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stress_at_work_analysis
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Explain the histogram better and explore long sessions.

communication
junos 2021-06-07 12:01:41 +02:00
parent 06c179f4dd
commit 8306e99392
1 changed files with 31 additions and 30 deletions
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61
exploration/expl_esm.py
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@ -34,9 +34,7 @@ from features.esm import *
# Only take data from the main part of the study. The pilot data have different structure, there were especially many additions to ESM_JSON.
# %%
participants_inactive_usernames = participants.query_db.get_usernames(
collection_start=datetime.date.fromisoformat("2020-08-01")
)
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)
# %%
@ -51,7 +49,7 @@ df_esm_preprocessed.columns
# %% [markdown]
# The purpose of concordance is to count the number of EMA sessions that a participant answered in a day and possibly compare it to some maximum number of EMAs that could theoretically be presented for that day.
# Traditionally, concordance (adherence) in EMA study is simply calculated as the ratio of (daily) answered EMAs.
# Traditionally, concordance (adherence) in EMA study is simply calculated as the ratio of (daily) answered EMAs.
# This is possible for studies with simple EMA design, such that they are presented at fixed schedule and expired within a certain limit.
#
# Since EMAs were triggered more flexibly in our study, a different approach is needed.
@ -63,9 +61,12 @@ df_esm_preprocessed.columns
# One approach would be to count distinct session IDs which are incremented for each group of EMAs. However, since not every question answered counts as a fulfilled EMA, some unique session IDs should be eliminated first.
# %%
session_counts = df_esm_preprocessed.groupby(["participant_id", "esm_session"])[
"esm_session"
].count()
session_counts = df_esm_preprocessed.groupby(["participant_id","esm_session"]).count()["id"]
# %% [markdown]
# Group data by participant_id and esm_session and count the number of instances (by id). Session counts are therefore counts of how many times a specific session ID appears *within* a specific participant.
#
# In the plot below, it is impossible to distinguish whether a specific count appears many times within the same or across different participants.
# %%
sns.displot(session_counts.to_numpy(), binwidth=1, height=8)
@ -74,11 +75,9 @@ sns.displot(session_counts.to_numpy(), binwidth=1, height=8)
# ### Unique session IDs
# %%
df_session_counts = pd.DataFrame(session_counts)
df_session_1 = df_session_counts[(df_session_counts["esm_session"] == 1)]
df_esm_unique_session = df_session_1.join(
df_esm_preprocessed.set_index(["participant_id", "esm_session"])
)
df_session_counts = pd.DataFrame(session_counts).rename(columns={"id": "esm_session_count"})
df_session_1 = df_session_counts[(df_session_counts["esm_session_count"] == 1)]
df_esm_unique_session = df_session_1.join(df_esm_preprocessed.set_index(["participant_id","esm_session"]))
# %%
df_esm_unique_session["esm_user_answer"].value_counts()
@ -88,14 +87,10 @@ df_esm_unique_session["esm_user_answer"].value_counts()
# What do the answers "Ne" represent?
# %%
df_esm_unique_session.query("esm_user_answer == 'Ne'")[
["esm_trigger", "esm_instructions", "esm_user_answer"]
].head()
df_esm_unique_session.query("esm_user_answer == 'Ne'")[[ "esm_trigger", "esm_instructions", "esm_user_answer"]].head()
# %%
df_esm_unique_session.loc[
df_esm_unique_session["esm_user_answer"].str.contains("Ne"), "esm_trigger"
].value_counts()
df_esm_unique_session.loc[df_esm_unique_session["esm_user_answer"].str.contains("Ne"), "esm_trigger"].value_counts()
# %% [markdown]
# These are all "first" questions of EMAs which serve as a way to postpone the daytime or evening EMAs.
@ -110,30 +105,36 @@ df_esm_unique_session.loc[
# There are some session IDs that only appear twice or three times.
# %%
df_session_counts[
(df_session_counts["esm_session"] < 4) & (df_session_counts["esm_session"] > 1)
]
df_session_counts[(df_session_counts["esm_session_count"] < 4) & (df_session_counts["esm_session_count"] > 1)]
# %% [markdown]
# Some represent the morning EMAs that only contained three questions.
# %%
df_esm_preprocessed.query("participant_id == 89 & esm_session == 158")[
["esm_trigger", "esm_instructions", "esm_user_answer"]
]
df_esm_preprocessed.query("participant_id == 89 & esm_session == 158")[[ "esm_trigger", "esm_instructions", "esm_user_answer"]]
# %%
df_esm_preprocessed.query("participant_id == 89 & esm_session == 157")[
["esm_trigger", "esm_instructions", "esm_user_answer"]
]
df_esm_preprocessed.query("participant_id == 89 & esm_session == 157")[[ "esm_trigger", "esm_instructions", "esm_user_answer"]]
# %% [markdown]
# Others represent interrupted EMA sessions.
# %%
df_esm_preprocessed.query("participant_id == 31 & esm_session == 77")[
["esm_trigger", "esm_instructions", "esm_user_answer"]
]
df_esm_preprocessed.query("participant_id == 31 & esm_session == 77")[[ "esm_trigger", "esm_instructions", "esm_user_answer"]]
# %% [markdown]
# ### Long sessions
# %%
df_session_counts[(df_session_counts["esm_session_count"] > 40)]
# %%
df_esm_preprocessed.query("participant_id == 83").sort_values("_id")[[ "esm_trigger","datetime_lj", "_id", "username"]]
# %% [markdown]
# Both, session ID and \_ID (and others) reset on application reinstall. Here, it can be seen that the application was reinstalled on 2 April (actually, the phone was replaced as reported by the participant).
#
# Session IDs should therefore be grouped while taking the timestamp into account (e.g. by sorting first).
# %% [markdown]
# ## Other possibilities