---
title: "The Quantified Self"
author: "Qiong Duan"
date: "December 6, 2019"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    source_code: embed
---
---

```{r setup, include=FALSE}
library(flexdashboard)
library(readxl)
```




Data Preparation {data-orientation=columns}
==========================================================================

Sidebar {.sidebar data-width=300}
-----------------------------------------------------------------------
### My Activity Report in 2018

1. How many calories I burned per month and day?

On average, I burned 52,992 calories per month and 1,743 calories per day. Based on the research "An average woman needs to burn 1500 calories per day to lose one pound of weight per week", I should be fit in this way.

2. What is the driver of the number of calories burned? Is there any pattern?

My main activity for exercise is walk - so my guess is that the number of steps and distance are postive correlated with the calories I burned. Based on the graph, they do have a positive correlation. I did more activity during winter and spring and burned more calories; while during summer and fall, I tend to walk less and burn less calories.

3. Any difference between monthly and daily numbers?

No, they are basically aligned. February is the only exception because it only has 28 days. The daily calories burned / distance / steps in February are all above average, while the total monthly numbers are lower than the average.

4. Any interesting findings for the "per step" numbers?

During summer, even though I tend to do less activity and walk less, I burned calories more efficiently. I burned more than 0.36 calories per step during hot season, which is 1/3 more than the average calories per step I burned among the entire year. The weather does help me a lot!

5. How can I burn more calories?

Another interesting finding is that per one step - I tend to burn more calories and have a shorter stride during summer season. There is a negative correlation between the calories burned and distance per step. We can say both of the hot weather and shorter stride may drive the efficiency of calories burn. 

Column
-----------------------------------------------------------------------

### Data Preperation

```{r}
setwd("/Users/carolduan/Documents/HU/512/")
activity_data <- read_excel("Carol's Activity by month.xlsx")
knitr::kable(activity_data, caption = "Activity Data in 2018")
```

***
Here is my activity data in 2018, I prepared three types of data:

1. Monthly data 
  - Calories Burned	
  - Steps
  - Distance (Mile)
2. Daily data = monthly / day in a month
  - Calories Burned	
  - Steps
  - Distance (Mile)
3. Calculated per step data 
  - Calories Burned per step
  - Distance per step (Centimeter)
  

Activity Analysis {data-orientation=columns}
==========================================================================

Sidebar {.sidebar data-width=300}
-----------------------------------------------------------------------
### My Activity Report in 2018

1. How many calories I burned per month and day?

On average, I burned 52,992 calories per month and 1,743 calories per day. Based on the research "An average woman needs to burn 1500 calories per day to lose one pound of weight per week", I should be fit in this way.

2. What is the driver of the number of calories burned? Is there any pattern?

My main activity for exercise is walk - so my guess is that the number of steps and distance are postive correlated with the calories I burned. Based on the graph, they do have a positive correlation. I did more activity during winter and spring and burned more calories; while during summer and fall, I tend to walk less and burn less calories.

3. Any difference between monthly and daily numbers?

No, they are basically aligned. February is the only exception because it only has 28 days. The daily calories burned / distance / steps in February are all above average, while the total monthly numbers are lower than the average.

4. Any interesting findings for the "per step" numbers?

During summer, even though I tend to do less activity and walk less, I burned calories more efficiently. I burned more than 0.36 calories per step during hot season, which is 1/3 more than the average calories per step I burned among the entire year. The weather does help me a lot!

5. How can I burn more calories?

Another interesting finding is that per one step - I tend to burn more calories and have a shorter stride during summer season. There is a negative correlation between the calories burned and distance per step. We can say both of the hot weather and shorter stride may drive the efficiency of calories burn. 


Column {.tabset .tabset-fade data-height=400}
-----------------------------------------------------------------------

### Monthly Calories Burned

```{r}
library(grid)
library(gridExtra)
library(ggplot2)
library(hrbrthemes)
activity_data$Month <- as.character(activity_data$Month)
activity_data$Month <- factor(activity_data$Month, levels=unique(activity_data$Month))

grob <- grobTree(textGrob("Mean: 52,992", x=0.80,  y=.93, hjust=0,
  gp=gpar(col="red", fontsize=10, fontface="italic")))

ggplot(activity_data, aes(x=Month, y=Calories_Burned))+
    geom_histogram(stat = "identity", fill="#69b3a2", color="#e9ecef", alpha=0.9) +
    ggtitle("Monthly Calories Burned in 2018") +
    theme_ipsum() +
    theme(plot.title = element_text(size=15), axis.title=element_blank())+
    geom_hline(yintercept=52992,linetype="dashed", color = "red") + 
    annotation_custom(grob)
```


### Monthly Distance

```{r}
library(grid)
library(gridExtra)
library(ggplot2)
library(hrbrthemes)
activity_data$Month <- as.character(activity_data$Month)
activity_data$Month <- factor(activity_data$Month, levels=unique(activity_data$Month))

grob <- grobTree(textGrob("Mean: 88", x=0.85,  y=.80, hjust=0,
  gp=gpar(col="red", fontsize=10, fontface="italic")))

ggplot(activity_data, aes(x=Month, y=Distance))+
    geom_histogram(stat = "identity", fill="#E69F00", color="#e9ecef", alpha=0.9) +
    ggtitle("Monthly Walking Distance in 2018 (Mile)") +
    theme_ipsum() +
    theme(plot.title = element_text(size=15), axis.title=element_blank())+
    geom_hline(yintercept=87.63,linetype="dashed", color = "red") + 
    annotation_custom(grob)
```


### Monthly Steps

```{r}
grob <- grobTree(textGrob("Mean: 207,314", x=0.80,  y=.6, hjust=0,
  gp=gpar(col="red", fontsize=10, fontface="italic")))

ggplot(activity_data, aes(x=Month, y=Steps, group = 1))+
  geom_line()+
  ggtitle("Monthly Steps in 2018") +
  theme_ipsum() +
  theme(plot.title = element_text(size=15), axis.title=element_blank())+
  geom_hline(yintercept=207314,linetype="dashed", color = "red") + 
  annotation_custom(grob)
```


Column {.tabset .tabset-fade data-height=400}
-----------------------------------------------------------------------

### Daily Calories Burned

```{r}
library(ggplot2)
library(hrbrthemes)
grob <- grobTree(textGrob("Mean: 1,743", x=0.80,  y=.93, hjust=0,
  gp=gpar(col="red", fontsize=10, fontface="italic")))

ggplot(activity_data, aes(x=Month, y=Daily_Calories_Burned))+
    geom_histogram(stat = "identity", fill="#69b3a2", color="#e9ecef", alpha=0.9) +
    ggtitle("Daily Calories Burned in 2018") +
    theme_ipsum() +
    theme(plot.title = element_text(size=15), axis.title=element_blank())+
    geom_hline(yintercept=1743,linetype="dashed", color = "red") + 
    annotation_custom(grob)
```


### Daily Distance

```{r}
library(grid)
library(gridExtra)
library(ggplot2)
library(hrbrthemes)
activity_data$Month <- as.character(activity_data$Month)
activity_data$Month <- factor(activity_data$Month, levels=unique(activity_data$Month))

grob <- grobTree(textGrob("Mean: 3", x=0.85,  y=.80, hjust=0,
  gp=gpar(col="red", fontsize=10, fontface="italic")))

ggplot(activity_data, aes(x=Month, y=Daily_Distance))+
    geom_histogram(stat = "identity", fill="#E69F00", color="#e9ecef", alpha=0.9) +
    ggtitle("Daily Walking Distance in 2018 (Mile)") +
    theme_ipsum() +
    theme(plot.title = element_text(size=15), axis.title=element_blank())+
    geom_hline(yintercept=2.88,linetype="dashed", color = "red") + 
    annotation_custom(grob)
```


### Daily Steps

```{r}
grob <- grobTree(textGrob("Mean: 6,813", x=0.80,  y=.6, hjust=0,
  gp=gpar(col="red", fontsize=10, fontface="italic")))

ggplot(activity_data, aes(x=Month, y=Daily_Steps, group = 1))+
  geom_line()+
  ggtitle("Daily Steps in 2018") +
  theme_ipsum() +
  theme(plot.title = element_text(size=15), axis.title=element_blank())+
  geom_hline(yintercept=6813,linetype="dashed", color = "red") + 
  annotation_custom(grob)
```


Column {.tabset .tabset-fade data-height=400}
-----------------------------------------------------------------------

### Calories Burned per Step


```{r}
grob <- grobTree(textGrob("Mean: 0.26", x=0.85,  y=0.25, hjust=0,
  gp=gpar(col="red", fontsize=10, fontface="italic")))

ggplot(activity_data, aes(x=Month, y=Calories_Burned_per_Step, group = 1))+
  geom_point(color="#69b3a2")+
  geom_line(color="#69b3a2")+
  ggtitle("Calories Burned per Step") +
  theme_ipsum() +
  theme(plot.title = element_text(size=15), axis.title=element_blank())+
  geom_hline(yintercept=0.26,linetype="dashed", color = "red") + 
  annotation_custom(grob)
```


### Distance per Step


```{r}
grob <- grobTree(textGrob("Mean: 68", x=0.85,  y=0.50, hjust=0,
  gp=gpar(col="red", fontsize=10, fontface="italic")))

ggplot(activity_data, aes(x=Month, y=Centimeter_per_Step, group = 1))+
  geom_point(color="#E69F00")+
  geom_line(color="#E69F00")+
  ggtitle("Distance per Step (Centimeter)") +
  theme_ipsum() +
  theme(plot.title = element_text(size=15), axis.title=element_blank())+
  geom_hline(yintercept=67.96,linetype="dashed", color = "red") + 
  annotation_custom(grob)
```

### Calories vs. Distance per Step

```{r}
ggplot(activity_data, aes(x=Centimeter_per_Step, y=Calories_Burned_per_Step, group = 1))+
  geom_point()+
  geom_smooth(method=lm)+
  ggtitle("Calories vs. Distance per Step") +
  xlab("Distance per Step (Centimeter)") + 
  ylab("Calories per Step")+
  theme_ipsum() +
  theme(plot.title = element_text(size=15))
```