Source file ⇒ Final_Project_Draft.Rmd

Introduction

For my STAT 184 Final Project, I decided to dive into my personal data since I haven’t had the opportunity to analyze it. There is plenty of consistency in the data since I’ve been wearing a FitBit since June 2015. There are probably only a handful of days that I didn’t wear the band. I downloaded my data from the online FitBit portal. FitBit breaks up your activity levels into Sedentary, Lightly Active Fairly Active and Very Active.

I was primarily interested in looking into the difference in my active lifestyle between the summer months and school year. I find that it is much easier to stay fit in the summer.

Data Wrangling

#Read all FitBit data
Oct <- read.csv(file = "october17.csv")
Sept <- read.csv(file = "Sept17FitbitData.csv")
August <- read.csv(file= "August.csv")
July <- read.csv(file= "July17.csv")
June <- read.csv(file= "June17.csv")

#add column for what month each day is a part of
Oct1 <- Oct %>%
  mutate(month = "October")
Sept1 <- Sept %>%
  mutate(month = "September")
Aug1 <- August %>%
  mutate(month = "August")
July1 <- July %>%
  mutate(month = "July")
June1 <- June %>%
  mutate(month = "June")

October Visualizations

I’d like to begin by analyzing the month of October 2017.

ggplot(Oct1, aes(x= Date, y= Distance)) + geom_point() + theme(axis.text.x = element_text(angle=30 , hjust=1)) 

ggplot(Oct1, aes(x= Date, y= Minutes.Fairly.Active)) + geom_point() + theme(axis.text.x = element_text(angle=30 , hjust=1))