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<h1 class="title toc-ignore">Best practices</h1>
<h4 class="author"><em>Robert Schlegel</em></h4>
<h4 class="date"><em>2019-03-19</em></h4>

</div>


<p><strong>Last updated:</strong> 2019-03-19</p>
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<p><details> <summary> <strong style="color:blue;">✔</strong> <strong>R Markdown file:</strong> up-to-date </summary></p>
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<p><details> <summary> <strong style="color:blue;">✔</strong> <strong>Repository version:</strong> <a href="https://github.com/robwschlegel/MHWdetection/tree/970b22c7c2cae4e09824722f8bf1d87fb5118a73" target="_blank">970b22c</a> </summary></p>
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated. <br><br> Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use <code>wflow_publish</code> or <code>wflow_git_commit</code>). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
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</code></pre>
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</td>
<td style="text-align:left;">
robwschlegel
</td>
<td style="text-align:left;">
2019-03-19
</td>
<td style="text-align:left;">
Publish analysis files
</td>
</tr>
</tbody>
</table>
</ul>
<p></details></p>
<hr />
<div id="overview" class="section level2">
<h2>Overview</h2>
<p>In this vignette the results from the main three vignettes are combined in order to visualise them simultaneously. Following this are individual sections on how to address the challenges presented by length, missing data, and decadal trends respectively.</p>
</div>
<div id="overview-of-reference-time-series" class="section level2">
<h2>Overview of reference time series</h2>
<pre class="r"><code># A multi-panel summary figure with images and tables
load(&quot;data/sst_ALL.Rdata&quot;)

# sst_ALL_summary &lt;- sst_ALL %&gt;% 

# Poential panels
# 1) The base time series
#   1.1) Rug pplot showing events
#   1.2) Events filled in with flames
#   1.3) Overlay thresholds
#   1.4) Overlay category thresholds
# 2) Mini map showing pixel location
# 3) Stats table (showing relevant stats for/from best practices)
# 4) The climatolgies/categories as there own doy x-axis panel
# 5) Lolli plot showing events

# ggplot(sst_ALL_summary)</code></pre>
</div>
<div id="overview-of-results" class="section level2">
<h2>Overview of results</h2>
<p>The following figures show the combined results from time series length, missing data, and decadal trends side by side for each of the three components of the results. This helps to create a more complete pictures of the importance of the different variables.</p>
<div id="climatologies" class="section level3">
<h3>Climatologies</h3>
</div>
<div id="event-metrics" class="section level3">
<h3>Event metrics</h3>
</div>
<div id="category-counts" class="section level3">
<h3>Category counts</h3>
<div id="refs">

</div>
</div>
</div>
<div id="session-information" class="section level2">
<h2>Session information</h2>
<pre class="r"><code>sessionInfo()</code></pre>
<pre><code>R version 3.5.3 (2019-03-11)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.5 LTS

Matrix products: default
BLAS: /usr/lib/openblas-base/libblas.so.3
LAPACK: /usr/lib/libopenblasp-r0.2.18.so

locale:
 [1] LC_CTYPE=en_CA.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_CA.UTF-8        LC_COLLATE=en_CA.UTF-8    
 [5] LC_MONETARY=en_CA.UTF-8    LC_MESSAGES=en_CA.UTF-8   
 [7] LC_PAPER=en_CA.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_CA.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
 [1] workflowr_1.1.1   Rcpp_0.12.18      digest_0.6.16    
 [4] rprojroot_1.3-2   R.methodsS3_1.7.1 backports_1.1.2  
 [7] git2r_0.23.0      magrittr_1.5      evaluate_0.11    
[10] stringi_1.2.4     whisker_0.3-2     R.oo_1.22.0      
[13] R.utils_2.7.0     rmarkdown_1.10    tools_3.5.3      
[16] stringr_1.3.1     yaml_2.2.0        compiler_3.5.3   
[19] htmltools_0.3.6   knitr_1.20       </code></pre>
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