Overview

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 website http://quantifiedself.com/ is a great place to start to understand more about the QS movement.

The value of the QS for our class is that its core mandate is to visualize and generate questions and insights about a topic that is of immense importance to most people – themselves. It also produces a wealth of data in a variety of forms. Therefore, designing this project around the QS movement makes perfect sense because it offers you the opportunity to be both the data and question provider, the data analyst, the vis designer, and the end user. This means you will be in the unique position of being capable of providing feedback and direction at all points along the data visualization/analysis life cycle.

Objective

Develop a visualization dashboard based on a series of data about your own life. The actual data used for this project can range from daily sleep regimes, TV shows watched, types of food eaten, spending habits, commute times to work, travel habits, to blood pressure and nutrient intake. The amount of data you collect and harvest will differ based on your specified objectives

Ultimately the project must meet certain key objectives:

1. You must provide an written summary of your data collection, analysis and visualization methods, including the why you chose your methods, and what tools you utilized.

2. Your summary must outline ≥ 5 questions that can be evaluated using a data-driven approach. These questions should be more than just “How many miles did I run”, although a couple of your questions could be stated that way.

3. You must collect, manage, and store the data necessary for this visualization.

4. You must design and create an appropriate set of visualizations (try not to use just one type of visualization) within a dashboard/storyboard that provides insight into your specified questions, with a minimum of ≥ 1 interactive graphical element.

Summary

There is no question that technology is an enormous part of out lives nowadays. Most of us use technology some way or another on a daily basis, likely spending several hours on it. Smartphones are perhaps the most popular piece of technology. It allows us to connect with others, provides internet access, and contains many other features and apps for entertainment, work, and social purposes. There is no denying that smartphones are helpful to humans, but there does exist some concern over the amount of time that people expose themselves to these devices. In this project, we gathered screen time information from our iphones in the hopes of discovering interesting patterns, trends, and potential issues regarding our smartphone use. Specifically, we looked at the following questions:

1. Is there a correlation between pickups and screentime?
2. Is there a significant difference in screentime between weekends and weekdays?
3. What type of apps do we use the most on a daily basis? Does this vary significantly by day?
4. Do daily pickups show any significant patterns or trends?
5. Overall, how do Carlos’s an Edwin’s screen time usage and pickup data differ?

For data collection, we relied on the Apple’s built in “Screen Time” feature in the Iphone’s settings. This feature provides information on how much time the user has spent on their phone, as well as what they do when they use their phone. It also provides data on how many times the user picks up their phone (these are referred to as “pickups”). Unfortunately, Apple does not have a feature that allows its users to download this information. This had two major setbacks for our project. First, it meant we had to manually enter the data from our phone into an Excel file in order to be able to import the data to R, which was time consuming and also provided more room for human error. Second, Apple only allows the user to see the data for the last 28 days, so we were limited to just under a month of data for the purposes of the project, which is not a large sample size. It wouldve been intersitng to have a year of data and we could analyze how our phone usage varies throguhout the year. For example, I expect we would see higher cellphone usage during the winter vs the summer, since people stay home for longer periods in the winter).

In terms of analysis and visualizations, we relied on basic ggplot graphs to answer the questions stated above. The following graphs were used:

Scatter plot - used to look at the # of daily pickups throughout the period being analyzed, split by weekends and weekdays.

Correlation plot - used to look at the correlation between # of daily pickups and # of screen time hours.

Stacked bar chart - used to look at total hours by category

Box plot - used to look at the the difference in screen time hours by weekend/weekdays (not that this plot is not that helpful with such a small sample size, but it still provides some useful information)

By analyzing the data we can answer the questions posed above:

1. There does seem to be a slight positive correlation between screen time hours and pickups. This is noticeable on both Carlos and Edwin’s correlation plots, but Carlos’s plot makes this relationship more clear.

2. Edwin’s data shows very little difference between weekends and weekdays, while Carlos’s data shows much higher usage on weekdays vs weekends

3. Both of use social apps the most (by quite a large margin) followed by entertainment apps. Neither of us use our phones too much for reading or for education activies (we should probably change that!)

4. Edwin’s daily pickup data looks random. However, Carlos’s data shows a positive trend, indicating that he’s been using his phone more over the last few weeks vs earlier in the month

5. Several differences exist between Edwin’s and Carlos’s screen time usage. Some interesting differences are below:

1. Edwin and Carlos both have different trends in terms of phone usage. Carlos has highest usage during the weekdays, whereas Edwin tends to show very consistent use between weekdays and weekends.

2. Carlos has a lot more daily pickups than Edwin, but lower phone usage than him, which might make us conclude that Carlos tends to check his phone a lot more frequently, but use it in shorter spurts of time.

3. Edwin’s pickup data does not show any discernible trend, while Carlos’s pickup data shows a positive trend, with more usage in recent days vs earlier in the month