stress_at_work_analysis/exploration/expl_esm_labels.py

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# -*- coding: utf-8 -*-
# ---
# jupyter:
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# text_representation:
# extension: .py
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# ---
# %%
import os
import sys
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import datetime
import seaborn as sns
nb_dir = os.path.split(os.getcwd())[0]
if nb_dir not in sys.path:
sys.path.append(nb_dir)
import participants.query_db
from features.esm import *
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from features.esm_JCQ import *
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from features.esm_SAM import *
# %%
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)
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# %% [markdown]
# Group by participants, date, and subscale and calculate daily means.
# %%
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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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)
# %% [markdown]
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# Next, calculate mean, median, and standard deviation across all days for each participant.
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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"])
.reset_index(col_level=1)
)
df_esm_PANAS_summary_participant.columns = df_esm_PANAS_summary_participant.columns.get_level_values(
1
)
df_esm_PANAS_summary_participant[
"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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# %%
sns.displot(
data=df_esm_PANAS_summary_participant, x="mean", hue="PANAS_subscale", binwidth=0.2
)
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# %%
sns.displot(
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data=df_esm_PANAS_summary_participant,
x="median",
hue="PANAS_subscale",
binwidth=0.2,
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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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# %%
df_esm_PANAS_summary_participant[df_esm_PANAS_summary_participant["std"] < 0.1]
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# %% [markdown]
# # Stress appraisal measure
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# %%
df_SAM_all = extract_stressful_events(df_esm_inactive)
# %%
df_SAM_all.head()
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# %%
df_esm_SAM = df_esm_preprocessed[
(df_esm_preprocessed["questionnaire_id"] >= 87)
& (df_esm_preprocessed["questionnaire_id"] <= 93)
]
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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]
# 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.
# %%
df_esm_SAM_event_summary_participant = (
df_esm_SAM_daily_events.groupby(["participant_id"])
.agg(["mean", "median", "std"])
.reset_index(col_level=1)
)
df_esm_SAM_event_summary_participant.columns = df_esm_SAM_event_summary_participant.columns.get_level_values(
1
)
# %%
sns.displot(data=df_esm_SAM_event_summary_participant, x="mean", binwidth=0.1)
# %%
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?"
# * Possible answers: 0 - Not at all, 1 - Slightly, 2 - Moderately, 3 - Considerably, 4 - Extremely
# %%
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"])
.agg(["mean", "median", "std"])
.reset_index(col_level=1)
)
df_esm_SAM_summary_participant.columns = df_esm_SAM_summary_participant.columns.get_level_values(
1
)
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# %%
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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[
"event_subscale"
] = 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",
hue="event_subscale",
binwidth=0.2,
)
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# %%
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sns.displot(
data=df_esm_SAM_threat_challenge_summary_participant,
x="std",
hue="event_subscale",
binwidth=0.1,
)
# %% [markdown]
# ## Stressfulness of period
# %%
df_esm_SAM_period_summary_participant = df_esm_SAM_summary_participant[
df_esm_SAM_summary_participant["questionnaire_id"] == 93
]
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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]
# # 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"])
.agg(["mean", "median", "std"])
.reset_index(col_level=1)
)
df_esm_JCQ_summary_participant.columns = df_esm_JCQ_summary_participant.columns.get_level_values(
1
)
df_esm_JCQ_summary_participant[
"JCQ_subscale"
] = df_esm_JCQ_summary_participant.questionnaire_id.astype(
"category"
).cat.rename_categories(
{10: "job demand", 11: "job control"}
)
# %%
sns.displot(
data=df_esm_JCQ_summary_participant, x="mean", hue="JCQ_subscale", binwidth=0.1,
)
# %%
sns.displot(
data=df_esm_JCQ_summary_participant, x="std", hue="JCQ_subscale", binwidth=0.05,
)