stress_at_work_analysis/exploration/expl_esm_labels.py

302 lines
7.8 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
2023-05-16 16:13:22 +02:00
from features.esm import clean_up_esm, get_esm_data, preprocess_esm
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)
# %%
save_figs = 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"] == 8)
| (df_esm_preprocessed["questionnaire_id"] == 9)
]
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
)
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"] >= 87)
& (df_esm_preprocessed["questionnaire_id"] <= 93)
]
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"] == 87].assign(
stressful_event=lambda x: (x.esm_user_answer_numeric > 0)
)
# %%
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"] == 88)
| (df_esm_SAM_daily["questionnaire_id"] == 89)
]
# %%
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
# %%
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"] == 88)
| (df_esm_SAM_summary_participant["questionnaire_id"] == 89)
]
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
# %% [markdown]
# ## Stressfulness of period
# %%
df_esm_SAM_period_summary_participant = df_esm_SAM_summary_participant[
df_esm_SAM_summary_participant["questionnaire_id"] == 93
]
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"] >= 10)
& (df_esm_preprocessed["questionnaire_id"] <= 11)
]
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"}
)
# %%
2023-05-16 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
# %%