Why Choose Nevron Chart for SSRS?

SQL Server Reporting Services 2005, 2008, 2008R2, 2012, 2014, 2016, 2017 and 2019

Bar Chart

Bar series is commonly used for visualizing discrete data ranges with clear boundaries between data points. They can be oriented vertically or horizontally and combined in various ways, such as stacked bars or clustered bars, to enhance data visibility and legibility. You can create visually compelling graphics communicating complex data by arranging multiple bar series in different depths. Overall, the bar series is a must for any data visualization component.

Sample Table Format

Item	Quantity
Product A	50
Product B	80
Product C	120
Product D	70
Product E	90
Product F	110
Product G	60
Product H	100

In this sample, the table shows the quantity of each product. The item names are listed in the first column, while the corresponding quantities are listed in the second column. This table can create a vertical bar chart, with the item names on the x-axis and the quantities on the y-axis. Each product's quantity will be represented by a vertical bar, making it easy to compare the quantities of different products.

Best practices for using bar charts

Keep it simple: Use a simple and clean design for your bar chart. Avoid adding too many colors or other design elements that can distract from the data.
Choose the correct type of bar chart: There are many different bar charts, such as stacked, grouped, or horizontal. Choose the type that best suits the data you are trying to visualize.
Label your axes:Always label the x and y axes to clarify what the data represents. Include units of measurement, if applicable.
Provide context to the data by including a title or subtitle explaining what the chart represents and any meaningful information relevant to interpreting the data.
Order your bars: Order your bars logically, making it easy to compare data. For example, you may want to order bars alphabetically or chronologically by size.
Use appropriate scales: Choose an appropriate scale for your y-axis. This will depend on the range of values you are visualizing. Avoid using a scale that exaggerates differences between bars, as this can be misleading.
Highlight important data: Use color or other visual elements to highlight important data or trends. For example, you may want to use a different color for the highest bar in a group.
Avoid clutter: Avoid cluttering your bar chart with too much data. Suppose you have a large amount of data to present. In that case, it may be beneficial to consider using a combination of multiple charts or alternative visualization types to display the information more effectively and in a more manageable way.


	Pie Chart

Pie charts visually represent the relative values of different entities in a sum total. Various pie shapes, labeling modes and options help arrange the chart and the labels within the scene, achieving best data visibility and visual impression.

Supported Technologies

SQL Server Reporting Services 2005, 2008, 2008R2, 2012, 2014, 2016, 2017 and 2019

Pie Chart

A pie chart is a popular data visualization tool used to represent the distribution of values relative to a total value. The chart is depicted as a circle divided into slices, each representing a different category. The size of each slice in a pie chart is proportional to the percentage of the total each category represents, making pie charts a valuable tool for visually presenting data and allowing viewers to understand the distribution of categorical variables quickly. They efficiently present data with precise categorical levels and can aid in making data-driven decisions. To use a pie chart, you need to have a whole amount divided into distinct parts, and your goal should be to compare each group's contribution to the whole rather than comparing groups to each other. However, if these criteria are not met, a pie chart may not be the most appropriate choice, and a different type of plot should be considered instead.

Sample Table Format

Item	Sales
Apples	150
Bananas	75
Oranges	100

The data shown represents the sales of three distinct fruits: apples, bananas, and oranges. With 150 units sold, apples had the largest overall sales of the three fruits. Bananas are available in 75 units, whilst oranges are supplied in 100 units. With this data, a pie chart that displays the sales of various fruits can be created.

Best practices for using pie charts

Make sure the data is suitable for a pie chart
When considering whether to use a pie chart, it's important to ensure that the data is suitable for this type of visualization. Pie charts are designed to show how a whole amount is divided into distinct parts and are most effective when the data is categorical, and each category represents a portion of the whole. It's also important to remember that the primary objective of a pie chart is to compare each group's contribution to the whole, as opposed to comparing groups to each other. If your data doesn't fit these criteria, choosing a different type of chart or visualization may be more appropriate.

Include annotations
Annotations are a crucial element in pie chart visualization as they provide precise information about the proportion of each category level. Pie charts can be challenging to interpret accurately, especially for not easily recognizable values such as small fractions. Additionally, pie charts often lack tick marks to estimate values directly from slice sizes when depicting amounts rather than proportions. Annotations help overcome these challenges by clearly understanding the data presented in a pie chart. Overall, annotations are essential for accurately communicating information through pie charts.

Consider the order of slices
Arranging the slices in a logical order can enhance the comprehensibility of a pie chart. Typically, the slices are ordered from largest to smallest, especially when categories have similar values. However, if the categories have a natural order, it is recommended to arrange the slices accordingly. In addition, it is advisable to start the slices from a cardinal direction, either from the right or the top, which are the conventional directions for measuring angles. Although starting from the top may seem more intuitive, given how we read from top to bottom and view time progression, starting from the right is mathematically grounded.

Avoid distorting effects
To ensure an accurate interpretation of a pie chart, it is crucial to maintain the correct area, arc length, and angle for each slice. It is essential to avoid any distortions that could misrepresent the data. Specifically, it is recommended to prevent 3D effects that could stretch or squish the circle and create unnecessary depth, as this can skew the size comparison of each slice. Another potential distortion to avoid is using "exploded" pie charts, where slices are pulled out from the center to emphasize specific data points. This can make it difficult to accurately determine the part-to-whole comparison due to gaps in the chart.


	Standard 3D Line Chart

A standard 3D line chart is a visualization that represents data points interconnected by lines. It's a helpful tool for displaying trends and patterns in data. The chart can be customized with markers of various shapes and fill options to highlight specific data points. This makes it easier for viewers to interpret and understand the presented data.

Supported Technologies

SQL Server Reporting Services 2005, 2008, 2008R2, 2012, 2014, 2016, 2017 and 2019

Line Chart

A line chart is a popular visualization tool that displays value changes over time or any other continuous progression. It uses points connected by line segments to depict trends and patterns in data. Line charts commonly track stock market trends, weather patterns, website traffic, and other metrics that change over time. Line charts are helpful when analyzing trends and patterns in data over a continuous period. They effectively show how data changes over time, how trends emerge, and how they fluctuate. Line charts can also reveal sudden changes or sharp dips in the data, which may be missed in other charts. Line charts are beneficial when there are many data points and multiple data series to compare. They can be customized to include various lines on the same chart, with different colors and labels for each line. This allows for easy comparison of different data sets and trends.
In summary, line charts are an excellent tool for visualizing trends and changes in data over time or any other continuous progression. They are beneficial when analyzing complex data sets with multiple data series and can help identify patterns and fluctuations that might not be visible in different charts.

Sample Table Format

Date	Sales Revenue	Expenses
2021-01-01	1000	500
2021-02-01	1200	550
2021-03-01	1300	600

In this example, the first column represents the date, the second column represents the sales revenue in USD, and the third column represents the expenses in USD. Using this data, a line chart could be created to visualize the sales revenue and expenses trend.

Best practices for using line charts

Selecting the proper measurement interval
Selecting the appropriate measurement interval is crucial when creating a line chart. The measurement interval, or bin size, should be sufficiently broad and short, as this can result in hiding the helpful signal or revealing only noise. It's best to test out different intervals or rely on domain knowledge to make an informed decision. Using multiple lines, one for a fine-grained interval and another for the overall trend can also be useful. This can involve averaging over a rolling window to obtain a clearer picture of the data trend.

Do not overload it with too many lines
To prevent clutter and make the line chart easily interpretable, limiting the number of lines plotted is crucial. It's recommended to keep the number of lines to five or fewer. Too many lines on a chart make reading and understanding the data trends difficult. But if the lines are distinct and separated, it's possible to include more lines while maintaining the chart's readability.

Use colors and markers effectively
Using colors and markers effectively is key to creating a visually appealing and informative line chart. Colors can differentiate between multiple lines or data sets, making it easier for viewers to identify and track specific trends. Markers can be utilized to draw attention to particular data points or trends, emphasizing their significance in the overall pattern. It is essential to select appropriate colors and markers that complement each other and are easily distinguishable, ensuring that the chart remains straightforward and easy to interpret.


	Standard 2D Area Chart

Area charts show trend or relative contributions over time that data series make to the total. They can be used to emphasize the volume or size of a data series over time (or any other dimension).

Supported Technologies

SQL Server Reporting Services 2005, 2008, 2008R2, 2012, 2014, 2016, 2017 and 2019

Area Series

An area chart is a data visualization tool that shows how the numerical values of one or more groups change over time by using a sequence of points connected by a line. In contrast to a line chart the area chart will fill the space between the value line(s) and a specified baseline with shade or color. Area charts are commonly used to display the data trend over time, like line charts. Stacked Area and Stacked Percent charts can be used to show how a whole is divided into portions, each represented by a different color, or to compare the relative sizes of several groups.

Sample Table Format

Year	Sales	Expenses	Profit
2015	$100	$70	$30
2016	$120	$80	$40
2017	$150	$90	$60
2018	$180	$100	$80

The sample data represents a company's sales, expenses, and profit figures over four years, from 2015 to 2018. The sales figures increase each year, indicating a growing business. Similarly, expenses and profits also increase over time, with profits showing a positive trend throughout the years. This data can be visualized using an area chart, with the shaded area representing the difference between the sales and expenses and the line representing the profit. The chart can provide insights into the business's overall performance and how each factor contributes to its success or failure.

Best practices for using area charts

Include a zero-baseline
It is a good practice to have a zero baseline when using shading in a line chart or an area chart, as this allows for an accurate comparison of the sizes of each group's values. Without a zero baseline, the group values ratio may not match what is shown in the plot. While it is generally recommended to have a zero baseline in a shaded area chart to compare the sizes of different groups accurately, there are some exceptions. For instance, when comparing two series in an overlapping area chart, limiting the shading to the area between the lines instead of extending to a common baseline allows for zooming the vertical axis limits into the area of interest without requiring a baseline. In such cases, the shading represents which group has a higher value, and the color's intensity indicates the difference's magnitude.

Limit the number of series in overlapping area chart
As the number of series increases in an overlapping area chart, the number of color combinations also increases. This can make it challenging to interpret the chart, as most colors will only be associated with one group. When comparing two series in an overlapping area chart, it is easy to mistake it for a stacked area plot if one series is consistently larger. The overlapping colors can also confuse readers, as they are not included in the general legend. It is best to limit overlapping area charts to only two series and consider using a line chart to show the comparison between groups more clearly.

Consider the order of lines in the stacked area chart
The sequence in which the lines of different groups are arranged for plotting does not change the shape of a plot, but it can affect visualization reading. Placing the largest or most stable groups at the bottom and the smallest or most variable groups at the top can improve readability. This makes the most important group easily distinguishable as the bottom-most.


	2D Float Bar Chart

A 2D float bar chart is a type of bar chart that allows you to display data with floating bars, which can represent a range of values rather than a single value. This is useful when you need to show the minimum and maximum values of a data series or when you want to show the spread of values across a range.
In a 2D float bar chart, each bar is represented by a rectangle that spans a horizontal distance based on the range of values it represents. The height of the bar represents the value of the data point. The bars can be stacked or grouped, allowing you to compare multiple data series.
Nevron's GDI+ graphics engine provides high-quality image rendering for 2D float bar charts, with features such as shadows, glow effects, and bevel/emboss effects. These features can enhance the visual appeal of your chart and make it easier to read and understand. Additionally, the engine conforms to

Supported Technologies

SQL Server Reporting Services 2005, 2008, 2008R2, 2012, 2014, 2016, 2017 and 2019

Float Bar Chart

A float bar chart is a graphical representation of data where bars represent a range of values. Unlike traditional bar charts, float bar charts allow for different bar origin value of each bars, thereby allowing for the representation of a range of values instead of single value like bar charts. Float bar charts are commonly used to show project timelines, where each bar represents a task or activity, and the bar's width represents the task's duration.

Sample Table Format

Category	Start Time	End Time	Value
Category A	8:00 AM	10:00 AM	50
Category B	11:30 AM	1:30 AM	75
Category C	2:00 PM	3:30 AM	40

In the this table, each row represents a category (e.g., a task or project), its start and end time, and its corresponding value. The start and end times define the bar's duration, and the height of the bar represents the value. Float bar charts can visualize the duration and value of various tasks or projects, making comparing and prioritizing them easier.

Best practices for using line charts

Choose an appropriate scale: Ensure that your chart scale can accommodate the range of your data without distorting it. You can use a linear or logarithmic scale, depending on the nature of your data.
Use consistent colors: Use consistent colors to represent the same data category across different float bars. This will make it easier for viewers to compare and contrast the different bars.
Provide clear labels: Make sure each float bar has a clear and concise label that accurately describes the data it represents. Use descriptive labels that are easy to understand.
Sort data categories: If possible, sort your data categories logically. This could be based on size, value, or other relevant factors. Sorting the data categories will make it easier for viewers to identify patterns and trends.
Keep it simple: Avoid cluttering your chart with too much information. Only include relevant data to the message you are trying to convey.


	2D Bubble Chart

Bubble charts are a highly effective way to visually represent complex, multidimensional data. In a 2D bubble chart, the data points are represented as circles (or other shapes) with varying sizes and positions corresponding to the displayed data. This lets you see patterns and trends in the data at a glance. One of the key advantages of a 2D bubble chart is its ability to represent up to four dimensions of data. This can include X and Y positions, size, and color. By incorporating these dimensions into the chart, you can gain deeper insights into the data and make more informed decisions based on the results. In particular, the size and color of each bubble can be used to convey important information about the data being displayed. For example, the size of the bubble can represent the magnitude of a particular variable, while the color can represent a categorical variable or another variable of interest.

Supported Technologies

SQL Server Reporting Services 2005, 2008, 2008R2, 2012, 2014, 2016, 2017 and 2019

Bubble Chart

A Bubble Chart is a type of chart that displays data in a way that allows the viewer to compare multiple variables simultaneously. It uses bubbles or circles to represent data points, with the size and color of the bubbles conveying additional information. Bubble Charts can be useful for understanding complex data sets, and they're especially helpful for visualizing the relationships between different variables. One of the advantages of Bubble Charts is that they can display a lot of data in a single chart.

Sample Table Format

Product	Sales	Revenue	Market Share
Product A	100	$10,000	5%
Product B	200	$20,000	10%
Product C	300	$30,000	15%

In this example, the first column represents different products, while the second and third columns represent sales and revenue. The fourth column represents the market share of each product, which determines the size of the bubble.

Best practices for using bubble charts

Bubble charts are a powerful data visualization tool that can help you understand complex datasets quickly and easily. They're beneficial for displaying data with three dimensions: size, value, and category. However, like any data visualization tool, there are best practices that you should follow to ensure that your bubble chart is effective and easy to understand.

Choosing the right data
Firstly, choosing the right data to display in your bubble chart is essential. Bubble charts work best with data that has clear categories and a range of values. This will help ensure that your chart is easy to read and provides valuable insights into your data.

Choosing the right scale
Next, choosing the right scale for your bubble chart is important. You should choose a scale that fits your data and is easy to read. It's also important to ensure that your bubble sizes are proportional to the data they represent.

Use color effectively
Another best practice for using a bubble chart is to use color effectively. Color can represent different categories or values in your data, making it easier to see patterns and trends. However, using color sparingly and consistently is important to avoid confusion.

Labeling
Finally, it's crucial to label your bubble chart effectively. This means including clear and concise labels for each category or value and providing a legend to help users understand what each bubble represents.

By following these practices, you can create a compelling and easy-to-understand bubble chart that provides valuable insights into your data. Remember to choose the right data, scale, and color, and label your chart effectively to ensure it's as valuable as possible for your audience.


	Grid Surface Chart

The Grid Surface chart is a powerful way to display complex data using a surface with customizable elevation values for each data point. The chart is based on a rectangular grid, with X and Y coordinates defining the location of each data point on the surface. This makes it easy to visualize relationships and patterns in multidimensional data sets, allowing you to identify areas of interest and make informed decisions based on the results. With the Grid Surface chart, you can create visually stunning representations of your data that are informative and easy to understand.

Supported Technologies

SQL Server Reporting Services 2005, 2008, 2008R2, 2012, 2014, 2016, 2017 and 2019

Grid Surface Chart

Grid surface charts are a type of data visualization that represents three-dimensional data, where data points are organized in a grid. The data points in each row and column in the grid share the same x and y values, whereas the z value is specified per data point. A variation of the grid surface allows the user to specify a color for each data point thus adding a fourth dimension to the plot. This type of chart is often used to visualize continuous functions or data sets that vary continuously across multiple dimensions, such as terrain data.

Sample Table Format

X Value	Y Value	Z Value
0	0	2.2
0	1	1.8
0	2	1.4

The table contains data points represented by X and Y values indicating their respective positions on the chart. The Z value signifies the data value or elevation for each point. By utilizing this data, you can generate a Contour Surface Chart that displays contour lines indicating the value or elevation of the data at each point on the chart.

Best practices for using surface charts

Surface charts are a great way to visualize data in three dimensions, making it easier to identify patterns and relationships. Here are some best practices for using Surface charts effectively:

Choose the right type of data: Surface charts are ideal for visualizing data with three or more variables, such as temperature, humidity, and pressure. Make sure that the data you choose is appropriate for the chart type.
Use appropriate colors: Surface charts can have multiple color schemes, and choosing colors that highlight the important aspects of the data is important. Make sure to use colors that are easy to distinguish and don't cause visual strain.
Use appropriate lighting: Lighting can significantly impact the appearance of Surface charts. Use lighting that highlights the important aspects of the data, such as areas of high or low value that don't obscure any important details.
Keep the chart simple: Surface charts can be complex, so keeping them as simple as possible is important. Remove any unnecessary elements that can distract from the important aspects of the data.
Use appropriate labeling: Labeling ensures the viewer understands the presented data. Label the axes and provide a legend explaining the meaning of any color schemes used in the chart.


	Mesh Surface Chart

The Mesh Surface chart displays a surface with user-defined y-position, x-position and z-position values for each data point, in contrast to Grid Surface where only y-position (elevation) is user defined. Data points are ordered in a regular network.

Supported Technologies

SQL Server Reporting Services 2005, 2008, 2008R2, 2012, 2014, 2016, 2017 and 2019

Mesh Surface Chart

A Mesh Surface chart is a type of graphical representation that displays a surface with user-defined Y-position, X-position, and Z-position values for each data point. Unlike a Grid Surface chart, which only allows users to define the y-position or elevation, Mesh Surface provides more precise control over the position of each data point. The data points are organized in a regular network, creating a mesh-like appearance. A variation of the Mesh Surface chart allows the user to specify a color per each data point, thus adding a fourth dimension of data to the chart. Mesh Surface charts are commonly used in scientific and engineering applications, allowing for more detailed visualizations of complex data sets.

Sample Table Format

X Value	Y Value	Z Value
1	1	10
1	2	20
1	3	30
2	1	40

The table includes x, y, and z positions for each data point. The x values represent the horizontal position, the y values represent the vertical position or elevation, and the z values represent the data or measurement at each point. This table can be used for creating a Mesh Surface chart that displays a surface with a regular network of data points.

Best practices for using mesh surface charts

Choose the right type of chart: Mesh Surface charts are best used when displaying data with multiple dimensions or parameters. Consider whether a Mesh Surface chart is the best way to represent your data or if a different chart type, such as a 3D Scatter plot or a Heatmap, would be more appropriate.
Simplify the data: Mesh Surface charts can be complex and overwhelming if there are too many data points or if the data is too detailed. Simplify your data by removing unnecessary data points or grouping data into larger categories.
Choose an appropriate color scheme: Colors are important in Mesh Surface charts, as they help to differentiate between different data points. Choose a color scheme that is appropriate for your data and easy to interpret. Use a legend to explain the meaning of each color.
Label axes clearly: Label the x, y, and z axes to ensure the chart is easily interpretable. Include units of measurement if necessary.
Use lighting and shading appropriately: Lighting and shading can enhance the 3D effect of Mesh Surface charts. Experiment with different lighting and shading options to find the best way to represent your data.
Provide context for the data by including a title, captions, and annotations. Explain the purpose of the chart, the meaning of the data, and any relevant background information.


	Triangulated Surface Chart

The Triangulated Surface chart series lets users visualize data points with customized X, Y, and Z coordinates as a 3D surface. Unlike the Mesh Surface series, the Triangulated Surface does not necessitate the data points to be arranged in a network. Instead, the chart automatically creates an irregular triangular network to render the surface. This chart type is beneficial when working with irregularly spaced data points or missing data in the set. It can help to fill in gaps and provide a complete representation of the data. The chart can be customized to suit the user's needs, with various options for color schemes, lighting effects, and other visual elements.

Supported Technologies

SQL Server Reporting Services 2005, 2008, 2008R2, 2012, 2014, 2016, 2017 and 2019

Triangulated Surface Chart

The Triangulated Surface Chart is a powerful visualization tool that displays three-dimensional data sets using a mesh of triangles to represent the surface of the data. It is ideal for displaying large and complex data sets clearly and concisely. The Triangulated Surface Chart is widely used in various industries such as engineering, scientific research, and finance.

The Triangulated Surface Chart offers many features that allow users to customize their charts according to their needs. The chart provides various shading options, including smooth and flat shading, and users can choose from a range of color maps to represent their data. The chart also allows users to add annotations and labels to help users better understand the data they view.

Sample Table Format

X Value	Y Value	Z Value
2	5	10
4	5	20
6	5	15
8	5	5
10	5	30

In this table, the X, Y, and Z values represent the coordinates of each data point. Users can input as many data points as necessary to create a comprehensive and detailed visualization of their data.

Best practices for using triangulated surface charts

Choose the Right Data: The Triangulated Surface Chart best displays continuous data sets with many data points. Ensure that your data is well-suited to this type of visualization before creating a Triangulated Surface Chart.
Choose the Right Color Map: Choose a color map that best represents your data. A good color map should provide contrast and allow users to identify areas of interest easily.
Label Your Chart: Add labels and annotations to your chart to provide context and make it easier for users to understand the data they view.
Customize Your Chart: Use the chart's customization features to customize the appearance of your chart, including shading, labels, and annotations. This can help make your data stand out and make it easier to read.
Use Appropriate Scaling: Ensure your chart is scaled appropriately to represent your data accurately. Use the chart's axis scaling features to adjust the scaling as necessary.


	Radar Line Chart

A Radar Line Chart, a Spider Chart, or Star Chart is a circular graph primarily used as a multi-parameter data comparison tool. This type of chart is created using the Radar Series, which renders the data as a series of lines radiating out from the center of the circle. The Radar Line Chart is a great way to compare multiple parameters or variables and easily identify strengths and weaknesses across different categories. The Radar Line Chart is widely used for presenting and analyzing complex data clearly and visually appealingly. Its easy-to-read format and visually appealing design make it popular among businesses and individuals.

Supported Technologies

SQL Server Reporting Services 2005, 2008, 2008R2, 2012, 2014, 2016, 2017 and 2019

Radar Chart

Radar charts are a powerful visualization tool for displaying multivariate data in a radial format. Also known as spider charts, they are ideal for comparing multiple data series concerning multiple quantitative or qualitative variables. By displaying data in a circular pattern, radar charts offer a clear and concise way to communicate large amounts of data. Radar charts are commonly used for market research, competitive analysis, and other data-driven decision-making processes. With their intuitive design and easy-to-understand visual representation, radar charts are a valuable tool for anyone looking to make sense of complex data sets.

Sample Table Format

Vitamin	Daily Recommended Intake	Your Intake
A	9000 IU	7500 IU
B	2.4 mcg	1.8 mcg
C	90 mg	120 mg
D	600 IU	400 IU
E	15mg	20 mg

In this table, the first column indicates the different vitamins being measured. The second column shows the recommended daily intake for each vitamin, while the third column displays your actual daily intake. The radar chart will plot these values to show how your vitamin intake compares to the recommended levels.

Best practices for using radar charts

Keep it simple: Avoid cluttering the chart with too many variables, making it difficult to read and interpret.
Use color carefully: Choose colors that are easy to distinguish from one another, and avoid using too many colors, as this can also make the chart difficult to read.
Label clearly: Ensure that each axis is labeled clearly and that the labels are positioned in such a way that they are easily visible.
Scale consistently: Use a consistent scale across all axes to ensure that the chart accurately represents the data.
Consider using multiple charts: If you have a lot of variables, consider breaking the data down into smaller groups and creating multiple radar charts to make it easier to read and interpret.
Provide context for the data by including a title, legend, and any relevant notes or explanations.
Use appropriate data: Make sure your data is relevant for a radar chart. Radar charts best compare relative differences between variables rather than showing precise values.
Test for readability: Before finalizing your chart, ensure it is easy to read, understand, and make any necessary adjustments.


	Polar Area Chart

A Polar Area chart visually represents data in a 2-dimensional polar coordinate system. It shows a filled closed contour created by connecting data points defined by their distance from the center and angle of rotation. This chart is an effective way to display large sets of data with many categories or levels. Polar Area charts help identify trends or patterns in data, especially in circular distributions. They can also compare relative values across different categories or levels. Overall, Polar Area charts provide an intuitive and visually appealing way to communicate complex data in a concise and easily understandable format.

Supported Technologies

SQL Server Reporting Services 2005, 2008, 2008R2, 2012, 2014, 2016, 2017 and 2019

Polar Chart

A Polar Chart, also known as a Radial Chart, is a data visualization tool that displays data points in a polar coordinate system. In this coordinate system, each data point X value is interpreted as an angle and its Y value as a distance from the coordinate system center. This type of chart is beneficial for showing patterns and trends in data with directional or periodic characteristics, such as weather patterns, radar and sonar images, seasonal trends, and others.

Sample Table Format

Year	Category 1	Category 2	Category 3
2010	100	50	75
2011	120	60	80
2012	150	75	90
2013	180	90	100
2014	200	100	110
2014	220	110	120

The table displays data for three categories over five years in this example. Each row represents a different year, and each column represents a different category. The values in the table are used to create the Area Chart, where the area under each line represents the magnitude or value of each category. The chart can be used to visualize trends and patterns in the data over time and compare the relative values of each category.

Best practices for using a radar chart

Polar charts are a powerful tool for visualizing data that depends on angle and magnitude. To ensure that your polar charts effectively convey your data and insights, consider the following best practices:

Choose an appropriate data set: Polar charts are most effective for data sets that depend on angle and magnitude. Consider whether this chart type is the best choice for your data.
Label your axes: Label your angle and value axes to ensure users can easily interpret the data. Use clear and concise labeling that is easy to understand.
Use appropriate scaling: Choose a proper scaling for your chart to ensure all data points are visible and easily interpreted. Consider using logarithmic scaling for data sets with a wide range of magnitudes.
Optimize axis positioning: Ensure that your angle and value axes are positioned to maximize clarity and readability. Use evenly spaced intervals and appropriate ranges to optimize axis positioning.
Use color coding or shading: To enhance the user experience, consider using color coding or shading to provide a clear visual representation of data.


	2D High Low Chart

A 2D High-Low chart displays an area connecting high and low value sets. This chart type helps represent changes in data over time and can be used to convey complex information simply and intuitively. High-Low charts typically display a vertical line representing the range of high and low values for a specific period. The area between the lines is filled in to indicate the magnitude or value of each category.

Supported Technologies

SQL Server Reporting Services 2005, 2008, 2008R2, 2012, 2014, 2016, 2017 and 2019

High-Low Chart

High-Low charts are a powerful data visualization tool used to represent changes in data over time. This chart type is particularly effective for displaying high and low data, such as stock prices or weather conditions. High-Low charts use a vertical line to represent the range of high and low values over a specific time, with optional additional information such as the opening and closing prices. This chart type is easy to read and interpret, allowing users to quickly gain insights into complex data sets. When used correctly, High-Low charts can be an essential tool for decision-making in various fields, from finance to meteorology.

Sample Table Format

Date	High	Low	Opening	Closing
2022-01-01	100.2	87.3	95.1	95.5
2022-01-02	98.5	89.7	93.2	91.1
2022-01-03	104.6	91.2	93.4	104.2
2022-01-04	110.1	96.5	98.5	107.3
2022-01-05	115.1	104.1	106.2	110.2

Each row in this sample table represents a specific date, with columns for that day's high and low values and the opening and closing values (optional). This format allows for easy input of data and the creation of a High-Low chart to visualize trends and changes over time.

Best practices for using high-low charts

Choose the appropriate data: High-Low charts are most effective when used with data ranging from high to low values, such as stock prices or weather conditions. Ensure that the data you are using is suitable for this chart type.
Use clear labeling: Make sure to label each axis clearly and concisely to help users understand the displayed data. This includes marking the x-axis (usually time) and the y-axis (usually values).
Use consistent scaling: Ensure that the scaling on the x and y axes is constant, so users can easily compare data points over time.
Use color coding or shading: Consider using color coding or shading to help users differentiate between different data sets or to highlight specific trends or changes over time.
Include relevant information: Additional information such as opening and closing prices or other data points may be useful depending on the data displayed.
Keep it simple: High-Low charts can quickly become cluttered and difficult to read if too much information is included. Focus on displaying the most important data points and trends, and avoid including unnecessary details.


	Error Bar Chart

An Error Chart can be used to indicate estimated error in a measurement. The error values are shown as "T" and upside-down "T" symbols above and below the primary value. The most common Error Chart displays errors only in Y values.

Supported Technologies

SQL Server Reporting Services 2005, 2008, 2008R2, 2012, 2014, 2016, 2017 and 2019

Error Bar Chart

An Error Bar chart is a statistical chart used to represent data variability. It displays the range and distribution of a data set by indicating the degree of uncertainty or error associated with each data point. Error bars can show standard deviation, standard error, confidence intervals, and other statistical measures. They are typically displayed as vertical or horizontal lines extending from each data point, with the line length indicating the magnitude of the error. Error Bar charts are useful for comparing data sets, identifying outliers, and determining the level of uncertainty associated with each data point.

Sample Table Format

Sample	Mean	Standart Deviation
A	10	2
B	20	3
C	15	1.5
D	12	2.2
E	18	1.8

To create an Error Bar chart, you can use a table that lists each data point with its corresponding mean value and standard deviation. The table can also include other statistical measures, such as standard error or confidence intervals, depending on the specific needs of your analysis. By incorporating this data into the chart, you can easily visualize the range and distribution of each data point, making it simpler to identify patterns and outliers.

Best practices for using error bar charts

Choose the appropriate type of error bar: Error bars can be vertical or horizontal, representing different statistical measures such as standard deviation or standard error. It's important to choose the type of error bar that best describes your data and your research question.
Ensure the error bars are clearly visible: It's essential to ensure that the error bars in your chart are clearly visible. They should be sized appropriately to make them easily distinguishable but not so large that they overpower the data points. Additionally, the color and thickness of the error bars should match the overall chart design to maintain consistency and clarity. By taking these measures, you can create an Error Bar chart that presents the data accurately and is easy to interpret.
Use appropriate scaling: The scale of the chart should be suitable for the range of the data. The error bars may be difficult to interpret if the scale is too large or too small.
Provide context: Error bars should be interpreted in the context of the data set and the research question. Providing labels, titles, and captions can help provide context and clarity.
Consider the audience: The chart's level of detail and information should be appropriate for the intended audience. It's important to keep in mind the level of statistical knowledge of your audience and tailor the chart accordingly.


	2D Funnel Chart

A 2D Funnel Chart is an excellent visual for displaying progressively decreasing values, such as sales process stages or any similar process of gradual elimination. This type of chart represents the flow of potential customers through various stages until only a few make a purchase. The funnel chart's distinctive shape, with a wide top and narrow bottom, makes it easy to see where the drop-off occurs and identify areas for improvement in the process.

Supported Technologies

SQL Server Reporting Services 2005, 2008, 2008R2, 2012, 2014, 2016, 2017 and 2019

Funnel Chart

Funnel charts represent stages in a process or data progression through a sequence. With a funnel chart, you can quickly see how the data flows through each stage and identify areas where the flow is hindered. Funnel charts get their name from their unique shape, which is widest at the top and narrowest at the bottom, resembling a funnel. This shape makes it easy to compare the values of each stage, and the colors can be customized to provide additional context or highlight specific stages. Funnel charts are versatile tools that analyze data types, such as sales or website traffic. By visualizing data in a funnel chart, you can gain insights into the progression of your data through different stages and identify areas for improvement. This makes funnel charts an effective way to optimize processes and achieve better results.

Sample Table Format

Stage	Visitors	Leads	Opportunities	Sales
Awareness	1000	500	100	20
Interest	500	250	75	15
Consideration	200	100	50	10
Decision	100	50	25	5

In this example, each row represents a process stage, such as awareness or consideration. The columns in the Funnel Chart represent the number of visitors at each stage and the number of conversions that occur, such as leads, opportunities, and sales. This helps to visualize the flow of potential customers through various stages and identify areas where the process may need improvement. This table can be used to create a funnel chart that visualizes the data flow through each stage, making it easy to spot any bottlenecks or areas where the flow is impeded.

Best practices for using funnel charts

Keep it simple: The funnel chart should be easy to read and understand, so it's best to avoid cluttering it with unnecessary details.
Use meaningful categories: The categories should be descriptive and meaningful so it's clear what each stage of the funnel represents.
Order the categories logically: The categories should be ordered in a logical sequence that reflects the data flow through the funnel.
Use consistent units: Make sure the units used for each funnel stage are consistent throughout the chart.
Avoid too many categories: A funnel chart with too many categories can be overwhelming and difficult to read. It's best to limit the number of categories to 5-7, if possible.
Highlight the drop-off rate: Use color or other visual cues to highlight the drop-off rate at each stage of the funnel, as this can help identify areas for improvement.


	Box and Whiskers Chart

A box and whiskers chart is a powerful type that provides a quick and easy summary of a dataset's overall data distribution. It presents the data in the form of a box and whiskers series, which enables the viewer to see the data's range, median, quartiles, and outliers at a glance. This makes it an excellent tool for identifying patterns and trends in the data and detecting any anomalies or outliers that may be present. With its ability to convey complex information clearly and concisely, a box and whiskers chart is a valuable resource for making informed decisions based on data.

Supported Technologies

SQL Server Reporting Services 2005, 2008, 2008R2, 2012, 2014, 2016, 2017 and 2019

Box and Whiskers Chart

A Box and Whisker chart, or a Box plot, is a powerful chart for visualizing statistical data. It displays the distribution of a dataset using quartiles and outliers, allowing users to quickly identify patterns and trends. The chart consists of a box representing the middle 50% of the data, with a line inside indicating the median value. The chart displays the minimum and maximum values as whiskers that extend from the box, and any outliers are represented as individual data points beyond the whiskers. Furthermore, it is highly customizable with options to adjust colors and labeling, making it an effective tool for data analysis.

Sample Table Format

Sample Name	Minimum	Lower Quartile	Median	Upper Quartile	Maximum
Sample A	5	8	10	15	20
Sample B	10	12	15	18	25
Sample C	8	10	12	16	22

This table represents three different samples of numerical data, each with five statistical measures. The minimum and maximum values indicate the range of values in each sample, while the lower and upper quartiles represent the 25th and 75th percentiles, respectively. The median is the value that divides the data into two halves. Comparing the values across the samples, we can observe differences in their distributions, ranges, and central tendencies. The box and whisker chart is a valuable tool to visually compare these characteristics and identify any outliers or unusual patterns in the data.

Best practices for using box and whiskers charts

Use Box and Whiskers charts to compare data: Box and Whiskers charts are excellent for comparing data between different groups. Use them to compare data points across multiple categories or periods.
Label your chart clearly: It's important to label it with a clear and descriptive title and label the axes with meaningful units of measure.
Ensure your data is accurate: Check it carefully before creating your chart to ensure it is accurate and up-to-date.
Use colors carefully: Sparingly and purposefully highlight important information in your chart. Avoid using too many bright colors that may distract from the data
Include relevant statistical information: Include relevant statistical data in your chart, such as the median, quartiles, and outliers.
Choose an appropriate scale: Choose a scale appropriate for the displayed data. A smaller scale may be more suitable if the data is tightly clustered.
Avoid clutter: Avoid cluttering your chart with too much information. Use a clear and simple design to make it easy to interpret the data.


	Combo Chart

The Combo Chart is versatile charting type, which allows you to specify a series type per each values data grouping, which is displayed in the combo chart area. This is a combination of clustered bar chart and line series.

Supported Technologies

SQL Server Reporting Services 2005, 2008, 2008R2, 2012, 2014, 2016, 2017 and 2019

Combo Chart

A Combo Chart is a versatile charting type that combines a clustered bar chart and line series, allowing you to display multiple data series in one chart area. This chart type allows you to specify a series type for each data grouping, giving you more control over how your data is displayed.
With a Combo Chart, you can easily compare data types, such as sales revenue and profit margin, in a single chart. The chart allows you to display each data series using the most appropriate chart type, such as a clustered bar chart or a line series.
The Combo Chart is a valuable tool for analyzing complex datasets and identifying patterns and trends in your data. With its ability to display multiple data series in one chart area, you can quickly and easily compare data and make informed decisions based on your findings.

Sample Table Format

Month	Sales Revenue (Bar)	Profit Margin (Line)	Customer Acquisition (Bar)	Employee Satisfaction (Line)
Jan-21	500	10%	50	75%
Feb-21	600	12%	60	80%
Mar-21	700	15%	70	85%
Apr-21	800	20%	80	90%
May-21	900	18%	90	95%
Jun-21	1000	22%	100	90%

In this example, the Sales Revenue and Customer Acquisition data series are represented as bar charts, while the Profit Margin and Employee Satisfaction data series are displayed as line charts. This allows for easy comparison of the different data types, with each series represented using the most appropriate chart type.

Best practices for using combo charts

Combo charts are helpful when you want to display multiple data types on a single chart. Here are some best practices for using combo charts:

Choose the right chart types: Combo charts can include different charts, such as bar, line, and scatter. Choose the chart types that best represent the data you want to display. For example, if you show how sales revenue and profit margin change over time, a line chart might be best for revenue, while a bar chart might be best for profit margin.
Use consistent axes: When using multiple chart types on a single chart, it's important to ensure they are consistent across all the chart types. This means that the scales and units used on the axes should be the same. If the scales or units differ, it can be difficult for viewers to compare the data accurately.
Limit the number of chart types: While combo charts can help display multiple data types, it's important not to overload the chart with too many. Generally, limiting the chart to at least two or three chart types is best.
Use color and labeling effectively: To help viewers understand the chart, use color and labeling. Use different colors for each chart type to clarify which data points belong to which one. Label each chart type clearly, and include a legend to help viewers understand what each color represents.
Choose the proper chart layout: There are different ways to layout a combo chart, such as overlaying the chart types or using multiple axes. Choose the layout that best suits the data you want to display and makes it easy for viewers to understand the relationships between the data points.


	Candle Stock Chart

Visualizing daily stock data, candlestick charts have become popular among investors and traders. Each data point is represented by a candlestick, with the candle body showing the open and close prices for the day. Vertical lines or "shadows" are displayed above and below the body to indicate the high and low prices. The color of the body fill depends on whether the stock price has gone up or down, with green usually indicating a rise and red indicating a drop. This chart type offers a quick and intuitive way to analyze daily stock data and identify trends.

Supported Technologies

SQL Server Reporting Services 2005, 2008, 2008R2, 2012, 2014, 2016, 2017 and 2019

Stock Chart

Stock charts are essential in data visualization for investors and traders to analyze historical market data and current trends, helping them to stay ahead of the curve, minimize risk, and maximize returns. These charts visualize stock prices over a period of time, such as days, weeks, months etc. The stock chart supports the two most common variations - candlestick and stick. Stock charts can easily be combined with build in technical analysis tools and indicators like moving averages, trend lines, and oscillators. By doing so, investors and traders can identify potential buy and sell signals, devise risk management strategies and determine entry and exit points for better investment decision-making.

Sample Table Format

Date	Open	High	Low	Close	Volume
2022-01-03	142.55	144.81	141.71	142.77	15574100
2022-01-04	141.23	141.79	138.64	139.53	17719200
2022-01-05	139.97	140.78	138.10	140.41	12134700

This is a simple example of a stock market data table with columns for Date, Open, High, Low, Close, and Volume. Each row represents a day's worth of data, with the date in the first column and the corresponding values for the other metrics in the subsequent columns. This format allows for easy sorting and analysis of the data and can be used to create charts and visualizations.

Best practices for using stock charts

Choose the right chart type: There are stock charts, such as line charts, candlestick charts, and bar charts. Choose the chart type best suits your data and the insights you want to convey.
Use a consistent time frame: When presenting historical stock data, use a consistent time frame throughout the chart. This allows viewers to easily compare trends and patterns over time.
Label your axes: Clearly label your chart's x and y axes so that viewers can understand the data. Use descriptive labels that include units of measurement and periods.
Avoid clutter: Avoid adding too much information to your stock chart, such as multiple indicators or excessive annotations. This can make the chart difficult to read and understand.
Use color strategically: Use color to highlight important data points or trends. However, be careful not to use too many colors or color schemes that are difficult to interpret.


	Kagi Chart

Kagi charts display series of vertical lines to illustrate general levels of supply and demand for certain assets. The thickness and direction of the lines are dependent on the price action. Thick lines are drawn when the price breaks above the previous high price and is interpreted as an increase in demand. Thin lines are used to represent increased supply when the price falls below the previous low. Kagi charts ignore the passage of time.

Supported Technologies

SQL Server Reporting Services 2005, 2008, 2008R2, 2012, 2014, 2016, 2017 and 2019

Kagi Charts

Kagi charts visually represent assets' supply and demand levels using vertical lines. The direction and thickness of the lines in Kagi charts depend on the price movement. A thick line indicates increased demand when the price breaks above the previous high, whereas a thin line represents increased supply when the price falls below the previous low. Unlike other financial charts, Kagi charts do not factor over time, allowing traders and investors to focus solely on significant price movements and market trends. With their unique approach to data visualization, Kagi charts can be valuable in financial analysis.

Sample Table Format

Date	Price	Direction	Line Thickness
2022-01-01	100	Up	Thick
2022-01-02	95	Down	Thin
2022-01-03	100	Up	Thick
2022-01-04	100	Up	Thick
2022-01-05	100	Down	Thin

In this example, each row represents a specific date and price point, with a column for the direction (up or down) and line thickness (thick or thin) based on the price movement relative to the previous high or low. This format allows traders and investors to quickly and easily identify trends and potential trading opportunities based on the thickness and direction of the lines in the chart.

Best practices for using kagi charts

Choose the right time frame: Kagi charts can be used for any time frame, but they are most effective for longer-term trends. Choose a time frame best suits your data and the insights you want to convey.
Label your chart: Clearly label your Kagi chart with the asset's name and displayed time frame. This helps viewers understand the data and the context in which it was generated.
Avoid over-analyzing: Kagi charts are best used for identifying long-term trends rather than short-term fluctuations. Avoid over-analyzing the chart or trying to make predictions based on short-term movements.


	Renko Chart

Renko charts display series of white and black boxes (bricks) to illustrate general price movement and trend reversals. A Renko chart is constructed by placing a brick in the next column once the price surpasses the top or bottom of the previous brick by a predefined amount. White bricks are used when the direction of the trend is up, black bricks are used when the trend is down. All bricks are equal in size. This type of chart helps traders to identify key support/resistance levels.

Supported Technologies

SQL Server Reporting Services 2005, 2008, 2008R2, 2012, 2014, 2016, 2017 and 2019

Renko Chart

Renko charts are a type of chart used to illustrate price movement and trend reversals. The chart consists of a series of white and black bricks, with each brick representing a predetermined price movement. A white brick indicates an upward trend, while a black brick indicates a downward trend. The size of each brick is the same, providing a uniform representation of price movements. Renko charts help identify critical support and resistance levels, allowing traders to make informed decisions based on price action.

Sample Table Format

Date	Open	Close	Brick Size
2022-01-08	130	120	10
2022-01-09	95	130	10
2022-01-10	100	120	10
2022-01-11	100	140	10

In this table, the Date column indicates the date of each trading day, the Open column indicates the opening price of each day, the Close column indicates the closing price of each day, and the Brick Size column indicates the size of each Renko brick. Renko chart is built using bricks, with each brick representing a fixed price movement. The size of the bricks is determined by the Brick Size column in the table

Best practices for using renko charts

Renko charts are a popular technical analysis tool that can give traders a unique way of viewing price action. Here are some best practices for using Renko charts effectively:

Choose an appropriate brick size: The size should be based on the underlying asset's volatility. A larger brick size will generate fewer signals, but they will be more reliable, while a smaller brick size will generate more signals but may be more prone to false signals.
Consider the time frame: Renko charts can be used in any time frame, but traders should be aware that the charts will look different in different time frames. Traders should choose a time frame that matches their trading style and objectives.


	Three Line Break Chart

Three Line Break charts display a series of vertical boxes ("lines") that are based on changes in prices. A new rising line is drawn if the closing price is higher than the previous one. A new falling line is drawn if the closing price is lower than the previous one. If a rally or a sell-off is powerful enough to form several consecutive lines with the same direction, then prices must reverse by the extreme price of the last several lines in order to create a new line. Usually three consecutive lines are used for the reversal criterion, hence the name Three Line Break. As with Kagi, Point and Figure, and Renko charts, Three Line Break charts ignore the passage of time.

Supported Technologies

SQL Server Reporting Services 2005, 2008, 2008R2, 2012, 2014, 2016, 2017 and 2019

Tree Line Break Chart

Tree Line Break charts are technical charts traders use to analyze price movements. These charts display vertical boxes or "lines" based on price changes. The lines are created based on whether the closing price is higher or lower than the previous one. If there is a powerful rally or sell-off, several consecutive lines with the same direction are formed. To create a new line, prices must reverse by the extreme price of the last several lines. Typically, three consecutive lines are used for the reversal criterion, giving the chart its name.
Like other types of technical charts, such as Kagi, Point and Figure, and Renko charts, Three Line Break charts ignore the passage of time and focus solely on price movements. This makes them particularly useful for identifying trends and potential trading opportunities. Traders can use Three Line Break charts to spot reversals, confirm trends, and set entry and exit points for trades.
To use Three Line Break charts effectively, it's important to understand the basic principles of this charting technique, including how to interpret price movements, identify key support and resistance levels, and spot chart patterns. Traders should also use Three Line Break charts with other technical analysis tools, such as moving averages, to confirm trading signals and improve their overall analysis.

Sample Table Format

Date	Close Price	Trend Direction
2022-01-01	100	N/A
2022-01-02	105	Up
2022-01-03	102	Down
2022-01-04	98	Down
2022-01-05	96	Down
2022-01-06	103	Up

In a Three-Line Break chart, each row represents a specific date, closing price, and trend direction. The trend direction column indicates whether the current price trend is up or down. A new rising line is drawn if the closing price is higher than the previous one, and a new falling line is drawn if the closing price is lower than the previous one. If a rally or a sell-off is powerful enough to form several consecutive lines with the same direction, then prices must reverse by the extreme price of the last several lines in order to create a new line.

Best practices for using a tree-line chart

Understand the concept: A three-line break chart is based on a series of price movements rather than time. It works by only drawing a new line when the price moves by a certain amount (usually three times the box size) in a certain direction. This helps filter out noise and provide a clearer picture of the trend.
Choose the box size carefully: The box size is the amount of price movement required to draw a new line. Choosing the right box size is important as it can affect the clarity of the chart. A larger box size will result in fewer lines and a clearer picture of the overall trend, while a smaller box size will result in more lines and more short-term fluctuations.
Combine with other indicators: Three-line break charts can be used in conjunction with other indicators such as moving averages or support and resistance levels to confirm trends or identify potential entry and exit points.
Use for medium to long-term analysis: Three-line break charts are best suited for medium to long-term analysis as they can help filter out short-term noise and provide a clearer picture of the overall trend.


	2D Vector Field

This example demonstrates a 2D Vector Field implemented with the Vector Series. A 2D vector field is a visualization that uses arrows to represent data points defined by four coordinates: Begin X, Begin Y, End X, and End Y. This allows for a clear and intuitive understanding of complex data sets and can be used in a variety of applications, from scientific research to business analysis.

Supported Technologies

SQL Server Reporting Services 2005, 2008, 2008R2, 2012, 2014, 2016, 2017 and 2019

Vector Chart

Vector charts use arrows or lines to represent data points. Vector charts help display complex data sets with magnitude and direction, such as wind or water currents. Vector charts are handy for scientific and engineering applications, as well as in geography and meteorology. They allow users to easily visualize complex data sets and identify patterns and trends in the data.

Sample Table Format

Begin X	Begin Y	End X	End Y
0	0	5	5
2	1	7	4
4	3	9	8
6	4	11	6
8	6	13	11

In this sample table, each row represents a data point with four coordinates: the Begin X and Begin Y coordinates define the starting point of the arrow, while the End X and End Y coordinate define the ending point of the arrow. The length and direction of each arrow will be determined by the values in the End X and End Y columns.

Best practices for using vector charts

Choose the right type of vector chart: Many types of vector charts are available, each with strengths and weaknesses. Consider the type of data you are trying to visualize and choose the chart type that will best represent that data.
Ensure data accuracy: Vector charts rely on accurate data points to create accurate representations of your data. Ensure your data is properly collected, processed, and inputted into the charting software.
Use appropriate scale and units: The scale of your chart should be relevant to the data you are visualizing. It's also important to ensure that the units used to define the magnitude and direction of the data points are consistent and appropriate.
Customize the appearance of the chart: Vector charts can be customized to fit the specific needs of your data. Choose arrow styles and colors that work well with your data, and adjust the size and position of the chart as needed.
Use labels and annotations: Labels and annotations can be added to a vector chart to provide additional context and make the chart easier to interpret. Consider adding labels to indicate the direction and magnitude of the arrows or annotations to highlight specific data points.
Keep it simple: Vector charts can quickly become complex and difficult to interpret. Keep your chart as simple as possible by limiting the number of data points and choosing a straightforward, easy-to-read design.


	2D Range Series

The Range series can display 2D or 3D shapes defined by the begin / end values of their bounding boxes. It can be used to display histograms, bars with custom sizes, 3D ranges etc. The Range Series lets you easily define 2D data points by four values, forming two ranges: [Begin X - End X], [Begin Y - End Y]. The following example shows histogram with varying x sizes, used to merge intervals with too scarce occurrences.

Supported Technologies