Creating different visualizations

Graph card

The Graph visualization type is ideal for displaying relationships between nodes in your graph database. It provides an interactive way to explore how different entities are connected.

Use the AI assistant to generate a graph card based on a natural language prompt, or write a Cypher query to fetch specific nodes and relationships.

For example, create a graph card to visualize the relationships between actors and movies:

Table card

The Table is best for detailed data representation. It allows you to display data in a structured format, making it easy to read and analyze.

Create a table card to show a list of movies along with their genres and release years:

Execute the following Cypher query in the card editor to get the results:

MATCH (u:User)-[r:RATED]->(m:Movie)
RETURN m.title AS movie, u.name AS user, r.rating AS rating
ORDER BY r.rating DESC
LIMIT 10

Line chart card

The Line Chart is useful for showing trends over time. It allows you to visualize data points connected by lines, making it easy to identify patterns and fluctuations.

Create a line chart card to visualize the average ratings of movies over time:

Execute the following Cypher query in the card editor to get the results:

MATCH (m:Movie)
WITH m, toInteger(m.year / 10) * 10 AS decade
RETURN decade, count(m) AS movieCount
ORDER BY decade

After running this query, select Line Chart from the visualization type dropdown to display the results.

You should obtain something similar to this:

Bar Chart card

The Bar Chart visualization type is suitable for comparing quantities across categories. It provides a clear visual representation of data, making it easy to identify trends and patterns.

Create a bar chart card to visualize the ratings distribution per location:

Execute the following Cypher query in the card editor to get the results:

MATCH (p:Person)-[:DIRECTED]->(m:Movie)
RETURN p.bornIn AS country, count(m) AS movieCount
ORDER BY movieCount DESC

Challenge: Create a bar chart card to visualize the number of movies directed by each person.

Pie Chart card

The Pie Chart visualization type is great for displaying proportions of a whole. It allows you to visualize how different categories contribute to the total.

Create a pie chart card to visualize the distribution of directors average movie ratings:

Ask the GenAI assistant to create a pie chart card using the following prompt: "Create a pie chart showing directors with the highest average movie ratings." or run the following Cypher query in the card editor to get the results:

/* Which directors have the highest average movie ratings? */

MATCH (d:Director)-[r:DIRECTED]->(m:Movie)<-[r2:RATED]-(u:User)
WITH d, avg(r2.rating) AS averageRating
ORDER BY averageRating DESC
RETURN d.name AS directorName, averageRating
LIMIT 10

You should obtain something similar to this:

Single Value card

The Single Value visualization type is perfect for highlighting a key metric. It allows you to display a single data point prominently, making it easy to focus on important information.

Create a single value card to show the total number of movies in the instance:

Execute the following Cypher query in the card editor to get the results:

MATCH (n)
RETURN COUNT(n) as Total

You should obtain something similar to this:

If you have obtained a different value, don’t worry! The data in your instance may vary based on other activities you have performed.

Text card

Adding text is a practical way to add context to your dashboard and help stakeholders understand the data being presented, as well as generating a local overview of all dashboards in one place by inserting the images of each dashboard.

For example, create a text card to describe the purpose of your favourite movie, in this example, The Matrix:

Copy and paste the following markdown text into the card editor:

# The Matrix (1999)

## Overview
A computer hacker learns from mysterious rebels about the true nature of his reality and his role in the war against its controllers.

## Details

**Director:** The Wachowskis
**Released:** March 31, 1999
**Runtime:** 136 minutes
**Rating:** R

### Genre
- Science Fiction
- Action
- Cyberpunk

### Cast
- **Keanu Reeves** as Neo
- **Laurence Fishburne** as Morpheus
- **Carrie-Anne Moss** as Trinity
- **Hugo Weaving** as Agent Smith

## Box Office & Reception

**Budget:** $63 million
**Box Office:** $467.2 million worldwide
**IMDb Rating:** 8.7/10
**Rotten Tomatoes:** 88% (Critics), 85% (Audience)

## Awards
- 🏆 4 Academy Awards (Visual Effects, Sound, Film Editing, Sound Effects Editing)
- 🏆 2 BAFTA Awards

## Key Relationships in Graph
```
(The Matrix)-[:DIRECTED_BY]->(The Wachowskis)
(Keanu Reeves)-[:ACTED_IN {role: "Neo"}]->(The Matrix)
(The Matrix)-[:GENRE]->(Science Fiction)
```

## Tagline
*"Free your mind."*

---

**Last Updated:** 2025-11-11
**Data Source:** Neo4j Movies Database

In the card editor, the markdown text will be rendered as follows:

In the screenshot above, the first part of the rendered markdown text is shown. Scrolling down reveals the rest of the content, and the menu on the bottom left allows you to edit, insert, or view the raw markdown text.

On the dashboard page, the text card will appear as a brief overview of the movie:

Challenge: Use your creativity to add more text cards to your dashboard, providing context and insights about the data being visualized.

Visualising aggregated data

You have seen multiple ways to visualize aggregated data using different card types in Neo4j Dashboards.

When working with aggregated data, it’s important to choose the right visualization type that effectively communicates the insights you want to convey. Consider the following factors when selecting a card type:

Hands on challenge: Create a new dashboard for the marketing team to visualize movie insights. Build cards that show:

Use the appropriate card types to represent each metric effectively, and add text cards to provide context and explanations for the visualizations. These insights will help the marketing team make data-driven decisions about content and acquisition.

Share your dashboard with your peers and discuss the choices you made for each visualization, or post it on our community forum for feedback Neo4j Community.

If you are new to data aggregation, these metrics will help you get started.

Run the following Cypher queries to get the aggregated data for each metric, in a separate card each time. Feel free to choose the visualization type that best represents the data.

MATCH (m:Movie)
RETURN count(m) AS total_movies

MATCH (m:Movie)-[:IN_GENRE]->(g:Genre)
RETURN g.name AS genre,
    avg(toFloat(m.rating)) AS average_rating,
    count(m) AS total_movies
ORDER BY average_rating DESC

MATCH (m:Movie)
WHERE m.rating IS NOT NULL
RETURN m.rating AS rating,
    count(*) AS count
ORDER BY rating DESC