Apple Watch Data in R

apple_watch.R

library(XML)
library(data.table)
library(lubridate)
library(ggplot2)
library(stringr)
library(leaflet)

# import and divide xml file ----------------------------------------------

# import
xml_dat = xmlParse("Data/apple_health_export/export.xml")

# split records & clean times
health_dat = as.data.table(XML:::xmlAttrsToDataFrame(xml_dat["//Record"]))
health_dat[, startDate := ymd_hms(startDate, tz = "America/Denver")]
health_dat[, endDate := ymd_hms(endDate, tz = "America/Denver")]

# split workouts & clean times
workout_dat = as.data.table(XML:::xmlAttrsToDataFrame(xml_dat["//Workout"]))
workout_dat[, startDate := ymd_hms(startDate, tz = "America/Denver")]
workout_dat[, endDate := ymd_hms(endDate, tz = "America/Denver")]

# split summaries & clean times
summary_dat = as.data.table(XML:::xmlAttrsToDataFrame(xml_dat["//ActivitySummary"]))
summary_dat[, dateComponents := ymd(dateComponents)]

# what records can we view? -----------------------------------------------

# health records
unique(health_dat$type)

# workout records
unique(workout_dat$workoutActivityType)

# mindfulness + HR data subset --------------------------------------------

# subset to mindfulness records
mindful_dat = health_dat[type == "HKCategoryTypeIdentifierMindfulSession"]

# only meaningful item is duration
mindful_dat = mindful_dat[, .(startDate, endDate)]
mindful_dat[, sessionLength := difftime(endDate, startDate, units = "mins")]
mindful_dat[, sessionLength := as.numeric(sessionLength, units = "mins")]
mindful_dat[, sessionID := 1:.N]

# subset to heart rate records
heart_dat = health_dat[type == "HKQuantityTypeIdentifierHeartRate"]
heart_dat = heart_dat[, .(startDate, endDate, HR = as.numeric(value))]

# subset further to records within ± 30 minutes of session, not including workouts
hr_mind = data.table()
for (record in 1:nrow(mindful_dat)) {
  # subset mindful record
  mindful_record = mindful_dat[record]
  
  # add ± 30 minute variables
  mindful_record[, startDateMinus := startDate - minutes(30)]
  mindful_record[, endDatePlus := endDate + minutes(30)]
  plusminus = c(mindful_record$startDateMinus, mindful_record$endDatePlus)
  actual = c(mindful_record$startDate, mindful_record$endDate)
  
  # subset heart rate records
  hr_record = heart_dat[startDate %between% plusminus | endDate %between% plusminus]
  hr_record[, duringSession := ifelse(startDate %between% actual |
                                        endDate %between% actual, 1, 0)]
  
  # remove HR's during workout
  for (workout in 1:nrow(workout_dat)) {
    
    # subset workout record
    workout_record = workout_dat[workout]
    
    # find interval
    workout_interval = c(workout_record$startDate, workout_record$endDate)
    
    # label in hr_record
    hr_record[, duringWorkout := ifelse(startDate %between% workout_interval |
                                          endDate %between% workout_interval, 1, 0)]
  }
  hr_record = hr_record[duringWorkout == 0]
  hr_record[, duringWorkout := NULL]
  
  # label session
  hr_record[, sessionID := mindful_record$sessionID]
  
  # save
  hr_mind = rbind(hr_mind, hr_record)
}

# mindful HR statistics & viz ---------------------------------------------

# convert duringSession to factor
hr_mind[, duringSession := as.factor(duringSession)]

# look at boxplot
ggplot(hr_mind, aes(x = duringSession, y = HR, fill = duringSession)) +
  geom_boxplot() +
  theme_minimal()

# remove outliers
hr_mind = hr_mind[,.SD[HR < quantile(HR, probs = 0.95)], by = duringSession]

# look at boxplot (again)
ggplot(hr_mind, aes(x = duringSession, y = HR, fill = duringSession)) +
  geom_boxplot() +
  theme_minimal()

# lm
mod = lm(HR ~ duringSession, data = hr_mind)
summary(mod)

# map a hike --------------------------------------------------------------

# get hikes
hikes = workout_dat[workoutActivityType == "HKWorkoutActivityTypeHiking"]

# get most recent hike date
hike_date = as_date(hikes[endDate == max(endDate)]$endDate)

# fetch .gpx file
route_list = list.files("Data/apple_health_export/workout-routes/")
route = str_which(route_list, paste0(hike_date))
filename = paste0("Data/apple_health_export/workout-routes/",route_list[route])
hike_gpx = htmlTreeParse(file = filename, useInternalNodes = TRUE)

# get coords & elevation
coords = xpathSApply(hike_gpx, path = "//trkpt", fun = xmlAttrs)
elevation = xpathSApply(hike_gpx, path = "//trkpt/ele", fun = xmlValue)

# create data.table
hike_route = data.table(
  LAT = as.numeric(coords["lat",]),
  LON = as.numeric(coords["lon",]),
  ELEVATION = as.numeric(elevation)
)

# leaflet
leaflet() %>%
  addTiles() %>%
  addPolylines(data = hike_route,
               lat = ~ LAT,
               lng = ~ LON,
               color = "#AE2573")

# ggplot elevation
hike_route[, TIME := 1:.N]
ggplot(hike_route, aes(x = TIME, y = ELEVATION)) + 
  geom_line(linewidth = 2) +
  theme_minimal()

# view standing hours -----------------------------------------------------

# plot
ggplot(summary_dat, aes(x = dateComponents, y = as.numeric(appleStandHours))) +
  geom_point(color = "#7cb7a3", size = 2) + 
  geom_smooth(color = "#AE2573", fill = "#AE2573") +
  xlab("Date") +
  ylab("Standing Hours") +
  theme_minimal()

This code accompanies a blog post, found here 💻