stress_at_work_analysis/exploration/expl_esm_labels.py

453 lines
12 KiB
Python
Raw Normal View History

2021-07-05 18:32:35 +02:00
# -*- coding: utf-8 -*-
# ---
# jupyter:
# jupytext:
# formats: ipynb,py:percent
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.3'
2023-05-16 16:13:22 +02:00
# jupytext_version: 1.14.5
# kernelspec:
# display_name: straw2analysis
# language: python
# name: straw2analysis
# ---
# %%
2022-08-23 16:41:41 +02:00
import datetime
import seaborn as sns
import participants.query_db
from features.esm import (
QUESTIONNAIRE_IDS,
clean_up_esm,
get_esm_data,
increment_answers,
preprocess_esm,
)
2023-07-03 20:27:33 +02:00
from features.esm_COPE import reassign_question_ids
2023-05-16 16:13:22 +02:00
from features.esm_JCQ import reverse_jcq_demand_control_scoring
from features.esm_SAM import extract_stressful_events
# import os
# import sys
# nb_dir = os.path.split(os.getcwd())[0]
# if nb_dir not in sys.path:
# sys.path.append(nb_dir)
# %%
2023-07-03 14:50:35 +02:00
save_figs = False
2023-07-03 18:25:40 +02:00
export_data = True
# %%
participants_inactive_usernames = participants.query_db.get_usernames(
collection_start=datetime.date.fromisoformat("2020-08-01")
)
df_esm_inactive = get_esm_data(participants_inactive_usernames)
# %%
df_esm_preprocessed = preprocess_esm(df_esm_inactive)
# %% [markdown]
# # PANAS
# %%
df_esm_PANAS = df_esm_preprocessed[
(
df_esm_preprocessed["questionnaire_id"]
== QUESTIONNAIRE_IDS["PANAS_positive_affect"]
)
| (
df_esm_preprocessed["questionnaire_id"]
== QUESTIONNAIRE_IDS["PANAS_negative_affect"]
)
]
df_esm_PANAS_clean = clean_up_esm(df_esm_PANAS)
2021-07-04 13:41:34 +02:00
# %% [markdown]
# Group by participants, date, and subscale and calculate daily means.
# %%
2021-07-04 13:41:34 +02:00
df_esm_PANAS_daily_means = (
2021-07-04 14:34:57 +02:00
df_esm_PANAS_clean.groupby(["participant_id", "date_lj", "questionnaire_id"])
2021-07-04 13:41:34 +02:00
.esm_user_answer_numeric.agg("mean")
2021-07-03 18:46:06 +02:00
.reset_index()
2021-07-04 13:41:34 +02:00
.rename(columns={"esm_user_answer_numeric": "esm_numeric_mean"})
2021-07-03 18:46:06 +02:00
)
# %% [markdown]
2023-05-16 16:37:34 +02:00
# Next, calculate mean, median, and standard deviation across all days for each participant.
2021-07-03 18:46:06 +02:00
# %%
df_esm_PANAS_summary_participant = (
2021-07-04 13:41:34 +02:00
df_esm_PANAS_daily_means.groupby(["participant_id", "questionnaire_id"])
.esm_numeric_mean.agg(["mean", "median", "std"])
2021-07-03 18:46:06 +02:00
.reset_index(col_level=1)
)
df_esm_PANAS_summary_participant[
2023-05-17 16:32:27 +02:00
"PANAS subscale"
2021-07-04 13:41:34 +02:00
] = df_esm_PANAS_daily_means.questionnaire_id.astype("category").cat.rename_categories(
2023-05-17 16:32:27 +02:00
{8.0: "positive affect", 9.0: "negative affect"}
2021-07-03 18:46:06 +02:00
)
2023-07-03 14:50:35 +02:00
# %%
df_esm_PANAS_summary_participant.groupby("PANAS subscale").describe()["mean"]
# %%
df_esm_PANAS_summary_participant.groupby("PANAS subscale").describe()["std"]
# %%
df_esm_PANAS_summary_participant.query("std == 0")
2021-07-04 13:41:34 +02:00
# %%
2023-05-16 16:13:22 +02:00
fig1 = sns.displot(
2023-05-17 16:32:27 +02:00
data=df_esm_PANAS_summary_participant, x="mean", hue="PANAS subscale", binwidth=0.2
2021-07-04 13:41:34 +02:00
)
2023-05-17 16:32:27 +02:00
fig1.set_axis_labels(x_var="participant mean", y_var="frequency")
2023-05-16 16:13:22 +02:00
if save_figs:
fig1.figure.savefig("PANAS_mean_participant.pdf", dpi=300)
2021-07-03 18:46:06 +02:00
# %%
sns.displot(
2021-07-04 14:34:57 +02:00
data=df_esm_PANAS_summary_participant,
x="median",
2023-05-17 16:32:27 +02:00
hue="PANAS subscale",
2021-07-04 14:34:57 +02:00
binwidth=0.2,
2021-07-03 18:46:06 +02:00
)
# %%
2023-05-16 16:13:22 +02:00
fig2 = sns.displot(
2023-05-17 16:32:27 +02:00
data=df_esm_PANAS_summary_participant, x="std", hue="PANAS subscale", binwidth=0.05
2021-07-03 18:46:06 +02:00
)
2023-05-17 16:32:27 +02:00
fig2.set_axis_labels(x_var="participant standard deviation", y_var="frequency")
2023-05-16 16:13:22 +02:00
if save_figs:
fig2.figure.savefig("PANAS_std_participant.pdf", dpi=300)
2021-07-04 13:41:34 +02:00
# %%
df_esm_PANAS_summary_participant[df_esm_PANAS_summary_participant["std"] < 0.1]
2021-07-04 14:34:57 +02:00
# %% [markdown]
# # Stress appraisal measure
2022-08-23 16:41:41 +02:00
# %%
df_SAM_all = extract_stressful_events(df_esm_inactive)
# %%
df_SAM_all.head()
2021-07-04 14:34:57 +02:00
# %%
df_esm_SAM = df_esm_preprocessed[
(
df_esm_preprocessed["questionnaire_id"]
>= QUESTIONNAIRE_IDS["appraisal_stressfulness_event"]
)
& (
df_esm_preprocessed["questionnaire_id"]
<= QUESTIONNAIRE_IDS["appraisal_stressfulness_period"]
)
2021-07-04 14:34:57 +02:00
]
2021-07-04 16:29:53 +02:00
df_esm_SAM_clean = clean_up_esm(df_esm_SAM)
# %% [markdown]
# ## Stressful events
# %%
df_esm_SAM_event = df_esm_SAM_clean[
df_esm_SAM_clean["questionnaire_id"]
== QUESTIONNAIRE_IDS["appraisal_stressfulness_event"]
].assign(stressful_event=lambda x: (x.esm_user_answer_numeric > 0))
2021-07-04 16:29:53 +02:00
# %%
df_esm_SAM_daily_events = (
df_esm_SAM_event.groupby(["participant_id", "date_lj"])
.stressful_event.agg("mean")
.reset_index()
.rename(columns={"stressful_event": "SAM_event_ratio"})
)
# %% [markdown]
2023-05-16 16:37:34 +02:00
# Calculate the daily mean of YES (1) or NO (0) answers to the question about stressful events. This is then the daily ratio of EMA sessions that included a stressful event.
2021-07-04 16:29:53 +02:00
# %%
df_esm_SAM_event_summary_participant = (
df_esm_SAM_daily_events.groupby(["participant_id"])
.SAM_event_ratio.agg(["mean", "median", "std"])
2021-07-04 16:29:53 +02:00
.reset_index(col_level=1)
)
# %%
2023-05-16 16:13:22 +02:00
fig6 = sns.displot(data=df_esm_SAM_event_summary_participant, x="mean", binwidth=0.1)
2023-05-17 16:32:27 +02:00
fig6.set_axis_labels(
x_var="participant proportion of stressful events", y_var="frequency"
)
2023-05-16 16:13:22 +02:00
if save_figs:
fig6.figure.savefig("SAM_events_mean_participant.pdf", dpi=300)
2021-07-04 16:29:53 +02:00
# %%
sns.displot(data=df_esm_SAM_event_summary_participant, x="std", binwidth=0.05)
# %% [markdown]
# ### Threat and challenge
# %% [markdown]
# * Example of threat: "Did this event make you feel anxious?"
# * Example of challenge: "How eager are you to tackle this event?"
2023-05-16 16:13:22 +02:00
# * Possible answers:
# 0 - Not at all,
# 1 - Slightly,
# 2 - Moderately,
# 3 - Considerably,
# 4 - Extremely
2021-07-04 16:29:53 +02:00
# %%
df_esm_SAM_daily = (
df_esm_SAM_clean.groupby(["participant_id", "date_lj", "questionnaire_id"])
.esm_user_answer_numeric.agg("mean")
.reset_index()
.rename(columns={"esm_user_answer_numeric": "esm_numeric_mean"})
)
# %%
df_esm_SAM_daily_threat_challenge = df_esm_SAM_daily[
(df_esm_SAM_daily["questionnaire_id"] == QUESTIONNAIRE_IDS["appraisal_threat"])
| (df_esm_SAM_daily["questionnaire_id"] == QUESTIONNAIRE_IDS["appraisal_challenge"])
2021-07-04 16:29:53 +02:00
]
# %%
df_esm_SAM_summary_participant = (
df_esm_SAM_daily.groupby(["participant_id", "questionnaire_id"])
.esm_numeric_mean.agg(["mean", "median", "std"])
2021-07-04 16:29:53 +02:00
.reset_index(col_level=1)
)
2021-07-04 14:34:57 +02:00
2023-07-03 14:50:35 +02:00
# %%
df_esm_SAM_event_stressfulness_summary_participant = df_esm_SAM_summary_participant[
df_esm_SAM_summary_participant["questionnaire_id"]
== QUESTIONNAIRE_IDS["appraisal_stressfulness_event"]
2023-07-03 14:50:35 +02:00
]
df_esm_SAM_event_stressfulness_summary_participant.describe()["mean"]
# %%
df_esm_SAM_event_stressfulness_summary_participant.describe()["std"]
# %%
sns.displot(
data=df_esm_SAM_event_stressfulness_summary_participant, x="mean", binwidth=0.2
)
2021-07-04 14:34:57 +02:00
# %%
2021-07-04 16:29:53 +02:00
df_esm_SAM_threat_challenge_summary_participant = df_esm_SAM_summary_participant[
(
df_esm_SAM_summary_participant["questionnaire_id"]
== QUESTIONNAIRE_IDS["appraisal_threat"]
)
| (
df_esm_SAM_summary_participant["questionnaire_id"]
== QUESTIONNAIRE_IDS["appraisal_challenge"]
)
2021-07-04 16:29:53 +02:00
]
df_esm_SAM_threat_challenge_summary_participant[
2023-05-17 16:32:27 +02:00
"event subscale"
2021-07-04 16:29:53 +02:00
] = df_esm_SAM_threat_challenge_summary_participant.questionnaire_id.astype(
"category"
).cat.rename_categories(
{88: "threat", 89: "challenge"}
)
# %%
sns.displot(
data=df_esm_SAM_threat_challenge_summary_participant,
x="mean",
2023-05-17 16:32:27 +02:00
hue="event subscale",
2021-07-04 16:29:53 +02:00
binwidth=0.2,
)
2021-07-04 14:34:57 +02:00
# %%
2023-05-16 16:13:22 +02:00
fig3 = sns.displot(
2021-07-04 16:29:53 +02:00
data=df_esm_SAM_threat_challenge_summary_participant,
x="std",
2023-05-17 16:32:27 +02:00
hue="event subscale",
2021-07-04 16:29:53 +02:00
binwidth=0.1,
)
2023-05-17 16:32:27 +02:00
fig3.set_axis_labels(x_var="participant standard deviation", y_var="frequency")
2023-05-16 16:13:22 +02:00
if save_figs:
fig3.figure.savefig("SAM_std_participant.pdf", dpi=300)
2021-07-04 16:29:53 +02:00
2023-07-03 14:50:35 +02:00
# %%
df_esm_SAM_threat_challenge_summary_participant.groupby("event subscale").describe()[
"mean"
]
# %%
df_esm_SAM_threat_challenge_summary_participant.groupby("event subscale").describe()[
"std"
]
2023-07-03 18:25:40 +02:00
# %%
df_esm_SAM_clean.columns
# %%
df_esm_SAM_clean.esm_status.value_counts()
# %%
if export_data:
df_esm_SAM_for_export = df_esm_SAM_clean[
[
"participant_id",
"username",
"device_id",
"_id",
"esm_trigger",
"esm_session",
"esm_notification_id",
"question_id",
"questionnaire_id",
2023-07-03 19:19:10 +02:00
"esm_instructions",
2023-07-03 18:25:40 +02:00
"double_esm_user_answer_timestamp",
"datetime_lj",
"date_lj",
"time",
"esm_user_answer",
"esm_user_answer_numeric",
]
]
df_esm_SAM_for_export = increment_answers(df_esm_SAM_for_export)
2023-07-03 18:25:40 +02:00
df_esm_SAM_for_export.sort_values(
2023-07-03 18:33:28 +02:00
by=["participant_id", "device_id", "_id"], ignore_index=True, inplace=True
2023-07-03 18:25:40 +02:00
)
print(df_esm_SAM_for_export.head())
df_esm_SAM_for_export.to_csv(
2023-07-03 18:33:28 +02:00
"../data/raw/df_esm_SAM_threat_challenge.csv", index=False
2023-07-03 18:25:40 +02:00
)
2021-07-04 16:29:53 +02:00
# %% [markdown]
# ## Stressfulness of period
# %%
df_esm_SAM_period_summary_participant = df_esm_SAM_summary_participant[
df_esm_SAM_summary_participant["questionnaire_id"]
== QUESTIONNAIRE_IDS["appraisal_stressfulness_period"]
2021-07-04 16:29:53 +02:00
]
2021-07-04 14:34:57 +02:00
2023-07-03 14:50:35 +02:00
# %%
df_esm_SAM_period_summary_participant.describe()["mean"]
# %%
df_esm_SAM_period_summary_participant.describe()["std"]
2021-07-04 14:34:57 +02:00
# %%
2021-07-04 16:29:53 +02:00
sns.displot(data=df_esm_SAM_period_summary_participant, x="mean", binwidth=0.2)
2021-07-04 14:34:57 +02:00
# %%
2021-07-04 16:29:53 +02:00
sns.displot(data=df_esm_SAM_period_summary_participant, x="std", binwidth=0.1)
2021-07-05 18:32:35 +02:00
# %% [markdown]
# # Job demand and control
# %%
df_esm_JCQ_demand_control = df_esm_preprocessed[
(df_esm_preprocessed["questionnaire_id"] >= QUESTIONNAIRE_IDS["JCQ_job_demand"])
& (df_esm_preprocessed["questionnaire_id"] <= QUESTIONNAIRE_IDS["JCQ_job_control"])
2021-07-05 18:32:35 +02:00
]
df_esm_JCQ_demand_control_clean = clean_up_esm(df_esm_JCQ_demand_control)
# %%
df_esm_JCQ_demand_control_reversed = reverse_jcq_demand_control_scoring(
df_esm_JCQ_demand_control_clean
)
# %%
df_esm_JCQ_daily = (
df_esm_JCQ_demand_control_reversed.groupby(
["participant_id", "date_lj", "questionnaire_id"]
)
.esm_user_score.agg("mean")
.reset_index()
.rename(columns={"esm_user_score": "esm_score_mean"})
)
df_esm_JCQ_summary_participant = (
df_esm_JCQ_daily.groupby(["participant_id", "questionnaire_id"])
.esm_score_mean.agg(["mean", "median", "std"])
2021-07-05 18:32:35 +02:00
.reset_index(col_level=1)
)
df_esm_JCQ_summary_participant[
2023-05-17 16:32:27 +02:00
"JCQ subscale"
2021-07-05 18:32:35 +02:00
] = df_esm_JCQ_summary_participant.questionnaire_id.astype(
"category"
).cat.rename_categories(
{10: "job demand", 11: "job control"}
)
2023-07-03 14:50:35 +02:00
# %%
df_esm_JCQ_summary_participant.groupby("JCQ subscale").describe()["mean"]
# %%
df_esm_JCQ_summary_participant.groupby("JCQ subscale").describe()["std"]
2021-07-05 18:32:35 +02:00
# %%
2023-05-16 16:13:22 +02:00
fig4 = sns.displot(
data=df_esm_JCQ_summary_participant,
x="mean",
2023-05-17 16:32:27 +02:00
hue="JCQ subscale",
2023-05-16 16:13:22 +02:00
binwidth=0.1,
2021-07-05 18:32:35 +02:00
)
2023-05-17 16:32:27 +02:00
fig4.set_axis_labels(x_var="participant mean", y_var="frequency")
2023-05-16 16:13:22 +02:00
if save_figs:
fig4.figure.savefig("JCQ_mean_participant.pdf", dpi=300)
2021-07-05 18:32:35 +02:00
# %%
2023-05-16 16:13:22 +02:00
fig5 = sns.displot(
data=df_esm_JCQ_summary_participant,
x="std",
2023-05-17 16:32:27 +02:00
hue="JCQ subscale",
2023-05-16 16:13:22 +02:00
binwidth=0.05,
2021-07-05 18:32:35 +02:00
)
2023-05-17 16:32:27 +02:00
fig6.set_axis_labels(x_var="participant standard deviation", y_var="frequency")
2023-05-16 16:13:22 +02:00
if save_figs:
fig5.figure.savefig("JCQ_std_participant.pdf", dpi=300)
2023-05-17 16:32:27 +02:00
# %% [markdown]
# # COPE Inventory
2023-05-17 16:32:27 +02:00
# %%
df_esm_COPE = df_esm_preprocessed[
(df_esm_preprocessed["questionnaire_id"] >= QUESTIONNAIRE_IDS["COPE_active"])
& (df_esm_preprocessed["questionnaire_id"] <= QUESTIONNAIRE_IDS["COPE_emotions"])
]
2023-07-03 18:33:28 +02:00
# %%
df_esm_COPE_clean = clean_up_esm(df_esm_COPE)
df_esm_COPE_clean = increment_answers(df_esm_COPE_clean)
2023-07-03 20:27:33 +02:00
df_esm_COPE_fixed = reassign_question_ids(df_esm_COPE_clean)
2023-07-03 18:33:28 +02:00
# %%
if export_data:
2023-07-03 20:27:33 +02:00
df_esm_COPE_for_export = df_esm_COPE_fixed[
2023-07-03 18:33:28 +02:00
[
"participant_id",
"username",
"device_id",
"_id",
"esm_trigger",
"esm_session",
"esm_notification_id",
"question_id",
"questionnaire_id",
2023-07-03 19:19:10 +02:00
"esm_instructions",
2023-07-03 18:33:28 +02:00
"double_esm_user_answer_timestamp",
"datetime_lj",
"date_lj",
"time",
"esm_user_answer",
"esm_user_answer_numeric",
]
]
df_esm_COPE_for_export.sort_values(
by=["participant_id", "device_id", "_id"], ignore_index=True, inplace=True
)
print(df_esm_COPE_for_export.head())
df_esm_COPE_for_export.to_csv("../data/raw/df_esm_COPE.csv", index=False)