Download Apple Watch Data in R

##Resources
https://github.com/deepankardatta/AppleHealthAnalysis
http://www.ryanpraski.com/apple-health-data-how-to-export-analyze-visualize-guide/
https://gist.github.com/ryanpraski/ba9baee2583cfb1af88ca4ec62311a3d#file-apple_health_load_analysis_r-r

##Set WD
```{r}
setwd("~/Dropbox/Apple Watch")
```

##Load Libraries
```{r}
library(AppleHealthAnalysis); library(xml2); library(purrr); library(lubridate); library(shiny); library(ggplot2)
```

##Import Data
```{r}
health_data <- ah_import_xml("export.xml")
names(health_data)
str(health_data)
```

##Data Cleaning
```{r}

health_data <- health_data[ c("endDate", "hour", "type" , "value" , "unit" , "day_name") ] #Subset the data to get rid of the columns we don't need

health_data$value <- as.numeric(as.character(health_data$value)) # Make the 'value' variable numeric
```

##Make Identifyer a Factor
```{r}
health_data$type <- health_data$type %>% as.factor()

health_data$type <- as.factor(health_data$type)
#why use the first instead of the second?
```

##Format Dates
```{r}
# Use lubridate package to format the dates
health_data$endDate <- health_data$endDate %>% ymd_hms()

# add in columns for dates and times
health_data$year <- health_data$endDate %>% year() %>% as.factor()
health_data$month <- health_data$endDate %>% month() %>% as.factor()
health_data$month_name <- health_data$endDate %>% month(label = TRUE, abbr = FALSE ) %>% as.factor()
health_data$day <- health_data$endDate %>% mday() %>% as.factor()
health_data$day_name <- health_data$endDate %>% wday( label=TRUE, abbr=FALSE ) %>% as.factor()
health_data$date <- health_data$endDate %>% as_date()
health_data$hour <- health_data$endDate %>% hour() %>% as.factor()
health_data$minute <- health_data$endDate %>% minute() %>% as.factor()
```

##Reorder Columns
```{r}
health_data_reorder<- health_data[c("endDate", "date", "month", "day", "year", "month_name", "day_name", "hour", "minute", "type", "value", "unit")]
```

##Pull Data
Note: ah_data_select Selects requested Apple Health information from the extracted data frame.

#Heart
```{r}
type_filter <- "RestingHeartRate"
ah_data_select(health_data , type_filter = "RestingHeartRate")
rhr_data <-ah_data_select( health_data , type_filter = "RestingHeartRate")
colnames(rhr_data)[4] <- "RestingHeartRate"

type_filter <- "HeartRate"
ah_data_select(health_data , type_filter = "HeartRate")
hr_data <-ah_data_select( health_data , type_filter = "HeartRate")
colnames(hr_data)[4] <- "HeartRate"

type_filter <- "HeartRateVariabilitySDNN"
ah_data_select(health_data , type_filter = "HeartRateVariabilitySDNN")
hrv_data <-ah_data_select( health_data , type_filter = "HeartRateVariabilitySDNN")
colnames(hrv_data)[4] <- "HeartRateVariabilitySDNN"

type_filter <- "HWalkingHeartRateAverage"
ah_data_select(health_data , type_filter = "WalkingHeartRateAverage")
whra_data <-ah_data_select( health_data , type_filter = "HWalkingHeartRateAverage")
colnames(whra_data)[4] <- "Walking Heart Rate Average"

```

#Physical Activity
```{r}
type_filter <- "StepCount"
ah_data_select(health_data , type_filter = "StepCount")
step_count_data <-ah_data_select( health_data , type_filter = "StepCount")
colnames(step_count_data)[4] <- "StepCount"

type_filter <- "DistanceWalkingRunning"
ah_data_select(health_data , type_filter = "DistanceWalkingRunning")
distance_data <-ah_data_select( health_data , type_filter = "DistanceWalkingRunning")
colnames(distance_data)[4] <- "DistanceWalkingRunning"

type_filter <- "FlightsClimbed"
ah_data_select(health_data , type_filter = "FlightsClimbed")
stairs_data <-ah_data_select( health_data , type_filter = "FlightsClimbed")
colnames(stairs_data)[4] <- "HFlightsClimbed"

type_filter <- "AppleExerciseTime"
ah_data_select(health_data , type_filter = "AppleExerciseTime")
exercise_time_data <-ah_data_select( health_data , type_filter = "AppleExerciseTime")
colnames(exercise_time_data)[4] <- "AppleExerciseTime"

type_filter <- "AppleStandHour"
ah_data_select(health_data , type_filter = "AppleStandHour")
stand_data <-ah_data_select( health_data , type_filter = "AppleStandHour")
colnames(stand_data)[4] <- "AppleStandHour"

```

#Calories. Note- studies suggest that calories from wearables can be up to 90% inaccurate
```{r}
type_filter <- "BasalEnergyBurned"
ah_data_select(health_data , type_filter = "BasalEnergyBurned")
basal_energy_data <-ah_data_select( health_data , type_filter = "BasalEnergyBurned")
colnames(basal_energy_data)[4] <- "BasalEnergyBurned"

type_filter <- "ActiveEnergyBurned"
ah_data_select(health_data , type_filter = "ActiveEnergyBurned")
active_energy_data <-ah_data_select( health_data , type_filter = "ActiveEnergyBurned")
colnames(active_energy_data)[4] <- "ActiveEnergyBurned"

```

#Blood Pressure
```{r}
type_filter <- "BloodPressureDiastolic"
ah_data_select(health_data , type_filter = "BloodPressureDiastolic")
bp_dia_data <-ah_data_select( health_data , type_filter = "BloodPressureDiastolic")
colnames(bp_dia_data)[4] <- "BloodPressureDiastolic"

type_filter <- "BloodPressureSystolic"
ah_data_select(health_data , type_filter = "BloodPressureSystolic")
bp_sys_data <-ah_data_select( health_data , type_filter = "BloodPressureSystolic")
colnames(bp_sys_data)[4] <- "BloodPressureSystolic"
```

#Body Mass
```{r}
type_filter <- "BodyMass"
ah_data_select(health_data , type_filter = "BodyMass")
body_mass_data <-ah_data_select( health_data , type_filter = "BodyMass")
colnames(body_mass_data)[4] <- "BodyMass"
```

#Mindfulness
```{r}
type_filter <- "MindfulSession"
ah_data_select(health_data , type_filter = "MindfulSession")
mindful_data <-ah_data_select( health_data , type_filter = "MindfulSession")
```