139 lines
3.6 KiB
Python
139 lines
3.6 KiB
Python
# ---
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# jupyter:
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# jupytext:
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# formats: ipynb,py:percent
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# text_representation:
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# extension: .py
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# format_name: percent
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# format_version: '1.3'
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# jupytext_version: 1.11.2
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# kernelspec:
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# display_name: straw2analysis
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# language: python
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# name: straw2analysis
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# ---
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import datetime
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# %%
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import os
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import sys
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import seaborn as sns
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nb_dir = os.path.split(os.getcwd())[0]
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if nb_dir not in sys.path:
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sys.path.append(nb_dir)
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import participants.query_db
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from features.esm import *
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# %% [markdown]
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# # ESM data
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# %% [markdown]
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# Only take data from the main part of the study. The pilot data have different structure, there were especially many additions to ESM_JSON.
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# %%
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participants_inactive_usernames = participants.query_db.get_usernames(
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collection_start=datetime.date.fromisoformat("2020-08-01")
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)
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df_esm_inactive = get_esm_data(participants_inactive_usernames)
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# %%
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df_esm_preprocessed = preprocess_esm(df_esm_inactive)
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df_esm_preprocessed.head()
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# %%
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df_esm_preprocessed.columns
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# %% [markdown]
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# # Concordance
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# %% [markdown]
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# 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.
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# %% [markdown]
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# ## Session IDs
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# %% [markdown]
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# 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.
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# %%
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session_counts = df_esm_preprocessed.groupby(["participant_id", "esm_session"])[
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"esm_session"
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].count()
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# %%
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sns.displot(session_counts.to_numpy(), binwidth=1, height=8)
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# %% [markdown]
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# ### Unique session IDs
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# %%
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df_session_counts = pd.DataFrame(session_counts)
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df_session_1 = df_session_counts[(df_session_counts["esm_session"] == 1)]
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df_esm_unique_session = df_session_1.join(
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df_esm_preprocessed.set_index(["participant_id", "esm_session"])
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)
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# %%
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df_esm_unique_session["esm_user_answer"].value_counts()
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# %% [markdown]
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# The "DayFinished3421" tag marks the last EMA, where the participant only marked "I finished with work for today" and did not answer any questions.
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# What do the answers "Ne" represent?
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# %%
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df_esm_unique_session.query("esm_user_answer == 'Ne'")[
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["esm_trigger", "esm_instructions", "esm_user_answer"]
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].head()
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# %%
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df_esm_unique_session.loc[
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df_esm_unique_session["esm_user_answer"].str.contains("Ne"), "esm_trigger"
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].value_counts()
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# %% [markdown]
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# These are all "first" questions of EMAs which serve as a way to postpone the daytime or evening EMAs.
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# %% [markdown]
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# The other answers signify expired or interrupted EMAs.
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# %% [markdown]
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# ### "Almost" unique session IDs
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# %% [markdown]
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# There are some session IDs that only appear twice or three times.
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# %%
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df_session_counts[
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(df_session_counts["esm_session"] < 4) & (df_session_counts["esm_session"] > 1)
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]
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# %% [markdown]
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# Some represent the morning EMAs that only contained three questions.
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# %%
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df_esm_preprocessed.query("participant_id == 89 & esm_session == 158")[
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["esm_trigger", "esm_instructions", "esm_user_answer"]
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]
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# %%
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df_esm_preprocessed.query("participant_id == 89 & esm_session == 157")[
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["esm_trigger", "esm_instructions", "esm_user_answer"]
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]
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# %% [markdown]
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# Others represent interrupted EMA sessions.
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# %%
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df_esm_preprocessed.query("participant_id == 31 & esm_session == 77")[
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["esm_trigger", "esm_instructions", "esm_user_answer"]
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]
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# %% [markdown]
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# ## Other possibilities
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# %% [markdown]
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# There are also answers that describe what happened to a pending question: "Removed%"
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