Custom Reports - Charts

All Reports

The report list is displayed in a table format that includes:

• Name: The title of the report. Use clear and descriptive names to easily identify its purpose and content.
• Type: Specifies the report type, such as Pivot Table, Big Number, Mixed Chart, Bar Chart, Line Chart, and more. Knowing the type helps in selecting the right report for analysis.
• Dataset: Indicates the data source used for generating the report.
• On Dashboards: Displays whether the report is linked to any dashboards. This helps in identifying reports that contribute to visual analytics.
• Owners: The user(s) responsible for the report. Contact the owner for updates, modifications, or troubleshooting.
• Last Modified: The date and time when the report was last updated. Sort reports by modification date to quickly access the most recent or relevant ones.

Creating Charts

Superset offers two main interfaces for building charts: the Chart Builder and the SQL-based SQL Lab.

A. Using the Chart Builder

The Explore view lets you build charts without writing SQL.

1. Start Chart Creation:
   • Navigate to Analytics > Advanced Analytics > Custom Reports > All Reports > Charts tab, click the +Chart button.

+Chart button.

• In the Create a new chart screen, choose your dataset from the dropdown list or click on Add a dataset button.

Choose a dataset or Add a dataset

2. Choose a Chart Type:

• Select the desired chart type from the available options. Then click the Create New Chart button.

Choose chart type

3. Configure Your Chart:

• Use the Metrics & Columns Panel below the Chart Source panel to search and select data fields.
• The Data tab provides key configuration options for your chart—such as setting the X-axis, time grouping, metrics, dimensions, and filters—along with advanced features like annotations and predictive analytics. Note that the available options may vary based on your chosen chart type and dataset.

Data tab

4. Create and Review:

• Click on the "Create chart" button in the control panel on the left to preview a visualization.

5. Customize Further:

• Use the Customize tab to adjust aesthetics such as colors, labels, and layout.
• Experiment with filters, time ranges, and grouping options to refine your visualization.

6. Save Your Chart:

• Click Save at the top right.
• Enter a name for your chart and choose whether to add it to an existing dashboard or create a new one.

Save chart

7. Other Options:

Next to the Save button in the top right of the Content panel, you'll find a meatballs (⋯) menu with additional chart tools to download, share, or further analyze your data.

• Download Options:
  • Export to CSV – Download raw data for further analysis.
  • Export to JSON – Get data in a flexible format for advanced use.
  • Download as Image – Capture a snapshot of your chart to use in reports or presentations.
  • Export to Excel – Open your data in Excel for additional formatting and calculations.
• Share Options:
  • Copy permanent link to clipboard – Generate a direct link to share your chart with colleagues.
  • Share chart by email – Send your chart via email.
  • Embed code – Easily integrate your chart into external websites or dashboards.
• For Advanced Users:
  • View Query – See the SQL code behind the chart.
  • Run in SQL Lab –Edit and test the SQL query.

Other options

Chart Filter - Important Note

When creating a chart, it is always necessary to apply an event_type filter.

• If the selected metric is, for example, Email Click, you must filter by EmailClick under event_type.
• If the selected metric is Email Click Rate, you need to filter by both EmailClick and EmailDelivery under event_type. This is because Click Rate is calculated by dividing Email Clicks by Email Deliveries, so both events must be included.

Advanced Analytics

Rolling Window

The Rolling Window feature helps smooth out fluctuations in time series data by calculating values over a set period instead of using individual data points. This makes it easier to see trends.

1. Rolling Function: Defines how the data is processed within the rolling window. Options include:
   • Mean: Calculates the average over the selected period.
   • Sum: Adds up values within the period.
   • Std: Measures how much values deviate from the average.
   • Cumsum: Shows a cumulative total over time.
2. Periods: Determines the number of time units (e.g., days, weeks) the function will use for calculations.
   • Example: A 7-day rolling mean calculates the average of the last 7 days for each data point.
3. Min Periods: Sets the minimum amount of data needed before displaying a value.
   • Example: If using a 7-day cumulative sum, setting Min Periods to 7 ensures that each result is based on a full 7-day total, preventing incomplete values at the beginning.

Rolling window

Using a rolling window is helpful when data has a lot of ups and downs, as it makes overall trends clearer.

Time Comparison

The Time Comparison feature allows you to compare data from different time periods, helping identify trends and patterns over time. This is useful for tracking performance changes across days, weeks, months, or even years.

1. Time Shift – Compares your current data with a past time period.
   • You can select a predefined shift such as:
     • 1 day ago
     • 1 week ago
     • 28 days ago
     • 30 days ago
     • 52 weeks ago
     • 1 year ago
     • 104 weeks ago
     • 2 years ago
     • 156 weeks ago
     • 3 years ago
   • Free text is also supported.
2. Calculation Type – Defines how the time shift data is displayed.
   • Actual Values – Shows both the original data and the time-shifted data as separate lines on the chart. This allows for a direct visual comparison.
   • Absolute Difference – Displays a single line representing the difference between the current value and the shifted value. This helps in understanding how much the metric has increased or decreased.
   • Percentage Change – Shows the relative change between the current value and the shifted value in percentage terms. This is useful for identifying trends in growth or decline.
   • Ratio – Compares the current value to the shifted value as a proportion. A value of 1 means no change, while values above or below 1 indicate an increase or decrease relative to the previous period.

Time Comparison

This feature helps in analyzing trends, identifying seasonal patterns, and comparing performance over different time periods.

Resample

Resampling allows you to change the time intervals of your data, making it easier to observe trends and patterns over different time periods. For example, you can adjust the frequency of your data from daily to weekly or even yearly.

There are two main parts to resampling:

1. Rule (Time Interval) – This defines how frequently you want your data to be displayed.
   You can select from options like:
   • 1 minutely frequency – Data will appear every minute.
   • 1 hourly frequency – Data will appear every hour.
   • 1 year start frequency – Data will appear at the start of each year.
2. Fill Method (How Missing Data is Handled) – After adjusting the time intervals, there may be missing data points. The fill method lets you choose how to fill those gaps. Options include:
   • Null Imputation – Missing data will be left empty (null).
   • Zero Imputation – Missing data will be filled with zeros.
   • Linear Interpolation – Missing data will be estimated based on the surrounding values.
   • Forward Values – Missing data will be filled with the value from the previous time period.
   • Backward Values – Missing data will be filled with the value from the next time period.
   • Median Values – Missing data will be filled with the median (middle) value of the surrounding data.
   • Mean Values – Missing data will be filled with the average value of the surrounding data.
   • Sum Values – Missing data will be filled with the total sum of the surrounding data.

Resample

Once you've done this, your data will be resampled according to the selected time intervals. For example, if you choose 1 hourly frequency with median values, your data will show the middle value for each hourly period. If you choose sum values, it will show the total sum for each period.

Chart Types

Area Chart

Uses the changing area under the lines to visualize how ratios, proportions, or percentages of metric(s) change over a dimension (e.g., time).

Bar Chart

Uses bars to visualize how metric(s) change over a dimension (e.g., time).

Big Number

Useful for emphasizing a single metric or KPI at a specific point in time.

Big Number with Trendline

Variation of Big Number chart that also includes a trendline to showcase recent changes in the metric.

Box Plot

Displays data distribution and outliers in a five-number summary (minimum, first quartile, median, third quartile, and maximum).

Bubble Chart

Uses circles to represent data points, where the position and size of the bubble convey additional information.

Bullet Chart

A variation of a bar chart that uses markers to compare performance against a target or benchmark.

Calendar Heatmap

Visualizes data across time using a calendar format, often used for tracking trends like daily activity.

Chord Diagram

Represents relationships between categories using circular arcs and connecting chords.

Country Map

Displays geographical data by highlighting countries based on a given metric.

Deck.gl 3D Hexagon

Represents spatial data in hexagonal bins with a three-dimensional perspective.

Deck.gl Arc

Shows flow or movement between two locations on a map.

Deck.gl Contour

Visualizes density variations in a given dataset using contour lines.

Deck.gl Geojson

Displays geographical shapes based on GeoJSON input data.

Deck.gl Grid

Aggregates spatial data into a grid format to show density.

Deck.gl Heatmap

Represents data density using color gradients on a map.

Deck.gl Multiple Layers

Combines multiple deck.gl visualizations within a single chart.

Deck.gl Path

Plots connected data points to visualize movement patterns.

Deck.gl Polygon

Displays filled polygons for geographical visualizations.

Deck.gl Scatterplot

Plots individual data points based on longitude and latitude.

Deck.gl Screen Grid

Displays high-density spatial data in a grid layout.

Event Flow

Visualizes sequences of events and their frequency.

Funnel Chart

Uses the thickness of components in a funnel shape to show progression through stages in a process.

Gauge Chart

Uses gauges from industrial settings to visualize partial progress toward a goal.

Generic Chart

A customizable chart for various use cases.

Graph Chart

Visualizes connected objects or events using nodes and edges.

Heatmap

Represents data density using color gradients.

Histogram

Displays data distribution across intervals using bars.

Horizon Chart

A compressed time-series visualization showing trends using color intensity.

Line Chart

Uses lines to visualize how metric(s) change over a dimension (e.g., time).

MapBox

Enables advanced mapping visualizations with customizable layers.

Mixed Chart

Combines multiple visualizations in a single container using a shared X-axis.

Nightingale Rose Chart

A variation of a pie chart where slices are equal in angle but differ in radius.

Paired t-test Table

Compares two related datasets to analyze statistical differences.

Parallel Coordinates

Displays multidimensional data using parallel axes.

Partition Chart

A hierarchical chart that breaks down data into segments.

Pie Chart

A circular chart showing proportions as slices of a pie.

Pivot Table

Aggregates and summarizes data across multiple dimensions.

Radar Chart

Visualizes multiple metrics across different categories in a circular layout.

Sankey Diagram

Shows flow relationships between different entities using proportional arrows.

Scatter Plot

Uses dots to represent relationships between two numeric variables.

Smooth Line Plot

A variation of the line chart with smoothed connections between points.

Stepped Line Chart

A variation of the line chart using stepped connections between data points.

Sunburst Chart

Displays hierarchical data as a series of concentric rings.

Table Chart

A classic row-by-column spreadsheet-like representation of data.

Time-series Area Chart

Visualizes time-based data using filled areas under the line.

Time-series Bar Chart

Represents time-based data using bars.

Time-series Line Chart

A variation of the line chart specifically for time-series data.

Time-series Percent Change

Shows percentage change over time in a dataset.

Time-series Period Pivot

Compares data across different time periods.

Time-series Table

Displays time-series data in tabular format.

Tree Chart

Excels at visualizing hierarchical data.

Treemap

Uses scaled rectangles to represent proportions across different groups.

Waterfall Chart

Visualizes incremental changes in a metric over a series of steps.

Word Cloud

Displays words sized by frequency of occurrence.

World Map

Displays global data using geographical regions.

Area Chart

An area chart is ideal for visualizing trends over time and illustrating the magnitude of values within a dataset. It helps convey patterns, cumulative totals, and part-to-whole relationships effectively.

When to Use an Area Chart

• Displaying Cumulative Values
  Area charts are useful for showing cumulative totals over time, making it easier to highlight overall growth or decline.
• Comparing Multiple Time Series
  When analyzing multiple time series, an area chart allows for easy comparison while showcasing the cumulative effect of each dataset.
• Highlighting Trends and Patterns
  This chart type effectively visualizes trends, fluctuations, and patterns in data, making it easier to spot changes over time.
• Illustrating Part-to-Whole Relationships
  Similar to a stacked area chart, it helps show how individual components contribute to the overall dataset.
• Visualizing Data Distribution
  An area chart can highlight the concentration or dispersion of values over time, offering insights into data distribution.
• Emphasizing Variability
  When showcasing fluctuations or volatility in data points over time, an area chart provides a visually intuitive representation.
• Segmenting Trends
  If you need to analyze variations within different categories or segments, an area chart effectively displays trends within distinct groups.
• Showing Time-Series Data with Magnitude
  When the magnitude of values is a key factor, area charts emphasize changes clearly over time.
• Comparing Overlapping Trends
  When trends overlap, an area chart helps illustrate how each dataset contributes to the overall pattern.

Creating an Area Chart

Data Tab Parameters:

1. X-Axis: Choose the column you want to display on the X-axis (e.g., Time, Categories).
2. Metrics: Select the metric(s) that will be shown on the Y-axis.
3. Dimensions: If needed, choose the column(s) to categorize or group the data by.

Simple Area Chart (without dimensions)

A basic area chart shows how a single metric changes over time. Here’s how to set it up:

1. X-Axis: Choose the column that represents the data (e.g., Time, Categories).
2. Time Grain: Choose the level of time granularity (Hourly, Daily, Weekly, etc.) for your X-axis (this option appears only for time-related columns).
3. Metric(s) on Y-axis: Drag and drop the metric(s) you want to display on the Y-axis.

Area Chart (with Dimensions)

If you want to add more complexity and group your data, you can include a dimension in the area chart. This chart is similar to the simple version but adds grouping.

1. Dimensions: Select columns that will slice or separate the X-series. This will group the data by the unique values in those columns and generate separate series for each group.

Customization Options

You can further customize your chart using the following options:

1. Series Style: By default, the chart uses a line to connect the data points, but you can change the style.
2. Area Chart Opacity: Adjust the opacity to control how transparent the area below the line appears.
3. Show Value: Check this box if you want to display the numeric values for each data point.
4. Stacked Style: This option stacks the series on top of each other, useful when you have multiple groups of data.
5. Only Total: If selected, only the total value for each point is shown. If unchecked, the individual values for each group are displayed.
6. Extra Controls: If selected, it allows users to switch between different stacking types quickly.
7. Marker: If checked, individual data points will be shown on the chart.
8. Marker Size: Use the slider to adjust the size of the markers.
9. Show Legend: Enable this option to display a legend showing each dimension and its corresponding color.

This will help you create an area chart tailored to your data and visualization needs.

Bar Chart

A bar chart is a versatile and easy-to-understand visualization tool that is commonly used to compare different categories or track changes over time. Here are some common use cases for bar charts:

When to Use a Bar Chart

1. Comparing Categories: Bar charts are great for comparing the values of different categories. Each bar represents a category, and its length corresponds to the value it represents.
2. Showing Trends Over Time: Bar charts can effectively compare values across different time periods, making them a good alternative to line charts for time-series data.
3. Part-to-Whole Relationships: Stacked or clustered bar charts can show how different parts contribute to a whole, making it easy to visualize the composition of a total.
4. Frequency Distribution: Bar charts (often in the form of histograms) can be used to display the distribution of values across different ranges or bins.
5. Categorical Comparison: Bar charts are ideal for comparing categorical data, where each bar represents a distinct category.
6. Simple Data Representation: Bar charts are easy to read and understand, making them suitable for a broad audience, including those who may not be familiar with more complex data visualizations.
7. Limited Data Points: If you have a small dataset, a bar chart is a concise way to display the data without overwhelming the viewer.

Creating a Bar Chart

Data Tab Parameters:

To generate a bar chart, specify the following parameters:

1. X-Axis: Choose the column you want to display on the X-axis (e.g., Time, Categories).
2. Metrics: Select the metric(s) to be shown on the Y-axis.
3. Dimensions: Optionally, choose columns for grouping or categorizing the data by.

Simple Bar Chart (without dimensions)

A simple bar chart shows how a single metric varies over time. Here’s how to set it up:

1. X-Axis: Choose the column that represents the data (e.g., Time, Categories).
2. Time Grain: Choose the level of time granularity for your X-axis (Hourly, Daily, Weekly, etc.). This option is available for time-based columns only.
3. Metric(s) on Y-Axis: Select or drag the metric(s) you want to display on the Y-axis.

Bar Chart (with Dimensions)

If you want to add more complexity and group your data, you can include a dimension in the bar chart. This chart is similar to the simple version but adds grouping.

1. Dimensions: Select columns to slice or separate the bar series. The unique values in these columns will be used to group the data and generate separate bars for each category.

Customization Options

You can further customize your bar chart with the following options:

1. Series Style: By default, bars are used to represent data, but you can customize the appearance.
2. Bar Opacity: Adjust the opacity to control how transparent the bars appear.
3. Show Value: Check this box to display numeric values at the top of each bar.
4. Stacked Style: Use stacked bars to show how different groups contribute to the total. This is useful when you have multiple data categories.
5. Only Total: When selected, only the total value is displayed for each bar. If unchecked, the individual values for each group are shown.
6. Extra Controls: This option enables a quick switcher between different stacking types for users.
7. Marker: If selected, individual data points will be marked on the chart.
8. Marker Size: Use the slider to adjust the size of the markers.
9. Show Legend: Enable this option to display a legend that explains the colors and dimensions used in the chart.

With these settings, you can tailor the bar chart to suit your data and visualization needs.

Big Number Chart

The Big Number chart is a powerful visualization tool used to highlight a crucial aggregate metric or Key Performance Indicator (KPI). It displays a single value that summarizes an important metric for easy interpretation. When paired with a trendline, the Big Number with Trendline chart can effectively show both the current status and the trend of the metric.

When to Use a Big Number Chart

• Highlighting Aggregate Metrics: Use this chart to emphasize an important metric, such as total sales, average number of users, or any other aggregate value.
• Showcasing KPIs: Ideal for tracking key performance indicators (KPIs) that represent a core measure of success.
• Monitoring Metrics Over Time (with Trendline): If you need to show both a key number and its trend, the Big Number with Trendline chart is perfect for monitoring the performance of an aggregate metric over time.

Creating a Big Number Chart

To create a basic Big Number chart, follow these steps:

1. Time (Optional):

• Time Range: Define a time range to be used when calculating the aggregate metric. This allows you to focus on specific periods of interest.
• Time Grain: The time grain (e.g., daily, weekly) isn't included in the visualization but helps calculate the aggregate over the selected time range.

2. Metric (Required):

• Select Metric: Choose the column you want to display as the Big Number (e.g., Total Sales, Total Users).
• Aggregate Function: Apply an aggregate function (e.g., SUM, AVG, MAX) to the column to calculate the value. For example, AVG(totalusers) will give you the average number of users.

3. Filters:

• Add Filters: You need to apply filters to narrow down the data used to compute the aggregate metric. This allows for more focused analysis by excluding unwanted data or focusing on specific segments.

4. Subheader:

• Add a Subheader: You can define a subheader that will appear below the Big Number. This subheader can provide additional context or clarification, such as the time period or description of the metric.

Big Number with Trendline Chart

For a more dynamic display, you can pair the Big Number chart with a trendline to show how the metric has changed over time.

• Trendline: The trendline represents the historical performance of the metric, providing valuable context to the current number shown in the Big Number chart.
• Time-Based Insights: This chart is especially useful for tracking metrics that change over time, helping you monitor trends while still focusing on the most current data.

Deck.gl Charts

deck.gl stands out as a robust data visualization library powered by WebGL. It empowers the creation of intricate visualizations through the composition of existing layers, facilitating the packaging and sharing of new visualizations as reusable layers. Within its chart picker, there is a selection of deck.gl charts.

deck.gl offers a range of chart layers, each with its unique set of properties and customization options.

Funnel Chart

A funnel chart is a visual representation that effectively demonstrates processes consisting of multiple stages, where the flow gradually narrows down, often highlighting conversion or filtering rates. It is particularly useful for showing drop-offs or conversions at each step of a process. Below are some common use cases for a funnel chart:

• Customer Acquisition Funnel: Tracks the journey of potential customers, starting from awareness through various engagement stages, leading to conversions or sign-ups.
• Content Engagement Funnel: Analyzes how visitors engage with content, from discovering blog posts or videos to deeper interactions like signing up for newsletters or downloading resources.
• Email Campaign Funnel: Maps the stages of an email campaign, starting with email opens, followed by clicks on calls to action, and ending with the conversion goal (e.g., form submission or purchase).
• Abandoned Cart Funnel: Tracks the steps a potential customer takes in the checkout process, from adding items to their cart to abandoning the cart and, if re-engaged, completing the purchase.

Building a Funnel Chart

To generate a funnel chart, your data should be formatted as follows:

Funnel Chart with Unaggregated Data

If your data is not aggregated, you will need to specify the column and decide how to aggregate the values in that column. The following steps outline the selections made in the chart builder interface:

• Dimensions: Choose a column that will categorize the various stages in the funnel. The distinct values in this column will define the individual stages.
• Metric: Specify how the numeric value for each stage will be calculated.

Funnel Chart with Aggregated Data

If your data is already grouped by stage, you can apply an aggregation function, such as MIN or MAX, to generate consistent values across the stages. The steps for this process are:

• Dimensions: Select a column that will define the stages of the funnel. The unique values in this column will determine each stage.
• Metric: For aggregated data, choose an aggregation function like MAX() or MIN() to calculate the value for each stage.

By following these guidelines, you can easily construct a funnel chart that visually represents the stages of your process and highlights critical metrics such as conversion or drop-off rates at each stage.

Gauge Charts

Gauge charts are an excellent tool for representing a single metric within a predefined range, making them ideal for visualizing progress, performance, and targets at a glance. These charts are particularly useful for monitoring KPIs, tracking goals, and displaying resource utilization efficiently.

Gauge charts work best when you have a clear benchmark or range for your metric. However, they are not ideal for handling multiple variables or complex datasets. Use them where simplicity and clarity are paramount.

When to Use a Gauge Chart

Consider using a gauge chart in the following scenarios:

• Performance Tracking: Gauge charts provide a clear way to display metrics like completion rates, success percentages, or efficiency levels.
• Goal Monitoring: These charts help visually assess how far a metric is from reaching its desired target.
• Highlighting Key Performance Indicators (KPIs): If you need to focus on a single KPI, a gauge chart effectively showcases its current status.

Creating a Gauge Chart

To construct a gauge chart, you need to select a single metric that you wish to visualize. However, defining an appropriate goal or target value enhances its effectiveness.

Data Configuration

• Metric: Choose a specific value you want to display (similar to a Big Number chart selection).

Customization Options

• Minimum Value (Min): Typically set to zero for most KPIs.
• Maximum Value (Max): Define the target value representing 100% of the gauge.

Gauge Chart with Dimensions

You can also introduce dimensions to segment and color-code different sections of the chart. The values in the selected dimension field will determine the breakdown of the gauge chart.

Customizing Your Gauge Chart

The customization panel offers various options to fine-tune your chart's appearance and functionality.

General Settings

• Min & Max Values: Adjust the starting and ending points of the gauge.
• Start & End Angle: Control the positioning and angle of the gauge arc.
• Show Pointer: Enables or disables the dial indicator (enabled by default).
• Animation: Activates smooth transitions when values update.

Axis Customization

• Show Axis Line Ticks: Displays minor ticks along the gauge.
• Show Split Lines: Adds major dividing lines for clarity.
• Split Number: Adjusts the number of sections within the gauge scale.

Progress Indicators

• Show Progress: Keeps the visual progress bars visible.
• Round Cap: Rounds the edges of the progress bars for a polished look.
• Overlap: Allows dimension-level bars to stack along a single progress bar (best disabled if multiple dimensions exist).

By leveraging these customization options, you can create visually compelling and highly informative gauge charts tailored to your specific data visualization needs.

Line Chart

Line chart is an ideal visualization tool when you need to illustrate data trends and fluctuations over a continuous scale or a period of time. Below are some scenarios where a line chart is particularly useful:

• Tracking Time-Based Trends: Best suited for displaying changes in data over time, such as sales performance, website traffic, or stock market trends.
• Comparing Multiple Trends: Allows for a side-by-side comparison of different data series over the same X-axis, making it easy to spot correlations and differences.
• Showing Growth or Decline: Useful for visualizing upward or downward progressions in datasets over time, such as revenue growth or temperature fluctuations.

A line chart is particularly effective when you need to present the progression or evolution of data points over time or a continuous scale.

Creating a Line Chart

To build a line chart, define the following elements:

• X-axis: Choose the column that represents the horizontal axis (e.g., time or category-based data).
• Y-axis Metric(s): Select one or more metrics to be plotted along the vertical axis.
• Grouping/Categorization: Specify any columns that should be used to categorize or group the data series.

Simple Line Chart (Without Dimensions)

A simple line chart represents a single metric plotted over time.

To create this chart, follow these steps:

1. X-axis: Select the column you want to display on the horizontal axis (e.g., time periods, categories).
2. Time Grain (If applicable): Define the level of detail for the X-axis, such as Hourly, Daily, Weekly, or Monthly. This option is available only for time-based dimensions.
3. Metric(s) on Y-axis: Drag or manually select the metric(s) to be plotted on the vertical axis.

Line Chart With Dimensions

A line chart can also include an additional dimension to segment the data into multiple series.

To add dimensions:

1. Use the same selections as in the simple line chart.
2. Dimensions: Choose a column that categorizes or groups the data into separate line series. Each unique value in this column will create a distinct line in the chart.
3. Customization: In the Customize panel, enable the “Show Values” option to display data values directly on the chart.

By following these steps, you can create an effective line chart tailored to your data visualization needs.

Mixed Chart

A Mixed Chart is a versatile visualization that combines multiple chart types in a single graph. This allows for a more comprehensive representation of data by comparing different metrics, highlighting trends, and showcasing relationships between variables.

When to Use a Mixed Chart

• Comparing Various Data Types: Perfect for showcasing different types of data in one visualization, such as tracking sales revenue with a line chart while displaying units sold using a bar chart.
• Emphasizing Trends and Comparisons: Great for illustrating trends over time with a line chart while simultaneously comparing specific categories through a bar chart.
• Identifying Relationships: Useful for analyzing correlations between variables, such as combining a scatter plot with a line chart to highlight patterns.
• Presenting Multiple Metrics: Ideal for visualizing multiple metrics with varying scales, ensuring a clearer and more comprehensive data representation.

Creating a Mixed Chart

To create a Mixed Chart, define the following values:

1. Shared X-axis: Select a dataset column (e.g., Time, Categories) for both visualizations.
   • Time Grain: Define the level of granularity (Hourly, Daily, Weekly, etc.). This option is available only for time-based dimensions.
2. First Visualization (Query A):
   • Select the metric(s) to display on the Y-axis.
   • Under the Customize tab, choose the chart type (Line, Scatter, Bar, etc.).
3. Second Visualization (Query B):
   • Select the metric(s) for the Y-axis.
   • Under the Customize tab, select the chart type.
4. Click Create Chart or Update Chart to generate the visualization.

Customization Options

General Settings

• X-Axis Section: Adjust the axis name and bottom margin (in pixels).
• Y-Axis Section: Customize the name, margin, and positioning (left or right).
• Color Scheme: Select colors for better visualization.

Query A & Query B Customization

• Series Type: Choose from Line, Scatter, Smooth Line, Bar, or Step.
• Stack Series: Enable this to stack multiple X-axis series, useful for bar charts.
• Area Chart & Opacity Slider: Shade the area under a line chart and adjust opacity.
• Show Values: Display data points as numbers.
• Marker & Marker Size: Enable markers for line charts and adjust their size.

Y-Axis Positioning

• Primary Y-Axis: Positions data on the left side.
• Secondary Y-Axis: Positions data on the right side.
• Assigning one chart to Primary and another to Secondary enables two Y-axis ranges.

Legend Customization

• Show Legend: Display metric names in the legend.
• Type:
  • Scroll: Shows a limited number of metrics at a time.
  • Plain: Displays all metrics simultaneously.
• Orientation: Set legend position (Top, Bottom, Left, or Right).
• Margin: Adjust whitespace between legend and chart.

X-Axis Formatting

• Format: Customize X-axis label formatting or use Adaptive Formatting.
• Rotate Axis Label: Option to rotate labels for better readability.

Tooltip Customization

• Rich Tooltip: Displays all metric/series values when hovering over the X-axis.
• Tooltip Sort by Metric: Sorts tooltip values in descending order.

Using these features, you can create a highly customized Mixed Chart to best visualize and analyze your data effectively.

Pie Chart

Pie charts are best suited for specific use cases where you need to illustrate the proportion of different components within a whole. Below are situations where a pie chart is an effective choice:

• Representing Proportions: Ideal for visually breaking down data into sections where each slice corresponds to a part of the total dataset.
• Displaying Percentage Breakdown: Useful for datasets that can be represented as percentages, making it easy to interpret the distribution of values.
• Categorical Data Representation: Works well when showcasing how different categories contribute to a total, particularly for discrete data sets.

However, it’s important to use pie charts only when necessary. If your dataset has many categories or needs detailed comparisons, bar charts or stacked bar charts might be a better choice for clearer insights.

Creating a Pie Chart

Pie Chart with a Single Category

To create a pie chart, you need to:

• Choose the data you want to measure (metric).
• Select the category (dimension) that will divide the data into slices.

Pie Chart with Multiple Categories

You can also create a pie chart using multiple categories. This will group the data into a combination of slices, showing more details within each section.

Customizing Your Pie Chart

The Customize tab provides several options to modify the look and feel of your pie chart:

Legend Settings

• Show Legend: Displays a legend when enabled.
• Type: Choose between a scrollable or standard legend format.
• Orientation: Set the legend’s position within the chart (e.g., top, bottom, left, or right).
• Margin: Adjusts the extra padding around the legend (measured in pixels).

Label Customization

• Show Labels: Enables labels for each slice of the pie.
• Put Labels Outside: Place labels outside the slices for better readability.
• Label Line: Adds connecting lines between labels and their corresponding slices.
• Show Total: Displays the total metric value across all categories.

Pie Shape Customization

• Outer Radius: Adjusts the overall size of the pie chart using a slider.
• Donut Mode: Converts the standard pie chart into a donut chart.
• Inner Radius: When in donut mode, modify the inner circle size using a slider.

Pivot Table

A Pivot Table helps you organize and analyze data by summarizing information from a large dataset. It allows you to group, sort, and calculate totals to find meaningful patterns.

Creating a Pivot Table

To create a Pivot Table, follow these steps:

1.Enter Data in the Query Section

Go to the Data tab and find the Query section. Here, you need to enter the required values:

1. Columns: Choose a data field to define how information is grouped horizontally.
2. Rows:Choose a data field to define how information is grouped vertically.
3. Metrics: Choose the value you want to calculate.
4. Apply Metrics On : Decide whether to apply the metric on columns or rows.

Additional Query Settings

• Filters: Narrow down data based on conditions.
• Series Limit: Limits the number of categories displayed.
• Cell Limit: Controls the number of data cells displayed.
• Sort By : Choose a column or metric to sort the data.

2: Adjust Settings in the Options Section

In the Options section, you can modify how the Pivot Table is displayed:

Aggregation Function (How Data is Calculated)

Select how the data should be calculated:

• SUM → Adds values together (e.g., total customers).
• COUNT → Counts the number of occurrences (e.g., number of orders).
• AVERAGE → Finds the mean value (e.g., average purchase amount).
• MEDIAN → Identifies the middle value in a dataset.

Display Options (How Data is Shown)

Enable or disable the following options based on what you need:

• Show Rows Total → Displays the total for each row.
• Show Rows Subtotal → Shows subtotals within row groups.
• Show Columns Total → Displays the total for each column.
• Show Columns Subtotal → Shows subtotals within column groups.
• Transpose Pivot → Swaps row and column placements for a different view.
• Combine Metrics → Merges multiple calculations into one table.

3: Click "Update Chart"

Once all values are set, click "Update Chart" to generate your Pivot Table. The system will process the data and display the results based on your selections.

Scatter Plot

A scatter plot is a simple and effective way to see how two things are related. It shows data points on a graph, helping you understand the connection between two variables. Scatter plots are useful for discovering patterns, finding outliers (unusual data points), and understanding how data behaves.

When to Use a Scatter Plot

• See Relationships Between Two Things: Use scatter plots to check if one thing (like sales) increases as another thing (like advertising budget) increases.
• Look for Trends:If you want to see how data changes over time or in relation to other factors, scatter plots make it easy to spot trends.
• Find Outliers:Sometimes, data points are very different from the rest (called outliers). Scatter plots help you quickly spot these outliers, which might be important to look at more closely.

Creating a Scatter Plot

To create a scatter plot, you'll need to decide on a few settings in the Data tab:

• X-Axis:Choose the data you want to appear on the horizontal axis (like time, categories, etc.).
• Metrics:Pick the numbers you want to show on the vertical axis.
• Dimensions:If needed, you can select categories that will group the data points (optional).

Types of Scatter Plots

1. Simple Scatter Plot (No Dimensions)
   • This is the most basic type of scatter plot. It shows how one metric changes over time or in relation to another continuous variable.
2. Steps to Create:
   • X-axis: Choose the column that will be shown on the horizontal axis (e.g., Time, Categories).
   • Time Grain: If you're working with time-based data, you can choose the level of detail for the time axis (like Hourly, Daily, Weekly).
   • Metric(s) on Y-axis: Choose the metric(s) you want to display on the vertical axis.
3. Scatter Plot (With Dimensions)
   • This is similar to the simple scatter plot, but it also includes a dimension, which means you can group or categorize the data to see how different groups compare.
4. Steps to Create:
   • Dimensions: Choose the columns that you want to use to group your data (like product categories, customer groups, etc.). This helps split the data into separate groups.
   • X-axis and Y-axis: Reuse the selections from the simple scatter plot, but now with added dimensions to organize the data by groups.

Time Series Line Chart

A Time-Series Line Chart is a type of chart that helps you track changes over time. It is useful for visualizing trends, patterns, or fluctuations in data.

Creating a Time-Series Line Chart

To create a Time-Series Line Chart, configure the following settings in the Data tab:

1. Time Settings

• Time Column: Select the column that contains time-based data (e.g., date, timestamp).
• Time Grain: Choose the level of detail for the timeline (Hourly, Daily, Weekly, etc.).
• Time Range: Optionally, filter the data to focus on a specific period.

2. Query Settings

• Metrics: Drag and drop the metric(s) to visualize on the Y-axis.
• Filters: Optionally, apply filters to refine the dataset by specific conditions.
• Dimensions: If you want to compare different categories within the same chart, drag a column here (e.g., Product Type, Region). Each unique value will create a separate line.
• Series Limit: Set a limit on the number of series (lines) displayed in the chart.
• Sort By: Choose a column or metric to define the sorting order of your data.
• Contribution : Enable this to compute each value’s contribution to the total.
• Row Limit: Set a limit on the number of data points included in the chart.

Customizing Time Series Line Chart

These settings control the overall display and interaction of the chart:

• Color Scheme: Choose a predefined color palette for your chart.
• Show Range Filter: Enable this option to allow users to filter data dynamically using a time range slider.
• Legend: Displays a legend to help identify different data series. You can position it inside or outside the chart.
• Rich Tooltip: Enables detailed tooltips that show extra information when hovering over data points.
• Show Markers: Displays markers (dots) on data points for better visibility
• Line Style: Defines how the line appears in the chart. The default option is linear, but other styles may be available depending on your settings.
• X-Axis Label: Add a custom label to describe the X-axis.
• Bottom Margin: Adjust spacing below the X-axis (set to auto by default).
• X Tick Layout: Defines how time labels appear (default is auto).
• X-Axis Format: Choose how dates/times are displayed (default is Adaptive formatting).
• X Bounds: Set custom limits for the X-axis if needed.
• Y-Axis Label: Add a custom label to describe the Y-axis.
• Left Margin: Adjust spacing on the left side of the chart (set to auto by default).
• Y Bounds: Set a fixed range for the Y-axis if needed.
• Y Log Scale: Enable logarithmic scaling for better visualization of data with large variations.
• Y-Axis Format: Defines the number format (default is Adaptive formatting).
• Y-Axis Bounds: Set custom limits for the Y-axis if necessary.

Tree chart

A Tree Chart is a visual representation of hierarchical data. It is useful for displaying relationships and is structured with nodes (circles) and edges (connecting lines). Unlike a general graph chart, a tree chart enforces a strict hierarchy.

Creating a Tree Chart

To create a tree chart, you'll need to decide on a few settings in the Data tab:

• ID: Name of the id column.
• Parent: Name of the column containing the id of the parent node.
• Name: Optional name for the data column.
• Root Node ID: Id of root node of the tree.
• Metric: A numerical value associated with each node.
• Filters: Conditions to refine data selection.
• Row Limit: Limits the number of the rows that are computed in the query that is the source of the data used for this chart.

Customizing Tree Chart

In the Customize tab, you can configure various aspects of the tree chart’s appearance and functionality.

Layout & Orientation

Choose between Orthogonal (structured with straight lines) or Radial (circular) layouts based on how you want to visualize relationships. The tree can expand in multiple directions: Left to Right, Right to Left, Top to Bottom, or Bottom to Top depending on your preference.

Labels & Emphasis

You can customize how labels appear on your chart. The Node Label Position defines where labels are placed relative to nodes (Left, Top, Right, or Bottom), while the Child Label Position determines how labels are positioned for child nodes. To highlight relationships, use the Emphasis option, selecting either Ancestor (highlighting the path from the root to a node) or Descendant (highlighting the path from a node to its children).

Symbols & Styling

Customize the visual representation of nodes by choosing from different Symbol Types such as Empty Circle, Circle, Pin, and more. Adjust the Symbol Size to control the appearance of nodes, ensuring clarity and consistency in your visualization.

Interaction & Roaming

To enhance user interaction, enable Graph Roaming to allow movement and zooming. You can choose between Scale and Move, Scale Only, Move Only, or disable roaming entirely based on how you want users to interact with the chart.

B. Chart Creation with SQL Lab

1. Access SQL Lab
   • Go to SQL Lab from the top navigation menu.
   • Select your data source from the left-hand panel.
   • Choose Database, Schema, and See Table Schema from the dropdown menus.

SQL Lab

2. Write and Run Your Query

• Type your SQL query in the text box.
• Click Run to execute the query.
• Once the results appear, click Save to proceed.