---
title: 'Final project for Data Visualization'
author:
date:
output:
flexdashboard::flex_dashboard:
storyboard: true
---

### Overview of the Quantified Self Data Visualisation

```{r}

```
![''](fitness.jpg)

***

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. The objective of the project is to develop a visualization dashboard based on
a series of data about my own fitness related data. The following answers
are summerised using visual methods.

1. Does feeling of activeness has an effect on step-counts on day by day basis?
2. How does weight reduces if body feeling active or inactive?
3. Which mood gives the longer hours of sleep?
4. How does hours of sleeps related to calories burned?
5. Which mood helps in reducing more number of calories?
6. In which year have the highest/lowest total calories burned?
7. In which year have the longest/lowest total hours sleep?
8. Does activeness improves the hours of sleep?

### Data Collection

```{r, echo=FALSE, warning=FALSE, message=FALSE}
library(knitr)
library(kableExtra)
library(ggplot2)
library(dplyr)
library(lubridate)
library(plotly)
```

```{r}
dat <- read.csv("fitness.csv")
head(dat, n=20) %>%
kable() %>%
kable_styling()
```

***

The data is collected from one of the fitness app that gives analytics of working-out/excersie behaviour to understand if it derives the activeness or not. The factors which might influece the activeness are step count, mood, hours of sleep, calories burned which might effects the weight of a person. Visualization methods used are:

1. line plot is created to plot the step-counts and weight over the days
2. bar plot is used to show the comparison of total sleep/calories burned year wise.
3. box plot is used to compare the spread of hours of sleep mood wise.
4. Pie chart is used to understand the calories burned with the mood.

### 1. Does feeling of activeness has an effect on step-counts?

```{r, message=FALSE, warning=FALSE}
dat <- read.csv("fitness.csv")

g <- ggplot(dat, aes(x=seq_along(step_count), y =step_count, col=Activeness))
g <- g + geom_line() + xlab("Day by Day") + ylab("Steps count") + ggtitle("Day by day steps-count along with activeness")
ggplotly(g)
```

Hand Computation: Y Y 16 X1 7.2 X1 X2 - - 9 - Mean Y = 5 Mean...