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<div class="md-content" data-md-component="content">
<article class="md-content__inner md-typeset">
<a href="https://github.com/carissalow/rapids/edit/master/docs/datastreams/fitbitparsed-csv.md" title="Edit this page" class="md-content__button md-icon">
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<h1 id="fitbitparsed_csv"><code>fitbitparsed_csv</code><a class="headerlink" href="#fitbitparsed_csv" title="Permanent link">&para;</a></h1>
<p>This <a href="../../datastreams/data-streams-introduction">data stream</a> handles Fitbit sensor data downloaded using the <a href="https://dev.fitbit.com/build/reference/web-api/">Fitbit Web API</a>, <strong>parsed</strong>, and stored in a CSV file. Please note that RAPIDS cannot query the API directly; you need to use other available tools or implement your own. Once you have your parsed sensor data in a CSV file, RAPIDS can process it.</p>
<div class="admonition info">
<p class="admonition-title">What is the difference between JSON and plain data streams</p>
<p>Most people will only need <code>fitbitjson_*</code> because they downloaded and stored their data directly from Fitbit&rsquo;s API. However, if, for some reason, you don&rsquo;t have access to that JSON data and instead only have the parsed data (columns and rows), you can use this data stream.</p>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>The CSV files have to use <code>,</code> as separator, <code>\</code> as escape character (do not escape <code>"</code> with <code>""</code>), and wrap any string columns with <code>"</code>.</p>
<details class="example"><summary>Example of a valid CSV file</summary><div class="highlight"><pre><span></span><code>&quot;device_id&quot;,&quot;heartrate&quot;,&quot;heartrate_zone&quot;,&quot;local_date_time&quot;,&quot;timestamp&quot;
&quot;a748ee1a-1d0b-4ae9-9074-279a2b6ba524&quot;,69,&quot;outofrange&quot;,&quot;2020-04-23 00:00:00&quot;,0
&quot;a748ee1a-1d0b-4ae9-9074-279a2b6ba524&quot;,69,&quot;outofrange&quot;,&quot;2020-04-23 00:01:00&quot;,0
&quot;a748ee1a-1d0b-4ae9-9074-279a2b6ba524&quot;,67,&quot;outofrange&quot;,&quot;2020-04-23 00:02:00&quot;,0
&quot;a748ee1a-1d0b-4ae9-9074-279a2b6ba524&quot;,69,&quot;outofrange&quot;,&quot;2020-04-23 00:03:00&quot;,0
</code></pre></div>
</details>
</div>
<h2 id="container">Container<a class="headerlink" href="#container" title="Permanent link">&para;</a></h2>
<p>The container should be a CSV file per sensor, each containing all participants&rsquo; data.</p>
<p>The script to connect and download data from this container is at:
<div class="highlight"><pre><span></span><code>src/data/streams/fitbitparsed_csv/container.R
</code></pre></div></p>
<h2 id="format">Format<a class="headerlink" href="#format" title="Permanent link">&para;</a></h2>
<p>The <code>format.yaml</code> maps and transforms columns in your raw data stream to the <a href="../mandatory-fitbit-format">mandatory columns RAPIDS needs for Fitbit sensors</a>. This file is at:</p>
<div class="highlight"><pre><span></span><code>src/data/streams/fitbitparsed_mysql/format.yaml
</code></pre></div>
<p>If you want to use this stream with your data, modify every sensor in <code>format.yaml</code> to map all columns except <code>TIMESTAMP</code> in <code>[RAPIDS_COLUMN_MAPPINGS]</code> to your raw data column names.</p>
<p>All columns are mandatory; however, all except <code>device_id</code> and <code>local_date_time</code> can be empty if you don&rsquo;t have that data. Just have in mind that some features will be empty if some of these columns are empty.</p>
<details class="info"><summary>FITBIT_HEARTRATE_SUMMARY</summary><p><strong>RAPIDS_COLUMN_MAPPINGS</strong></p>
<table>
<thead>
<tr>
<th>RAPIDS column</th>
<th>Stream column</th>
</tr>
</thead>
<tbody>
<tr>
<td>TIMESTAMP</td>
<td>FLAG_TO_MUTATE</td>
</tr>
<tr>
<td>LOCAL_DATE_TIME</td>
<td>local_date_time</td>
</tr>
<tr>
<td>DEVICE_ID</td>
<td>device_id</td>
</tr>
<tr>
<td>HEARTRATE_DAILY_RESTINGHR</td>
<td>heartrate_daily_restinghr</td>
</tr>
<tr>
<td>HEARTRATE_DAILY_CALORIESOUTOFRANGE</td>
<td>heartrate_daily_caloriesoutofrange</td>
</tr>
<tr>
<td>HEARTRATE_DAILY_CALORIESFATBURN</td>
<td>heartrate_daily_caloriesfatburn</td>
</tr>
<tr>
<td>HEARTRATE_DAILY_CALORIESCARDIO</td>
<td>heartrate_daily_caloriescardio</td>
</tr>
<tr>
<td>HEARTRATE_DAILY_CALORIESPEAK</td>
<td>heartrate_daily_caloriespeak</td>
</tr>
</tbody>
</table>
<p><strong>MUTATION</strong></p>
<ul>
<li>
<p><strong>COLUMN_MAPPINGS</strong> (None)</p>
</li>
<li>
<p><strong>SCRIPTS</strong> </p>
<div class="highlight"><pre><span></span><code>src/data/streams/mutations/fitbit/add_zero_timestamp.py
</code></pre></div>
</li>
</ul>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><code>add_zero_timestamp</code> adds an all-zero column called <code>timestamp</code> that will be filled in later in the pipeline by <code>readable_time.R</code> converting LOCAL_DATE_TIME to a unix timestamp taking into account single or multiple time zones.</p>
<details class="example"><summary>Example of the raw data RAPIDS expects for this data stream</summary><table>
<thead>
<tr>
<th>device_id</th>
<th>local_date_time</th>
<th>heartrate_daily_restinghr</th>
<th>heartrate_daily_caloriesoutofrange</th>
<th>heartrate_daily_caloriesfatburn</th>
<th>heartrate_daily_caloriescardio</th>
<th>heartrate_daily_caloriespeak</th>
</tr>
</thead>
<tbody>
<tr>
<td>a748ee1a-1d0b-4ae9-9074-279a2b6ba524</td>
<td>2020-10-07</td>
<td>72</td>
<td>1200.6102</td>
<td>760.3020</td>
<td>15.2048</td>
<td>0</td>
</tr>
<tr>
<td>a748ee1a-1d0b-4ae9-9074-279a2b6ba524</td>
<td>2020-10-08</td>
<td>70</td>
<td>1100.1120</td>
<td>660.0012</td>
<td>23.7088</td>
<td>0</td>
</tr>
<tr>
<td>a748ee1a-1d0b-4ae9-9074-279a2b6ba524</td>
<td>2020-10-09</td>
<td>69</td>
<td>750.3615</td>
<td>734.1516</td>
<td>131.8579</td>
<td>0</td>
</tr>
</tbody>
</table>
</details>
</div>
</details>
<details class="info"><summary>FITBIT_HEARTRATE_INTRADAY</summary><p><strong>RAPIDS_COLUMN_MAPPINGS</strong></p>
<table>
<thead>
<tr>
<th>RAPIDS column</th>
<th>Stream column</th>
</tr>
</thead>
<tbody>
<tr>
<td>TIMESTAMP</td>
<td>FLAG_TO_MUTATE</td>
</tr>
<tr>
<td>LOCAL_DATE_TIME</td>
<td>local_date_time</td>
</tr>
<tr>
<td>DEVICE_ID</td>
<td>device_id</td>
</tr>
<tr>
<td>HEARTRATE</td>
<td>heartrate</td>
</tr>
<tr>
<td>HEARTRATE_ZONE</td>
<td>heartrate_zone</td>
</tr>
</tbody>
</table>
<p><strong>MUTATION</strong></p>
<ul>
<li>
<p><strong>COLUMN_MAPPINGS</strong> (None)</p>
</li>
<li>
<p><strong>SCRIPTS</strong> </p>
<div class="highlight"><pre><span></span><code>src/data/streams/mutations/fitbit/add_zero_timestamp.py
</code></pre></div>
</li>
</ul>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><code>add_zero_timestamp</code> adds an all-zero column called <code>timestamp</code> that will be filled in later in the pipeline by <code>readable_time.R</code> converting LOCAL_DATE_TIME to a unix timestamp taking into account single or multiple time zones.</p>
<details class="example"><summary>Example of the raw data RAPIDS expects for this data stream</summary><table>
<thead>
<tr>
<th>device_id</th>
<th>local_date_time</th>
<th>heartrate</th>
<th>heartrate_zone</th>
</tr>
</thead>
<tbody>
<tr>
<td>a748ee1a-1d0b-4ae9-9074-279a2b6ba524</td>
<td>2020-10-07 00:00:00</td>
<td>68</td>
<td>outofrange</td>
</tr>
<tr>
<td>a748ee1a-1d0b-4ae9-9074-279a2b6ba524</td>
<td>2020-10-07 00:01:00</td>
<td>67</td>
<td>outofrange</td>
</tr>
<tr>
<td>a748ee1a-1d0b-4ae9-9074-279a2b6ba524</td>
<td>2020-10-07 00:02:00</td>
<td>67</td>
<td>outofrange</td>
</tr>
</tbody>
</table>
</details>
</div>
</details>
<details class="info"><summary>FITBIT_SLEEP_SUMMARY</summary><p><strong>RAPIDS_COLUMN_MAPPINGS</strong></p>
<table>
<thead>
<tr>
<th>RAPIDS column</th>
<th>Stream column</th>
</tr>
</thead>
<tbody>
<tr>
<td>TIMESTAMP</td>
<td>FLAG_TO_MUTATE</td>
</tr>
<tr>
<td>LOCAL_DATE_TIME</td>
<td>local_date_time</td>
</tr>
<tr>
<td>LOCAL_START_DATE_TIME</td>
<td>local_start_date_time</td>
</tr>
<tr>
<td>LOCAL_END_DATE_TIME</td>
<td>local_end_date_time</td>
</tr>
<tr>
<td>DEVICE_ID</td>
<td>device_id</td>
</tr>
<tr>
<td>EFFICIENCY</td>
<td>efficiency</td>
</tr>
<tr>
<td>MINUTES_AFTER_WAKEUP</td>
<td>minutes_after_wakeup</td>
</tr>
<tr>
<td>MINUTES_ASLEEP</td>
<td>minutes_asleep</td>
</tr>
<tr>
<td>MINUTES_AWAKE</td>
<td>minutes_awake</td>
</tr>
<tr>
<td>MINUTES_TO_FALL_ASLEEP</td>
<td>minutes_to_fall_asleep</td>
</tr>
<tr>
<td>MINUTES_IN_BED</td>
<td>minutes_in_bed</td>
</tr>
<tr>
<td>IS_MAIN_SLEEP</td>
<td>is_main_sleep</td>
</tr>
<tr>
<td>TYPE</td>
<td>type</td>
</tr>
</tbody>
</table>
<p><strong>MUTATION</strong></p>
<ul>
<li>
<p><strong>COLUMN_MAPPINGS</strong> (None)</p>
</li>
<li>
<p><strong>SCRIPTS</strong> </p>
<div class="highlight"><pre><span></span><code>src/data/streams/mutations/fitbit/add_zero_timestamp.py
</code></pre></div>
</li>
</ul>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><code>add_zero_timestamp</code> adds an all-zero column called <code>timestamp</code> that will be filled in later in the pipeline by <code>readable_time.R</code> converting LOCAL_DATE_TIME to a unix timestamp taking into account single or multiple time zones.</p>
<p>Fitbit API has two versions for sleep data, v1 and v1.2. We support both but ignore v1&rsquo;s <code>count_awake</code>, <code>duration_awake</code>, and <code>count_awakenings</code>, <code>count_restless</code>, <code>duration_restless</code> columns.</p>
<details class="example"><summary>Example of the expected raw data</summary><table>
<thead>
<tr>
<th>device_id</th>
<th>local_start_date_time</th>
<th>local_end_date_time</th>
<th>efficiency</th>
<th>minutes_after_wakeup</th>
<th>minutes_asleep</th>
<th>minutes_awake</th>
<th>minutes_to_fall_asleep</th>
<th>minutes_in_bed</th>
<th>is_main_sleep</th>
<th>type</th>
</tr>
</thead>
<tbody>
<tr>
<td>a748ee1a-1d0b-4ae9-9074-279a2b6ba524</td>
<td>2020-10-10 15:36:30</td>
<td>2020-10-10 16:37:00</td>
<td>92</td>
<td>0</td>
<td>55</td>
<td>5</td>
<td>0</td>
<td>60</td>
<td>0</td>
<td>classic</td>
</tr>
<tr>
<td>a748ee1a-1d0b-4ae9-9074-279a2b6ba524</td>
<td>2020-10-10 01:46:30</td>
<td>2020-10-10 08:10:00</td>
<td>88</td>
<td>0</td>
<td>318</td>
<td>65</td>
<td>0</td>
<td>383</td>
<td>1</td>
<td>stages</td>
</tr>
<tr>
<td>a748ee1a-1d0b-4ae9-9074-279a2b6ba524</td>
<td>2020-10-11 00:12:30</td>
<td>2020-10-11 11:47:00</td>
<td>89</td>
<td>1</td>
<td>562</td>
<td>132</td>
<td>0</td>
<td>694</td>
<td>1</td>
<td>stages</td>
</tr>
<tr>
<td>a748ee1a-1d0b-4ae9-9074-279a2b6ba524</td>
<td>2020-10-12 01:31:00</td>
<td>2020-10-12 09:34:30</td>
<td>93</td>
<td>0</td>
<td>415</td>
<td>68</td>
<td>0</td>
<td>483</td>
<td>1</td>
<td>stages</td>
</tr>
</tbody>
</table>
</details>
</div>
</details>
<details class="info"><summary>FITBIT_SLEEP_INTRADAY</summary><p><strong>RAPIDS_COLUMN_MAPPINGS</strong></p>
<table>
<thead>
<tr>
<th>RAPIDS column</th>
<th>Stream column</th>
</tr>
</thead>
<tbody>
<tr>
<td>TIMESTAMP</td>
<td>FLAG_TO_MUTATE</td>
</tr>
<tr>
<td>LOCAL_DATE_TIME</td>
<td>local_date_time</td>
</tr>
<tr>
<td>DEVICE_ID</td>
<td>device_id</td>
</tr>
<tr>
<td>TYPE_EPISODE_ID</td>
<td>type_episode_id</td>
</tr>
<tr>
<td>DURATION</td>
<td>duration</td>
</tr>
<tr>
<td>IS_MAIN_SLEEP</td>
<td>is_main_sleep</td>
</tr>
<tr>
<td>TYPE</td>
<td>type</td>
</tr>
<tr>
<td>LEVEL</td>
<td>level</td>
</tr>
</tbody>
</table>
<p><strong>MUTATION</strong></p>
<ul>
<li>
<p><strong>COLUMN_MAPPINGS</strong> (None)</p>
</li>
<li>
<p><strong>SCRIPTS</strong> </p>
<div class="highlight"><pre><span></span><code>src/data/streams/mutations/fitbit/add_zero_timestamp.py
</code></pre></div>
</li>
</ul>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><code>add_zero_timestamp</code> adds an all-zero column called <code>timestamp</code> that will be filled in later in the pipeline by <code>readable_time.R</code> converting LOCAL_DATE_TIME to a unix timestamp taking into account single or multiple time zones.</p>
<p>Fitbit API has two versions for sleep data, v1 and v1.2, we support both.</p>
<details class="example"><summary>Example of the expected raw data</summary><table>
<thead>
<tr>
<th>device_id</th>
<th>type_episode_id</th>
<th>local_date_time</th>
<th>duration</th>
<th>level</th>
<th>is_main_sleep</th>
<th>type</th>
</tr>
</thead>
<tbody>
<tr>
<td>a748ee1a-1d0b-4ae9-9074-279a2b6ba524</td>
<td>0</td>
<td>2020-10-10 15:36:30</td>
<td>60</td>
<td>restless</td>
<td>0</td>
<td>classic</td>
</tr>
<tr>
<td>a748ee1a-1d0b-4ae9-9074-279a2b6ba524</td>
<td>0</td>
<td>2020-10-10 15:37:30</td>
<td>660</td>
<td>asleep</td>
<td>0</td>
<td>classic</td>
</tr>
<tr>
<td>a748ee1a-1d0b-4ae9-9074-279a2b6ba524</td>
<td>0</td>
<td>2020-10-10 15:48:30</td>
<td>60</td>
<td>restless</td>
<td>0</td>
<td>classic</td>
</tr>
<tr>
<td>a748ee1a-1d0b-4ae9-9074-279a2b6ba524</td>
<td>&hellip;</td>
<td>&hellip;</td>
<td>&hellip;</td>
<td>&hellip;</td>
<td>&hellip;</td>
<td>&hellip;</td>
</tr>
<tr>
<td>a748ee1a-1d0b-4ae9-9074-279a2b6ba524</td>
<td>1</td>
<td>2020-10-10 01:46:30</td>
<td>420</td>
<td>light</td>
<td>1</td>
<td>stages</td>
</tr>
<tr>
<td>a748ee1a-1d0b-4ae9-9074-279a2b6ba524</td>
<td>1</td>
<td>2020-10-10 01:53:30</td>
<td>1230</td>
<td>deep</td>
<td>1</td>
<td>stages</td>
</tr>
</tbody>
</table>
</details>
</div>
</details>
<details class="info"><summary>FITBIT_STEPS_SUMMARY</summary><p><strong>RAPIDS_COLUMN_MAPPINGS</strong></p>
<table>
<thead>
<tr>
<th>RAPIDS column</th>
<th>Stream column</th>
</tr>
</thead>
<tbody>
<tr>
<td>TIMESTAMP</td>
<td>FLAG_TO_MUTATE</td>
</tr>
<tr>
<td>DEVICE_ID</td>
<td>device_id</td>
</tr>
<tr>
<td>LOCAL_DATE_TIME</td>
<td>local_date_time</td>
</tr>
<tr>
<td>STEPS</td>
<td>steps</td>
</tr>
</tbody>
</table>
<p><strong>MUTATION</strong></p>
<ul>
<li>
<p><strong>COLUMN_MAPPINGS</strong> (None)</p>
</li>
<li>
<p><strong>SCRIPTS</strong> </p>
<div class="highlight"><pre><span></span><code>src/data/streams/mutations/fitbit/add_zero_timestamp.py
</code></pre></div>
</li>
</ul>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><code>add_zero_timestamp</code> adds an all-zero column called <code>timestamp</code> that will be filled in later in the pipeline by <code>readable_time.R</code> converting LOCAL_DATE_TIME to a unix timestamp taking into account single or multiple time zones.</p>
<details class="example"><summary>Example of the expected raw data</summary><table>
<thead>
<tr>
<th>device_id</th>
<th>local_date_time</th>
<th>steps</th>
</tr>
</thead>
<tbody>
<tr>
<td>a748ee1a-1d0b-4ae9-9074-279a2b6ba524</td>
<td>2020-10-07</td>
<td>1775</td>
</tr>
<tr>
<td>a748ee1a-1d0b-4ae9-9074-279a2b6ba524</td>
<td>2020-10-08</td>
<td>3201</td>
</tr>
<tr>
<td>a748ee1a-1d0b-4ae9-9074-279a2b6ba524</td>
<td>2020-10-09</td>
<td>998</td>
</tr>
</tbody>
</table>
</details>
</div>
</details>
<details class="info"><summary>FITBIT_STEPS_INTRADAY</summary><p><strong>RAPIDS_COLUMN_MAPPINGS</strong></p>
<table>
<thead>
<tr>
<th>RAPIDS column</th>
<th>Stream column</th>
</tr>
</thead>
<tbody>
<tr>
<td>TIMESTAMP</td>
<td>FLAG_TO_MUTATE</td>
</tr>
<tr>
<td>DEVICE_ID</td>
<td>device_id</td>
</tr>
<tr>
<td>LOCAL_DATE_TIME</td>
<td>local_date_time</td>
</tr>
<tr>
<td>STEPS</td>
<td>steps</td>
</tr>
</tbody>
</table>
<p><strong>MUTATION</strong></p>
<ul>
<li>
<p><strong>COLUMN_MAPPINGS</strong> (None)</p>
</li>
<li>
<p><strong>SCRIPTS</strong> </p>
<div class="highlight"><pre><span></span><code>src/data/streams/mutations/fitbit/add_zero_timestamp.py
</code></pre></div>
</li>
</ul>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><code>add_zero_timestamp</code> adds an all-zero column called <code>timestamp</code> that will be filled in later in the pipeline by <code>readable_time.R</code> converting LOCAL_DATE_TIME to a unix timestamp taking into account single or multiple time zones.</p>
<details class="example"><summary>Example of the expected raw data</summary><table>
<thead>
<tr>
<th>device_id</th>
<th>local_date_time</th>
<th>steps</th>
</tr>
</thead>
<tbody>
<tr>
<td>a748ee1a-1d0b-4ae9-9074-279a2b6ba524</td>
<td>2020-10-07 00:00:00</td>
<td>5</td>
</tr>
<tr>
<td>a748ee1a-1d0b-4ae9-9074-279a2b6ba524</td>
<td>2020-10-07 00:01:00</td>
<td>3</td>
</tr>
<tr>
<td>a748ee1a-1d0b-4ae9-9074-279a2b6ba524</td>
<td>2020-10-07 00:02:00</td>
<td>0</td>
</tr>
</tbody>
</table>
</details>
</div>
</details>
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