Published
Edited
Feb 13, 2022
1 fork
1 star
Apple Health Data Analysis and Visualization
Routine data
WalkingSpeed
walking_Speed = FileAttachment("google://WalkingSpeed.csv").csv({typed: true})
vl.layer(
vl.markBar({color:'black',width:0.5}),
)
.encode(
vl.x().fieldT('creationDate').timeUnit('monthdate').title('Date'),
vl.y().fieldQ('value').title('km/hr')
)
.data(walking_Speed)
.width(800)
.height(200)
.render()
Walking Asymmetry Percentage
walking_Asymtry_Percentage = FileAttachment("google://WalkingAsymmetryPercentage.csv").csv({typed: true})
vl.layer(
vl.markBar({color:'black',width:2}),
)
.encode(
vl.x().fieldT('creationDate').timeUnit('monthdate').title('Date'),
vl.y().fieldQ('value').title('%')
)
.data(walking_Asymtry_Percentage)
.width(800)
.height(200)
.render()
StepCount
stp_ct = FileAttachment("stepCount_clean.csv").csv({typed: true})
vl.layer(
vl.markBar({color:'black',width:1}),
)
.encode(
vl.x().fieldT('startDate').timeUnit('yearmonthdate').title('Date'),
vl.y().average('value').title('Step count')
)
.data(stp_ct)
.width(800)
.height(200)
.render()
vl.layer(
vl.markBar({color:'black',width:1}),
)
.encode(
vl.x().fieldT('startDate').timeUnit('monthdate').title('Date'),
vl.y().average('value').title('Average step count per monthdate')
)
.data(stp_ct)
.width(800)
.height(200)
.render()
vl.layer(
vl.markBar({color:'black',width:20}),
vl.markText({color:'Black',dx: 20, dy: -5, align:'left',size:'10'})
.encode(
vl.text().mean('value').format('.0f')
)
)
.encode(
vl.x().fieldT('startDate').timeUnit('month').title('Date'),
vl.y().average('value').title('Average step count per month')
)
.data(stp_ct)
.width(800)
.height(200)
.render()
vl.layer(
vl.markBar({color:'black',width:40}),
vl.markText({color:'white',dx: 55, dy: 10, align:'left',size:'10'})
.encode(
vl.text().mean('value').format('.0f')
),
vl.markLine({color:'red'}).encode(vl.y().aggregate({average:['value']}))
)
.encode(
vl.x().fieldT('startDate').timeUnit('year').title('Date'),
vl.y().average('value').title('Average step count per year')
)
.data(stp_ct)
.width(800)
.height(200)
.render()
HeartRate
heartrate_var = FileAttachment("HeartRateVariabilitySDNN.csv").csv({typed:true})
wkheartrate = FileAttachment("WalkingHeartRateAverage.csv").csv()
HeartRate Variability SDNN average by date
vl.layer(
vl.markBar({color:'black',width:0.5}),
)
.encode(
vl.x().fieldT('startDate').title('Date').timeUnit('monthyeardate'),
vl.y().average('value').title('')
)
.data(heartrate_var)
.width(800)
.height(200)
.render()
rst_heartrate =FileAttachment("RestingHeartRate@1.csv").csv({typed:true})
Resting heart rate
vl.markBar({color:'black',width:0.5})
.encode(
vl.x().fieldT('startDate').title('Date').timeUnit('monthdate'),
vl.y().fieldQ('value').title('Heart rate')
)
.data(rst_heartrate)
.width(800)
.height(200)
.render()
vl.layer(
vl.markBar({color:'black'}),
vl.markText({color:'white',dx: -10, dy: 10, align:'left',size:'10'})
.encode(
vl.text().average('value').format('.0f')
),
vl.markLine({color:'gold'}).encode(vl.y().average('value'))
)
.encode(
vl.x().fieldO('startDate').timeUnit('day').title('Date').scale({domain:['Mon','Tue','Wed','Thu','Fri','Sat','Sun']}),
vl.y().average('value').title('Heart rate(Bpm)')
)
.title('Resting heart rate')
.data(rst_heartrate)
.width(800)
.height(150)
.render()
Walking heart rate average
vl.markBar({color:'black',width:0.5})
.encode(
vl.x().fieldT('startDate').title('Date').timeUnit('monthdate'),
vl.y().average('value').title('')
)
.data(wkheartrate)
.width(800)
.height(200)
.render()
vl.layer(
vl.markBar({color:'black'}),
vl.markText({color:'white',dx: -18, dy: 10, align:'left',size:'10'})
.encode(
vl.text().average('value').format('.0f')
),
vl.markLine({color:'gold'}).encode(vl.y().average('value'))
)
.encode(
vl.x().fieldO('startDate').timeUnit('day').title('Date').scale({domain:['Mon','Tue','Wed','Thu','Fri','Sat','Sun']}),
vl.y().average('value').title('Heart rate(Bpm)')
)
.title('Walking heart rate')
.data(wkheartrate)
.width(800)
.height(150)
.render()
Rest vs Walking heart rate comparison
heartrate = (await Promise.all([ FileAttachment("RestingHeartRate@1.csv"), FileAttachment("HeartRateVariabilitySDNN.csv")]
.map(async file => [file.name.slice(0, -1).toUpperCase(), await file.csv({typed: "true"})])))
.flatMap(([Symbol, data]) => data.map(d => ({Symbol, ...d})))
Plot.plot({
marginRight: 50,
y: {
grid: true,
label: "Heart Rate"
},
x: {
axis: null
},
marks: [
Plot.line(heartrate, {x: "endDate", y: "value", stroke: "Symbol"}),
Plot.text(heartrate, Plot.selectLast({x: "endDate", y: "value", z: "Symbol", text: "Symbol", textAnchor: "start", dx: 1}))
],
width:800
})
Workout
wkot = FileAttachment("Workout.csv").csv({type:true})
vl.markBar({color:'black',width:3})
.encode(
vl.x().fieldT('startDate').title('Date').timeUnit('yearmonthdate'),
vl.y().fieldQ('duration').title('totalEnergyBurned/Unit:Kcal')
)
.data(wkot)
.width(800)
.height(200)
.render()
Sleep
slp = FileAttachment("sleep_clean").csv({type:true})
vl.markBar({color:'black'})
.encode(
vl.x().fieldO('endDate').timeUnit('yearmonthdate').title('Date'),
vl.y().average('sleepHour').title('Sleep time(hour)')
)
.data(slp)
.title('Sleep time')
.width(800)
.height(150)
.render()
vl.markBar({color:'black'})
.encode(
vl.x().fieldO('endDate').timeUnit('month').title('Date'),
vl.y().average('sleepHour').title('Sleep time(hour)')
)
.data(slp)
.width(800)
.height(150)
.render()
vl.layer(
vl.markBar({color:'black'}),
vl.markText({color:'white',dx: -15, dy: 10, align:'left',size:'10'})
.encode(
vl.text().average('sleepHour').format('.0f')
),
vl.markLine({color:'red'}).encode(vl.y().aggregate({average:['value']}))
)
.encode(
vl.x().fieldO('endDate').timeUnit('day').title('Date').scale({domain:['Mon','Tue','Wed','Thu','Fri','Sat','Sun']}),
vl.y().average('sleepHour').title('Sleep time(hour)')
)
.data(slp)
.title('Average sleep time per weekday')
.width(800)
.height(150)
.render()
stp_slp_cor = FileAttachment("step_sleep_corr.csv").csv({type:true})
vl.markBar({color:'black'})
.encode(
vl.y().fieldO('value').sort('ascending').title('Step count'),
vl.x().average('sleepHour').title('Sleep Time(hour)')
)
.data(stp_slp_cor)
.width(600)
.height(800)
.render()
Plot.plot({
"data": "url:stp_slp_cor.csv",
"layer": [
{
"mark": {
"type": "point",
"filled": true
},
"encoding": {
"x": {
"field": "value",
"type": "quantitative"
},
"y": {
"field": "sleepHour",
"type": "quantitative"
}
}
},
{
"mark": {
"type": "line",
"color": "firebrick"
},
"transform": [
{
"regression": "value",
"on": "sleepHour"
}
],
"encoding": {
"x": {
"field": "sleepHour",
"type": "quantitative"
},
"y": {
"field": "value",
"type": "quantitative"
}
}
},
{
"transform": [
{
"regression": "value",
"on": "sleepHour",
"params": true
}],
"mark": {
"type": "text",
"color": "firebrick",
"x": "width",
"align": "right",
"y": -5
},
"encoding": {
"text": {"type": "nominal", "field": "R2"}
}
}
]
})
Purpose-built for displays of data
Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
