom: 1px solid grey; border-top: 2px solid grey; text-align: center;'>pCode</th>
<th style='border-bottom: 1px solid grey; border-top: 2px solid grey; text-align: center;'>shortName</th>
</tr>
</thead>
<tbody>
<tr>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: center;'>00060</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: center;'>Discharge [ft<sup>3</sup>/s]</td>
</tr>
<tr style='background-color: #f7f7f7;'>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: center;'>00065</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: center;'>Gage height [ft]</td>
</tr>
<tr>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: center;'>00010</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: center;'>Temperature [C]</td>
</tr>
<tr style='background-color: #f7f7f7;'>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: center;'>00045</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: center;'>Precipitation [in]</td>
</tr>
<tr>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; border-bottom: 2px solid grey; text-align: center;'>00400</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; border-bottom: 2px solid grey; text-align: center;'>pH</td>
</tr>
</tbody>
</table>
<p>Two output columns that may not be obvious are “srsname” and “casrn”. Srsname stands for “Substance Registry Services”. More information on the srs name can be found <a href="http://ofmpub.epa.gov/sor_internet/registry/substreg/home/overview/home.do">here</a>.</p>
<p>Casrn stands for “Chemical Abstracts Service (CAS) Registry Number”. More information on CAS can be found <a href="http://www.cas.org/content/chemical-substances/faqs">here</a>.</p>
<p>For unit values data (sensor data measured at regular time intervals such as 15 minutes or hourly), knowing the parameter code and <code>siteNumber</code> is enough to make a request for data. For most variables that are measured on a continuous basis, the USGS also stores the historical data as daily values. These daily values are statistical summaries of the continuous data, e.g. maximum, minimum, mean, or median. The different statistics are specified by a 5-digit statistics code.</p>
<p>Some common codes are shown in Table 3.</p>
<table class='gmisc_table' style='border-collapse: collapse; margin-top: 1em; margin-bottom: 1em;' >
<thead>
<tr><td colspan='2' style='text-align: left;'>
Table 3: Commonly used USGS Stat Codes</td></tr>
<tr>
<th style='border-bottom: 1px solid grey; border-top: 2px solid grey; text-align: center;'>StatCode</th>
<th style='border-bottom: 1px solid grey; border-top: 2px solid grey; text-align: center;'>shortName</th>
</tr>
</thead>
<tbody>
<tr>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: center;'>00001</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: center;'>Maximum</td>
</tr>
<tr style='background-color: #f7f7f7;'>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: center;'>00002</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: center;'>Minimum</td>
</tr>
<tr>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: center;'>00003</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: center;'>Mean</td>
</tr>
<tr style='background-color: #f7f7f7;'>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; border-bottom: 2px solid grey; text-align: center;'>00008</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; border-bottom: 2px solid grey; text-align: center;'>Median</td>
</tr>
</tbody>
</table>
<p>Examples for using these site numbers, parameter codes, and statistic codes will be presented in subsequent sections.</p>
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<p>There are occasions where NWIS values are not reported as numbers, instead there might be text describing a certain event such as “Ice”. Any value that cannot be converted to a number will be reported as NA in this package (not including remark code columns), unless the user sets an argument <code>convertType</code> to <code>FALSE</code>. In that case, the data is returned as a data frame that is entirely character columns.</p>
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<div id="site-information" class="section level2">
<h2><span class="header-section-number">1.1</span> Site Information</h2>
<div id="readnwissite" class="section level3">
<h3><span class="header-section-number">1.1.1</span> readNWISsite</h3>
<p>Use the <code>readNWISsite</code> function to obtain all of the information available for a particular USGS site (or sites) such as full station name, drainage area, latitude, and longitude. <code>readNWISsite</code> can also access information about multiple sites with a vector input.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">siteNumbers &lt;-<span class="st"> </span><span class="kw">c</span>(<span class="st">&quot;01491000&quot;</span>, <span class="st">&quot;01645000&quot;</span>)
siteINFO &lt;-<span class="st"> </span><span class="kw">readNWISsite</span>(siteNumbers)</code></pre></div>
<p>Site information is obtained from: <a href="https://waterservices.usgs.gov/rest/Site-Test-Tool.html" class="uri">https://waterservices.usgs.gov/rest/Site-Test-Tool.html</a></p>
<p>Information on the returned data can be found with the <code>comment</code> function as described in the <a href="#embedded-metadata">Metadata</a> section.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">comment</span>(siteINFO)</code></pre></div>
</div>
<div id="whatnwisdata" class="section level3">
<h3><span class="header-section-number">1.1.2</span> whatNWISdata</h3>
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<p>To discover what data is available for a particular USGS site, including measured parameters, period of record, and number of samples (count), use the <code>whatNWISdata</code> function. It is possible to limit the retrieval information to a subset of services. The possible choices for services are: “dv” (daily values), “uv”, or “iv” (unit values), “qw” (water-quality), “sv” (sites visits), “pk” (peak measurements), “gw” (groundwater levels), “ad” (sites included in USGS Annual Water Data Reports External Link), “aw” (sites monitored by the USGS Active Groundwater Level Network External Link), and “id” (historical instantaneous values).</p>
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<p>In the following example, we limit the retrieved data to only daily data. The default for “service” is <code>all</code>, which returns all of the available data for that site. Likewise, there are arguments for parameter code (<code>parameterCd</code>) and statistic code (<code>statCd</code>) to filter the results. The default for both is to return all possible values (<code>all</code>). The returned <code>count_nu</code> for “uv” data is the count of days with returned data, not the actual count of returned values.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Continuing from the previous example: This pulls out just</span>
<span class="co"># the daily, mean data:</span>

dailyDataAvailable &lt;-<span class="st"> </span><span class="kw">whatNWISdata</span>(siteNumbers, <span class="dt">service =</span> <span class="st">&quot;dv&quot;</span>, 
    <span class="dt">statCd =</span> <span class="st">&quot;00003&quot;</span>)</code></pre></div>
<table class='gmisc_table' style='border-collapse: collapse; margin-top: 1em; margin-bottom: 1em;' >
<thead>
<tr><td colspan='6' style='text-align: left;'>
Table 4: Reformatted version of output from the whatNWISdata function for the Choptank River near Greensboro, MD, and from Seneca Creek at Dawsonville, MD from the daily values service [Some columns deleted for space considerations]</td></tr>
<tr>
<th style='border-bottom: 1px solid grey; border-top: 2px solid grey; text-align: center;'>siteNumber</th>
<th style='border-bottom: 1px solid grey; border-top: 2px solid grey; text-align: center;'>srsname</th>
<th style='border-bottom: 1px solid grey; border-top: 2px solid grey; text-align: center;'>startDate</th>
<th style='border-bottom: 1px solid grey; border-top: 2px solid grey; text-align: center;'>endDate</th>
<th style='border-bottom: 1px solid grey; border-top: 2px solid grey; text-align: center;'>count</th>
<th style='border-bottom: 1px solid grey; border-top: 2px solid grey; text-align: center;'>units</th>
</tr>
</thead>
<tbody>
<tr>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: center;'>01491000</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: center;'>Temperature, water</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: center;'>2010-10-01</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: center;'>2012-05-09</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: center;'>529</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: center;'>deg C</td>
</tr>
<tr style='background-color: #f7f7f7;'>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: center;'>01491000</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: center;'>Stream flow, mean daily</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: center;'>1948-01-01</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: center;'>2017-05-17</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: center;'>25340</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: center;'>ft<sup>3</sup>/s</td>
</tr>
<tr>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: center;'>01645000</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: center;'>Stream flow, mean daily</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: center;'>1930-09-26</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: center;'>2017-05-17</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: center;'>31646</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: center;'>ft<sup>3</sup>/s</td>
</tr>
<tr style='background-color: #f7f7f7;'>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: center;'>01491000</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: center;'>Specific conductance</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: center;'>2010-10-01</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: center;'>2012-05-09</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: center;'>527</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: center;'>uS/cm @25C</td>
</tr>
<tr>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: center;'>01491000</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: center;'>Suspended sediment concentration (SSC)</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: center;'>1980-10-01</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: center;'>1991-09-30</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: center;'>4017</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: center;'>mg/l</td>
</tr>
<tr style='background-color: #f7f7f7;'>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; border-bottom: 2px solid grey; text-align: center;'>01491000</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; border-bottom: 2px solid grey; text-align: center;'>Suspended sediment discharge</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; border-bottom: 2px solid grey; text-align: center;'>1980-10-01</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; border-bottom: 2px solid grey; text-align: center;'>1991-09-30</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; border-bottom: 2px solid grey; text-align: center;'>4017</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; border-bottom: 2px solid grey; text-align: center;'>tons/day</td>
</tr>
</tbody>
</table>
<p>See <a href="#creating-tables-in-microsoft-software-from-r">Creating Tables</a> for instructions on converting an R data frame to a table in Microsoft® software Excel or Word to display a data availability table similar to Table 4. Excel, Microsoft, PowerPoint, Windows, and Word are registered trademarks of Microsoft Corporation in the United States and other countries.</p>
</div>
</div>
<div id="parameter-information" class="section level2">
<h2><span class="header-section-number">1.2</span> Parameter Information</h2>
<p>To obtain all of the available information concerning a measured parameter (or multiple parameters), use the <code>readNWISpCode</code> function:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Using defaults:</span>
parameterCd &lt;-<span class="st"> &quot;00618&quot;</span>
parameterINFO &lt;-<span class="st"> </span><span class="kw">readNWISpCode</span>(parameterCd)</code></pre></div>
</div>
<div id="daily-data" class="section level2">
<h2><span class="header-section-number">1.3</span> Daily Data</h2>
<p>To obtain daily records of USGS data, use the <code>readNWISdv</code> function. The arguments for this function are <code>siteNumber</code>, <code>parameterCd</code>, <code>startDate</code>, <code>endDate</code>, and <code>statCd</code> (defaults to “00003”). If you want to use the default values, you do not need to list them in the function call. Daily data is pulled from <a href="https://waterservices.usgs.gov/rest/DV-Test-Tool.html" class="uri">https://waterservices.usgs.gov/rest/DV-Test-Tool.html</a>.</p>
<p>The dates (start and end) must be in the format “YYYY-MM-DD” (note: the user must include the quotes). Setting the start date to “” (no space) will prompt the program to ask for the earliest date, and setting the end date to “” (no space) will prompt for the latest available date.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Choptank River near Greensboro, MD:</span>
siteNumber &lt;-<span class="st"> &quot;01491000&quot;</span>
parameterCd &lt;-<span class="st"> &quot;00060&quot;</span>  <span class="co"># Discharge</span>
startDate &lt;-<span class="st"> &quot;2009-10-01&quot;</span>
endDate &lt;-<span class="st"> &quot;2012-09-30&quot;</span>

discharge &lt;-<span class="st"> </span><span class="kw">readNWISdv</span>(siteNumber, parameterCd, startDate, endDate)</code></pre></div>
<p>The column “datetime” in the returned data frame is automatically imported as a variable of class “Date” in R. Each requested parameter has a value and remark code column. The names of these columns depend on the requested parameter and stat code combinations. USGS daily value qualification codes are often “A” (approved for publication) or “P” (provisional data subject to revision).</p>
<p>Another example would be a request for mean and maximum daily temperature and discharge in early 2012:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">siteNumber &lt;-<span class="st"> &quot;01491000&quot;</span>
parameterCd &lt;-<span class="st"> </span><span class="kw">c</span>(<span class="st">&quot;00010&quot;</span>, <span class="st">&quot;00060&quot;</span>)  <span class="co"># Temperature and discharge</span>
statCd &lt;-<span class="st"> </span><span class="kw">c</span>(<span class="st">&quot;00001&quot;</span>, <span class="st">&quot;00003&quot;</span>)  <span class="co"># Mean and maximum</span>
startDate &lt;-<span class="st"> &quot;2012-01-01&quot;</span>
endDate &lt;-<span class="st"> &quot;2012-05-01&quot;</span>

temperatureAndFlow &lt;-<span class="st"> </span><span class="kw">readNWISdv</span>(siteNumber, parameterCd, startDate, 
    endDate, <span class="dt">statCd =</span> statCd)</code></pre></div>
<p>The column names can be shortened and simplified using the <code>renameNWISColumns</code> function. This is not necessary, but may streamline subsequent data analysis and presentation. Site information, daily statistic information, and measured parameter information is attached to the data frame as attributes. This is discussed further in the <a href="#embedded-metadata">metadata</a> section.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">names</span>(temperatureAndFlow)</code></pre></div>
<pre><code>## [1] &quot;agency_cd&quot;        &quot;site_no&quot;          &quot;Date&quot;            
## [4] &quot;X_00010_00001_cd&quot; &quot;X_00010_00001&quot;    &quot;X_00010_00003_cd&quot;
## [7] &quot;X_00010_00003&quot;    &quot;X_00060_00003_cd&quot; &quot;X_00060_00003&quot;</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">temperatureAndFlow &lt;-<span class="st"> </span><span class="kw">renameNWISColumns</span>(temperatureAndFlow)
<span class="kw">names</span>(temperatureAndFlow)</code></pre></div>
<pre><code>## [1] &quot;agency_cd&quot;    &quot;site_no&quot;      &quot;Date&quot;        
## [4] &quot;Wtemp_Max_cd&quot; &quot;Wtemp_Max&quot;    &quot;Wtemp_cd&quot;    
## [7] &quot;Wtemp&quot;        &quot;Flow_cd&quot;      &quot;Flow&quot;</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Information about the data frame attributes:</span>
<span class="kw">names</span>(<span class="kw">attributes</span>(temperatureAndFlow))</code></pre></div>
<pre><code>## [1] &quot;names&quot;         &quot;row.names&quot;     &quot;url&quot;          
## [4] &quot;siteInfo&quot;      &quot;variableInfo&quot;  &quot;disclaimer&quot;   
## [7] &quot;statisticInfo&quot; &quot;queryTime&quot;     &quot;class&quot;</code></pre>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">statInfo &lt;-<span class="st"> </span><span class="kw">attr</span>(temperatureAndFlow, <span class="st">&quot;statisticInfo&quot;</span>)
variableInfo &lt;-<span class="st"> </span><span class="kw">attr</span>(temperatureAndFlow, <span class="st">&quot;variableInfo&quot;</span>)
siteInfo &lt;-<span class="st"> </span><span class="kw">attr</span>(temperatureAndFlow, <span class="st">&quot;siteInfo&quot;</span>)</code></pre></div>
<p>An example of plotting the above data:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">variableInfo &lt;-<span class="st"> </span><span class="kw">attr</span>(temperatureAndFlow, <span class="st">&quot;variableInfo&quot;</span>)
siteInfo &lt;-<span class="st"> </span><span class="kw">attr</span>(temperatureAndFlow, <span class="st">&quot;siteInfo&quot;</span>)

<span class="kw">par</span>(<span class="dt">mar =</span> <span class="kw">c</span>(<span class="dv">5</span>, <span class="dv">5</span>, <span class="dv">5</span>, <span class="dv">5</span>))  <span class="co">#sets the size of the plot window</span>

<span class="kw">plot</span>(temperatureAndFlow$Date, temperatureAndFlow$Wtemp_Max, <span class="dt">ylab =</span> variableInfo$parameter_desc[<span class="dv">1</span>], 
    <span class="dt">xlab =</span> <span class="st">&quot;&quot;</span>)
<span class="kw">par</span>(<span class="dt">new =</span> <span class="ot">TRUE</span>)
<span class="kw">plot</span>(temperatureAndFlow$Date, temperatureAndFlow$Flow, <span class="dt">col =</span> <span class="st">&quot;red&quot;</span>, 
    <span class="dt">type =</span> <span class="st">&quot;l&quot;</span>, <span class="dt">xaxt =</span> <span class="st">&quot;n&quot;</span>, <span class="dt">yaxt =</span> <span class="st">&quot;n&quot;</span>, <span class="dt">xlab =</span> <span class="st">&quot;&quot;</span>, <span class="dt">ylab =</span> <span class="st">&quot;&quot;</span>, 
    <span class="dt">axes =</span> <span class="ot">FALSE</span>)
<span class="kw">axis</span>(<span class="dv">4</span>, <span class="dt">col =</span> <span class="st">&quot;red&quot;</span>, <span class="dt">col.axis =</span> <span class="st">&quot;red&quot;</span>)
<span class="kw">mtext</span>(variableInfo$parameter_desc[<span class="dv">2</span>], <span class="dt">side =</span> <span class="dv">4</span>, <span class="dt">line =</span> <span class="dv">3</span>, <span class="dt">col =</span> <span class="st">&quot;red&quot;</span>)
<span class="kw">title</span>(<span class="kw">paste</span>(siteInfo$station_nm, <span class="st">&quot;2012&quot;</span>))
<span class="kw">legend</span>(<span class="st">&quot;topleft&quot;</span>, variableInfo$param_units, <span class="dt">col =</span> <span class="kw">c</span>(<span class="st">&quot;black&quot;</span>, 
    <span class="st">&quot;red&quot;</span>), <span class="dt">lty =</span> <span class="kw">c</span>(<span class="ot">NA</span>, <span class="dv">1</span>), <span class="dt">pch =</span> <span class="kw">c</span>(<span class="dv">1</span>, <span class="ot">NA</span>))</code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
</div>
<div id="unit-data" class="section level2">
<h2><span class="header-section-number">1.4</span> Unit Data</h2>
<p>Any data collected at regular time intervals (such as 15-minute or hourly) are known as “unit values”. Many of these are delivered on a real time basis and very recent data (even less than an hour old in many cases) are available through the function <code>readNWISuv</code>. Some of these unit values are available for many years, and some are only available for a recent time period such as 120 days. Here is an example of a retrieval of such data.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">parameterCd &lt;-<span class="st"> &quot;00060&quot;</span>  <span class="co"># Discharge</span>
startDate &lt;-<span class="st"> &quot;2012-05-12&quot;</span>
endDate &lt;-<span class="st"> &quot;2012-05-13&quot;</span>
dischargeUnit &lt;-<span class="st"> </span><span class="kw">readNWISuv</span>(siteNumber, parameterCd, startDate, 
    endDate)
dischargeUnit &lt;-<span class="st"> </span><span class="kw">renameNWISColumns</span>(dischargeUnit)</code></pre></div>
<p>The retrieval produces a data frame that contains 96 rows (one for every 15 minute period in the day). They include all data collected from the <code>startDate</code> through the <code>endDate</code> (starting and ending with midnight locally-collected time). The dateTime column is converted to UTC (Coordinated Universal Time), so midnight EST will be 5 hours earlier in the dateTime column (the previous day, at 7pm).</p>
<p>To override the UTC timezone, specify a valid timezone in the tz argument. Default is “”, which will keep the dateTime column in UTC. Other valid timezones are:</p>
<pre><code>America/New_York
America/Chicago
America/Denver
America/Los_Angeles
America/Anchorage
America/Honolulu
America/Jamaica
America/Managua
America/Phoenix
America/Metlakatla</code></pre>
<p>Data are retrieved from <a href="https://waterservices.usgs.gov/rest/IV-Test-Tool.html" class="uri">https://waterservices.usgs.gov/rest/IV-Test-Tool.html</a>. There are occasions where NWIS values are not reported as numbers, instead a common example is “Ice”. Any value that cannot be converted to a number will be reported as NA in this package. Site information and measured parameter information is attached to the data frame as attributes. This is discussed further in <a href="#embedded-metadata">metadata</a> section.</p>
</div>
<div id="water-quality-data" class="section level2">
<h2><span class="header-section-number">1.5</span> Water Quality Data</h2>
<p>To get USGS water quality data from water samples collected at the streamgage or other monitoring site (as distinct from unit values collected through some type of automatic monitor) we can use the function <code>readNWISqw</code>, with the input arguments: <code>siteNumber</code>, <code>parameterCd</code>, <code>startDate</code>, and <code>endDate</code>. Additionally, the argument <code>expanded</code> is a logical input that allows the user to choose between a simple return of datetimes/qualifier/values (<code>expanded=FALSE</code>), or a more complete and verbose output (<code>expanded=TRUE</code>). <code>expanded = TRUE</code> includes such columns as remark codes, value qualifying text, and detection level for each parameter code. There also includes an argument “reshape”, that converts the expanded dataset to a “wide” format (each requested parameter code gets individual columns). The defaults are <code>expanded=TRUE</code>, and <code>reshape=FALSE</code>.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Dissolved Nitrate parameter codes:</span>
parameterCd &lt;-<span class="st"> </span><span class="kw">c</span>(<span class="st">&quot;00618&quot;</span>, <span class="st">&quot;71851&quot;</span>)
startDate &lt;-<span class="st"> &quot;1985-10-01&quot;</span>
endDate &lt;-<span class="st"> &quot;2012-09-30&quot;</span>

dfLong &lt;-<span class="st"> </span><span class="kw">readNWISqw</span>(siteNumber, parameterCd, startDate, endDate)

<span class="co"># Or the wide return:</span>
dfWide &lt;-<span class="st"> </span><span class="kw">readNWISqw</span>(siteNumber, parameterCd, startDate, endDate, 
    <span class="dt">reshape =</span> <span class="ot">TRUE</span>)</code></pre></div>
<p>Site information and measured parameter information is attached to the data frame as attributes. This is discussed further in the <a href="#embedded-metadata">metadata</a> section. Additional metadata, such as information about the column names can be found by using the <code>comment</code> function, also described in the <a href="#embedded-metadata">metadata</a> section.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">comment</span>(dfLong)</code></pre></div>
</div>
<div id="groundwater-level-data" class="section level2">
<h2><span class="header-section-number">1.6</span> Groundwater Level Data</h2>
<p>Groundwater level measurements can be obtained with the <code>readNWISgwl</code> function. Information on the returned data can be found with the <code>comment</code> function, and attached attributes as described in the <a href="#embedded-metadata">metadata</a> section.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">siteNumber &lt;-<span class="st"> &quot;434400121275801&quot;</span>
groundWater &lt;-<span class="st"> </span><span class="kw">readNWISgwl</span>(siteNumber)</code></pre></div>
</div>
<div id="peak-flow-data" class="section level2">
<h2><span class="header-section-number">1.7</span> Peak Flow Data</h2>
<p>Peak flow data are instantaneous discharge or stage data that record the maximum values of these variables during a flood event. They include the annual peak flood event but can also include records of other peaks that are lower than the annual maximum. Peak discharge measurements can be obtained with the <code>readNWISpeak</code> function. Information on the returned data can be found with the <code>comment</code> function and attached attributes as described in the <a href="#embedded-metadata">metadata</a> section.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">siteNumber &lt;-<span class="st"> &quot;01594440&quot;</span>
peakData &lt;-<span class="st"> </span><span class="kw">readNWISpeak</span>(siteNumber)</code></pre></div>
</div>
<div id="rating-curve-data" class="section level2">
<h2><span class="header-section-number">1.8</span> Rating Curve Data</h2>
<p>Rating curves are the calibration curves that are used to convert measurements of stage to discharge. Because of changing hydrologic conditions these rating curves change over time. Information on the returned data can be found with the <code>comment</code> function and attached attributes as described in the <a href="#embedded-metadata">metadata</a> section.</p>
<p>Rating curves can be obtained with the <code>readNWISrating</code> function.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">ratingData &lt;-<span class="st"> </span><span class="kw">readNWISrating</span>(siteNumber, <span class="st">&quot;base&quot;</span>)
<span class="kw">attr</span>(ratingData, <span class="st">&quot;RATING&quot;</span>)</code></pre></div>
</div>
<div id="surface-water-measurement-data" class="section level2">
<h2><span class="header-section-number">1.9</span> Surface-Water Measurement Data</h2>
<p>These data are the discrete measurements of discharge that are made for the purpose of developing or revising the rating curve. Information on the returned data can be found with the <code>comment</code> function and attached attributes as described in the <a href="#embedded-metadata">metadata</a> section.</p>
<p>Surface-water measurement data can be obtained with the <code>readNWISmeas</code> function.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">surfaceData &lt;-<span class="st"> </span><span class="kw">readNWISmeas</span>(siteNumber)</code></pre></div>
</div>
<div id="water-use-data" class="section level2">
<h2><span class="header-section-number">1.10</span> Water Use Data</h2>
<p>Retrieves water use data from USGS Water Use Data for the Nation. See <a href="https://waterdata.usgs.gov/nwis/wu" class="uri">https://waterdata.usgs.gov/nwis/wu</a> for more information. All available use categories for the supplied arguments are retrieved.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">allegheny &lt;-<span class="st"> </span><span class="kw">readNWISuse</span>(<span class="dt">stateCd =</span> <span class="st">&quot;Pennsylvania&quot;</span>, <span class="dt">countyCd =</span> <span class="st">&quot;Allegheny&quot;</span>)


national &lt;-<span class="st"> </span><span class="kw">readNWISuse</span>(<span class="dt">stateCd =</span> <span class="ot">NULL</span>, <span class="dt">countyCd =</span> <span class="ot">NULL</span>, <span class="dt">transform =</span> <span class="ot">TRUE</span>)</code></pre></div>
</div>
<div id="statistics-data" class="section level2">
<h2><span class="header-section-number">1.11</span> Statistics Data</h2>
<p>Retrieves site statistics from the USGS Statistics Web Service beta.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">discharge_stats &lt;-<span class="st"> </span><span class="kw">readNWISstat</span>(<span class="dt">siteNumbers =</span> <span class="kw">c</span>(<span class="st">&quot;02319394&quot;</span>), 
    <span class="dt">parameterCd =</span> <span class="kw">c</span>(<span class="st">&quot;00060&quot;</span>), <span class="dt">statReportType =</span> <span class="st">&quot;annual&quot;</span>)</code></pre></div>
</div>
</div>
<div id="water-quality-portal-web-retrievals" class="section level1">
<h1><span class="header-section-number">2</span> Water Quality Portal Web Retrievals</h1>
<p>There are additional water quality data sets available from the <a href="https://www.waterqualitydata.us/">Water Quality Data Portal</a>. These data sets can be housed in either the STORET database (data from EPA), NWIS database (data from USGS), STEWARDS database (data from USDA), and additional databases are slated to be included in the future. Because only USGS uses parameter codes, a “characteristic name” must be supplied. The <code>readWQPqw</code> function can take either a USGS parameter code, or a more general characteristic name in the parameterCd input argument. The Water Quality Data Portal includes data discovery tools and information on characteristic names. The following example retrieves specific conductance from a DNR site in Wisconsin.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">specificCond &lt;-<span class="st"> </span><span class="kw">readWQPqw</span>(<span class="st">&quot;WIDNR_WQX-10032762&quot;</span>, <span class="st">&quot;Specific conductance&quot;</span>, 
    <span class="st">&quot;2011-05-01&quot;</span>, <span class="st">&quot;2011-09-30&quot;</span>)</code></pre></div>
<p>A tool for finding NWIS characteristic names can be found <a href="https://www.waterqualitydata.us/public_srsnames/">here</a></p>
</div>
<div id="generalized-retrievals" class="section level1">
<h1><span class="header-section-number">3</span> Generalized Retrievals</h1>
<p>The previous examples all took specific input arguments: <code>siteNumber</code>, <code>parameterCd</code> (or characteristic name), <code>startDate</code>, <code>endDate</code>, etc. However, the Web services that supply the data can accept a wide variety of additional arguments.</p>
<div id="nwis" class="section level2">
<h2><span class="header-section-number">3.1</span> NWIS</h2>
<div id="sites-whatnwissites" class="section level3">
<h3><span class="header-section-number">3.1.1</span> Sites: whatNWISsites</h3>
<p>The function <code>whatNWISsites</code> can be used to discover NWIS sites based on any query that the NWIS Site Service offers. This is done by using the <code>...</code> argument, which allows the user to use any arbitrary input argument. We can then use the service <a href="https://waterservices.usgs.gov/rest/Site-Test-Tool.html">here</a> to discover many options for searching for NWIS sites. For example, you may want to search for sites in a lat/lon bounding box, or only sites tidal streams, or sites with water quality samples, sites above a certain altitude, etc. The results of this site query generate a URL. For example, the tool provided a search within a specified bounding box, for sites that have daily discharge (parameter code = 00060) and temperature (parameter code = 00010). The generated URL is:</p>
<p><a href="https://waterservices.usgs.gov/nwis/site/?format=rdb&amp;bBox=-83.0,36.5,-81.0,38.5&amp;parameterCd=00010,00060&amp;hasDataTypeCd=dv" class="uri">https://waterservices.usgs.gov/nwis/site/?format=rdb&amp;bBox=-83.0,36.5,-81.0,38.5&amp;parameterCd=00010,00060&amp;hasDataTypeCd=dv</a></p>
<p>The following <code>dataRetrieval</code> code can be used to get those sites:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">sites &lt;-<span class="st"> </span><span class="kw">whatNWISsites</span>(<span class="dt">bBox =</span> <span class="kw">c</span>(-<span class="dv">83</span>, <span class="fl">36.5</span>, -<span class="dv">81</span>, <span class="fl">38.5</span>), <span class="dt">parameterCd =</span> <span class="kw">c</span>(<span class="st">&quot;00010&quot;</span>, 
    <span class="st">&quot;00060&quot;</span>), <span class="dt">hasDataTypeCd =</span> <span class="st">&quot;dv&quot;</span>)</code></pre></div>
</div>
<div id="data-readnwisdata" class="section level3">
<h3><span class="header-section-number">3.1.2</span> Data: readNWISdata</h3>
<p>For NWIS data, the function <code>readNWISdata</code> can be used. The argument listed in the R help file is <code>...</code> and <code>service</code> (only for data requests). Table 5 describes the services are available.</p>
<table class='gmisc_table' style='border-collapse: collapse; margin-top: 1em; margin-bottom: 1em;' >
<thead>
<tr><td colspan='3' style='text-align: left;'>
Table 5: NWIS general data calls</td></tr>
<tr>
<th style='border-bottom: 1px solid grey; border-top: 2px solid grey; text-align: center;'>Service</th>
<th style='border-bottom: 1px solid grey; border-top: 2px solid grey; text-align: center;'>Description</th>
<th style='border-bottom: 1px solid grey; border-top: 2px solid grey; text-align: center;'>URL</th>
</tr>
</thead>
<tbody>
<tr>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>dv</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>Daily</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'><a href='https://waterservices.usgs.gov/rest/DV-Test-Tool.html' target='_blank'>https://waterservices.usgs.gov/rest/DV-Test-Tool.html<a></td>
</tr>
<tr style='background-color: #f7f7f7;'>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>iv</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>Instantaneous</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'><a href='https://waterservices.usgs.gov/rest/IV-Test-Tool.html' target='_blank'>https://waterservices.usgs.gov/rest/IV-Test-Tool.html<a></td>
</tr>
<tr>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>gwlevels</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>Groundwater Levels</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'><a href='https://waterservices.usgs.gov/rest/GW-Levels-Test-Tool.html' target='_blank'>https://waterservices.usgs.gov/rest/GW-Levels-Test-Tool.html<a></td>
</tr>
<tr style='background-color: #f7f7f7;'>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>qwdata</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>Water Quality</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'><a href='https://nwis.waterdata.usgs.gov/nwis/qwdata' target='_blank'>https://nwis.waterdata.usgs.gov/nwis/qwdata<a></td>
</tr>
<tr>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>measurements</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'>Surface Water Measurements</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; text-align: left;'><a href='https://waterdata.usgs.gov/nwis/measurements/' target='_blank'>https://waterdata.usgs.gov/nwis/measurements/<a></td>
</tr>
<tr style='background-color: #f7f7f7;'>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>peak</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'>Peak Flow</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; background-color: #f7f7f7; text-align: left;'><a href='https://nwis.waterdata.usgs.gov/usa/nwis/peak/' target='_blank'>https://nwis.waterdata.usgs.gov/usa/nwis/peak/<a></td>
</tr>
<tr>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; border-bottom: 2px solid grey; text-align: left;'>stat</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; border-bottom: 2px solid grey; text-align: left;'>Statistics Service</td>
<td style='padding-bottom: 0.0em; padding-right: 0.5em; padding-top: 0.0em; border-bottom: 2px solid grey; text-align: left;'><a href='https://waterservices.usgs.gov/rest/Statistics-Service-Test-Tool.html' target='_blank'>https://waterservices.usgs.gov/rest/Statistics-Service-Test-Tool.html<a></td>
</tr>
</tbody>
</table>
<p>The <code>...</code> argument allows the user to create their own queries based on the instructions found in the web links above. The links provide instructions on how to create a URL to request data. Perhaps you want sites only in Wisconsin, with a drainage area less than 50 mi<sup>2</sup>, and the most recent daily discharge data. That request would be done as follows:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">dischargeWI &lt;-<span class="st"> </span><span class="kw">readNWISdata</span>(<span class="dt">service =</span> <span class="st">&quot;dv&quot;</span>, <span class="dt">stateCd =</span> <span class="st">&quot;WI&quot;</span>, <span class="dt">parameterCd =</span> <span class="st">&quot;00060&quot;</span>, 
    <span class="dt">drainAreaMin =</span> <span class="st">&quot;50&quot;</span>, <span class="dt">statCd =</span> <span class="st">&quot;00003&quot;</span>)

siteInfo &lt;-<span class="st"> </span><span class="kw">attr</span>(dischargeWI, <span class="st">&quot;siteInfo&quot;</span>)</code></pre></div>
</div>
</div>
<div id="wqp" class="section level2">
<h2><span class="header-section-number">3.2</span> WQP</h2>
<p>Just as with NWIS, the Water Quality Portal (WQP) offers a variety of ways to search for sites and request data. The possible Web service arguments for WQP site searches is found <a href="https://www.waterqualitydata.us/webservices_documentation">here</a>.</p>
<div id="sites-whatwqpsites" class="section level3">
<h3><span class="header-section-number">3.2.1</span> Sites: whatWQPsites</h3>
<p>To discover available sites in the WQP in New Jersey that have measured Chloride, use the function <code>whatWQPsites</code>.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">sitesNJ &lt;-<span class="st"> </span><span class="kw">whatWQPsites</span>(<span class="dt">statecode =</span> <span class="st">&quot;US:34&quot;</span>, <span class="dt">characteristicName =</span> <span class="st">&quot;Chloride&quot;</span>)</code></pre></div>
</div>
<div id="data-readwqpdata" class="section level3">
<h3><span class="header-section-number">3.2.2</span> Data: readWQPdata</h3>
<p>To get data from the WQP using generalized Web service calls, use the function <code>readWQPdata</code>. For example, to get all the pH data in Wisconsin:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">dataPH &lt;-<span class="st"> </span><span class="kw">readWQPdata</span>(<span class="dt">statecode =</span> <span class="st">&quot;US:55&quot;</span>, <span class="dt">characteristicName =</span> <span class="st">&quot;pH&quot;</span>)</code></pre></div>
</div>
<div id="availability-whatwqpdata" class="section level3">
<h3><span class="header-section-number">3.2.3</span> Availability: whatWQPdata</h3>
<p>The function <code>whatWQPdata</code> returns a data frame with information on the amount of data collected at a site. For example:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">type &lt;-<span class="st"> &quot;Stream&quot;</span>
sites &lt;-<span class="st"> </span><span class="kw">whatWQPdata</span>(<span class="dt">countycode =</span> <span class="st">&quot;US:55:025&quot;</span>, <span class="dt">siteType =</span> type)</code></pre></div>
<p>This returns a data frame with all of the sites that were measured in streams in Dane County, WI. Also, in that table, there is a measure of <code>activityCount</code> (how often the site was sampled), and <code>resultCount</code> (how many individual results are available).</p>
</div>
<div id="samples-whatwqpsamples" class="section level3">
<h3><span class="header-section-number">3.2.4</span> Samples: whatWQPsamples</h3>
<p>The function <code>whatWQPsamples</code> returns information on the individual samples collected at a site. For example:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">site &lt;-<span class="st"> </span><span class="kw">whatWQPsamples</span>(<span class="dt">siteid =</span> <span class="st">&quot;USGS-01594440&quot;</span>)</code></pre></div>
<p>This returns one row for each instance that a sample was collect.</p>
</div>
<div id="metrics-whatwqpmetrics" class="section level3">
<h3><span class="header-section-number">3.2.5</span> Metrics: whatWQPmetrics</h3>
<p>The function <code>whatWQPmetrics</code> provides metric information. This is only currently available for STORET data:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">type &lt;-<span class="st"> &quot;Stream&quot;</span>
sites &lt;-<span class="st"> </span><span class="kw">whatWQPmetrics</span>(<span class="dt">countycode =</span> <span class="st">&quot;US:55:025&quot;</span>, <span class="dt">siteType =</span> type)</code></pre></div>
</div>
</div>
</div>
<div id="embedded-metadata" class="section level1">
<h1><span class="header-section-number">4</span> Embedded Metadata</h1>
<p>All data frames returned from the Web services have some form of associated metadata. This information is included as attributes to the data frame. All data frames will have a <code>url</code> (returning a character of the url used to obtain the data), <code>siteInfo</code> (returning a data frame with information on sites), and <code>queryTime</code> (returning a POSIXct datetime) attributes. For example, the url and query time used to obtain the data can be found as follows:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">attr</span>(dischargeWI, <span class="st">&quot;url&quot;</span>)

<span class="kw">attr</span>(dischargeWI, <span class="st">&quot;queryTime&quot;</span>)

siteInfo &lt;-<span class="st"> </span><span class="kw">attr</span>(dischargeWI, <span class="st">&quot;siteInfo&quot;</span>)</code></pre></div>
<p>Depending on the format that the data was obtained (RDB, WaterML1, etc), there will be additional information embedded in the data frame as attributes. To discover the available attributes:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">names</span>(<span class="kw">attributes</span>(dischargeWI))</code></pre></div>
<p>For data obtained from <code>readNWISuv</code>, <code>readNWISdv</code>, <code>readNWISgwl</code> there are two attributes that are particularly useful: <code>siteInfo</code> and <code>variableInfo</code>.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">siteInfo &lt;-<span class="st"> </span><span class="kw">attr</span>(dischargeWI, <span class="st">&quot;siteInfo&quot;</span>)

variableInfo &lt;-<span class="st"> </span><span class="kw">attr</span>(dischargeWI, <span class="st">&quot;variableInfo&quot;</span>)</code></pre></div>
<p>Data obtained from <code>readNWISpeak</code>, <code>readNWISmeas</code>, and <code>readNWISrating</code>, the <code>comment</code> attribute is useful.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">comment</span>(peakData)

<span class="co"># Which is equivalent to:</span>
<span class="kw">attr</span>(peakData, <span class="st">&quot;comment&quot;</span>)</code></pre></div>
</div>
<div id="getting-started-in-r" class="section level1">
<h1><span class="header-section-number">5</span> Getting Started in R</h1>
<p>This section describes the options for downloading and installing the <code>dataRetrieval</code> package.</p>
<div id="new-to-r" class="section level2">
<h2><span class="header-section-number">5.1</span> New to R?</h2>
<p>If you are new to R, you will need to first install the latest version of R, which can be found [here] (www.R-project.org).</p>
<p>At any time, you can get information about any function in R by typing a question mark before the functions name. This will open a file (in RStudio, in the Help window) that describes the function, the required arguments, and provides working examples. This will open a help file similar to the image below. To see the raw code for a particular code, type the name of the function, without parentheses.</p>
<pre><code>?readNWISpCode</code></pre>
<div class="figure">
<img src="data:image/png;base64,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" alt="A simple R help file" />
<p class="caption">A simple R help file</p>
</div>
<p>Additionally, many R packages have vignette files attached (such as this paper). To view the vignette:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">vignette</span>(dataRetrieval)</code></pre></div>
</div>
<div id="r-user-installing-dataretrieval" class="section level2">
<h2><span class="header-section-number">5.2</span> R User: Installing dataRetrieval</h2>
<p>The following command installs <code>dataRetrieval</code> and subsequent required packages:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">install.packages</span>(<span class="st">&quot;dataRetrieval&quot;</span>)</code></pre></div>
<p>After installing the package, you need to open the library each time you re-start R. This is done with the simple command:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(dataRetrieval)</code></pre></div>
</div>
</div>
<div id="creating-tables-in-microsoft-software-from-r" class="section level1">
<h1><span class="header-section-number">6</span> Creating Tables in Microsoft® Software from R</h1>
<p>There are a few steps that are required in order to create a table in Microsoft® software (Excel, Word, PowerPoint, etc.) from an R data frame. There are certainly a variety of good methods, one of which is detailed here. The example we will step through here will be to create a table in Microsoft Excel based on the data frame tableData:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">availableData &lt;-<span class="st"> </span><span class="kw">whatNWISdata</span>(siteNumber, <span class="st">&quot;dv&quot;</span>)
dailyData &lt;-<span class="st"> </span>availableData[<span class="st">&quot;00003&quot;</span> ==<span class="st"> </span>availableData$stat_cd, 
    ]

tableData &lt;-<span class="st"> </span><span class="kw">with</span>(dailyData, <span class="kw">data.frame</span>(<span class="dt">shortName =</span> srsname, 
    <span class="dt">Start =</span> begin_date, <span class="dt">End =</span> end_date, <span class="dt">Count =</span> count_nu, <span class="dt">Units =</span> parameter_units))</code></pre></div>
<p>First, save the data frame as a tab delimited file (you don’t want to use comma delimited because there are commas in some of the data elements):</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">write.table</span>(tableData, <span class="dt">file =</span> <span class="st">&quot;tableData.tsv&quot;</span>, <span class="dt">sep =</span> <span class="st">&quot;</span><span class="ch">\t</span><span class="st">&quot;</span>, <span class="dt">row.names =</span> <span class="ot">FALSE</span>, 
    <span class="dt">quote =</span> <span class="ot">FALSE</span>)</code></pre></div>
<p>This will save a file in your working directory called tableData.tsv. You can see your working directory by typing <code>getwd()</code> in the R console. Opening the file in a general-purpose text editor, you should see the following:</p>
<pre><code>shortName  Start  End   Count   Units
Temperature, water  2010-10-01  2012-06-24  575 deg C
Stream flow, mean. daily    1948-01-01  2013-03-13  23814   ft3/s
Specific conductance    2010-10-01  2012-06-24  551 uS/cm 25C
Suspended sediment concentration (SSC)  1980-10-01  1991-09-30  3651    mg/l
Suspended sediment discharge    1980-10-01  1991-09-30  3652    tons/day</code></pre>
<p>Next, follow the steps below to open this file in Excel:</p>
<ol style="list-style-type: decimal">
<li>Open Excel</li>
<li>Click on the File tab</li>
<li>Click on the Open option</li>
<li>Navigate to the working directory (as shown in the results of <code>getwd()</code>)</li>
<li>Next to the File name text box, change the drop down type to All Files (<em>.</em>)</li>
<li>Double click tableData.tsv</li>
<li>A text import wizard will open up, in the first window, choose the Delimited radio button if it is not automatically picked, then click on Next.</li>
<li>In the second window, click on the Tab delimiter if it is not automatically checked, then click Finished.</li>
<li>Use the many formatting tools within Excel to customize the table</li>
</ol>
<p>From Excel, it is simple to copy and paste the tables in other Microsoft® software. An example using one of the default Excel table formats is here. Additional formatting could be required in Excel, for example converting u to <span class="math inline">\(\mu\)</span>.</p>
<div class="figure">
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" alt="A simple table produced in Microsoft ® Excel." />
<p class="caption">A simple table produced in Microsoft ® Excel.</p>
</div>
</div>
<div id="disclaimer" class="section level1">
<h1><span class="header-section-number">7</span> Disclaimer</h1>
<p>This information is preliminary and is subject to revision. It is being provided to meet the need for timely best science. The information is provided on the condition that neither the U.S. Geological Survey nor the U.S. Government may be held liable for any damages resulting from the authorized or unauthorized use of the information.</p>
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