The TRR 265 analysis pipeline.
Project description
TRR 265
This module handles analysis of the TRR265 data.
Install
pip install trr265
pip install biuR (optional but needed for most analyses)
How to use
from pygments.formatters import HtmlFormatter
from pygments import highlight
import IPython
import inspect
from pygments.lexers import PythonLexer
def display_function(the_function):
formatter = HtmlFormatter()
return IPython.display.HTML('<style type="text/css">{}</style>{}'.format(
formatter.get_style_defs('.highlight'),
highlight(inspect.getsource(the_function), PythonLexer(), formatter)))
display_function(dp.get_mov_data)
<style type="text/css">pre { line-height: 125%; }
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span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
.highlight .hll { background-color: #ffffcc }
.highlight { background: #f8f8f8; }
.highlight .c { color: #408080; font-style: italic } /* Comment */
.highlight .err { border: 1px solid #FF0000 } /* Error */
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.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
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.highlight .mf { color: #666666 } /* Literal.Number.Float */
.highlight .mh { color: #666666 } /* Literal.Number.Hex */
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.highlight .mo { color: #666666 } /* Literal.Number.Oct */
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.highlight .vc { color: #19177C } /* Name.Variable.Class */
.highlight .vg { color: #19177C } /* Name.Variable.Global */
.highlight .vi { color: #19177C } /* Name.Variable.Instance */
.highlight .vm { color: #19177C } /* Name.Variable.Magic */
.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */</style>@patch
@get_efficiently
def get_mov_data(self:DataProvider):
"""
This function gets Movisense data
1) We create unique participnat IDs (e.g. "b001"; this is necessary as sites use overapping IDs)
2) We merge double IDs, so participants with two IDs only have one (for this duplicate_ids.csv has to be updated)
3) We remove pilot participants
4) We get starting dates (from the participant overviews in movisense; downloaded as html)
5) We calculate sampling days and end dates based on the starting dates
"""
# Loading raw data
mov_berlin = pd.read_csv(self.mov_berlin_path, sep = ';')
mov_dresden = pd.read_csv(self.mov_dresden_path, sep = ';')
mov_mannheim = pd.read_csv(self.mov_mannheim_path, sep = ';')
<span class="c1"># Merging (participant numbers repeat so we add the first letter of location)</span>
<span class="n">mov_berlin</span><span class="p">[</span><span class="s1">'location'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'berlin'</span>
<span class="n">mov_dresden</span><span class="p">[</span><span class="s1">'location'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'dresden'</span>
<span class="n">mov_mannheim</span><span class="p">[</span><span class="s1">'location'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'mannheim'</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">mov_berlin</span><span class="p">,</span><span class="n">mov_dresden</span><span class="p">,</span><span class="n">mov_mannheim</span><span class="p">])</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'participant'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'location'</span><span class="p">]</span><span class="o">.</span><span class="n">str</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">df</span><span class="o">.</span><span class="n">Participant</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="s1">'</span><span class="si">%03d</span><span class="s1">'</span><span class="o">%</span><span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span> <span class="o">=</span> <span class="s1">'Participant'</span><span class="p">,</span> <span class="n">inplace</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span> <span class="c1"># Dropping old participant column to avoid mistakes</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'trigger_date'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">Trigger_date</span> <span class="o">+</span> <span class="s1">' '</span> <span class="o">+</span> <span class="n">df</span><span class="o">.</span><span class="n">Trigger_time</span><span class="p">)</span>
<span class="c1"># Merging double IDs (for participants with several movisense IDs)</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'participant'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">participant</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_duplicate_mov_ids</span><span class="p">())</span>
<span class="c1"># Removing pilot participants</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="o">~</span><span class="n">df</span><span class="o">.</span><span class="n">participant</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">contains</span><span class="p">(</span><span class="s1">'test'</span><span class="p">)]</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="o">~</span><span class="n">df</span><span class="o">.</span><span class="n">participant</span><span class="o">.</span><span class="n">isin</span><span class="p">([</span><span class="s1">'m157'</span><span class="p">,</span> <span class="s1">'b010'</span><span class="p">,</span> <span class="s1">'b006'</span><span class="p">,</span> <span class="s1">'d001'</span><span class="p">,</span> <span class="s1">'d002'</span><span class="p">,</span> <span class="s1">'d042'</span><span class="p">,</span> <span class="s1">'m024'</span><span class="p">,</span> <span class="s1">'m028'</span><span class="p">,</span> <span class="s1">'m071'</span><span class="p">,</span> <span class="s1">'m079'</span><span class="p">,</span> <span class="s1">'m107'</span><span class="p">])]</span>
<span class="c1"># Adding starting dates to get sampling days</span>
<span class="k">def</span> <span class="nf">get_starting_dates</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">pp_prefix</span> <span class="o">=</span> <span class="s1">''</span><span class="p">):</span>
<span class="n">soup</span> <span class="o">=</span> <span class="n">bs</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="n">path</span><span class="p">)</span><span class="o">.</span><span class="n">read</span><span class="p">())</span>
<span class="n">ids</span> <span class="o">=</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">text</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">soup</span><span class="o">.</span><span class="n">find_all</span><span class="p">(</span><span class="s2">"td"</span><span class="p">,</span> <span class="n">class_</span> <span class="o">=</span> <span class="s1">'simpleId'</span><span class="p">)]</span>
<span class="n">c_dates</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">find_all</span><span class="p">(</span><span class="s2">"span"</span><span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="s1">'title'</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">soup</span><span class="o">.</span><span class="n">find_all</span><span class="p">(</span><span class="s2">"td"</span><span class="p">,</span> <span class="n">class_</span> <span class="o">=</span> <span class="s1">'coupleDate'</span><span class="p">)]</span>
<span class="n">s_dates</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="s1">'value'</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">soup</span><span class="o">.</span><span class="n">find_all</span><span class="p">(</span><span class="s2">"input"</span><span class="p">,</span> <span class="n">class_</span> <span class="o">=</span> <span class="s1">'dp startDate'</span><span class="p">)]</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'participant'</span><span class="p">:</span><span class="n">ids</span><span class="p">,</span><span class="s1">'coupling_date'</span><span class="p">:</span><span class="n">c_dates</span><span class="p">,</span><span class="s1">'starting_date'</span><span class="p">:</span><span class="n">s_dates</span><span class="p">})</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'coupling_date'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">coupling_date</span><span class="p">)</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'starting_date'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">starting_date</span><span class="p">)</span>
<span class="n">df</span><span class="o">.</span><span class="n">starting_date</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">coupling_date</span><span class="p">,</span><span class="n">inplace</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'participant'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pp_prefix</span> <span class="o">+</span> <span class="n">df</span><span class="o">.</span><span class="n">participant</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="s1">'</span><span class="si">%03d</span><span class="s1">'</span><span class="o">%</span><span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="k">return</span> <span class="n">df</span>
<span class="n">starting_dates</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span>
<span class="n">get_starting_dates</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mov_berlin_starting_dates_path</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">),</span>
<span class="n">get_starting_dates</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mov_dresden_starting_dates_path</span><span class="p">,</span> <span class="s1">'d'</span><span class="p">),</span>
<span class="n">get_starting_dates</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mov_mannheim_starting_dates_path</span><span class="p">,</span> <span class="s1">'m'</span><span class="p">)</span>
<span class="p">])</span>
<span class="c1"># For participants with several movisense IDs we use the first coupling date</span>
<span class="n">starting_dates</span><span class="o">.</span><span class="n">participant</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_duplicate_mov_ids</span><span class="p">(),</span> <span class="n">inplace</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
<span class="n">starting_dates</span> <span class="o">=</span> <span class="n">starting_dates</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'participant'</span><span class="p">)[[</span><span class="s1">'starting_date'</span><span class="p">,</span><span class="s1">'coupling_date'</span><span class="p">]]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">starting_dates</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="s2">"participant"</span><span class="p">,</span> <span class="n">how</span> <span class="o">=</span> <span class="s1">'left'</span><span class="p">,</span> <span class="n">indicator</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
<span class="c1"># Checking if starting dates were downloaded</span>
<span class="k">if</span> <span class="s2">"left_only"</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">_merge</span><span class="o">.</span><span class="n">unique</span><span class="p">():</span>
<span class="n">no_starting_dates</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="s1">'_merge == "left_only"'</span><span class="p">)</span><span class="o">.</span><span class="n">participant</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Starting dates missing for participants below. Did you download the participant overviews as html?"</span><span class="p">,</span> <span class="n">no_starting_dates</span><span class="p">)</span>
<span class="c1"># Calculating movisense sampling day, adding date and end_date</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'sampling_day'</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'trigger_date'</span><span class="p">]</span> <span class="o">-</span> <span class="n">df</span><span class="p">[</span><span class="s1">'starting_date'</span><span class="p">])</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">days</span> <span class="o">+</span> <span class="mi">1</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'date'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">trigger_date</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">date</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'end_date'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">date</span> <span class="o">+</span> <span class="n">pd</span><span class="o">.</span><span class="n">DateOffset</span><span class="p">(</span><span class="n">days</span> <span class="o">=</span> <span class="mi">365</span><span class="p">)</span>
<span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="s1">'mov_index'</span><span class="p">,</span><span class="n">inplace</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
<span class="c1"># Adding redcap IDs</span>
<span class="n">ids_table</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_ba_data</span><span class="p">()[[</span><span class="s1">'participant_id'</span><span class="p">,</span><span class="s1">'mov_id'</span><span class="p">]]</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="s1">'mov_id==mov_id'</span><span class="p">)</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'mov_id'</span><span class="p">)</span><span class="o">.</span><span class="n">first</span><span class="p">()</span>
<span class="n">ids_table</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'redcap_id'</span><span class="p">]</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">ids_table</span><span class="p">,</span> <span class="n">left_on</span><span class="o">=</span><span class="s1">'participant'</span><span class="p">,</span> <span class="n">right_index</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">how</span> <span class="o">=</span> <span class="s1">'left'</span><span class="p">)</span>
<span class="c1"># Filtering out participants with no associated redcap data</span>
<span class="n">no_redcap</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="s2">"redcap_id.isna()"</span><span class="p">)</span><span class="o">.</span><span class="n">participant</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Participants: </span><span class="si">%s</span><span class="s2"> have no associated redcap IDs and are excluded from the following analyses."</span><span class="o">%</span><span class="s1">', '</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">no_redcap</span><span class="p">))</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">redcap_id</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">==</span><span class="kc">False</span><span class="p">]</span>
<span class="k">return</span> <span class="n">df</span>
#%load_ext autoreload
#%autoreload 2
from trr265.data_provider import DataProvider
dp = DataProvider('/Users/hilmarzech/Projects/trr265/trr265/data/') # Path to data folder (containing raw, interim, external, and processed)
dp.get_two_day_data().iloc[:20][['participant','date','MDBF_zufrieden','g_alc']]
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| participant | date | MDBF_zufrieden | g_alc | |
|---|---|---|---|---|
| two_day_index | ||||
| 0 | b001 | 2020-02-22 | NaN | 6.4 |
| 1 | b001 | 2020-02-23 | NaN | 35.2 |
| 2 | b001 | 2020-02-24 | 2.0 | NaN |
| 3 | b001 | 2020-02-25 | NaN | NaN |
| 4 | b001 | 2020-02-26 | NaN | NaN |
| 5 | b001 | 2020-02-27 | NaN | NaN |
| 6 | b001 | 2020-02-28 | NaN | NaN |
| 7 | b001 | 2020-02-29 | NaN | NaN |
| 8 | b001 | 2020-03-01 | NaN | NaN |
| 9 | b001 | 2020-03-02 | NaN | NaN |
| 10 | b001 | 2020-03-03 | NaN | NaN |
| 11 | b001 | 2020-03-04 | NaN | NaN |
| 12 | b001 | 2020-03-05 | NaN | 0.0 |
| 13 | b001 | 2020-03-06 | NaN | 57.6 |
| 14 | b001 | 2020-03-07 | 3.0 | NaN |
| 15 | b001 | 2020-03-08 | NaN | NaN |
| 16 | b001 | 2020-03-09 | NaN | NaN |
| 17 | b001 | 2020-03-10 | NaN | NaN |
| 18 | b001 | 2020-03-11 | NaN | NaN |
| 19 | b001 | 2020-03-12 | NaN | NaN |
Required data
Phone screening
- data/external/b7_participants.xlsx <- from Hilmar
- data/raw/phonescreening.csv <- from redcap
- data/external/phone_codebook.html <- from redcap
Basic assessment (from redcap)
- data/raw/ba.csv <- from redcap
- data/external/ba_codebook.html <- from redcap
Movisens
- all basic assessment data (see above)
- data/raw/mov_data_b.csv
- data/raw/mov_data_d.csv
- data/raw/mov_data_m.csv
- data/raw/starting_dates_b.csv
- data/raw/starting_dates_d.csv
- data/raw/starting_dates_m.csv
- data/external/alcohol_per_drink.csv <- from Hilmar
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