276 lines
7.0 KiB
Python
276 lines
7.0 KiB
Python
# -*- coding: utf-8 -*-
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# ---
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# jupyter:
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# jupytext:
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# formats: ipynb,py:percent
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# text_representation:
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# extension: .py
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# format_name: percent
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# format_version: '1.3'
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# jupytext_version: 1.13.0
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# kernelspec:
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# display_name: straw2analysis
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# language: python
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# name: straw2analysis
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# ---
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# %%
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import os
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import sys
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import datetime
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import seaborn as sns
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nb_dir = os.path.split(os.getcwd())[0]
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if nb_dir not in sys.path:
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sys.path.append(nb_dir)
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import participants.query_db
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from features.esm import *
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from features.esm_JCQ import *
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from features.esm_SAM import *
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# %%
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participants_inactive_usernames = participants.query_db.get_usernames(
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collection_start=datetime.date.fromisoformat("2020-08-01")
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)
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df_esm_inactive = get_esm_data(participants_inactive_usernames)
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# %%
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df_esm_preprocessed = preprocess_esm(df_esm_inactive)
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# %% [markdown]
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# # PANAS
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# %%
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df_esm_PANAS = df_esm_preprocessed[
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(df_esm_preprocessed["questionnaire_id"] == 8)
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| (df_esm_preprocessed["questionnaire_id"] == 9)
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]
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df_esm_PANAS_clean = clean_up_esm(df_esm_PANAS)
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# %% [markdown]
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# Group by participants, date, and subscale and calculate daily means.
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# %%
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df_esm_PANAS_daily_means = (
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df_esm_PANAS_clean.groupby(["participant_id", "date_lj", "questionnaire_id"])
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.esm_user_answer_numeric.agg("mean")
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.reset_index()
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.rename(columns={"esm_user_answer_numeric": "esm_numeric_mean"})
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)
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# %% [markdown]
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# Next, calculate mean, median, and standard deviation across all days for each participant.
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# %%
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df_esm_PANAS_summary_participant = (
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df_esm_PANAS_daily_means.groupby(["participant_id", "questionnaire_id"])
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.agg(["mean", "median", "std"])
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.reset_index(col_level=1)
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)
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df_esm_PANAS_summary_participant.columns = df_esm_PANAS_summary_participant.columns.get_level_values(
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1
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)
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df_esm_PANAS_summary_participant[
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"PANAS_subscale"
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] = df_esm_PANAS_daily_means.questionnaire_id.astype("category").cat.rename_categories(
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{8.0: "PA", 9.0: "NA"}
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)
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# %%
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sns.displot(
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data=df_esm_PANAS_summary_participant, x="mean", hue="PANAS_subscale", binwidth=0.2
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)
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# %%
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sns.displot(
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data=df_esm_PANAS_summary_participant,
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x="median",
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hue="PANAS_subscale",
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binwidth=0.2,
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)
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# %%
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sns.displot(
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data=df_esm_PANAS_summary_participant, x="std", hue="PANAS_subscale", binwidth=0.05
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)
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# %%
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df_esm_PANAS_summary_participant[df_esm_PANAS_summary_participant["std"] < 0.1]
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# %% [markdown]
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# # Stress appraisal measure
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# %%
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df_SAM_all = extract_stressful_events(df_esm_inactive)
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# %%
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df_SAM_all.head()
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# %%
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df_esm_SAM = df_esm_preprocessed[
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(df_esm_preprocessed["questionnaire_id"] >= 87)
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& (df_esm_preprocessed["questionnaire_id"] <= 93)
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]
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df_esm_SAM_clean = clean_up_esm(df_esm_SAM)
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# %% [markdown]
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# ## Stressful events
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# %%
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df_esm_SAM_event = df_esm_SAM_clean[df_esm_SAM_clean["questionnaire_id"] == 87].assign(
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stressful_event=lambda x: (x.esm_user_answer_numeric > 0)
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)
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# %%
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df_esm_SAM_daily_events = (
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df_esm_SAM_event.groupby(["participant_id", "date_lj"])
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.stressful_event.agg("mean")
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.reset_index()
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.rename(columns={"stressful_event": "SAM_event_ratio"})
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)
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# %% [markdown]
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# Calculate the daily mean of YES (1) or NO (0) answers to the question about a stressful events. This is then the daily ratio of EMA sessions that included a stressful event.
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# %%
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df_esm_SAM_event_summary_participant = (
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df_esm_SAM_daily_events.groupby(["participant_id"])
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.agg(["mean", "median", "std"])
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.reset_index(col_level=1)
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)
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df_esm_SAM_event_summary_participant.columns = df_esm_SAM_event_summary_participant.columns.get_level_values(
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1
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)
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# %%
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sns.displot(data=df_esm_SAM_event_summary_participant, x="mean", binwidth=0.1)
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# %%
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sns.displot(data=df_esm_SAM_event_summary_participant, x="std", binwidth=0.05)
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# %% [markdown]
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# ### Threat and challenge
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# %% [markdown]
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# * Example of threat: "Did this event make you feel anxious?"
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# * Example of challenge: "How eager are you to tackle this event?"
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# * Possible answers: 0 - Not at all, 1 - Slightly, 2 - Moderately, 3 - Considerably, 4 - Extremely
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# %%
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df_esm_SAM_daily = (
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df_esm_SAM_clean.groupby(["participant_id", "date_lj", "questionnaire_id"])
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.esm_user_answer_numeric.agg("mean")
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.reset_index()
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.rename(columns={"esm_user_answer_numeric": "esm_numeric_mean"})
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)
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# %%
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df_esm_SAM_daily_threat_challenge = df_esm_SAM_daily[
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(df_esm_SAM_daily["questionnaire_id"] == 88)
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| (df_esm_SAM_daily["questionnaire_id"] == 89)
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]
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# %%
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df_esm_SAM_summary_participant = (
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df_esm_SAM_daily.groupby(["participant_id", "questionnaire_id"])
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.agg(["mean", "median", "std"])
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.reset_index(col_level=1)
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)
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df_esm_SAM_summary_participant.columns = df_esm_SAM_summary_participant.columns.get_level_values(
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1
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)
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# %%
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df_esm_SAM_threat_challenge_summary_participant = df_esm_SAM_summary_participant[
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(df_esm_SAM_summary_participant["questionnaire_id"] == 88)
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| (df_esm_SAM_summary_participant["questionnaire_id"] == 89)
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]
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df_esm_SAM_threat_challenge_summary_participant[
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"event_subscale"
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] = df_esm_SAM_threat_challenge_summary_participant.questionnaire_id.astype(
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"category"
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).cat.rename_categories(
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{88: "threat", 89: "challenge"}
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)
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# %%
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sns.displot(
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data=df_esm_SAM_threat_challenge_summary_participant,
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x="mean",
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hue="event_subscale",
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binwidth=0.2,
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)
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# %%
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sns.displot(
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data=df_esm_SAM_threat_challenge_summary_participant,
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x="std",
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hue="event_subscale",
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binwidth=0.1,
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)
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# %% [markdown]
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# ## Stressfulness of period
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# %%
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df_esm_SAM_period_summary_participant = df_esm_SAM_summary_participant[
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df_esm_SAM_summary_participant["questionnaire_id"] == 93
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]
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# %%
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sns.displot(data=df_esm_SAM_period_summary_participant, x="mean", binwidth=0.2)
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# %%
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sns.displot(data=df_esm_SAM_period_summary_participant, x="std", binwidth=0.1)
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# %% [markdown]
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# # Job demand and control
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# %%
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df_esm_JCQ_demand_control = df_esm_preprocessed[
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(df_esm_preprocessed["questionnaire_id"] >= 10)
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& (df_esm_preprocessed["questionnaire_id"] <= 11)
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]
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df_esm_JCQ_demand_control_clean = clean_up_esm(df_esm_JCQ_demand_control)
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# %%
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df_esm_JCQ_demand_control_reversed = reverse_jcq_demand_control_scoring(
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df_esm_JCQ_demand_control_clean
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)
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# %%
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df_esm_JCQ_daily = (
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df_esm_JCQ_demand_control_reversed.groupby(
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["participant_id", "date_lj", "questionnaire_id"]
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)
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.esm_user_score.agg("mean")
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.reset_index()
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.rename(columns={"esm_user_score": "esm_score_mean"})
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)
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df_esm_JCQ_summary_participant = (
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df_esm_JCQ_daily.groupby(["participant_id", "questionnaire_id"])
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.agg(["mean", "median", "std"])
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.reset_index(col_level=1)
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)
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df_esm_JCQ_summary_participant.columns = df_esm_JCQ_summary_participant.columns.get_level_values(
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1
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)
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df_esm_JCQ_summary_participant[
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"JCQ_subscale"
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] = df_esm_JCQ_summary_participant.questionnaire_id.astype(
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"category"
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).cat.rename_categories(
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{10: "job demand", 11: "job control"}
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)
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# %%
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sns.displot(
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data=df_esm_JCQ_summary_participant, x="mean", hue="JCQ_subscale", binwidth=0.1,
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)
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# %%
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sns.displot(
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data=df_esm_JCQ_summary_participant, x="std", hue="JCQ_subscale", binwidth=0.05,
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)
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