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Data-Crunching.html
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<h1 class="title toc-ignore">Data Crunching</h1>
</div>
<div id="TOC">
<ul>
<li><a href="#challenges-with-household-survey-analysis">Challenges with Household Survey analysis</a></li>
<li><a href="#data-crunching-automation">Data Crunching Automation</a></li>
<li><a href="#using-koboloader-to-facilitate-reproducibily.">Using KoboloadeR to facilitate reproducibily.</a><ul>
<li><a href="#import-raw-data">Import raw data</a></li>
<li><a href="#recode-relabel">Recode & Relabel</a></li>
<li><a href="#clean-records">Clean records</a></li>
<li><a href="#reweight-dataset-according-to-sampling-strategy">Reweight dataset according to sampling strategy,</a></li>
<li><a href="#build-new-indicators-from-existing-variable-etc.">Build new indicators from existing variable, etc.</a></li>
</ul></li>
<li><a href="#koboloader">KoboloadeR</a><ul>
<li><a href="#collaborative-development">Collaborative Development</a></li>
</ul></li>
</ul>
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<div class="note">
<p><strong>Key Take Away</strong> :</p>
</div>
<div id="challenges-with-household-survey-analysis" class="section level2">
<h2>Challenges with Household Survey analysis</h2>
<p>Household survey often results in dataset with over 300 variables to process & explore. Deadline to get insight from the dataset are often tight and Manual processing is very lengthy and can be done only for a limited part of the dataset. Often, because of those challenges, a lot of potential insights are not discovered.</p>
<p>To address this, it’s necessary to move from a reactive support to a proactive one.</p>
<p><img src="images/unknowquestions.png" /></p>
<p>Data Crunching is about automating processes:</p>
<ul>
<li><p>Upstream process of data interpretation: consists of the <strong>preparation of a dataset</strong> so that it can be processed, sorted and structured to run algorithms and program sequences on it;</p></li>
<li><p>Can <strong>save a lot of time</strong> as the processes do not need to be performed manually (different from data munging and data wrangling that refers manual processing of data);</p></li>
<li><p>Can be <strong>iterative</strong> when the output of the crunching process includes new data or reveals errors. This means that the program sequences may be repeated until the desired result is achieved;</p></li>
<li><p>Enable better <a href="http://edouard-legoupil.github.io/humanitaRian-data-science/slides/"><strong>analysis reproducibility</strong></a> (minimize point & click interventions) to facilitate peer review</p></li>
</ul>
</div>
<div id="data-crunching-automation" class="section level2">
<h2>Data Crunching Automation</h2>
<p>Using the right combination of packages from the R statistical language, it is possible to integrate all necessary data analysis steps into <strong>scripts</strong>:</p>
<ul>
<li><p>Data management (clean, recode, merge, reshape)</p></li>
<li><p>Data analysis (test, regression, multivariate analysis, etc…)</p></li>
<li><p>Data visualisation (plot, map, graph…)</p></li>
<li><p>Writing up results (report and presentation generation)</p></li>
</ul>
<p><img src="images/analysis.png" /></p>
</div>
<div id="using-koboloader-to-facilitate-reproducibily." class="section level2">
<h2>Using KoboloadeR to facilitate reproducibily.</h2>
<p>Before data visualization & interpretation many steps are required:</p>
<div id="import-raw-data" class="section level3">
<h3>Import raw data</h3>
<p>In a structured survey with numbered questions, the <strong>flat file</strong> type of data structure has a column for each question, and a row for each respondent. A more complex survey data structure arises if the data are <strong>hierarchical</strong>. A common type of hierarchy is where a series of questions is repeated say for each child in the household, and combined with a household questionnaire, and maybe data collected at community level. For analysis, we can create a rectangular flat file, at the ‘child level’, by repeating relevant household information in separate rows for each child. Similarly, we can summarise information for the children in a household, to create a ‘household level’ analysis file.</p>
<p>In the case of hierearchical dataset, it is required to use <a href="https://opendatakit.org/downloads/download-category/briefcase/">ODK Briefcase</a> to export and configure the key to join the frame together.</p>
</div>
<div id="recode-relabel" class="section level3">
<h3>Recode & Relabel</h3>
<ul>
<li>Leverage the same <a href="http://xlsform.org">xlsform</a> file (saved as .xls – not .xlsx) already used to encode the questionnaire to generate a data dictionary</li>
<li>Extend xlsform by adding additional column (chapter, disaggregation, correlation, etc.)</li>
<li>Potentially revise label wording to make them more concise when they will appear on the output chart</li>
</ul>
</div>
<div id="clean-records" class="section level3">
<h3>Clean records</h3>
<ul>
<li>Cleaning Log defined though the iteration of the crunching</li>
<li>Log stored as a worksheet, act as documented data audit trail</li>
<li>Log actions to be sorted as “update” or “delete”</li>
<li>Log to be re-applied every time to raw date</li>
</ul>
</div>
<div id="reweight-dataset-according-to-sampling-strategy" class="section level3">
<h3>Reweight dataset according to sampling strategy,</h3>
<ul>
<li>Associate a weight to each record</li>
<li>Weight defined by the sampling script (can be based on cluster, or Respondent Driven Sample)</li>
<li>Possibility to use post-stratification to re-compute corrected weights in case of low coverage of the sample</li>
</ul>
</div>
<div id="build-new-indicators-from-existing-variable-etc." class="section level3">
<h3>Build new indicators from existing variable, etc.</h3>
<p>Indicators are summary measures. They often provide a baseline from which to weigh up the finer points. It is important not to create unnecessary confusion. An indicator should synthesise information and serve to represent a reasonable measure of some issue or concept. The concept should have an agreed name so that users can discuss it meaningfully.</p>
<ul>
<li><p>Create new indicators from existing one: Need to define in a worksheet for each indicator: type, name, label, chapter, correlation, aggregation, formula, frame</p></li>
<li><p>Indicators formula written with a R-ready syntax: Allow for complex notation:</p>
<ul>
<li>May need to use dcast if you want to calculate an indicator based on values from a nested data frame. dcast will work as pivot table using the unique ID used for the join.</li>
<li>May need to use if when trying to do a calculation where you could have potential zero as numerator Indicators are calculated, appended to the right data frame and then the indicator definition is appended in the data dictionary</li>
</ul></li>
</ul>
</div>
</div>
<div id="koboloader" class="section level2">
<h2>KoboloadeR</h2>
<p>It is An R packages (i.e. a series of functions) that can be plugged to a configuration file in order to separate “input”, “processing”, and “output”</p>
<ul>
<li>The “output” is an Rmd (Rmarkdown) file than produce word, pdf or html reports</li>
</ul>
<p><img src="images/rm-cheatsheet1.png" /></p>
<ul>
<li><p>The configuration file includes references to all “input”:</p>
<pre><code> Path to raw data files
Path to form (in xlsform) in order to build a data dictionary
Path to the sample weight
Path to the data cleaning log
Path to the indicator calculation sheet </code></pre></li>
</ul>
<div id="collaborative-development" class="section level3">
<h3>Collaborative Development</h3>
<ul>
<li><p>Open Source Package maintained in <a href="https://github.com/Edouard-Legoupil/koboloadeR">GitHub</a></p></li>
<li><p>Submit issues for <a href="https://github.com/Edouard-Legoupil/koboloadeR/issues">bug report or feature request</a> in Github</p></li>
<li><p><a href="https://github.com/Edouard-Legoupil/koboloadeR/tree/master/R#fork-destination-box">Fork and submit pull request</a> for code review and integration</p></li>
</ul>
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