Notion for Habits: How To Build A Habit Tracker In 2022 (free template)

TL;DR

Create a daily “my habits” calendar database with checkbox properties for each habit and a date property that anchors entries to the calendar.

Briefing Cornell Notes

Briefing

A habit tracker in Notion can be upgraded from simple checkboxes into an analytics dashboard by linking a “daily habits” calendar to a separate “analytics” database. The key move is building an analytical table that uses relations and formulas to calculate completion rates, then visualizing those rates as progress bars or rings—so daily effort turns into measurable trends.

The workflow starts with a calendar-style database called “my habits,” where each day contains checkbox properties for multiple habits (five in the example) plus a date property that anchors the entry to the calendar. To turn that raw activity into metrics, a second database named “analytics” is created as an analytical table. A relation property connects “analytics” back to “my habits,” with the relation configured to show the linked daily pages and no limit on related entries. Once the connection is in place, every daily page automatically links to the corresponding analytics page.

To compute daily completion, a formula property in the “my habits” pages converts each checkbox into a numeric value using a two-number function (0 for unchecked, 1 for checked). Those five values are summed and divided by the total number of habits to produce a percentage. The result is rounded down using a floor-based approach (scaling by 1,000, flooring, then dividing by 1,000) to keep the metric stable. That percentage is then formatted as a progress bar (with configurable color and optional percentage display), and the formula is surfaced in the calendar view so completion visibly rises and falls as checkboxes are toggled.

Next, the analytics database is expanded with roll-ups that measure completion by individual habit across all linked days. Each roll-up uses the relation to pull in daily checkbox data for one habit (e.g., “meditate,” “publish article,” “Read 50 pages,” “tweet 2 times,” “workout”) and converts “percent checked” into a progress ring with distinct colors per habit. A final formula aggregates those roll-up values into an overall “success rate” across the set of habits, again using floor-based rounding and formatting it as a bar or ring.

For deeper analysis, the analytics table is duplicated into views like “total” and “weekdays.” The “weekdays” view filters out the “habit completion rate” page and creates seven pages—one for each day of the week. Each weekday page is manually linked to the appropriate dates in the calendar (e.g., Thursday entries), allowing the roll-ups to update based on which days are connected. The same structure can be extended to other time slices like quarters or additional months.

Finally, the system can automate habit completion when the underlying data lives elsewhere. An “article database” example is built with a relation back to habits via a “published” link. Instead of manually checking “publish article 2 times,” the tracker converts the roll-up count of related articles into a formulaic checkbox, so the habit’s completion updates automatically when new article records are marked as due on a given date. The result is a flexible habit tracker that combines manual checkboxes, relational analytics, and automated progress updates—without losing the calendar-style simplicity people want for daily routines.

Cornell Notes

The setup uses two Notion databases: a daily “my habits” calendar with checkbox properties, and an “analytics” table that links back to those daily entries. Relations let each analytics page pull in all related daily pages, while formulas convert checkboxes into numeric values to calculate daily completion percentages. Roll-ups then summarize completion by each individual habit across linked days, and another formula aggregates those roll-ups into an overall success rate. Duplicating the analytics view into “weekdays” (Monday–Sunday pages) enables day-of-week performance analysis, and connecting external databases (like an “article database”) can automate habit checkboxes based on related record counts.

How does the tracker turn checkbox habits into a measurable daily completion rate?

Each daily habit checkbox is converted into a number using a two-number function: unchecked becomes 0 and checked becomes 1. A formula sums the five habit values, divides by 5 to get a completion percentage, and then rounds down using a floor-based method (scaling by 1,000, flooring, then dividing by 1,000). That percentage is formatted as a progress bar (or ring) and displayed directly in the calendar view so completion visibly updates as checkboxes change.

What role does the “analytics” database play, and how is it connected to daily habit pages?

“Analytics” is the metrics layer. A relation property inside “analytics” links back to “my habits” (the daily calendar database). The relation is configured to show the linked daily pages and uses a single analytics page per day (so each daily entry maps to the correct completion-rate record). After the relation exists, the calendar can be filtered so every daily page automatically connects to the right “habit completion rate” analytics page.

How are individual habits summarized across days without manually calculating totals?

Roll-ups in the “analytics” database summarize each habit across all related daily pages. For each habit (e.g., meditate, publish article, Read 50 pages, tweet 2 times, workout), a roll-up selects the relation to daily habits and then uses a “percent checked” calculation from the corresponding checkbox property. Each roll-up is displayed as a progress ring with a distinct color, making habit-by-habit performance easy to scan.

How does the system compute an overall success rate from multiple roll-ups?

A final formula in “analytics” adds the numeric values from the individual habit roll-ups (meditate + publish article + Read 50 pages + tweet 2 times + workout). That sum is divided by the total number of habits (5) to normalize it into an overall completion rate. The formula then applies floor-based rounding (again using a 1,000 scale) and formats the result as a progress bar or ring.

How can the tracker analyze performance by day of the week?

The analytics view is duplicated into a “weekdays” version and filtered so it excludes the “habit completion rate” page. Seven pages are created—one for Monday through Sunday. Each weekday page is manually linked to the corresponding dates in the calendar (e.g., Thursday entries connect to the Thursday weekday page). Once linked, roll-ups update so the rings/bars reflect completion patterns for that specific weekday.

How can habit completion be automated using data from another database?

An “article database” example replaces manual checkbox work. The article records are linked to habits via a relation (e.g., a “published” relation shown in the habits calendar). A roll-up counts related articles per day, and a formula converts that count (e.g., equals 2) into a formulaic checkbox for “publish article 2 times.” The calendar then shows an empty checkbox label because the completion is driven by the underlying article data, not manual toggling.

Review Questions

If you wanted to add a sixth habit to the tracker, what parts of the formulas and roll-ups would need adjustment (and why)?
What configuration choices in the relation and filtering steps ensure that each daily page automatically connects to the correct analytics page?
How would you modify the weekday analysis approach to analyze performance by month or quarter instead of Monday–Sunday?

Key Points

1
Create a daily “my habits” calendar database with checkbox properties for each habit and a date property that anchors entries to the calendar.
2
Build a separate “analytics” database and connect it to daily pages using a relation so each day’s analytics page can pull in all related habit entries.
3
Use formulas to convert checkboxes into 0/1 values, sum them, divide by the number of habits, and round down for a stable daily completion percentage.
4
Use roll-ups in “analytics” to compute per-habit completion across days (via percent checked) and display results as progress rings with distinct colors.
5
Add an overall success-rate formula that averages the per-habit roll-ups and formats it as a progress bar or ring.
6
Duplicate analytics into filtered views like “weekdays” and create Monday–Sunday pages linked to the corresponding calendar dates for day-of-week insights.
7
Automate habit completion by linking external data (like an “article database”) and turning roll-up counts into formulaic checkboxes (e.g., equals 2).

Highlights

Daily completion becomes a real metric by converting checkboxes into numbers (0/1), summing, dividing by the habit count, and rounding down before displaying as a progress bar.

The analytics layer works because relations let roll-ups pull checkbox completion data from every linked daily page, enabling per-habit progress rings.

Weekday performance is created by duplicating analytics into a “weekdays” view and manually linking each weekday page to the matching dates in the calendar.

Habit automation is achieved by replacing manual checks with formulaic checkboxes driven by roll-up counts from an external database (like articles due on a date).

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