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install.packages("lubridate")

library(lubridate)

#make endDate in a date-time variable POSIXct using lubridate with Indian time zone
df$endDate <-ymd_hms(df$endDate,tz="UTC")

#new features 
df$month<-format(df$endDate,"%m")
df$year<-format(df$endDate,"%Y")

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install.packages("XML")

library(XML)

xml <- xmlParse("export.xml")
summary(xml)

#convert the XML object to data frame
df <- XML:::xmlAttrsToDataFrame(xml["//Record"])

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heart_rate <- df %>%
  filter(type == 'HKQuantityTypeIdentifierHeartRate') %>%
  group_by(date,year,month) %>%
  summarize(heart_rate=mean(value)) 

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p1 <- ggplot(heart_rate,aes(x=date, y=heart_rate, group=year)) + 
  geom_line(aes(colour=year))+
  ggtitle("Mean heartrate over the months")

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heart_rate <- df %>%
  filter(type == 'HKQuantityTypeIdentifierHeartRate') %>%
  group_by(date,year,month) %>%
  summarize(heart_rate=median(value))+
  xlab("Month/Year")

heart_rate$date <- as.Date(heart_rate$date,"%Y-%m-%d")

plot <- ggplot(heart_rate,aes(x=date, y=heart_rate, group=year)) + 
  geom_line(aes(colour=year))+

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steps <- df %>%
  filter(type == 'HKQuantityTypeIdentifierStepCount') %>%
  group_by(date,year,month) %>%
  summarize(steps=sum(value))

steps$date <- as.Date(steps$date,"%Y-%m-%d")

plot2 <- ggplot(steps,aes(x=date, y=steps, group=year)) + 
  geom_line(aes(colour=year))+
  geom_snooth(se=F)+

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step_count <- df %>%
  filter(type == 'HKQuantityTypeIdentifierStepCount') %>%
  filter(year==2019) %>% 
  group_by(dayofweek,year,month) %>%
  summarize(step_count=median(value))

plot <- ggplot(step_count,aes(x=month, y=step_count, group=dayofweek)) + 
  geom_line(aes(colour=dayofweek),size=1.5)+
  theme_minimal()+
  ggtitle("Weekly median stepcount")

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str(df)

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table(df$type)

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install.packages("tidyverse")
install.packages("ggplot2")
library(tidyverse)
library(ggplot2)