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stress_at_work_analysis
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Add SAM event and period analysis.

communication
junos 2021-07-04 16:29:53 +02:00
parent 48d7be780c
commit e8005aadc6
1 changed files with 103 additions and 11 deletions
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114
exploration/expl_esm_labels.py
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@ -102,22 +102,114 @@ df_esm_SAM = df_esm_preprocessed[
(df_esm_preprocessed["questionnaire_id"] >= 87)
& (df_esm_preprocessed["questionnaire_id"] <= 93)
]
df_esm_SAM_clean = clean_up_esm(df_esm_SAM)
# %% [markdown]
# ## Stressful events
# %%
clean_up_esm(df_esm_SAM)[["esm_user_answer", "esm_user_answer_numeric"]].head(9)
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_PANAS_clean[["esm_user_answer", "esm_user_answer_numeric"]].head(n=10)
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[
[
"esm_instructions",
"question_id",
"questionnaire_id",
"esm_user_answer",
"esm_type",
]
].head(n=10)
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
)
# %%
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,
)
# %%
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
]
# %%
sns.displot(data=df_esm_SAM_period_summary_participant, x="mean", binwidth=0.2)
# %%
sns.displot(data=df_esm_SAM_period_summary_participant, x="std", binwidth=0.1)