Deep Work Dashboard 2023

This is a detailed article about my Deep Work Dashboard 2023 project where I analyzed my deep work data from 2023. I used the following tools:

Quick Links:

Google Looker Dashboard | LinkedIn Post

Table of Contents:

1. Introduction

2. Process

3. How I track my Time

4. Google Sheets

5. Google Looker

1. Finished Project

2. Overview of Data

3. Insights

4. What I Learned

5. Conclusion

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Introduction

This data visualization focuses on my deep work time from January 1, 2023 to December 31, 2023. The main metric is time spent on a deep work activity in hours and to compare it with 2022.

If you're interested to see how I track my data check out the [How I tracked my data] section below.

Goal: To get an overview of my deep work time for 2023.

Question: What have been my habits for deep work? Are there certain days I spend more time on deep work? Which category do I spend the most time on? etc.

How is this dashboard used? 

• Reflect on my productive time. It lets me reflect on my year as a whole, where have I made progress, where can I adjust my schedule or take more time off.

• Lets me prepare for the next year. Based on this I can adjust what I want to fix or change for the upcoming year.

• Keep me accountable. I view it as a "leveling up system" like a video game. Every time I work on a topic (e.g. data analytics or math) I'm increasing my level. It gamifies my work. You can read more about my system in this article.

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Process

Overview: The original data was inputted, cleaned and manipulated in Google Sheets, then I used Google Looker exclusively to create this dashboard (sadly no Figma this time around). I did make a similar dashboard last year if you want to see that, check out my article here. 

How I Tracked My Time

First, before putting my data into Google sheets I needed a way to track my time. I use an app called Forest. Every time you start a timer or a stopwatch you plant a tree. If you try to leave the app the tree dies. And your forest (which is where all your planted trees are) has a dead tree. This encourages you to focus because you don't want an ugly tree in your Forest. It seems like a silly idea but it's worked for me.

When I enter a deep work session. I put the stopwatch on (plant a tree), pick the appropriate label (or category), and write the description. This lets me get right into the work without having to think about anything else like data collection. 

Why Do I Use this App?

The app does let you export your data but only if you have the pro version. If you're asking why do I use this tool instead of something that can track my time on apps/websites (like RescueTime) that track my data on a computer:

1. It's because my deep work doesn't always involve a computer (e.g. practicing Japanese). 

2. I use the timer as a focus tool and it doesn’t let me go to certain apps while the timer is on like Instagram, so it helps keep me focused. 

3. It only takes me about  5-10 minutes a day to record my data from the app. 
   

Why didn’t I export my data? 

• It was not an available feature when I started this process. 

• I was  also not diligent about adding notes to every time I planted a tree, so the data would be unclean (and I don’t remember exactly what I did on X day of 2023). 

Now that I have this feature next year I will be utilizing it for my 2024 data and will adjust my data cleaning methods accordingly. 

Google Sheets

Then every day (or so) I would take the data I gathered in the Forest app (the app lets you view your activities in a list view) and input it manually into Google Sheets. I input the date, time, activity, and day total. The hours column is done at the end of every week. Day of the week and month is done automatically, I just have to fill down the row. 

Each row is a record of the activity I do. Below are the columns I have:

1. Hours - The total hours per week (Sunday-Saturday)

2. Date - The date of the activity

3. Day of the Week - The day of the week that the activity was done

4. Month - The month that the activity was done

5. Time - Time spent on the activity (in minutes)

6. Activity - A description of the activity (e.g. blog post or LinkedIn Dashboard)

7. Day Total - The total time (in minutes) per day

Below is a screenshot of my deep work log for the week of 1/29/23 to 2/3/23. At some point in the future I will provide a link to make a copy of this worksheet if you’re interested. So stay tuned for that. 

I also created a dashboard in my spreadsheet (shown below). The dashboard displays the time spent and count of each type of deep work. Along with the monthly totals and how much time was spent for each day of the week.

Clean the Data

I prepared my data for analysis with a new Google Sheet, I used Google Sheets over BigQuery or uploading a CSV because:

• The data I had was relatively small

• If I need to make any last minutes changes in the spreadsheet it’s easy to do. 

• It’s easy to get started with a Looker dashboard. 
  

In the cleaning process I removed redundant columns and sheets:

• Deleted the previous dashboard sheet and the 'Day Total' column, as I planned to recreate these in Looker.

• Removed the 'Hours' column since Looker can handle these computations itself. 
  

To have a year-over-year comparison I:

• Copied in my 2022 data for a direct comparison with 2023.

• Inserted week Number which was extracted from the Date column so I could look at the total hours spent over a time period by weeks. 

• Used a nested IF statement to replace numerical identifiers with actual category names (Column F), enhancing readability for grouping in analyses. For example, `IF(category_number=1, 'Business', IF(category_number=2, 'General', ...))`.
  

Google Looker 

Then when I created my Looker Dashboard I added this new Google Sheet as a  new data source with the specific sheet. I created another sheet that had all of the dates I had deep work and created a column displaying my “streak”. It basically it counted how many days in a row I did deep work. This was the second data source I added into my Looker Dashboard and I used that for the “Longest Streak” KPI. 

My main goal was to create a compact dashboard displaying key metrics in one screen. I included the following KPIs (Key Performance Indicators):

• Longest Streak (Days)

• Total Time (Hr)

• Average Session in Minutes

• Total Sessions
  

I also had the following graphs:

• Total Time per Month (2022 vs. 2023)

• Total Time per Day of the Week (2022 vs. 2023)

• Top 5 Categories (2023)

• Total Time Spent over Week Number (2023) 
  

I included a universal filter. This lets you filter out the category type and applies to every graph. For my dashboard I kept it simple and focused on clarity. I laid out my dashboard as follows: 

• Title and subtitle at the top.

• Universal filter and navigation on the top right. 

• The main KPIs across the top (to display them first). 

• Then the graphs below that. 
  

All of the elements are in a grid layout and have ample amount of whitespace. 

I also did not use Figma for my background, I wanted to try using everything in Looker. It looks pretty clean but I still wish there were a few things I could customize. 

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Finished Project

View my dashboard using Google Looker here.

Note: If you came directly from LinkedIn you’ll see that my screenshot here is different from my post. This is because I changed: Total Time per Day of the Week (2022 vs. 2023) and Total Time Spent over Week Number (2023). I changed the dates (e.g. Day of the Week & Week Number) in chronological order. I can’t edit or change the LinkedIn post but the live dashboard is correct. 

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Overview of Data

Below I've included an overview of the data (all in hours).

1. Longest Streak (Days) - 103 days (106% increase compared to 2022)

2. Total Time (Hr) - 947 hours (76.6% increase compared to 2022)

1. Average Session in Minutes - 44 minutes (56.9% increase compared to 2022)

2. Total Sessions - 1283 (12.5% increase compared to 2022) 

3. Total Time per Month (2022 vs. 2023) - 

   1. 2022

      1. January - 37

      2. February - 37

      3. March - 38

      4. April - 52

      5. May - 27

      6. June - 45

      7. July - 52

      8. August - 68

      9. September - 61

      10. October - 31

      11. November - 46

      12. December - 43

   2. 2023

      1. January - 65

      2. February - 64

      3. March - 79

      4. April - 50

      5. May - 74

      6. June - 42

      7. July - 57

      8. August - 90

      9. September - 112

      10. October - 116

      11. November - 103

      12. December - 94

4. Total Time per Day of the Week (2022 vs. 2023)

   1. 2022

      1. Sunday - 39

      2. Monday - 80

      3. Tuesday - 95

      4. Wednesday - 112

      5. Thursday - 93

      6. Friday - 91

      7. Saturday - 27

   2. 2023

• Sunday - 34

• Monday - 181

• Tuesday - 185

• Wednesday - 193

• Thursday - 192

• Friday - 141

• Saturday - 21

1. Top 5 Categories (2023)

   1. Data Science - 460

   2. Writing - 109

   3. Reading - 107

   4. Career - 80

   5. Business - 75

2. Total Time Spent over Week Number (2023) 

   1. I won’t be writing down the sum of every week.

   2. But the range of these hours is: 2.5 - 32

   3. Average hours per week: 18

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Insights

General Insights

Below are general insights from the dashboard:

• Deep Work Time

  • The category I spent the most time on was data science with 460 hours.

  • Wednesday was my most productive day at 193 total hours. The other weekdays were similar in the time spent except for Friday.

  • My average time per week in hours was 18 which I feel was productive. But to note, in the beginning of the year I didn’t spend as much time on deep work my average increased once I started my job. 

• Time Management

  • I was not as productive on the weekends. Which makes sense since I take breaks on the weekends except when I’m working on my freelance gig. 

  • My average time per month was around 79 hours. The month that had the most was October with 116 hours. The least was in June with 42 hours. In June I was honestly feeling discourage with my job search and didn’t spend a lot of time working on projects. 

• Work Patterns

  • My most productive weekday was Wednesday at 193 hours and the lowest was Saturday with 21 hours. I try to take the weekends off from work (as much as I can), so it’s understandable why Saturday is the weekday with the lowest and then Sunday is the next one. 

  • I spent the most time on data science at 460.67 hours and the least on general (which is not even shown on the graph) at 44.83 hours. This includes anything that doesn’t fall into my other categories. My job is in data so this is one reason why my data science time increased so much, especially since I got my job in July 2023. 

• Notes: 

  • The reason why 2022 deep was so low wasn’t because I didn’t work. In 2022 I did work a full-time job but it was not in data so I didn’t track the time there. I’m sure if I tracked time spent drafting legal documents I’m sure that would’ve been a better comparison. 

  • So while my time in 2023 did increase significantly it’s not just because I worked more, it’s that I worked more on what I considered deep work (data science). 
    

Reflection

Below are some general reflections I’ve had about my deep work time. 

• Through all of this I realized I have a pretty good system down for deep work. Taking breaks on the weekends allow me to focus on the weekdays and get my goal of 10 hours per week. 

• I’ve been able to increase my deep work time to 45 minute blocks on each activity work for me. The techniques I’ve been using to get into deep focus (like time blocking, listening to music/podcasts only aka don’t watch anything with video format).
  

The So What

What does this mean? How can I change my thinking or habits? 

• Schedule intensive data science work for Wednesdays, which are my most productive days. 

• I didn’t spend as much time learning about business and it showed in my analysis at work, I did the technical portion of my job well but I struggled with tying it into the job. Which I hope to remedy this year. 

• Reflected on work-life balance so I don’t experience burnout, from this I decided to take a step back from a new projects I didn’t deem as a priority in the last few months. 
  

What’s Next 

This is what I want to look at for my 2024 deep work dashboard/analysis. That will be published in 2025. 

• Look at the impact of my deep work on career progression, am I moving to a more senior analyst role? Or learning more about other data fields like data engineering or data science, and how has that helped me?

• Analyze the actual time (get the date and time I spent on each deep work session) to see what kind of correlation or patterns I can find there. 

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What I Learned

1. How to compare two time periods in Google Looker (e.g. 2022 vs. 2023) and show the percentage change, for my KPIs at the top. 

2. How to display and manipulate dates better: years, weeks, months. Looker can be finnicky about dates, especially if you’re trying to order by a date range (in chronological order.

3.  What other things I could look at for a a deeper analysis like the actual time periods I spent working on specific projects. 

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Conclusion

Overall this was an enjoyable mini project. I wanted to create another dashboard in a similar style to my LinkedIn Metrics Dashboard. The dashboard was more popular than I thought. I wasn't sure if people would be interested in viewing my deep work/productive time. But I was proven wrong. Hopefully this article answered any questions about the project. I'll continue making another dashboard for 2024 in 2025. It’s also a good way for me to compare my data visualization skills from then to now. I’m excited to see what 2024 has in store for me.