Data Recording

Lifestyle Data Recording

As I explore the recording process, I will share my embarrassing lifestyle captured in April and how I progressed to live a more consistent life in a later month. 

Since the purpose of the self-evaluation project is to analyze my lifestyle, recording meaningful data that can capture my day-to-day life is critical. Data is powerful because it provides insights that would otherwise be impossible to find if anyone was to analyze by simply looking back at one's life. It also allows the implementation of advanced Data Science techniques such as machine learning to create forecasts or to get suggestions on decision making. By utilizing Python and Notion API, I wanted to create an evaluation system that can be applied towards creating healthy habits, tracking project progress, or even evaluating the company's growth. In the end, I successfully assembled such a system by utilizing Python and various APIs. 

Thus, first, we will begin by explaining which variables best describe my daily performance and how they are recorded.

Notion provides a built-in function where basic coding can be implemented within the dataset. As shown above, these mathematical functions generate unique outputs(%) to demonstrate how I lived each day.

Notion: Built-in Coding feature

Total to-do list (%)

The above shows that when there are less than 14 tasks, it calculates the percentage to have less ratio. In other words, the more tasks I have each day(more than 14), the more it would affect the total percentage. 

Rise Time (%)

For the convenience of data handling, I have set the default wake-up time to 8:00 am. Thus, the input value 0 demonstrates waking on time at 8 am. A positive value would indicate oversleep, and a negative value would mean waking up earlier than 8.

How are the recorded data visualized?

The top plot is from the data recorded in April 2021, and the bottom is from November 2021. These two visualizations best represent the different lifestyles of different time periods. As shown below, there were numerous fluctuations and inconsistencies in April. On the other hand, the plot of November is more stable and fulfilling. With these data visualizations, different lifestyles and motivation levels can be easily observed.

Plot of April, 2021

Top Plot ​Description

Purple line: total percentage of how I lived each day (left y-axis)
Green dashed line: wake-up time (right y-axis)
Red bar: Reading (minutes)
Yellow bar: Meditation (minutes)
Red dot: Days I drank beer

Bottom Plot ​Description

Rise Time Histogram: It gives average wake up time and its distribution

Plot of November, 2021

  • The further apart the purple line is from the ​green dashed line, the more likely it was a productive day.

  • Total average in November (72.73%) was significantly higher than it was in April (47.25%). 

  • Right skewed Rise Time Histogram indicates that there were more early risen days, which was the case for November.

Connecting to Notion API

If these datasets were to be downloaded every time new evaluations are recorded, that would be extremely redundant. Hence, writing a code that directly connects to the Notion API enhances efficiency when retrieving data.

With such data, there are powerful things that can be done. In my case, these are a few analyzations that were done through python and R.

  1. Visual Analyzation (using Matplotlib Module)
  2. Various correlation tests among variables
  3. Automate visualization update using Python
  4. Connect to Slack API for hourly status update
  5. Linear Regression Model
  6. Time series Analysis (In Progress)


All codes can be viewed on Github

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