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  • Writer's pictureKelly Adams

Deep Work Dashboard 2022

Updated: Feb 17, 2023

This is a detailed article about my deep work dashboard where I visualized my time spent in deep work. I used the following tools: Tableau, Google Sheets, and Figma.

Quick Links:

Table of Contents:

  1. Introduction

  2. Process

    1. Google Sheets

    2. Tableau

    3. Misc

  3. Finished Project

  4. Overview of Data

  5. Insights

  6. What I Learned

  7. Conclusion



This data visualization focuses on my deep work time from January 1, 2022 to December 31, 2022. This is part of a challenge I've given myself: create a dashboard in 4-6 hours. This is to practice creating quick dashboards so I can find out what I need to improve (data cleaning, labels, etc.). The dashboard took me about 3 hours to complete. The main metric is time spent on a deep work activity in both minutes and hours.

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 2022.

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? Helps keep myself accountable to deep work. 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.

What is Deep Work?

The idea of "deep work" is based off of Cal Newport's book called Deep Work. It is "professional activities performed in a state of distraction-free concentration that push your cognitive capabilities to their limit". These efforts create new value, improve your skill, and are hard to replicate. For me it's time I spend working on difficult tasks like writing blog articles, working on data analytics projects, or reading non-fiction books.



Overview: The original data was cleaned and manipulated in Google Sheets, then I used Tableau Public to create an interactive dashboard. I used Figma to create the background.

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.

The app doesn't let you export your data (at least not what I could find). If you're asking why do I use this tool instead of something that can track my time on apps/websites (like RescueTime) and let export my data. It's because my deep work doesn't always involve the computer. And while this takes longer than if I was able to export my data I like it because it lets me control my data and set it up how I want (and the timer works as a focus tool). It only takes me about 5 minutes a day to record my data from the app.

Google Sheets

Then every day 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 into Google Sheets. 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. If you're interested in this spreadsheet you can copy it in Google Sheets here (note: you need to be signed into a gmail account for this to work).

I also created a basic 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.

Tableau Public

Before moving my data into Tableau Public I edited my spreadsheet. I deleted the following:

  • sheet with my dashboard

  • the Hours columns

  • the Day of the Week column

  • the Month column; and

  • the Day total column

After that all that was left was the: date, time, category (1-7), and activity columns. Since Tableau can calculate the day of the week and month. Along with other calculations. It made it easier to convert this to a .csv file. I imported the data (in .csv format) into Tableau Public.

My main goal was to create a compact dashboard displaying key metrics in one screen. I included the following metrics:

  • Total Time (in minutes and hours)

  • Average Time (in minutes and hours)

  • Average Time per Week (in minutes and hours)

  • Total Entries

I also had the following graphs:

  • Time per Calendar Day - displays the sum of the time for each calendar day. To see if I could gain any trends from this

  • Time per Month - shows the total time for each month for 2022

  • Time per Day of the Week - the total time for each day of the week

  • Categories - time spent for each type of category (e.g. reading, writing, data analytics)

  • Top Activities - the top 5 activities I spent the most time on

All of these are displayed in minutes and hours depending on which page you're looking at.

I also included a universal filter. This lets you filter out the category type. The filter applies to every graph but the Top Activities. I wasn't able to get the top 5 activities for each type of activity only all of the activities combined. Like if I wanted to see my metrics for the Data Analytics Category everything would change (top metrics and charts) except for the top activities. This lets me see the differences in my habits between categories.

For my dashboard I kept it simple and focused on clarity. I had my title and subtitle (time period) at the top. Along with my universal filter and navigation on the top right. The topic metrics along the top. Then the graphs below that. All of the graphs are in a grid layout.


I created the background using Figma. For this dashboard I focused on using Tableau as much as I could. Meaning I made the top metrics (e.g. total time) in Tableau instead of Figma. I created a more compact view so you can see all of the charts/graphs in one screen. The color scheme was based off of the Google Drive logo since I use Google Sheets for my deep work log. The icons were from a Figma plugin called Iconify.


Finished Project

View my dashboard in Tableau Public. The first photo is the Minute view (showing all of my time in minutes).

The second photo is the Hours view (showing all of my time in hours).


Overview of Data

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

  1. Total Time: 32,210

  2. Average Time: 28.23

  3. Average Time per Week: 619.42

  4. Total Entries: 1,141

  5. Time per Calendar Day

  6. Time per Day of the Week

    1. Sunday: 2,310

    2. Monday: 4,780

    3. Tuesday: 5,700

    4. Wednesday: 6,735

    5. Thursday: 5,600

    6. Friday: 5,495

    7. Saturday: 1,590

  7. Time per Category

    1. Reading: 8,905

    2. Writing: 8,075

    3. Data Analytics: 5,395

    4. General: 4,345

    5. Math: 4,300

    6. Business: 845

    7. Website Development: 345

  8. Top Activities

    1. Blog Posts: 4,000

    2. Notebook: 2,020

    3. LinkedIn Posts: 1,620

    4. Weightlifting Project: 1,330

    5. Python Bootcamp: 1,310



Below are general insights from the dashboard:

  • The activity I spent the most time on was blog posts with 4,000 minutes or 66 hours.

  • Wednesday was my most productive day at 6,735 minutes or 112.25 hours. The other weekdays were similar in the time spent.

  • I was not as productive on the weekends. Which makes sense since I take breaks on the weekends except when I read or the occasional blog post.

  • My average time per month was around 40 hours. The month that had the most was August with 67.83 hours. The least was in May with 27 hours. In May I went on vacation for 7 days and didn't do anything productive.

  • My most productive calendar day was the 1st at 23 hours and the lowest was on the 17th at 10.92 hours. It may be because the 17th fell on the weekends more than any other day of the month.

  • I spent the most time reading non-fiction books at 148.4 hours and the least on web development at 5.8 hours. Because of this I decided to get rid of web development category and replace it with a career category (e.g. mock interviews, networking, etc.). I also need to spend more time on data analytics, I was slacking last year.

  • My average time per week in hours was 10 which I felt was a reasonable goal and one I reached every week. I spent about 2 hours every weekday in deep work.

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. Working in 30 minute chunks on each activity work for me. It allows me to focus on the task but I don't feel like the time is impossible to do (like having to focus for an hour). I need to spend more time on data analytics. Last year I slacked a bit and it showed, I only completed 3 portfolio projects last year.


What I Learned

  1. How to create navigation buttons in Tableau.

  2. Display the min and max values for specific charts

  3. Create my own calculated field (converting the minutes value to hours)

  4. Having the universal filter affecting the top metrics (e.g. total time)



Overall this was an enjoyable mini project. I wanted to create another dashboard in a similar style to my LinkedIn Metrics Dashboard. I tried to use Tableau as much as possible for this project. I also wanted my universal filter, in this case the categories, to be more useful. I've used it frequently to see the difference between the category times. I used Figma as little as possible except to create the dashboard background and design.

As I said in my LinkedIn Metrics Dashboard article, it may not be realistic to complete a dashboard end-to-end in 3 hours, it was still a good experience. After posting on LinkedIn I already had a few critics that were helpful. One of the reasons I post on LinkedIn is to gain feedback on things I may have missed. I was also familiar with the dataset so it was easy for me to clean and manipulate.

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 probably make another dashboard for 2023. It will be a great way for me to compare my data visualization skills from then to now.


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