Table of Contents
- For this project we had to track our daily activity for seven subsequent days using a smartwatch that measures and records the data which would be then converted into a CSV format that would be then used for our main analysis.
- Our main goal of this project was to visualise our activities and track our sleeping schedule to learn more about our daily routine and think of different ways to improve on it to become healthier and more organised.
- The raw data are found in the “collected_data.csv” file while the main code is found in the “Living_Data_Project.pde” file.
For my sketch, I used my own hourly data. I had seven shapes that represent the seven days of the week starting on Monday and ending on Sunday. Each shape has 24 projection that represents the number of hours per day.
In my sketch, I used three variables:
- float “avSteps” _This variable represents the data from the “Steps (average per min)” column.
- float “avActivity” _ This variable represents the data from the “Mean Activity (g)” column.
- float “avSedentary” _ This variable represents the data from the “Sedentary Activity (proportion)” column.
Mapping of the Variables was Conducted as Follows:
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I mapped the Steps in my sketch to the length of the projections. (The longer the projection the more steps have been done).
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I mapped the activity in my sketch to the fill colour of the star-like shapes that are highlighted in the following picture:
(The redder the colour is the more Activity has been done, the more yellow the colour is the less activity has been done).
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I mapped the sedentary activity in my sketch to the stroke of the projections. (The bluer the colour of the projection is the more sedentary I was, the greener the colour is the less sedentary I was).
Based on the length and colour of the projection and star-like shapes, we were able to note that the longer the projection is, the redder the subsequent star-like shape, and the greener the colour of the projection is. This demonstrates that when our movements increase (Longer projections), our mean activity would also increase (Redder star-like shapes), while our sedentary activity would decrease (Greener projections). By looking at the sketch, we can also note that I spend very little energy on activities on Sunday which is not really a big surprise :p
This report was built using Processing Software & Language and the extracted data comes from a CSV file.
To get the main code up and running, please follow these simple steps:
- First, fork the project to your repository.
- Download the CSV file named “collected_data.csv” into your desired folder.
- Create a new Processing file in the same folder.
- Open the PDE file named “Living_Data_Project.pde”.
- Copy the code using the “Copy Raw Contents” options in the top ribbon.
- Paste your code into the newly created processing file and run the code.
- You should be able to get the same visualisation presented in the README file.
To get a quick look at the raw data or the code, you could simply do that by clicking the desired file without the need to download them.
This project provides a brief introduction to using Processing language in tackling real-world problems. Users can play around with the code to learn how the shapes could change while changing the underlying code. Furthermore, a scientist could use this code as a template for their own analysis of different data sets that shares the same number of variables (In this case three variables).
Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated. If you have a suggestion that would make this better, please fork the repo and create a pull request. Don't forget to give the project a star! Thanks again!
Distributed under the Apache-2.0 license. See LICENSE.txt for more information.
My Name – Shehab Shahin
Email: shahin.shehab21@gmail.com