The ultimate guide to Notion Charts (9 examples)

Creating Native Charts in Notion

Introduction to Chart Creation

• The ability to create native charts in Notion has finally arrived, allowing users to add chart views to any database with ease.

• Various practical applications of charts include task grouping by project or assignee and creating sales dashboards for better data visualization.

Understanding Charts

• A chart is essentially a synonym for a graph and serves as a new layout type within Notion's database tool.

• Charts can be placed anywhere in Notion, enabling the creation of dynamic dashboards using four different chart types available at launch.

Preparing Data for Charting

• The presenter has prepared three datasets: tasks and projects, a Pokedex, and a comprehensive sales database with numerous records.

• These datasets will be used to demonstrate how to create charts at varying levels of complexity.

Creating Your First Chart

• The first example involves creating a simple bar chart displaying HP stats of selected Pokémon from the dataset.

• Users can switch between table layouts and bar charts easily; the name property on the X-axis allows for visual representation through page icons.

Steps to Build the Chart

• To create a chart, start by selecting "chart" as the layout type in your database block.

• Users have options for different chart types including vertical/horizontal bar charts, line charts, and pie charts.

• For this example, select a vertical bar chart and configure properties for both axes (X-axis: Pokémon names; Y-axis: HP).

Customizing Chart Settings

• Users can choose various numeric display options on the Y-axis such as sum or average; these become crucial when dealing with grouped data later on.

• Sorting options allow users to arrange data based on specific criteria like HP values from high to low.

Finalizing Your First Chart

Chart Customization and Data Visualization Techniques

Exploring Chart Options

• The speaker discusses various chart customization options, emphasizing the use of colorful palettes over monochromatic ones due to their more appealing hue, luminosity, and transparency distributions.

• The ability to toggle data labels on or off is highlighted; turning them off can enhance aesthetic appeal while still allowing for hover-over visibility of individual values.

Creating an Average HP Chart by Type

• A new chart will be created to display the average HP of Pokémon categorized by type, showcasing a comparison between dragon types (average HP: 81.55) and bug types (average HP: 56.53).

• The process involves linking a database named "Pokedex video version" and selecting a vertical bar chart format to represent the average HP by type.

• The Y-axis will reflect average HP instead of count, providing a clearer representation of data across the entire database.

Practical Application: Tasks Done Per Day Chart

• Transitioning from Pokémon data visualization to practical applications, the speaker introduces a project dashboard that tracks tasks related to projects.

• A "tasks done per day" chart will be created using automation that sets completion dates when tasks are marked as complete.

Building the Tasks Done Per Day Chart

• Demonstration of how marking a task as complete updates its status with today's date in the completed area.

• A line graph will be selected for visualizing daily task completions, utilizing date properties such as day for accurate tracking.

Automation and Manual Tracking Considerations

• The completed date property is crucial for mapping data points on the X-axis; users without automation access can manually set this date.

• Emphasizes that regardless of plan type (free or paid), having a property that tracks completion dates is essential for effective chart creation.

Grouping Tasks by Project

Creating Charts in Notion

Setting Up a Vertical Bar Chart

• The process begins with creating a vertical bar chart to visualize project task statuses.

• The "what to show" property is set to the project relation, which connects tasks to their respective projects.

• A one-to-many relationship is established where each project can have multiple tasks, but each task belongs to only one project.

• To enhance clarity, the option to omit zero values for tasks without projects is utilized.

• Status properties are grouped into categories like "to do," "in progress," and "completed," allowing for detailed tracking of task statuses.

Analyzing Workload Distribution

• A workload graph is introduced, displaying the number of tasks per team member categorized by status.

• This graph helps identify workload distribution among team members, highlighting who may be overworked or underutilized.

• The creation of this chart follows similar steps as before but introduces interaction with other database features in Notion.

• A horizontal bar chart titled "workload" is created, focusing on assignees and their respective task counts.

• Zero values are omitted from the display for better clarity regarding assigned tasks.

Filtering Data for Enhanced Insights

• Filters are applied to exclude completed tasks from the workload analysis, refining data visibility.

• Grouping by status values on the X-axis further organizes the information presented in the chart.

Exploring Advanced Charting Techniques

• Transitioning to more complex charts, a single versus dual type chart illustrates Pokémon types within a Pokedex context.

• Formulas are introduced as a method for creating additional groups based on existing data properties in Notion databases.

Creating a Type Count Formula in a Pokedex

Setting Up the Type Count Property

• The speaker plans to add a formula property called "type count" to track the number of types for Pokémon.

• By referencing the type property, it outputs a list of string values representing different types, which can be counted using the length function.

Visualizing Data with Charts

• A new chart titled "single versus dual" is created to visualize Pokémon types using a donut chart format.

• The chart displays only two possible values (one or two), reflecting current data limitations; future updates could allow for three or more types.

Transitioning to Sales Data Dashboard

Exploring Customer Records

• The discussion shifts from Pokémon to sales data dashboards, highlighting charts like top customers and VIPs with low recent spend.

• A specific customer record (Scott Summers) is examined, showcasing his sales by month through a dedicated chart linked to individual purchases.

Creating Order Total Calculations

• To enhance customer records, an order total formula property will be added based on product prices related to each sale.

• Instead of manually entering prices, the speaker suggests pulling prices directly from the products table for accuracy and efficiency.

Building an Order Total Formula

Implementing Price Retrieval Logic

• The need arises to sum up multiple product prices within sales records; this requires referencing product relations effectively.

Understanding the Map Function in Notion

Introduction to the Map Function

• The map function allows for element-wise operations on lists, utilizing a keyword called "current" to reference each specific element during execution.

• As the map function iterates through elements, it applies an expression (e.g., current.price) to generate a new output list based on the input list.

Calculating Order Totals

• After generating a new list with prices, this list can be passed into the sum function to calculate the order total.

• The resulting formula property can be formatted as US dollars, enhancing clarity when presenting financial data.

Creating Customer-Specific Charts

• To visualize customer spending, a chart is created that filters sales records specifically for Scott Summers, showcasing his total expenditures.

• A line chart is utilized to display monthly sales data; adjustments are made to show cumulative dollar amounts rather than just counts of sales.

Implementing Database Templates in Notion

Setting Up Customer Templates

• A database template is created for customers that includes previously established charts and properties.

• The filter criteria within this template must be adjusted from a specific customer (Scott Summers) to a generic customer template for broader applicability.

Utilizing Self Referential Filters

• Self-referential filters allow templates to dynamically update based on the specific customer record being viewed or edited.

• This feature enhances functionality by ensuring that when new pages are created using the template, they automatically reflect relevant data without manual adjustments.

Creating Top Customers Graph

Overview of Sales Data Visualization

• The final graph discussed is designed to highlight top customers for the current month, building upon previous visualizations like "sales by customer."

Creating a Customer Spending Graph

Introduction to Sales Data Analysis

• The discussion begins with the intention to simplify understanding of complex formulas needed for analyzing sales data, specifically focusing on the last 30 days.

Setting Up Customer Table Formulas

• A new formula property called "total spend" is created in the customers table to aggregate individual customer spending data.

• The map function is utilized to iterate through sales records and calculate the total amount spent by Scott Summers at the store.

Calculating Total Spend

• The total spend calculation involves summing up all order totals from sales records associated with Scott Summers, formatted as US dollars for clarity.

Filtering Sales Data for Recent Activity

• To analyze top customers within the last month, another property named "30 day spend" is introduced alongside total spend.

• This requires filtering sales records to include only those transactions that occurred within the past 30 days before applying further calculations.

Implementing Date Filters

• A filter function is applied to create a new list containing only sales that meet specific date criteria using a date between function.

• The test condition checks if the number of days since each sale is less than 31, ensuring only recent transactions are considered.

Finalizing Customer Spend Calculation

• After filtering, the same map function calculates and sums up order totals for Scott's purchases over the last 30 days, which will be used in visualizations.

Creating Visual Representations of Data

• A vertical bar chart titled "Top Customers This Month" is proposed to visualize customer spending data effectively.

• The X-axis will represent customer names while the Y-axis displays their respective 30-day spending amounts.

Enhancing Chart Readability

• To improve clarity, zero values are omitted from display; additional filters can be applied based on minimum spending thresholds (e.g., $750).

Conclusion and Further Exploration

VIPs with Low Recent Spend

Understanding VIP Status in Customer Databases

• The discussion begins with a focus on identifying VIP customers who have low recent spending. This involves utilizing a checkbox property in the customer database to denote VIP status.

• The speaker encourages viewers to engage with the challenge of analyzing this data, suggesting that it is not significantly different from previous tasks.

Creating Multi-Series Charts

Introduction to Multi-Series Charting

• Level three introduces the concept of multi-series charts, which allow for multiple Y-axis values against a single X-axis value.

• An example is provided comparing sales data from two consecutive 30-day periods, enabling day-to-day sales comparisons between these periods.

Practical Applications and Limitations

• A transition back to discussing limitations within Notion charts highlights the inability to graph multiple individual stats (like HP or attack) for Pokémon records directly.

• The speaker contrasts this limitation with an example from Google Sheets where various Pokémon stats are effectively graphed together.

Database Architecture Insights

Structuring Data for Multi-Series Charts

• To create multi-series charts in Notion, users must establish a separate table for stat values associated with each Pokémon record.

• Each stat value must be categorized by type (e.g., HP, attack), allowing for effective grouping when visualizing data.

Grouping and Summarizing Data

• The process involves summing all stats per Pokémon and then grouping them by stat type to visualize their contributions collectively.

• Various chart styles can be applied, including horizontal representations and stacked views, enhancing clarity in displaying cumulative statistics.

Sales Data Analysis Challenge

Transitioning Between Time Period Comparisons

• The next challenge focuses on transforming date properties into groups representing current versus previous 30-day sales periods.

Understanding Notion Formulas and Charting

Creating Variables with the let Function

• The discussion begins with extracting information from order dates using the let function in Notion to create variables.

• An example is provided where two variables, a and b, are defined, demonstrating how to concatenate them into a single string output.

• The value of the variable period is calculated as the number of days between now and the order date, initially returning a simple integer.

Defining Time Periods

• To enhance clarity, instead of just returning integers for periods, descriptive strings like "current period" or "last period" are introduced based on the value of period.

• The use of conditional statements (if, ifs) allows for multiple outputs based on different conditions related to time periods.

Calculating Cycle Days

• A new formula property called cycle day is created to determine which day it is within a given cycle (e.g., 30-day cycles).

• The modulus operation is explained as a way to find remainders when dividing by 30, indicating which day in the cycle corresponds to current orders.

Visualizing Data with Charts

• With both cycle day and period established, a line chart can be created that displays data across these parameters.

• The X-axis represents cycle days ranging from zero to thirty; sorting options are discussed for better visualization.

Multi-Series Graphing and FAQs

• Transitioning from showing counts to summing order totals allows for more informative multi-series graphs.

• Acknowledgment that creating these charts took longer than expected leads into FAQs about chart functionality on free versus paid plans.

Notion Charts: Features and Future Enhancements

Overview of Chart Functionality

• Users on the free plan can utilize one chart for temporary reports or a permanent display, but extensive chart needs may require a paid plan.

Exporting Charts

• Charts cannot be exported using the standard export feature; attempting to do so results in a blank space.

• Users can save charts as PNG or SVG files, copy them to the clipboard, and share them on social media platforms like Slack.

Upcoming Features for Charts

• A new number type chart will display simple numerical data, such as total sales figures, enhancing dashboard visibility.

• The "drill down" feature will allow users to click on data points to view underlying database pages or rows, improving data accessibility.

Feedback and Suggestions

• While impressed with current features, there is a desire for more access to charts on the free plan and multi-series chart capabilities for individual properties in databases.

Community Engagement and Learning Resources

• Tutorials will be created for integrating charts into existing templates within their customer community while maintaining accessibility for users on free plans.

Conclusion and Further Learning