96 lines
3.1 KiB
Python
96 lines
3.1 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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# %%
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import os
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import sys
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import datetime
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import seaborn as sns
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import pandas as pd
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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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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 = pd.concat([baseline_si, baseline_be_1, baseline_be_2], join="inner").reset_index().drop(columns="index")
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# %%
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participants_inactive_usernames = participants.query_db.get_usernames(collection_start=datetime.date.fromisoformat("2020-08-01"))
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# %%
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baseline_inactive = baseline[baseline["Gebruikersnaam"].isin(participants_inactive_usernames)]
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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_session_counts = classify_sessions_adherence(df_esm_preprocessed)
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# %%
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tbl_session_outcomes = df_session_counts.reset_index()["session_response"].value_counts()
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# %%
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print("All sessions:", len(df_session_counts))
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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))
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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(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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df_session_counts
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# %%
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df_session_finished = df_session_counts[df_session_counts["session_response"] == "esm_finished"].reset_index()
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# %%
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df_participant_finished_sessions = df_session_finished.groupby("participant_id").count()["esm_session"].rename("finished_sessions")
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# %%
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df_adherence = baseline_inactive[["username", "gender", "age", "startlanguage"]].merge(df_esm_preprocessed[["username", "participant_id"]].drop_duplicates(), how="left", on="username")
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df_adherence = df_adherence.merge(df_participant_finished_sessions, how="left", left_on="participant_id", right_index=True)
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# %% tags=[]
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df_adherence
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# %%
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lm_adherence = smf.ols('finished_sessions ~ C(gender) + C(startlanguage) + age', data=df_adherence).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('finished_sessions ~ C(gender) + C(startlanguage) + age', data=df_adherence)
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ls_result = lr_ols.fit()
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ls_result.summary()
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