429 lines
12 KiB
Python
429 lines
12 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: 1.12.0
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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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# %%
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# %matplotlib inline
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import datetime
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import os
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import sys
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import matplotlib.pyplot as plt
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import pandas as pd
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import seaborn as sns
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import statsmodels.api as sm
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import statsmodels.formula.api as smf
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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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# %%
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SAVE_FIGS = True
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FIG_HEIGHT = 5
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FIG_ASPECT = 1.7
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FIG_COLOUR = "#28827C"
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SMALL_SIZE = 14
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MEDIUM_SIZE = SMALL_SIZE + 2
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BIGGER_SIZE = MEDIUM_SIZE + 2
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plt.rc("font", size=SMALL_SIZE) # controls default text sizes
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plt.rc("axes", titlesize=SMALL_SIZE) # fontsize of the axes title
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plt.rc("axes", labelsize=MEDIUM_SIZE) # fontsize of the x and y labels
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plt.rc("xtick", labelsize=SMALL_SIZE) # fontsize of the tick labels
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plt.rc("ytick", labelsize=SMALL_SIZE) # fontsize of the tick labels
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plt.rc("legend", fontsize=SMALL_SIZE) # legend fontsize
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plt.rc("figure", titlesize=BIGGER_SIZE) # fontsize of the figure title
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# %%
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baseline_si = pd.read_csv("E:/STRAWbaseline/results-survey637813.csv")
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baseline_be_1 = pd.read_csv("E:/STRAWbaseline/results-survey358134.csv")
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baseline_be_2 = pd.read_csv("E:/STRAWbaseline/results-survey413767.csv")
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baseline = (
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pd.concat([baseline_si, baseline_be_1, baseline_be_2], join="inner")
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.reset_index()
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.drop(columns="index")
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)
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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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# %%
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baseline_inactive = baseline[
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baseline["Gebruikersnaam"].isin(participants_inactive_usernames)
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]
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# %%
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VARIABLES_TO_TRANSLATE = {
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"Gebruikersnaam": "username",
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"Geslacht": "gender",
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"Geboortedatum": "date_of_birth",
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}
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baseline_inactive.rename(columns=VARIABLES_TO_TRANSLATE, copy=False, inplace=True)
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now = pd.Timestamp("now")
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baseline_inactive = baseline_inactive.assign(
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date_of_birth=lambda x: pd.to_datetime(x.date_of_birth),
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age=lambda x: (now - x.date_of_birth).dt.days / 365.25245,
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)
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# %%
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df_esm_inactive = get_esm_data(participants_inactive_usernames)
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# %% [markdown]
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# # Classify EMA sessions
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# %%
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df_esm_preprocessed = preprocess_esm(df_esm_inactive)
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df_session_counts_time = classify_sessions_by_completion_time(df_esm_preprocessed)
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# %% [markdown]
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# Sessions are now classified according to the type of a session (a true questionnaire or simple single questions) and users response.
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# %%
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df_session_counts_time["session_response_cat"] = df_session_counts_time["session_response"].astype("category")
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df_session_counts_time["session_response_cat"] = df_session_counts_time["session_response_cat"].cat.remove_categories(['during_work_first', 'ema_unanswered', 'evening_first', 'morning', 'morning_first'])
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df_session_counts_time["session_response_cat"] = df_session_counts_time["session_response_cat"].cat.add_categories("interrupted")
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df_session_counts_time.loc[df_session_counts_time["session_response_cat"].isna(), "session_response_cat"] = "interrupted"
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#df_session_counts_time["session_response_cat"] = df_session_counts_time["session_response_cat"].cat.rename_categories({
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# "ema_unanswered": "interrupted",
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# "morning_first": "interrupted",
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# "evening_first": "interrupted",
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# "morning": "interrupted",
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# "during_work_first": "interrupted"})
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# %%
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df_session_counts_time.session_response_cat
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# %%
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tbl_session_outcomes = df_session_counts_time.reset_index()[
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"session_response_cat"
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].value_counts()
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# %%
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tbl_session_outcomes_relative = tbl_session_outcomes / len(df_session_counts_time)
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# %%
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print(tbl_session_outcomes_relative.to_latex(escape=True))
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# %%
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print("All sessions:", len(df_session_counts_time))
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print("-------------------------------------")
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print(tbl_session_outcomes)
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print("-------------------------------------")
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print(tbl_session_outcomes / len(df_session_counts_time))
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# %% [markdown]
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# ## Consider only true EMA sessions
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# %%
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df_session_finished = df_session_counts_time[
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df_session_counts_time["session_response"] == SESSION_STATUS_COMPLETE
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].reset_index()
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# %%
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df_participant_finished_sessions = (
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df_session_finished.groupby("participant_id")
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.count()["esm_session"]
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.rename("finished_sessions")
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)
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# %%
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df_adherence = baseline_inactive[["username", "gender", "age", "startlanguage"]].merge(
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df_esm_preprocessed[["username", "participant_id"]].drop_duplicates(),
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how="left",
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on="username",
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)
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df_adherence = df_adherence.merge(
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df_participant_finished_sessions,
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how="left",
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left_on="participant_id",
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right_index=True,
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)
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# %% tags=[]
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df_adherence
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# %%
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df_adherence.describe()
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# %%
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df_adherence[["gender", "startlanguage"]].value_counts()
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# %%
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sns.displot(df_adherence["finished_sessions"], binwidth=5, height=FIG_HEIGHT)
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# %%
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lm_adherence = smf.ols(
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"finished_sessions ~ C(gender) + C(startlanguage) + age", data=df_adherence
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).fit()
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table = sm.stats.anova_lm(lm_adherence, typ=2) # Type 2 ANOVA DataFrame
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print(table)
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# %%
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lr_ols = smf.ols(
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"finished_sessions ~ C(gender) + C(startlanguage) + age", data=df_adherence
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)
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ls_result = lr_ols.fit()
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ls_result.summary()
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# %% [markdown]
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# # Concordance by type
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# %% [markdown]
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# ## Workday EMA
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# %% [markdown]
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# ### Filter the EMA of interest.
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# %% [markdown]
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# Work with only completed EMA.
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# %% tags=[]
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df_session_counts_time_completed = df_session_counts_time[
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df_session_counts_time.session_response == "ema_completed"
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]
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# %% [markdown]
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# To be able to compare EMA sessions *within* one day, add a date-part column.
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#
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# **NOTE**: Since daytime EMAs could *theoretically* last beyond midnight, but never after 4 AM, the datetime is first translated to 4 h earlier.
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# %%
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df_session_counts_time_completed = df_session_counts_time_completed.assign(
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date_lj=lambda x: (x.datetime_lj - datetime.timedelta(hours=4)).dt.date
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)
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# %%
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df_session_counts_time_completed
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# %% [markdown]
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# Next, calculate differences between subsequent record. But first group them by participant and device ID (as usual) and *time*. This way, the differences between the same type of EMA sessions are calculated.
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# %% tags=[]
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df_session_time_diff = (
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df_session_counts_time_completed[["datetime_lj", "date_lj", "time"]]
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.groupby(["participant_id", "device_id", "time"])
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.diff()
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.rename(
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columns={
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"datetime_lj": "previous_same_type_time_diff",
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"date_lj": "time_diff_days",
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}
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)
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)
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# %%
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df_session_time_diff
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# %% tags=[]
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df_session_counts_time_diff = df_session_counts_time_completed.join(
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df_session_time_diff, how="left"
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)
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# %% [markdown]
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# Now, select only the daytime EMAs of interest. Discard the differences between *different day* EMAs.
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# %% tags=[]
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time_workday_completed_less_than_1_day = (
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(df_session_counts_time_diff.time == "daytime") # Only take daytime EMAs.
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& ~(
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df_session_counts_time_diff.previous_same_type_time_diff.isna()
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) # Only where the diff was actually calculated.
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& (df_session_counts_time_diff.time_diff_days == datetime.timedelta(0))
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) # Only take differences *within* a day.
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# %% tags=[]
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df_session_workday = df_session_counts_time_diff[time_workday_completed_less_than_1_day]
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# %%
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df_session_workday = df_session_workday.assign(
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time_diff_minutes=lambda x: x.previous_same_type_time_diff.dt.seconds / 60
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)
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# %%
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g1 = sns.displot(
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df_session_workday["time_diff_minutes"],
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binwidth=5,
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height=FIG_HEIGHT,
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aspect=FIG_ASPECT,
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color=FIG_COLOUR,
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)
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g1.set_axis_labels("Time difference [min]", "Session count")
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g1.set(xlim=(0, 570))
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if SAVE_FIGS:
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g1.savefig("WorkdayEMAtimeDiff.pdf")
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# %% [markdown]
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# There are some sessions that are really close together. By design, none should be closer than 30 min. Let's take a look at those.
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# %%
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df_session_workday[df_session_workday.time_diff_minutes < 30]
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# %% [markdown]
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# There are only 2 instances, look at them individually.
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# %%
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df_esm_preprocessed.loc[
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(df_esm_preprocessed.participant_id == 35)
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& (df_esm_preprocessed.esm_session == 7)
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& (df_esm_preprocessed.device_id == "62a44038-3ccb-401e-a69c-6f22152c54a6"),
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[
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"esm_trigger",
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"esm_session",
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"datetime_lj",
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"esm_instructions",
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"device_id",
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"_id",
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],
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]
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# %%
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df_esm_preprocessed.loc[
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(df_esm_preprocessed.participant_id == 45)
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& (df_esm_preprocessed.esm_session < 3)
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& (df_esm_preprocessed.device_id == "d848b1c4-33cc-4e22-82ae-96d6b6458a33"),
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["esm_trigger", "esm_session", "datetime_lj", "esm_instructions"],
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]
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# %% [markdown]
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# As these signify bugs, we can safely discard them in the following analysis.
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# %%
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df_session_workday = df_session_workday[df_session_workday.time_diff_minutes > 29]
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# %% [markdown]
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# ### All participants
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# %%
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df_session_workday.describe()
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# %%
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df_session_workday[df_session_workday["time_diff_minutes"] < 120].shape[
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0
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] / df_session_workday.shape[0]
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# %% [markdown]
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# These statistics look reasonable.
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# %% [markdown]
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# ### Differences between participants
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# %%
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df_mean_daytime_interval = df_session_workday.groupby("participant_id").median()
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# %%
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df_mean_daytime_interval.describe()
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# %%
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g2 = sns.displot(
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df_mean_daytime_interval.time_diff_minutes,
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binwidth=5,
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height=FIG_HEIGHT,
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aspect=FIG_ASPECT,
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color=FIG_COLOUR,
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)
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g2.set_axis_labels("Median time difference [min]", "Participant count")
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if SAVE_FIGS:
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g2.savefig("WorkdayEMAtimeDiffMedianParticip.pdf")
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# %%
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df_adherence = df_adherence.merge(
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df_mean_daytime_interval, how="left", left_on="participant_id", right_index=True
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)
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# %%
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lr_ols_time_diff_median = smf.ols(
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"time_diff_minutes ~ C(gender) + C(startlanguage) + age", data=df_adherence
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)
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ls_result_time_diff_median = lr_ols_time_diff_median.fit()
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ls_result_time_diff_median.summary()
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# %%
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df_count_daytime_per_participant = df_session_workday.groupby(
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["participant_id", "date_lj"]
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).count()
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# %%
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df_count_daytime_per_participant["time"].describe()
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# %%
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sns.displot(
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df_count_daytime_per_participant.time,
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binwidth=1,
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height=FIG_HEIGHT,
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aspect=FIG_ASPECT,
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color=FIG_COLOUR,
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)
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# %% [markdown]
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# ## Evening EMA
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# %% [markdown]
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# For evening EMA, determine whether in a day that any EMA session was completed, an evening EMA is also present.
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#
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# Note, we are only dealing with true EMA sessions, non-sessions etc. have already been filtered out.
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# %%
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s_evening_completed = df_session_counts_time_completed.groupby(
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["participant_id", "device_id", "date_lj"]
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).apply(lambda x: (x.time == "evening").any())
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# %%
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df_session_counts_time_completed
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# %%
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s_evening_completed.sum()
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# %%
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s_evening_completed_ratio = (
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s_evening_completed.groupby("participant_id").sum()
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/ s_evening_completed.groupby("participant_id").count()
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)
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# %%
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s_evening_completed_ratio.describe()
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# %%
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g3 = sns.displot(
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s_evening_completed_ratio - 0.001,
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binwidth=0.05,
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height=FIG_HEIGHT,
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aspect=FIG_ASPECT,
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color=FIG_COLOUR,
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)
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g3.set_axis_labels("Ratio of days with the evening EMA filled out", "Participant count")
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g3.set(xlim=(1.01, 0.59))
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if SAVE_FIGS:
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g3.savefig("EveningEMAratioParticip.pdf")
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# %%
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df_adherence = df_adherence.merge(
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s_evening_completed_ratio.rename("evening_EMA_ratio"),
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how="left",
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left_on="participant_id",
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right_index=True,
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)
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# %%
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lr_ols_evening_ratio = smf.ols(
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"evening_EMA_ratio ~ C(gender) + C(startlanguage) + age", data=df_adherence
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)
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ls_result_evening_ratio = lr_ols_evening_ratio.fit()
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ls_result_evening_ratio.summary()
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