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title: "Final Project"
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# **Introduction**

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### **Overview** 

This project aimed to collect and analyze data about three individuals’ daily activities, behaviors, 
and habits. The goal was to help the group members better understand themselves and make informed decisions about their lifestyle choices.

Coined in 2007 by two Wired Magazine editors, Gary Wolf, and Kevin Kelly. Since then, the “Quantified Self” movement has gained popularity. Thus, we adopted it and started an analysis project as its extension with the help of wearable devices, mobile apps, and other technologies which track various aspects of our lives.

We tracked various metrics, such as sleep, steps, workout, screen time, and food spending. We used devices like fitness trackers, smartwatches, and Apple Health to collect data and used R to analyze and visualize that data.

The data collected through this process helped us identify patterns in our daily life, especially in the physical health field, and make more informed decisions about our health and well-being.

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### **Objective** 

The objective of this quantified self-data collection and analysis project is to gather information about our daily activities, behaviors, and habits in order to gain insights into our overall health and well-being. We can better understand our patterns and behaviors by collecting data on various metrics such as exercise, sleep, food spending, and screen time. This can help us identify areas where we can improve our lifestyle, optimize our performance, or even detect potential health issues.

In addition, the quantified self-data collection and analysis project aims to empower group members with the tools and knowledge to take control of their health and well-being. 


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### **Data Acquistion & Storage** 

Our group collected life data from various sources, such as Apple Health, fitness apps, and bank apps. Once the data was collected at the end of the week, we recorded it in an excel report, making it easy for data analysis and access to all team members.
 
For example, the steps data from the Apple Health app was used to input the weekly average number of steps taken. The weekly food spending data was extracted from our bank accounts and was then inputted into the excel file.  

Overall, data acquisition and storage were critical components of our data analysis process and helped us derive insights and make informed decisions.

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### **Visulization Methods** 

Many visualization methods are used to present data and information in a visual format that is easy to understand and analyze. We have used the following visualization methods in our project:

Bar charts: A graphical representation of data in which bars of different lengths represent different categories or values.

Line charts: A graphical representation of data in which a line connects data points to show trends over time.

Scatter plot: A graphical representation that uses dots to represent values for two different numeric variables. The position of each dot on the horizontal and vertical axis indicates values for an individual data point. It was used in our analysis to examine relationships between 2 variables.

# **Avg Weekly Sleep Hours Analysis**

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```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)

install.packages("maps",repos = "http://cran.us.r-project.org")
install.packages("scatterpie",repos = "http://cran.us.r-project.org")
install.packages("ggiraph",repos = "http://cran.us.r-project.org")
install.packages("dplyr",repos = "http://cran.us.r-project.org")
install.packages("ggplot2",repos = "http://cran.us.r-project.org")
install.packages("plotly",repos = "http://cran.us.r-project.org")
install.packages("rnoaa",repos = "http://cran.us.r-project.org")
install.packages("usmap",repos = "http://cran.us.r-project.org")
install.packages("gdtools",repos = "http://cran.us.r-project.org")
install.packages("readxl",repos = "http://cran.us.r-project.org")

library(maps)
library(scatterpie)
library(ggiraph)
library(dplyr)
library(ggplot2)
library(plotly)
library(rnoaa)
library(usmap)
library(gdtools)
library(readxl)
```

```{r, echo=FALSE, message=FALSE}
life_data <- read.csv("/Users/samanthama/Downloads/life_data - Dataset.csv")
sleep<-ggplot(data=life_data, aes(x=Week, y=Weekly_Sleep_Time)) +
  geom_bar(stat="identity")
sleep

```

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The bar chart provides information about the average number of hours we sleep during a week. The highest is 60 hours during week eight, ten and fourteen while the lowest is 40 hours during week nine. 60 hours of sleep during a week, accounts to more than 8 hours of sleep on average per day during that week which is more than the recommended sleep time for adults according to ‘The American Academy of Sleep Medicine and the Sleep Research Society’*. 

However, 40 hours of sleep during a week, accounts to a little less than 6 hours of sleep on average per day. (Which is below the recommended hours of sleep for an adult)

On average we sleep for around 53 hours in a week (more than 7 hours per day) which is healthy. With all of us having full time jobs as well as grad student course work, we believe that we have had a healthy amount of sleep time. 

References:
*Are You Getting Enough Sleep? (cdc.gov)
*Recommended Hours of Sleep for adults is 7 hours or more

# **Average Weekly Screen Time Analysis**

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```{r,echo = FALSE, message = FALSE}
data1 <- read.csv("/Users/samanthama/Downloads/life_data - Dataset.csv")
ggplot(data1, aes(x=Week, y =Weekly_Screen_Time_In_Hours
)) +
  geom_line(color = "red")+
  labs(title="Avg Weekly Screen Time", x="Week", y="Weekly Screen Time In Hours")
```

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-------------------------------------

This data refers to the amount of time we spent using electronic devices such as smartphones, tablets, computers, and televisions over the course of a week. Screen time has become increasingly prevalent in recent years due to the growing use of electronic devices in everyday life, especially with the shift to remote work and virtual learning during the COVID-19 pandemic.

During the holiday season, our screen time was much shorter because we have taken time off of work and spent more time offline with our loved ones. Outside of the holiday season, since we work in jobs that require extensive computer use, this resulted in higher weekly screen time than those who work in other fields. 

# **Average Weekly Workout Hours Analysis**

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```{r,echo = FALSE, message = FALSE}
x <- life_data$Week
y <- life_data$Weekly_Workout_Hours
plot(x, y, main = "Avg Weekly Workout Hours", xlab = "Week", ylab = "Avg Weekly Workout Hours")
```

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The scatter plot shows that our weekly workout hours fluctuate based on seasonality. While approaching Christmas, we felt less motivated to work out and the numbers decreased week over week. However, we still manage to keep it above 2 hours a week. Starting in January, the New Year motivation worked it magic and encouraged us to increase our weekly workout hours up to 6 hours a week.Overall, we managed to keep our workout hour between 3 - 4 hours a week, which falls within the healthy range.


References: 
* https://www.mayoclinic.org/healthy-lifestyle/fitness/expert-answers/exercise/faq-20057916#:~:text=Answer%20From%20Edward%20R.%20Laskowski,of%20moderate%20and%20vigorous%20activity

# **Average Weekly Food Spend Analysis**

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```{r,echo = FALSE, message = FALSE}
data2 <- read.csv("/Users/samanthama/Downloads/life_data - Dataset (1).csv")
ggplot(data2, aes(x=Week, y = Weekly_Food_Spend)) +
  geom_line(color = "blue")+
  labs(title="Food Spend", x="Week", y="Weekly Food Spend")
```

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Consumer expenditure on food in New York City is an important part of the city's economy, as it is one of the largest urban centers in the United States. New York City's diverse population has resulted in a wide variety of food options, ranging from street vendors to high-end restaurants. 

The graph on the left illustrates the average food expenditure among the three of us, which includes both food at home and food outside. We can see that our spend on food was the highest in Week 7, which was the week of Thanksgiving, and also Week 10, the week of Christmas. The holiday season is often associated with feasting and indulging in special treats, leading to increased spending on food and drinks. 

It's important to note that consumer expenditure on food in New York City may have been affected by the ongoing COVID-19 pandemic and associated economic impacts. The pandemic has led to the closure of many restaurants and other food businesses, as well as changes in consumer behavior and spending patterns.

# **Average Weekly Steps Analysis**

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```{r, echo=FALSE, message=FALSE}

steps<-ggplot(data=life_data, aes(x=Week, y=Weekly_Steps)) +
  geom_bar(stat="identity")
steps
```

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The horizontal bar chart gives us a view about the average number of steps taken during a week. The highest is 57,890 steps during week eight, while the lowest is 29,727 steps during week thirteen. 57,890 steps during a week, accounts to 8,270 steps on average per day during that week which is more than the recommended number of steps needed for an adult according to cdc.**

The data shows us that our step count on average was higher than the recommended weekly step count of 30,000 steps per week for all the weeks except for week 13. 

With our hybrid and work from home jobs, our lifestyle isn’t too sedentary as expected. Based on our weekly step count, we are physically healthy. This should reduce the risk of heart disease, stroke, high blood pressure, diabetes and obesity in the future. 

References:
*https://www.healthline.com/health/average-steps-per-day#guidelines
*“For more health benefits, the CDC recommends upping that goal to 300 minutes. This equals about 30,000 steps per week (just under 5,000 steps per day).”

# **Questions and Conclusion**

Questions:

What is the average weekly sleep in the group during the data collection period? And, based on the data we gathered, have we had enough sleep?

How much screen time do we usually spend every week?

How much time do we spend in the gym per week? And, if the numbers fluctuate, what could be the reason behind it?

How does the weekly food spending fluctuate during the data collection period? And what is the trend behind the data?

How many steps did we take every week? And, are we physically healthy based on the numbers we face?

Are we overall healthy young adults?



Conclusion:  

Are we overall healthy young adults? This was the main question we wanted to answer for our final project. With physical and mental health issues on the rise (especially during and after the pandemic) due to isolation, unhealthy habits  and an unbalanced lifestyle, we wanted to gauge our overall health by monitoring our data. Having a full time job as well as graduate student course work, can be extremely stressful. This project gave us an opportunity to document our numbers and provide information about our health. What is working and what areas of our lives need more work. From our data we are getting enough sleep during most of our weeks, we are getting a healthy dose of physical activity from the weekly steps data and number of workout hours. One area of improvement is to reduce the amount of weekly screen time. (which can be difficult given how glued we are to our devices), but this is an area we can work on.