Sleep EMA Exploratory Data Analysis

Replication Data for Wu et al. (2020) “Multi-Modal Data Collection for Measuring Health, Behavior, and Living Environment of Large-Scale Participant Cohorts: Conceptual Framework and Findings from Deployments”: Ecological Momentary Assessment Data (Beiwe)

import pandas as pd
import numpy as np
from glob import glob
import seaborn as sns
import matplotlib.pyplot as plt

if "jms_style_sheet" in plt.style.available:
    plt.style.use("jms_style_sheet")

df = pd.read_csv("data/UT1000_ema_wide.csv")
display(df.head()); df.shape

	pid	survey.date	content	energy	lonely	refreshed	restful	sad	sleep	stress
0	1193rv5x	2019-02-14	3.0	3.0	2.0	2.0	3.0	1.0	5.0	1.0
1	1193rv5x	2019-02-15	2.0	3.0	3.0	1.0	2.0	2.0	6.0	1.0
2	1193rv5x	2019-02-16	1.0	2.0	3.0	1.0	2.0	2.0	9.0	1.0
3	1193rv5x	2019-02-17	1.0	1.0	3.0	2.0	2.0	3.0	9.0	2.0
4	1193rv5x	2019-02-18	3.0	3.0	1.0	1.0	2.0	1.0	5.0	1.0

(31669, 10)

df.describe()

	content	energy	lonely	refreshed	restful	sad	sleep	stress
count	24950.000000	24633.000000	24936.000000	29336.000000	29328.000000	24944.000000	24617.000000	24945.000000
mean	1.573347	1.606747	0.321744	1.717531	1.771856	1.234806	6.345574	1.522309
std	0.863883	0.897869	0.754577	0.790531	0.909771	1.088472	3.592835	1.131327
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	1.000000	1.000000	0.000000	1.000000	1.000000	0.000000	4.000000	1.000000
50%	2.000000	2.000000	0.000000	2.000000	2.000000	1.000000	6.000000	1.000000
75%	2.000000	2.000000	0.000000	2.000000	2.000000	2.000000	8.000000	3.000000
max	3.000000	4.000000	3.000000	3.000000	3.000000	3.000000	12.000000	3.000000

_ = plt.figure(figsize=(20, 5))
_ = sns.stripplot(data=df.select_dtypes(float).melt(), x="variable", y="value", alpha=0.5)

plot_df = df.set_index(["pid", "survey.date"]).head(10000)
_ = plt.figure(figsize=(30, 5))
_ = sns.heatmap(data=plot_df.T)

mask = np.triu(np.ones_like(df.corr(), dtype=bool))
_ = sns.heatmap(df.corr(method="spearman"), mask=mask, annot=True)