---
title: "ANLY 512 Final Project - Haoqi Yang, Jingxian Hu"
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)

fitbit <- read_excel("fitbit.xlsx")

```


Overview of the Quantified Self Movement
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![quant_self](Image.png)

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**ANLY 512 Data Visualization Final Project**

The quantified self refers both to the cultural phenomenon of self-tracking with technology and to a community of users and makers of self-tracking tools who share an interest in “self-knowledge through numbers.” Quantified Self supports every person’s right and ability to learn from their own data. In this project, we will use the health data from Fitbit to analyze people’s workout preference and have a better understanding of both the good habit to keep up and get rid of the bad habit.

For this research, our data were mostly collected through self-monitoring and self-sensing combines wearable sensors, called Fitbit. The data contains one year of Haoqi’s activity, variables contains calories, steps, distance in meters, floors. The data was gathered with a Fitbit Charge HT fitness tracker and every observation one day. 

For this project, we are going to have a quantified self analysis, which is self-knowledge through self-tracking with technology. It is interesting for our team to see how to analyze data strongly related to our daily life, and the main goal here is to get some insights or our daily activities, to find out good habits to continue working on, or even some hidden bad habits in our daily life. In the analysis, we are planning to create a dashboard, to visualize all the data, and answer a few questions that we were curious about, for example, what is my average heart rate over a year, and does it match the average heart rate for a person my age etc. 

Calories Analysis {data-orientation=rows}
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```{r}
Dates<-as.Date(fitbit$Date,format="%m/%d/%Y")
ggplot(fitbit)+aes(Dates,Calories_Activity)+geom_bar(stat = "identity", color='red')+theme(axis.text.x = element_text(angle = 60,hjust = 1))
```


row {data-height=350}
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###
**Q1: How many calories burned each day?**

For the first question, we used the total daily calories burned data collected from Haoqi’s Fitbit wristband. The data collected is from August 2018 to August 2019, and graphically shows the total calories burned for each day. We run the data in R, we found out that on average, the total daily calories burned was 3037, compare to the average non-exercise daily calories on average, which is 350 according to (https://www.health.harvard.edu/staying-healthy/burning-calories-without-exercise), it could showed that Haoqi exercise regularly during the year.


Steps Analysis {data-orientation=rows}
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row {data-height=650}
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```{r}
ggplot(fitbit)+aes(Dates, Steps)+geom_bar(stat = "identity", color = 'blue')
```

row {data-height=350}
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###
**Q2: How many steps taken each day?**

The average steps Haoqi had taken per day was around 11,000 with the median of 10,413steps steps, and maximum steps was 26,444 steps. Same as calories burned, there’s no specific regularity about my daily steps. However, from the plot and the data we analyzed we can see that the average steps taken was slightly increased starting from 2019, since Haoqi moved to a new apartment in 2019, and he usually walked to work and walked home as well.


Calories vs Steps {data-orientation=rows}
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```{r}
ggplot(fitbit)+aes(Steps, Calories_Activity)+geom_point()+geom_smooth(method = "lm", se=FALSE)+labs(title = "Relationship between Calories Burned and Steps", x="Steps",y="Calories Burned")+theme_minimal()
```

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###
**Q3: What is the relationship between calories and steps?**

From the ggplot, we can see that there's an inverse proportional relationship between calories burned and steps. For the most of the time, with more steps taken, the calories burned increased also. In addition, we discussed why the relationship is not shown as positive linear relations, the conclusion we got is because Haoqi worked out regularly during the time, so there are other variables like intense exercise also affect the total calories burned.


Minutes Sitting Analysis {data-orientation=rows}
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row {data-height=650}
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```{r}
ggplot(fitbit)+aes(Dates, Minutes_sitting)+geom_bar(stat = "identity", color = 'green')

```

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###
**Q4: Minutes setting analysis?**

From the graph and our analysis, we acknowledge that even though Haoqi is a person who exercises regularly, we could even say daily, however, the average sitting minute daily was 748 minute, which equals to 12 hours a day in total. This data actually shocked both of us, and we did some research online, we found out that on average an adult would sit for 6.5 hours according to(https://www.washingtonpost.com/national/health-science/the-big-numberthe-average-us-adult-sits-65-hours-a-day-for-teens-its-even-more/2019/04/26/7c29e4c2-676a-11e9-a1b6-b29b90efa879_story.html). We were thinking probably because of working in the office, we basically would have 8 hours fixed each day for weekdays, and usually would be sitting mostly at home after working out from the gym.


Calories vs Intense activities {data-orientation=rows}
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row {data-height=650}
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```{r}
ggplot(fitbit)+aes(Minutes_of_intense_activity, Calories_Activity)+geom_point()+geom_smooth(method = "lm", se=FALSE)+labs(title = "Relationship between Calories burned and Minutes of intense activity", x="Minutes of intense activity",y="Calories Burned")+theme_minimal()
```

row {data-height=350}
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###
**Q5: What's the relationship between calories burned and minutes of intense activity?**

We could see from the graph that there is an obvious relationship between intense activities and calories burned in total, but the origin was from around 1,000 calories and above, we were thinking the reason might be varied. For example, for some days, the exercise would be focused on heavy weight lifting, so it could be difficult for a device like the Fitbit to measure and record the calories burned very accurately.