forestprecipitation

<p>A completely fictional dataset of daily precipitation</p>

datasets

Verification of continually updating time series data where we
expect new values, but want to ensure previous data remains unchanged. Data
previously recorded could change for a number of reasons, such as discovery
of an error in model code, a change in methodology or instrument recalibration.
Monitoring data sources for these changes is not always possible. Other unnoticed
changes could include a jump in time or measurement frequency, due to instrument
failure or software updates. Functionality is provided that can be
used to check and flag changes to previous data to prevent changes going
unnoticed, as well as unexpected jumps in time.

Thomas Zwagerman

butterfly

Verification for Continually Updating Time Series Data

British Antarctic Survey 

Quentin Read

forestprecipitation function


<div class="section" id="forestprecipitation">
<h3><code>forestprecipitation</code></h3><p></p><p>
</p><p>A list with 2 dataframes (january, february) containing 2 columns,
and 6 rows. February intentionally resets to 1970-01-01</p><dl>
<dt>time</dt>
<dd><p>The date on which the imaginary rainfall was measured took
place, in yyyy-mm-dd format</p></dd><p></p><p><dt>rainfall_mm</dt>
<dd><p>Rainfall in mm</p></dd></p><p>...</p><p></p></dl><p></p><p></p></div>


Format

Forest precipitation dummy data — forestprecipitation


<div class='section' id='forestprecipitation'>
<h3><code>forestprecipitation</code></h3>


<p>A list with 2 dataframes (january, february) containing 2 columns,
and 6 rows. February intentionally resets to 1970-01-01</p><dl>
<dt>time</dt>
<dd><p>The date on which the imaginary rainfall was measured took
place, in yyyy-mm-dd format</p></dd>

<dt>rainfall_mm</dt>
<dd><p>Rainfall in mm</p></dd>

...

</dl>

</div>


forestprecipitation: Forest precipitation dummy data

Description

Usage

Arguments

Format

`forestprecipitation`