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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) |
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