Analyze and Visualize Apple Health Kit Steps Data using R

apple_health_analysis_R.r

library(dplyr)
library(ggplot2)
library(lubridate)

#load steps data into data frame
dfsteps <- read.csv("C:\\Users\\praskry\\Desktop\\apple_health_data\\StepCount.csv")
str(dfsteps)

#make endDate in a date time variable POSIXct using lubridate with eastern time zone
dfsteps$endDate <-ymd_hms(dfsteps$endDate,tz="America/New_York")
str(dfsteps)

##add in year month date dayofweek hour columns
dfsteps$month<-format(dfsteps$endDate,"%m")
dfsteps$year<-format(dfsteps$endDate,"%Y")
dfsteps$date<-format(dfsteps$endDate,"%Y-%m-%d")
dfsteps$dayofweek <-wday(dfsteps$endDate, label=TRUE, abbr=FALSE)
dfsteps$hour <-format(dfsteps$endDate,"%H")
str(dfsteps)

#show steps by month by year using dplyr then graph using ggplot2
dfsteps %>%
  group_by(year,month) %>%
  summarize(steps=sum(value)) %>%
  #print table steps by month by year
  print (n=100) %>%
  #graph data by month by year
  ggplot(aes(x=month, y=steps, fill=year)) + 
  geom_bar(position='dodge', stat='identity') +
  scale_y_continuous(labels = scales::comma) +
  scale_fill_brewer() +
  theme_bw() +  
  theme(panel.grid.major = element_blank())

#boxplot data by month by year
dfsteps %>%
  group_by(date,month,year) %>%
  summarize(steps=sum(value)) %>%
  #print table steps by date by month by year
  print (n=100) %>%
  ggplot(aes(x=month, y=steps)) + 
  geom_boxplot(aes(fill=(year))) + 
  scale_fill_brewer() +
  theme_bw() +  
  theme(panel.grid.major = element_blank())

#summary statistics by month for 2015
dfsteps %>%
  group_by(date,month,year) %>%
  summarize(steps=sum(value)) %>%
  filter(year==2015) %>%
  group_by(month) %>%
  summarize(mean = round(mean(steps), 2), sd = round(sd(steps), 2), 
            median = round(median(steps), 2), max = round(max(steps), 2), 
            min = round(min(steps), 2),`25%`= quantile(steps, probs=0.25),
            `75%`= quantile(steps, probs=0.75))

#boxplot data by day of week year
dfsteps %>%
  group_by(dayofweek,date,year) %>%
  summarize(steps=sum(value)) %>%
  #print table steps by date by month by year
  print (n=100) %>%
  ggplot(aes(x=dayofweek, y=steps)) + 
  geom_boxplot(aes(fill=(year))) + 
  scale_fill_brewer() +
  theme_bw() +  
  theme(panel.grid.major = element_blank())

#summary statistics by day of week for 2015
dfsteps %>%
  group_by(dayofweek,date,year) %>%
  summarize(steps=sum(value)) %>%
  filter(year==2015) %>%
  group_by(dayofweek) %>%
  summarize(mean = round(mean(steps), 2), sd = round(sd(steps), 2), 
            median = round(median(steps), 2), max = round(max(steps), 2), 
            min = round(min(steps), 2),`25%`= quantile(steps, probs=0.25),
            `75%`= quantile(steps, probs=0.75)) %>%
  arrange(desc(median))