---
title: "ANLY 512 Final Project: The Quantified Self"
author: "Yujia Liao"
date: "`r Sys.Date()`"
output: 
  flexdashboard::flex_dashboard:
    storyboard: true
    social: menu
    source: embed
    orientation: columns
    vertical_layout: fill
---

```{r setup, include=FALSE}
library(flexdashboard)
library(knitr)
library(ggplot2)
library(tidyverse)
library(readxl)
library(dplyr)
library(xts)
library(zoo)
library(lubridate)
setwd("/Users/charlene/Desktop/Harrisburg/ANLY 512-90-O/Final Project/")
```
###Overview of the Quantified Self movement
![quant_self](Quantified Self.png)


***
Final project for ANLY512: Data Visualization

The Quantified Self (QS) is a movement motivated to leverage the synergy of wearables, analytics, and “Big Data”. This movement exploits the ease and convenience of data acquisition through the internet of things (IoT) to feed the growing obsession of personal informatics and quotidian data. In short, quantified self is self-knowledge through self-tracking with technology.

Data collection through self-monitoring and self-sensing combines wearable sensors (e.g. EEG, ECG) and wearable computing.

I use the Health App to collet daily data. The data collected is from April 2018 to the end of March, 2019.  
The Quantified Self application was used to convert this daily data into a csv.
I then clenaed the data and added columns to create the graph. 

Working on this project I will focus on five questions as following:
1. How much total active Calories do I burnt every day? 
2. What is the distance I covered in this year and is there any relationship between my walking distance and my Calories burnt?
3. What is the amount of flight climbs each day in the past year?
4. How well do I sleep?
5. How many steps did I walk every day?


###Q1: Active Calories burned each day
```{r}
data <- read.csv("//Users/charlene/Desktop/Harrisburg/ANLY 512-90-O/Final Project/Health Data.csv")
Dates <- as.Date(data$Date, format = "%m/%d/%Y")

# plot
ggplot(data) + 
  aes(Dates, data$Active.Calories..kcal.) +
  geom_bar(stat = "identity", color = 'Purple') +
  theme(axis.text.x=element_text(angle=60, hjust=1))


```

***
During the past one year, I have a maximum Calories burned of around 2500 kcal and the average is about 2000 kcal. Based on the fact that I usually did not eat too much, such Calories burning situation is enough for me to keep fit. However, many days below 1500 Calories per day reminds me to increase the frequency of excise.

###Q2: Walking Distance Analysis 

```{r}
ggplot(data) +
  aes(Dates, data$Distance..mi. ) +
  geom_bar(stat = "identity", color = 'Purple')
ggplot(data) +
  aes(Distance..mi., Active.Calories..kcal.) +
  geom_point(color = 'Pink') +
  geom_smooth(method = "lm",color = 'Purple') +
  theme(axis.text.x=element_text(angle=60, hjust=1))
```

***
Here we see that the maximum distances happened when I was on my vacation to my hometown during the summer. Except for that period, the distances I walked everyday is not very lone at an average of about 2 miles.
And according to the plot showing the relationship between my walking distance and Calories spent, a highly positive relationship as my expected is not appear. It seems that they do not have a strong relationship.

### Q3: Flight Climbs Situation

```{r}

library(plotly)
plot_ly(data, x=data$Date, y=data$Flights.Climbed..count. , type="scatter", mode="makers+lines", name="Flight Climbed Count") 

```

***
We see that my average flights climbed during the past year is about 5 floors with the maximum of 25 floors. The same question appeared for me that I need to improve the frequency of exercise in the next year.

###Q4: Sleep Situation Analysis 

```{r}

ggplot(data) +
  aes(Dates, data$Sleep.Hours..hr.) +
  geom_bar(stat = "identity", color = 'Purple') +
  labs(title = 'Sleep Pattern', x = 'Days', y = 'Time in Hours')
```

***
It's showed in this plot that my average sleep hours during the past year is about 7 hours with the maximum of 8 and minimal 6 hours. 



###Q5: Steps in the past year

```{r}

ggplot(data) +
  aes(Dates, data$Steps..count.) +
  geom_bar(stat = "identity", color = 'Purple')

```

***
We see that each month the maximum steps I have taken are in the 5 digits. The Steps graph is similar to the distance graph that showing that I stepped a lot during the past summer on my vacation. Except for that, my average daily steps is below 5000, which should be improved.