# Visual types in Amazon Quick Sight Visual types Amazon Quick Sight offers a range of visual types that you can use to display your data. Use the topics in this section to learn more about the capabilities of each visual type. **Topics** + [ ## Measures and dimensions ](#measures-and-dimensions) + [ ## Display limits ](#display-limits) + [ ## Hiding or displaying the other category ](#other-category) + [ ## Customizing the number of data points to display ](#customizing-number-of-data-points) + [ # Using AutoGraph ](autograph.md) + [ # Using bar charts ](bar-charts.md) + [ # Using box plots ](box-plots.md) + [ # Using combo charts ](combo-charts.md) + [ # Using custom visual content ](custom-visual-content.md) + [ # Using donut charts ](donut-chart.md) + [ # Using funnel charts ](funnel-charts.md) + [ # Using gauge charts ](gauge-chart.md) + [ # Using heat maps ](heat-map.md) + [ # Using Highcharts ](highchart.md) + [ # Using histograms ](histogram-charts.md) + [ # Using image components ](image-component.md) + [ # Using KPIs ](kpi.md) + [ # Using layer maps ](layered-maps.md) + [ # Using line charts ](line-charts.md) + [ # Creating maps and geospatial charts ](geospatial-charts.md) + [ # Using small multiples ](small-multiples.md) + [ # Using pie charts ](pie-chart.md) + [ # Using pivot tables ](pivot-table.md) + [ # Using radar charts ](radar-chart.md) + [ # Using Sankey diagrams ](sankey-diagram.md) + [ # Using scatter plots ](scatter-plot.md) + [ # Using tables as visuals ](tabular.md) + [ # Using text boxes ](textbox.md) + [ # Using tree maps ](tree-map.md) + [ # Using waterfall charts ](waterfall-chart.md) + [ # Using word clouds ](word-cloud.md) ## Measures and dimensions We use the term *measure* to refer to numeric values that you use for measurement, comparison, and aggregation in visuals. A measure can be either a numeric field, like product cost, or a numeric aggregate on a field of any data type, like count of transaction IDs. We use the term *dimension* or *category* to refer to text or date fields that can be items, like products, or attributes that are related to measures and can be used to partition them. Examples are sales date for sales figures or product manufacturer for customer satisfaction numbers. Amazon Quick Sight automatically identifies a field as a measure or a dimension based on its data type. Numeric fields can act as dimensions, for example ZIP codes and most ID numbers. It's helpful to give such fields a string data type during data preparation. This way, Amazon Quick Sight understands that they are to be treated as dimensions and are not useful for performing mathematical calculations. You can change whether a field is displayed as a dimension or measure on an analysis-by-analysis basis instead. For more information, see [Fields as dimensions and measures](creating-a-visual.md#dimensions-and-measures). ## Display limits All visual types limit the number of data points they display, so that the visual elements (like lines, bars, or bubbles) are still easy to view and analyze. The visual selects the first *n* number of rows for display up to the limit for that visual type. The selection is either according to sort order, if one has been applied, or in default order otherwise. The number of data points supported varies by visual type. To learn more about display limits for a particular visual type, see the topic for that type. The visual title identifies the number of data points displayed if you have reached the display limit for that visual type. If you have a large dataset and want to avoid running into the visual display limit, use one or more filters to reduce the amount of data displayed. For more information about using filters with visuals, see [Filtering data in Amazon Quick Sight](adding-a-filter.md). For dashboards and analyses, Amazon Quick Sight supports the following: + 50 datasets per dashboard + 20 sheets per dashboard + 30 visualization objects per sheet **Note** Amazon Quick Sight supports over 30 different *visual types* (categories of charts and visualizations such as bar charts, pie charts, and line charts). Each analysis sheet can contain up to 30 *visual instances* (individual chart objects) of any combination of types. You can also choose to limit how many data points you want to display in your visual, before they are added to the **other** category. This category contains the aggregated data for all the data beyond the cutoff limit for the visual type you are using—either the one you impose, or the one based on display limits. You can use the on-visual menu to choose whether to display the **other** category. The **other** category doesn't show on scatter plots, heat maps, maps, tables (tabular reports), or key performance indicators (KPIs). It also doesn't show on line charts when the x-axis is a date. Drilling down into the **other** category is not supported. ## Hiding or displaying the other category Use the following procedure to hide or display the "other" category. **To hide or display the "other" category** 1. On the analysis page, choose the visual that you want to modify. 1. Choose the on-visual menu at the upper-right corner of the visual, and then choose **Hide "other" category** or **Show "other" category**, as appropriate. ## Customizing the number of data points to display You can choose the number of data points to display on the main axis of some visuals. After this number is displayed in the chart, any additional data points are included in the "other" category. For example, if you choose to include 10 data points out of 200, 10 display in the chart and 190 become part of the "other" category. To find this setting, choose the **v**-shaped on-visual menu, then choose **Format visual**. You can use the following table to determine which field well contains the data point setting and what number of data points the visual type displays by default. | Visual type | Where to find the data point setting | Default number of data points | | --- | --- | --- | | Bar chart, horizontal | **Y-axis** – **Number of data points displayed** | 10,000 | | Bar chart, vertical | **X-axis** – **Number of data points displayed** | 10,000 | | Combo chart | **X-axis** – **Number of data points displayed** | 2,500 | | Heat map | **Rows** – **Number of rows displayed** **Columns** – **Number of columns displayed** | 100 | | Line chart | **X-axis** – **Number of data points displayed** | 10,000 | | Pie chart | **Group/Color** – **Number of slices displayed** | 20 | | Tree map | **Group by** – **Number of squares displayed** | 100 | # Using AutoGraph AutoGraph isn't a visual type itself, but instead lets you tell Amazon Quick to choose the visual type for you. When you create a visual by choosing AutoGraph and then selecting fields, Amazon Quick uses the most appropriate visual type for the number and data types of the fields you select. ## Creating a visual using AutoGraph Use the following procedure to create a visual using AutoGraph. **To create a visual using AutoGraph** 1. On the analysis page, choose **Visualize** on the tool bar. 1. Choose **Add** on the application bar, and then choose **Add visual**. 1. On the **Visual types** pane, choose the AutoGraph icon. 1. On the **Fields list** pane, choose the fields that you want to use. # Using bar charts Amazon Quick supports the following types of bar charts, with either horizontal or vertical orientation: + **Single-measure** – A *single-measure bar chart* shows values for a single measure for a dimension. + **Multi-measure** – A *multi-measure bar chart* shows values for multiple measure for a dimension. + **Clustered** – A *clustered bar chart* shows values for a single measure for a dimension, grouped by another dimension. + **Stacked** – A *stacked bar chart* is similar to a clustered bar chart in that it displays a measure for two dimensions. However, instead of clustering bars for each child dimension by the parent dimension, it displays one bar per parent dimension. It uses color blocks within the bars to show the relative values of each item in the child dimension. The color blocks reflect the value of each item in the child dimension relative to the total for the measure. A stacked bar chart uses a scale based on the maximum value for the selected measure. + **Stacked 100 percent** – A *stacked 100 percent bar chart* is similar to a stacked bar chart. However, in a stacked 100 percent bar chart, the color blocks reflect the percentage of each item in the child dimension, out of 100 percent. Bar charts show up to 10,000 data points on the axis for visuals that don't use group or color. For visuals that do use group or color, they show up to 50 data points on the axis and up to 50 data points for group or color. For more information about how Amazon Quick handles data that falls outside display limits, see [Display limits](working-with-visual-types.md#display-limits). ## Creating single-measure bar charts Single-measure bar charts Use the following procedure to create a single-measure bar chart. **To create a single-measure bar chart** 1. On the analysis page, choose **Visualize** on the toolbar at left. 1. On the application bar at upper left, choose **Add**, and then choose **Add visual**. 1. On the **Visual types** pane, choose the **Horizontal bar chart** or **Vertical bar chart** icon. 1. From the **Fields list** pane, drag a dimension to the **X-axis** or **Y-axis** field well. 1. From the **Fields list** pane, drag a measure to the **Value** field well. ## Creating multi-measure bar charts Multi-measure bar charts Use the following procedure to create a multi-measure bar chart. **To create a multi-measure bar chart** 1. On the analysis page, choose **Visualize** on the toolbar at left. 1. On the application bar at upper-left, choose **Add**, and then choose **Add visual**. 1. On the **Visual types** pane, choose the **Horizontal bar chart** or **Vertical bar chart** icon. 1. From the **Fields list** pane, drag a dimension to the **X-axis** or **Y-axis** field well. 1. From the **Fields list** pane, drag two or more measures to the **Value** field well. ## Creating clustered bar charts Clustered bar charts Use the following procedure to create a clustered bar chart. **To create a clustered bar chart** 1. On the analysis page, choose **Visualize** on the toolbar at left. 1. On the application bar at upper left, choose **Add**, and then choose **Add visual**. 1. On the **Visual types** pane, choose the **Horizontal bar chart** or **Vertical bar chart** icon. 1. From the **Fields list** pane, drag a dimension to the **X-axis** or **Y-axis** field well. 1. From the **Fields list** pane, drag a measure to the **Value** field well. 1. From the **Fields list** pane, drag a dimension to the **Group/Color** field well. ## Creating stacked bar charts Stacked bar charts Use the following procedure to create a stacked bar chart. **To create a stacked bar chart** 1. On the analysis page, choose **Visualize** on the toolbar at left. 1. On the application bar at upper-left, choose **Add**, and then choose **Add visual**. 1. On the **Visual types** pane, choose the **Horizontal stacked bar chart** or **Vertical stacked bar chart** icon. 1. From the **Fields list** pane, drag a dimension to the **X-axis** or **Y-axis** field well. 1. From the **Fields list** pane, drag a dimension to the **Group/Color** field well. 1. From the **Fields list** pane, drag a measure to the **Value** field well. 1. (Optional) Add data labels and show totals: 1. On the menu in the upper-right corner of the visual, choose the **Format visual** icon. 1. In the **Visual** pane, choose **Data labels**. 1. Toggle the switch to display data labels. Labels for each measure value appear in the chart and the option to show totals appears in the pane. 1. Check **Show totals**. Totals appear for each bar in the chart. ## Creating stacked 100 percent bar charts Stacked 100% bar charts Use the following procedure to create a stacked 100 percent bar chart. **To create a stacked 100 percent bar chart** 1. On the analysis page, choose **Visualize** on the toolbar at left. 1. On the application bar at upper-left, choose **Add**, and then choose **Add visual**. 1. On the **Visual types** pane, choose the **Horizontal stacked 100% bar chart** or **Vertical stacked 100% bar chart** icon. 1. From the **Fields list** pane, drag a dimension to the **X-axis** or **Y-axis** field well. 1. From the **Fields list** pane, drag two or more measures to the **Value** field well. ## Bar chart features To understand the features supported by bar charts, use the following table. **** | Feature | Supported? | Comments | For more information | | --- | --- | --- | --- | | Changing the legend display | Yes, with exceptions | Multi-measure and clustered bar charts display a legend, while single-measure horizontal bar charts don't. | [Legends on visual types in Quick](customizing-visual-legend.md) | | Changing the title display | Yes | | [Titles and subtitles on visual types in Quick](customizing-a-visual-title.md) | | Changing the axis range | Yes | | [Range and scale on visual types in Quick](changing-visual-scale-axis-range.md) | | Showing or hiding axis lines, grid lines, axis labels, and axis sort icons | Yes | | [Axes and grid lines on visual types in Quick](showing-hiding-axis-grid-tick.md) | | Changing the visual colors | Yes | | [Colors in visual types in Quick](changing-visual-colors.md) | | Focusing on or excluding elements | Yes, with exceptions | You can focus on or exclude any bar on the chart, except when you are using a date field as the dimension for the axis. In that case, you can only focus on a bar, not exclude it. | [Focusing on visual elements](focusing-on-visual-elements.md) [Excluding visual elements](excluding-visual-elements.md) | | Sorting | Yes | You can sort on the fields you choose for the axis and the values. | [Sorting visual data in Amazon Quick](sorting-visual-data.md) | | Performing field aggregation | Yes | You must apply aggregation to the field or fields you choose for the value, and can't apply aggregation to the fields you choose for the axis or group/ color. | [Changing field aggregation](changing-field-aggregation.md) | | Adding drill-downs | Yes | You can add drill-down levels to the axis and Group/Color field wells. | [Adding drill-downs to visual data in Quick Sight](adding-drill-downs.md) | | Showing data labels | Yes | | [Data labels on visual types in Quick](customizing-visual-data-labels.md) | | Showing stacked bar chart totals | Yes | Showing totals in a stacked bar chart is only available when you choose to show data labels. | [Stacked bar charts](#create-bar-chart-stacked) | # Using box plots *Box plots*, also known as box and whisker plots, display data pooled from multiple sources into one visual, helping you make data-driven decisions. Use a box plot to visualize how data is distributed across an axis or over time, for example flights delayed over a 7-day time period. Typically, a box plot details information in quarters: + **Minimum** – The lowest data point excluding outliers. + **Maximum** – The highest data point excluding outliers. + **Median** – The middle value of the dataset. + **First Quartile** – The middle value between the smallest number and the median of the dataset. The first quartile doesn't include the minimum or the median. + **Third Quartile** – The middle value between the largest number and the median of the dataset. The third quartile doesn't include the maximum or the median. *Outliers* are extreme data points that aren't included in the calculation of a box plot's key values. Because outliers are calculated separately, their data points don't appear immediately after a box plot is created. Box plots display up to 10,000 data points. If a dataset contains more than 10,000 data points, a warning appears at the upper-right corner of the visual. Box plots support up to five metrics and one group-by, but don't render if duplicate metrics are supplied. Box plots support some calculated fields, but not all. Any calculated field that uses a window function, for example `avgOver`, results in a SQL error. Box plot visuals aren't compatible with MySQL 5.3 and earlier. **To create a basic box plot visual** 1. Sign in to Amazon Quick at [https://quicksight.aws.amazon.com/](https://quicksight.aws.amazon.com/). 1. Open Quick and choose **Analyses** on the navigation pane at left. 1. Choose one of the following: + To create a new analysis, choose **New analysis** at upper right. For more information, see [Starting an analysis in Quick Sight](creating-an-analysis.md). + To use an existing analysis, choose the analyses that you want to edit. 1. Choose **Add**, **Add visual**. 1. At lower left, choose the box plot icon from **Visual types**. 1. On the **Fields list** pane, choose the fields that you want to use for the appropriate field wells. Box plots require at least one unique measure field. 1. (Optional) Add drill-down layers by dragging one or more additional fields to the **Group/Color** field well. For more information about adding drill-downs, see [Adding drill-downs to visual data in Quick Sight](adding-drill-downs.md). To understand the features supported by box plots, see [Analytics formatting per type in Quick](analytics-format-options.md). For customization options, see [Formatting in Amazon Quick](formatting-a-visual.md). # Using combo charts Using a combo chart, you can create one visualization that shows two different types of data, for example trends and categories. Combo charts are also known as line and column (bar) charts, because they combine a line chart with a bar chart. Bar charts are useful for comparing categories. Both bar charts and line charts are useful for displaying changes over time, although bar charts should show a greater difference between changes. Amazon Quick supports the following types of combo charts: + **Clustered bar combo charts** – display sets of single-color bars where each set represents a parent dimension and each bar represents a child dimension. Use this chart to make it easy to determine values for each bar. + **Stacked bar combo charts** – display multi-color bars where each bar represents a parent dimension and each color represents a child dimension. Use this chart to make it easy to see relationships between child dimensions within a parent dimension. This chart shows the total value for the parent dimension and how each child adds to the total value. To determine the value for each child dimension, the chart reader must compare the size of the color section to the data labels for that axis. Both types of combo chart require only one dimension on the **X axis**, but are usually more effective when also displaying at least one measure under **Lines**. Use a combo chart only if you want to show a relationship between the bars and the lines. A good rule of thumb is that if you need to explain how the two chart types relate, you should probably use two separate charts instead. Because each chart works differently, it can be helpful to understand the following points before you begin: + The data points in each series render on different scales. Combo charts use a scale based on the maximum value for the selected measure. + The distance between the numbers on the axis won't match between the lines and bars, even if you select the same scale for each chart type. + For clarity, try to use different units for the measure in each data series. The combo chart is like using two different types of visualization at the same time. Make sure that the data in the bars (or columns) directly relates to the data in the line or lines. This relationship is not technically enforced by the tool, so it's essential that you determine this relationship yourself. Without some relation between the lines and bars, the visual loses meaning. You can use the combo chart visual type to create a single-measure or single-line chart. A single-measure combo chart shows one measure for one dimension. To create a multi-measure chart, you can choose to add multiple lines, or multiple bars. A multi-measure bar chart shows two or more measures for one dimension. You can group the bars in clusters, or stack them. For the bars, use a dimension for the axis and a measure for the value. The dimension is typically a text field that is related to the measure in some way and can be used to segment it to see more detailed information. Each bar in the chart represents a measure value for an item in the dimension you chose. Bars and lines show up to 2,500 data points on the axis for visuals that don't use group or color. For visuals that do use group or color, bars show up to 50 data points on the axis and up to 50 data points for group or color, while lines show 200 data points on the axis and up to 25 data points for group or color. For more information about how Amazon Quick handles data that falls outside display limits, see [Display limits](working-with-visual-types.md#display-limits). ## Combo chart features To understand the features supported by combo charts, use the following table. **** | Feature | Supported? | Comments | For more information | | --- | --- | --- | --- | | Changing the legend display | Yes, with exceptions | Multi-measure combo charts display a legend, and single-measure combo charts don't. | [Legends on visual types in Quick](customizing-visual-legend.md) | | Changing the title display | Yes | | [Titles and subtitles on visual types in Quick](customizing-a-visual-title.md) | | Changing the axis range | Yes | You can set the range for the axis. | [Range and scale on visual types in Quick](changing-visual-scale-axis-range.md) | | Showing or hiding axis lines, grid lines, axis labels, and axis sort icons | Yes | | [Axes and grid lines on visual types in Quick](showing-hiding-axis-grid-tick.md) | | Changing the visual colors | Yes | | [Colors in visual types in Quick](changing-visual-colors.md) | | Focusing on or excluding elements | Yes, with exceptions | You can focus on or exclude any bar on the chart, except when you are using a date field as the dimension for the axis. In that case, you can only focus on a bar, not exclude it. | [Focusing on visual elements](focusing-on-visual-elements.md) [Excluding visual elements](excluding-visual-elements.md) | | Sorting | Yes | You can sort on the fields you choose for the axis and the values. | [Sorting visual data in Amazon Quick](sorting-visual-data.md) | | Performing field aggregation | Yes | You must apply aggregation to the field or fields you choose for the value. You can't apply aggregation to the fields you choose for the axis or group/color. | [Changing field aggregation](changing-field-aggregation.md) | | Adding drill-downs | Yes | You can add drill-down levels to the axis and Group/Color field wells. | [Adding drill-downs to visual data in Quick Sight](adding-drill-downs.md) | | Synchronizing y-axis | Yes | Synchronize the y-axes for both bars and lines into a single axis. | [Range and scale on visual types in Quick](changing-visual-scale-axis-range.md) | ## Creating a combo chart Use the following procedure to create a combo chart. **To create a combo chart** 1. On the analysis page, choose **Visualize** on the tool bar. 1. Choose **Add** on the application bar, and then choose **Add visual**. 1. On the **Visual types** pane, choose one of the combo chart icons. 1. From the **Fields list** pane, drag the fields that you want to use to the appropriate field wells. Typically, you want to use dimension or measure fields as indicated by the target field well. If you choose to use a dimension field as a measure, the **Count** aggregate function is automatically applied to it to create a numeric value. You can create combo charts as follows: + Choose a dimension for the **X axis**. + To create a single-measure combo chart, choose one measure for either **Bars** or **Lines**. + To create a multi-measure combo chart, choose two or more measures for the **Bars** or **Lines** field well. + Optionally, add a dimension to the **Group/Color** field well. If you have a field in **Group/Color**, you can't have more than one field under **Bars**. ![\[alt text not found\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/combo-chart-example2-clustered.png) 1. (Optional) Add drill-down layers by dragging one or more additional fields to the **X axis** or **Group/Color** field wells. For more information about adding drill-downs, see [Adding drill-downs to visual data in Quick Sight](adding-drill-downs.md). # Using custom visual content Using custom visual content You can embed webpages and online videos, forms, and images in your Quick dashboards using the custom visual content chart type. For example, you can embed the image of your company logo in your dashboards. You can also embed an online video from your organization's latest conference, or embed an online form asking readers of the dashboard if the dashboard is helpful. After you create custom visual content, you can use navigation actions to navigate within them. You can also use parameters to control what appears in them. The following limitations apply to custom visual content: + Only `https` URL schemes are supported. + Custom visual content isn't supported in email reports. + Images and websites that use hotlink protection won't load in custom visuals. To embed a webpage, video, online form, or image in your dashboard, choose the custom visual content icon in the **Visual types** pane. For more information about adding visuals to a dashboard, see [Adding a visual](creating-a-visual.md#create-a-visual). Use the following procedures to learn how to embed custom visuals in your dashboards. ## Best practices for using custom visual content Best practices When embedding web content using the custom visual content chart type, we recommend the following: + Choose web content from sources that support viewing or opening the content in an IFrame. If the source of the web content doesn't support being viewed or opened in an IFrame, the content doesn't appear in Quick, even if the URL is accurate. + When possible, use embeddable URLs, especially for videos, online forms, spreadsheets, and documents. Embeddable URLs create a better experience for readers of your dashboard and make interacting with the content easier. You can usually find the embeddable URL for content when you choose to share the content from the source website. + To embed internal URLs or URLs that you own, you might need to set them to be opened in an IFrame. + When viewing custom visual content in an analysis or dashboard, make sure you enable all cookies. If third-party cookies are blocked in your browser, images that are part of the website that is embedded within the custom content visual do not render. **Note** Chrome has announced plans to deprecate all third-party cookies by the end of 2024. This means that websites that are embedded within Quick custom content visuals will no longer show any contents that rely on third-party cookies in Chrome. For more information about Chrome's plans to deprecate third party cookies, see [Chrome is deprecating third-party cookies](https://cloud.google.com/looker/docs/best-practices/chrome-third-party-cookie-deprecation). ## Embedding images in a dashboard Embedding images You can embed an online image in a dashboard using the image URL. Use the following procedure to embed an image using the custom visual content chart type. Embedded images don't appear in a browser that has third-party cookies blocked. To see embedded images in a dashboard, enable third-party cookies in your browser settings. **To embed an image in a dashboard** 1. In the **Visual types** pane, choose the custom visual content icon. 1. In the visual, choose **Customize visual**. 1. In the **Properties** pane that opens, under **Custom content**, enter the image URL for the image that you want to embed. 1. Choose **Apply**. The image appears as a webpage in the visual. 1. Choose **Show as image**. If the URL is an image, the image appears in the visual. If the URL is not an image, such as a URL to a slide show, gallery, or webpage, the following message appears: `This URL doesn't appear to be an image. Update the URL to an image`. To do so, open the image that you want to embed in a separate browser tab, or choose an embeddable URL for the image (usually found when you choose to share the image). 1. (Optional) For **Image sizing options**, choose one of the following options: + **Fit to width** – This option fits the image to the width of the visual. + **Fit to height** – This option fits the image to the height of the visual. + **Scale to visual** – This option scales the image to the width and height of the visual. This option might contort the image. + **Do not scale** – This option keeps the image at its original scale and doesn't fit the image to the dimensions of the visual. With this option, the image is centered in the visual and the parts of the image that are within the width and height of the visual appear. Some parts of the image might not appear if the visual is smaller than the image. If the visual is larger than the image, however, the image is centered in the visual and is surrounded by white space. ## Embedding online forms in a dashboard Embedding online forms You can embed an online form in a dashboard using the embeddable URL. Use the following procedure to embed an online form using the custom visual content chart type. **To embed an online form in a dashboard** 1. In the **Visual types** pane, choose the custom visual content icon. 1. In the visual, choose **Customize visual**. 1. In the **Properties** pane that opens, under **Custom content**, enter the form URL for the online form that you want to embed. If possible, use an embeddable URL for the form. Using an embeddable URL creates a better experience for readers of your dashboard who might want to interact with the form. You can often find the embeddable URL when you choose to share the form on the site where you create it. 1. Choose **Apply**. The form appears in the visual. ## Embedding webpages in a dashboard Embedding webpages You can embed webpage in a dashboard using the URL. Use the following procedure to embed webpage using the custom visual content chart type. **To embed a webpage in a dashboard** 1. In the **Visual types** pane, choose the custom visual content icon. 1. In the visual, choose **Customize visual**. 1. In the **Properties** pane that opens, under **Custom content**, enter the URL for the webpage that you want to embed. 1. Choose **Apply**. The webpage appears in the visual. ## Embedding online videos in a dashboard Embedding online videos You can embed an online video in a dashboard using the embeddable video URL. Use the following procedure to embed an online video using the custom visual content chart type. **To embed an online video in a dashboard** 1. In the **Visual types** pane, choose the custom visual content icon. 1. In the visual, choose **Customize visual**. 1. In the **Properties** pane that opens, under **Custom content**, enter the embeddable URL for the video that you want to embed. To find the embeddable URL for a video, share the video and copy the embed URL from IFrame code. The following is an example of an embed URL for a YouTube video: `https://www.youtube.com/embed/uniqueid`. For a Vimeo video, the following is an example of an embed URL: `https://player.vimeo.com/video/uniqueid`. 1. Choose **Apply**. The video appears in the visual. # Using donut charts Use donut charts to compare values for items in a dimension. The best use for this type of chart is to show a percentage of a total amount. Each wedge in a donut chart represents one value in a dimension. The size of the wedge represents the proportion of the value for the selected measure that the item represents compared to the whole for the dimension. Donut charts are best when precision isn't important and there are few items in the dimension. To learn how to use donut charts in Amazon Quick, you can watch this video: [![AWS Videos](http://img.youtube.com/vi/vR6H4bXaRBY/0.jpg)](http://www.youtube.com/watch?v=vR6H4bXaRBY) To create a donut chart, use one dimension in the **Group/Color** field well. With only one field, the chart displays the division of values by row count. To display the division of dimension values by a metric value, you can add a metric field to the **Value** field well. Donut charts show up to 20 data points for group or color. For more information about how Amazon Quick handles data that falls outside display limits, see [Display limits](working-with-visual-types.md#display-limits). ## Donut chart features To understand the features supported by donut charts, use the following table. **** | Feature | Supported? | Comments | For more information | | --- | --- | --- | --- | | Changing the legend display | Yes | | [Legends on visual types in Quick](customizing-visual-legend.md) | | Changing the title display | Yes | | [Titles and subtitles on visual types in Quick](customizing-a-visual-title.md) | | Changing the axis range | Not applicable | | [Range and scale on visual types in Quick](changing-visual-scale-axis-range.md) | | Changing the visual colors | Yes | | [Colors in visual types in Quick](changing-visual-colors.md) | | Focusing on or excluding elements | Yes, with exceptions | You can focus on or exclude a wedge in a donut chart, except when you are using a date field as a dimension. In that case, you can only focus on a wedge, not exclude it. | [Focusing on visual elements](focusing-on-visual-elements.md) [Excluding visual elements](excluding-visual-elements.md) | | Sorting | Yes | You can sort on the field that you choose for the value or the group or color. | [Sorting visual data in Amazon Quick](sorting-visual-data.md) | | Performing field aggregation | Yes | You must apply aggregation to the field that you choose for the value, and can't apply aggregation to the field that you choose for group or color. | [Changing field aggregation](changing-field-aggregation.md) | | Adding drill-downs | Yes | You can add drill-down levels to the Group/Color field well. | [Adding drill-downs to visual data in Quick Sight](adding-drill-downs.md) | | Choosing size | Yes | You can choose how thick the donut chart is: small, medium, and large. | [Formatting in Amazon Quick](formatting-a-visual.md) | | Showing totals | Yes | You can choose to display or hide the aggregate of the Value field. By default, this displays the total count of the Group/Color field, or the total sum of the Value field. | [Formatting in Amazon Quick](formatting-a-visual.md) | ## Creating a donut chart Use the following procedure to create a donut chart. **To create a donut chart** 1. On the analysis page, choose **Visualize** on the tool bar. 1. Choose **Add** on the application bar, and then choose **Add visual**. 1. On the **Visual types** pane, choose the donut chart icon. 1. From the **Fields list** pane, drag the fields that you want to use to the appropriate field wells. Typically, you want to use dimension or measure fields as indicated by the target field well. If you choose to use a dimension field as a measure, the **Count** aggregate function is automatically applied to it to create a numeric value. To create a donut chart, drag a dimension to the **Group/Color** field well. Optionally, drag a measure to the **Value** field well. 1. (Optional) Add drill-down layers by dragging one or more additional fields to the **Group/Color** field well. For more information about adding drill-downs, see [Adding drill-downs to visual data in Quick Sight](adding-drill-downs.md). # Using funnel charts Use a funnel chart to visualize data that moves across multiple stages in a linear process. In a funnel chart, each stage of a process is represented in blocks of different shapes and colors. The first stage, known as the *head*, is the largest block and is followed by the smaller stages, known as the *neck*, in a funnel shape. The size of the block representing each stage in a funnel chart is a percentage of the total, and is proportionate to its value. The bigger the size of the block, the bigger its value. Funnel charts are often useful in business contexts because you can view trends or potential problem areas in each stage, such as bottlenecks. For example, they can help you visualize the amount of the potential revenue in each stage of a sale, from first contact to final sale and on through maintenance. **To create a basic funnel chart visual** 1. Open Amazon Quick and choose **Analyses** on the navigation pane at left. 1. Choose one of the following: + To create a new analysis, choose **New analysis** at upper right. For more information, see [Starting an analysis in Quick Sight](creating-an-analysis.md). + To use an existing analysis, choose the analysis that you want to edit. 1. Choose **Add (\$1), Add Visual**. 1. At lower left, choose the funnel chart icon from **Visual types**. 1. On the **Fields list** pane, choose the fields that you want to use for the appropriate field wells. Funnel charts require one dimension in **Group**. 1. (Optional) Add drill-down layers by dragging one or more additional fields to the **Group/Color** field well. For more information about adding drill-downs, see [Adding drill-downs to visual data in Quick Sight](adding-drill-downs.md). To understand the features supported by funnel charts, see [Analytics formatting per type in Quick](analytics-format-options.md). For customization options, see [Formatting in Amazon Quick](formatting-a-visual.md). # Using gauge charts Use gauge charts to compare values for items in a measure. You can compare them to another measure or to a custom amount. A gauge chart is similar to a nondigital gauge, for example a gas gauge in an automobile. It displays how much there is of the thing you are measuring. In a gauge chart, this measurement can exist alone or in relation to another measurement. Each color section in a gauge chart represents one value. In the following example, we are comparing actual sales to the sales goal, and the gauge shows that we must sell an additional 33.27% to meet the goal. To learn how to use gauge charts in Amazon Quick, you can watch this video: [![AWS Videos](http://img.youtube.com/vi/03gYx4-iGak/0.jpg)](http://www.youtube.com/watch?v=03gYx4-iGak) To create a gauge chart, you need to use at least one measure. Put the measure in the **Value** field well. If you want to compare two measures, put the additional measure in the **Target value** field well. If you want to compare a single measure to a target value that isn't in your dataset, you can use a calculated field that contains a fixed value. You can choose a variety of formatting options for the gauge chart, including the following settings in **Format visual**. + ****Value displayed**** – Hide value, display actual value, or display a comparison of two values + ****Comparison method**** – Compare values as a percent, the actual difference between values, or difference as a percent + ****Axis style**** – + **Show axis label** – Show or hide the axis label + **Range** – The numeric minimum and maximum range to display in the gauge chart + **Reserve padding (%)** – Added to the top of the range (target, actual value, or max) + ****Arc style**** – Degrees the arc displays (180° to 360°) + ****Thickness**** – Thickness of the arc (small, medial, or large) ## Gauge chart features To understand the features supported by gauge charts, use the following table. **** | Feature | Supported? | Comments | For more information | | --- | --- | --- | --- | | Changing the legend display | Yes | | [Legends on visual types in Quick](customizing-visual-legend.md) | | Changing the title display | Yes | | [Titles and subtitles on visual types in Quick](customizing-a-visual-title.md) | | Formatting gauge | Yes | You can customize the value displayed, the comparison method, the axis style, the arc style, and the thickness of the gauge. | | | Changing the axis range | No | | | | Changing the visual colors | Yes | The foreground color the filled area; it represents the Value. The background color the unfilled area; it represents the Target value if one is selected. | [Colors in visual types in Quick](changing-visual-colors.md) | | Focusing on or excluding elements | No | | | | Sorting | No | | [Sorting visual data in Amazon Quick](sorting-visual-data.md) | | Performing field aggregation | Yes | | [Changing field aggregation](changing-field-aggregation.md) | | Adding drill-downs | No | | | ## Creating a gauge chart Use the following procedure to create a gauge chart. **To create a gauge chart** 1. On the analysis page, choose **Visualize** on the tool bar. 1. Choose **Add** on the application bar, and then choose **Add visual**. 1. On the **Visual types** pane, choose the gauge chart icon. 1. From the **Fields list** pane, drag the fields that you want to use to the appropriate field wells. To create a gauge chart, drag a measure to the **Value** field well. To add a comparison value, drag a different measure to the **Target value** field well. # Using heat maps Use heat maps to show a measure for the intersection of two dimensions, with color-coding to easily differentiate where values fall in the range. Heat maps can also be used to show the count of values for the intersection of the two dimensions. Each rectangle on a heat map represents the value for the specified measure for the intersection of the selected dimensions. Rectangle color represents where the value falls in the range for the measure, with darker colors indicating higher values and lighter colors indicating lower ones. Heat maps and pivot tables display data in a similar tabular fashion. Use a heat map if you want to identify trends and outliers, because the use of color makes these easier to spot. Use a pivot table if you want to further analyze data on the visual, for example by changing column sort order or applying aggregate functions across rows or columns. To create a heat map, choose at least two fields of any data type. Amazon Quick populates the rectangle values with the count of the x-axis value for the intersecting y-axis value. Typically, you choose a measure and two dimensions. Heat maps show up to 50 data points for rows and up to 50 data points for columns. For more information about how Amazon Quick handles data that falls outside display limits, see [Display limits](working-with-visual-types.md#display-limits). ## Heat map features To understand the features supported by heat maps, use the following table. | Feature | Supported? | Comments | For more information | | --- | --- | --- | --- | | Changing the legend display | Yes | | [Legends on visual types in Quick](customizing-visual-legend.md) | | Changing the title display | Yes | | [Titles and subtitles on visual types in Quick](customizing-a-visual-title.md) | | Changing the axis range | Not applicable | | [Range and scale on visual types in Quick](changing-visual-scale-axis-range.md) | | Changing the visual colors | No | | [Colors in visual types in Quick](changing-visual-colors.md) | | Focusing on or excluding elements | Yes, with exceptions | You can focus on or exclude a rectangle in a heat map, except when you are using a date field as the rows dimension. In that case, you can only focus on a rectangle, not exclude it. | [Focusing on visual elements](focusing-on-visual-elements.md) [Excluding visual elements](excluding-visual-elements.md) | | Sorting | Yes | You can sort by the fields you choose for the columns and the values. | [Sorting visual data in Amazon Quick](sorting-visual-data.md) | | Performing field aggregation | Yes | You must apply aggregation to the fields you choose for the value, and can't apply aggregation to the fields you choose for the rows or columns. | [Changing field aggregation](changing-field-aggregation.md) | | Adding drill-downs | Yes | You can add drill-down levels to the Rows and Columns field wells. | [Adding drill-downs to visual data in Quick Sight](adding-drill-downs.md) | | Conditional formatting | No | | [Conditional formatting on visual types in Quick](conditional-formatting-for-visuals.md) | ## Creating a heat map Use the following procedure to create a heat map. **To create a heat map** 1. On the analysis page, choose **Visualize** on the tool bar. 1. Choose **Add** on the application bar, and then choose **Add visual**. 1. On the **Visual types** pane, choose the heat map icon. 1. From the **Fields list** pane, drag the fields that you want to use to the appropriate field wells. Typically, you want to use dimension or measure fields as indicated by the target field well. If you choose to use a dimension field as a measure, the **Count** aggregate function is automatically applied to it to create a numeric value. To create a heat map, drag a dimension to the **Rows** field well, a dimension to the **Columns** field well, and a measure to the **Values** field well. 1. (Optional) Add drill-down layers by dragging one or more additional fields to the **Rows** or **Columns** field wells. For more information about adding drill-downs, see [Adding drill-downs to visual data in Quick Sight](adding-drill-downs.md). # Using Highcharts Use Highcharts visuals to create custom chart types and visuals that use the [Highcharts Core library](https://www.highcharts.com/blog/products/highcharts/). Highcharts visuals provide Quick authors direct access to the [Highcharts API](https://api.highcharts.com/highcharts/). To configure a Highcharts visual, Quick authors need to add a Highcharts JSON schema to the visual in Quick. Authors can use Quick expressions to reference Quick fields, and formatting options in the JSON schema that they use to generate the Highcharts visual. The JSON **Chart code** editor provides contextual assistance for autocomplete and real time validation to ensure that the input JSON schemas are configured properly. To maintain security, the Highcharts visual editor does not accept CSS, JavaScript, or HTML code input. For more information about Highcharts visuals in Amazon Quick, see the [Highcharts Visual QuickStart Guide](https://democentral.learnquicksight.online/#Dashboard-FeatureDemo-Highcharts-Visual) in [DemoCentral](https://democentral.learnquicksight.online/#). The following image shows a lipstick chart that is configured in the **Chart code** JSON editor of a Highcharts visual in Quick. ![\[alt text not found\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/highcharts-example1.png) For more examples of visuals that you can create with the Highcharts visual in Quick, see [Highcharts demos](https://www.highcharts.com/demo). ## Considerations Before you start creating Highcharts visuals in Amazon Quick, review the following limitations that apply to Highcharts visuals. + The following JSON values are not supported in the Highcharts **Chart code** JSON editor: + Functions + Dates + Undefined values + Links to GeoJSON files or other images are not supported for Highcharts visuals. + Field colors are not available for Highcharts visuals. Default theme colors are applied to all Highcharts visuals. ## Creating a Highcharts visual Use the following procedure to create a Highcharts visual in Amazon Quick. 1. Open the [Quick console](https://quicksight.aws.amazon.com/). 1. Open the Quick analysis that you want to add a Highcharts visual to. 1. On the application bar, choose **Add**, and then choose **Add visual**. 1. On the **Visual types** pane, choose the Highcharts visual icon. An empty visual appears on the analysis sheet and the **Properties** pane opens on the left. 1. In the **Properties** pane, expand the **Display settings** section and perform the following actions: 1. For **Edit title**, choose the paintbrush icon, enter the title that you want the visual to have, and then choose **SAVE**. Alternatively, choose the eyeball icon to hide the title. 1. (Optional) For **Edit subtitle**, choose the paintbrush icon, enter the subtitle that you want the visual to have, and then choose **SAVE**. Alternatively, choose the eyeball icon to hide the subtitle. 1. (Optional) For **Alt text**, add the alt text that you want the visual to have. 1. Expand the **Data point limit** section. For **Number of data points to show**, enter the number of data points that you want the visual to show. Highcharts visuals can show up to 10,000 data points. 1. Expand the **Chart code** section. 1. Enter a JSON schema into the **Chart code** JSON editor. The editor provides contextual assistance and real time validation to ensure that your input JSON is configured properly. Any errors that Quick identifies can be viewed in the **Errors** dropdown. The example below shows a JSON schema that creates a lipstick chart that shows current year sales by industry. ``` { "xAxis": { "categories": ["getColumn", 0] }, "yAxis": { "min": 0, "title": { "text": "Amount ($)" } }, "tooltip": { "headerFormat": "{point.key}", "pointFormat": "", "footerFormat": "
{series.name}: ${point.y:,.0f}
", "shared": true, "useHTML": true }, "plotOptions": { "column": { "borderWidth": 0, "grouping": false, "shadow": false } }, "series": [ { "type": "column", "name": "Current Year Sales", "color": "rgba(124,181,236,1)", "data": ["getColumn", 1], "pointPadding": 0.3, "pointPlacement": 0.0 } ] } ``` 1. Choose **APPLY CODE**. Quick converts the JSON schema into a visual that appears in the analysis. To make changes to the rendered visual, update the appropriate properties in the JSON schema and choose **APPLY CODE**. 1. (Optional) Open the **Reference** dropdown to access links to helpful Highctarts reference material. When you are happy with the rendered visual, close the properties pane. For more information about Quick Sight specific expressions that can be used to configure a Highcharts visual, see [Amazon Quick JSON expression language for Highcharts visuals](highchart-expressions.md). ## Interactive Highchart features Highchart visualizations in Amazon Quick Sight support custom actions, highlighting, and custom field color consistencies, allowing you to create interactive and visually cohesive charts that integrate seamlessly with other Quick Sight visuals. ### Custom actions With custom actions, you can define specific behaviors for any data point in your Highchart visualizations. This feature seamlessly integrates with Quick Sight's existing action framework, enabling you to create interactive charts that respond to user clicks. The system currently supports single data point selection, giving you precise control over user interactions. Custom actions can be implemented across various chart types, including line charts, bar charts, and stacked bar charts, among others. To implement custom actions, you'll need to modify your Highcharts JSON configuration. Add an event block to your series configuration, specifying the click event and the corresponding action. For example: ``` { "series": [{ "type": "line", "data": ["getColumn", 1], "name": "value", "events": { "click": [ "triggerClick", { "rowIndex": "point.index" } ] } }] ``` This configuration enables click events on your chart's data points, allowing Quick Sight to handle custom actions based on the selected data. ### Cross-visual highlighting Cross-visual highlighting enhances the interactivity of your dashboards by creating visual connections between different charts. When a user selects elements in one chart, related elements in other visuals are automatically highlighted, while unrelated elements are dimmed. This feature helps users quickly identify relationships and patterns across multiple visualizations, improving data comprehension and analysis. To enable cross-visual highlighting and maintain field color consistency, use the `quicksight` clause in your Highcharts JSON configuration. This clause acts as a bridge between Highcharts rendering and Quick's visual interaction system. Here's an example of how to set it up: ``` { "quicksight": { "pointRender": ["updatePointAttributes", { "opacity": ["case", ["dataMarkMatch", ["getColumnName", 0], "series.name"], 1, // Full opacity for matching elements 0.1 // Dim non-matching elements ], "color": ["getColumnColorOverrides", ["getColumnName", 0], "series.name"] }] } } ``` This configuration uses Quick Sight's JSON expression language to dynamically modify visual properties like opacity and color based on user interactions and predefined color schemes. For more complex scenarios, you can set up highlighting based on multiple conditions. This allows for more nuanced interactivity in your visualizations. The following example highlights elements based on either the quarter or day of the week: ``` { "quicksight": { "pointRender": ["updatePointAttributes", { "opacity": ["case", ["||", ["dataMarkMatch", "quarter", "series.name"], ["dataMarkMatch", "day_of_week", "point.name"] ], 1, // Full opacity for matching elements 0.1 // Dim non-matching elements ], }] } } ``` ### Field-level color consistency Maintaining visual coherence across your dashboard is crucial for effective data interpretation. The field-level color consistency feature ensures that colors assigned to specific dimensions perist across all visuals in your dashboard. This consistency helps users quickly recognize and track particular data categories across different chart types and views, enhancing the overall user experience and data comprehension. # Amazon Quick JSON expression language for Highcharts visuals JSON expression language for Highcharts Highcharts visuals accept most [valid JSON values](https://www.w3schools.com/js/js_json_datatypes.asp), standard arithmetic operators, string operators, and conditional operators. The following JSON values are not supported for Highcharts visuals: + Functions + Dates + Undefined values Quick authors can use JSON expression language create JSON schemas for a highcharts visual. JSON expression language is used to bind JSON to APIs or datasets to allow dynamic population and modification of JSON structures. Developers can also use JSON expression language to inflate and transform JSON data with concise and intuitive expressions. With JSON expression language, expressions are represented as arrays, where the first element specifies the operation and subsequent elements are the arguments. For example, `["unique", [1, 2, 2]]` applies the `unique` operation to the array `[1, 2, 2]`, resulting in `[1, 2]`. This array-based syntax allows for flexible expressions, that allow complex transformations on JSON data. JSON expression language supports *nested expressions*. Nested expressions are expressions that contain other expressions as arguments. For example `["split", ["toUpper", "hello world"], " "]` first converts the string `hello world` into an uppercase, then splits it into array of words, resulting in `["HELLO", "WORLD"]`. Use the following sections to learn more about JSON expression language for Highcharts visuals in Amazon Quick. **Topics** + [ # Arithmetics ](jle-arithmetics.md) + [ # Array operations ](jle-arrays.md) + [ # Amazon Quick expressions ](jle-qs-expressions.md) # Arithmetics The following table shows arithmetic expressions that can be used with JSON expression language. | Operation | Expression | Input | Output | | --- | --- | --- | --- | | Addition | ["\$1", operand1, operand2] | \$1 sum: ["\$1", 2, 4] \$1 | \$1 sum: 6 \$1 | | Subtraction | ["-", operand1, operand2] | \$1 difference: ["-", 10, 3] \$1 | \$1 difference: 7 \$1 | | Multiplication | ["\$1", operand1, operand2] | \$1 product: ["\$1", 5, 6] \$1 | \$1 product: 30 \$1 | | Division | ["/", operand1, operand2] | \$1 quotient: ["/", 20, 4] \$1 | \$1 quotient: 5 \$1 | | Modulo | ["%", operand1, operand2] | \$1 remainder: ["%", 15, 4] \$1 | \$1 remainder: 3 \$1 | | Exponentiation | ["\$1\$1", base, exponent] | \$1 power: ["\$1\$1", 2, 3] \$1 | \$1 power: 8 \$1 | | Absolute Value | ["abs", operand] | \$1 absolute: ["abs", -5] \$1 | \$1 absolute: 5 \$1 | | Square Root | ["sqrt", operand] | \$1 sqroot: ["sqrt", 16] \$1 | \$1 sqroot: 4 \$1 | | Logarithm (base 10) | ["log10", operand] | \$1 log: ["log10", 100] \$1 | \$1 log: 2 \$1 | | Natural Logarithm | ["ln", operand] | \$1 ln: ["ln", Math.E] \$1 | \$1 ln: 1 \$1 | | Round | ["round", operand] | \$1 rounded: ["round", 3.7] \$1 | \$1 rounded: 4 \$1 | | Floor | ["floor", operand] | \$1 floor: ["floor", 3.7] \$1 | \$1 floor: 3 \$1 | | Ceiling | ["ceil", operand] | \$1 ceiling: ["ceil", 3.2] \$1 | \$1 ceiling: 4 \$1 | | Sine | ["sin", operand] | \$1 sine: ["sin", 0] \$1 | \$1 sine: 0 \$1 | | Cosine | ["cos", operand] | \$1 cosine: ["cos", 0] \$1 | \$1 cosine: 1 \$1 | | Tangent | ["tan", operand] | \$1 tangent: ["tan", Math.PI] \$1 | \$1 tangent: 0 \$1 | # Array operations JSON expression language allows generic array manipulation for the following functions: + `map` – Applies a mapping function to each element of an array and returns a new array with the transformed values. For example, `["map", [1, 2, 3], ["*", ["item"], 2]]` maps each element of the array `[1, 2, 3]` by multiplying it by 2. + `filter` – Filters an array based on a given condition and returns a new array containing only the elements that satisfy the condition For example, `["filter", [1, 2, 3, 4, 5], ["==", ["%", ["item"], 2], 0]]` filters the array `[1, 2, 3, 4, 5]` to include only the even numbers. + `reduce` – Reduces an array to a single value by applying a reducer function to each element and accumulating the result. For example, `["reduce", [1, 2, 3, 4, 5], ["+", ["acc"], ["item"]], 0]` reduces the array `[1, 2, 3, 4, 5]` to the sum of its elements. + `get` – Retrieves a value from an object or an array by specifying a key or index. For example, `["get", ["item"], "name"]` retrieves the value of the `"name"` property from the current item. + `unique` – Given an array returns only unique items inside this array. For example, `["unique", [1, 2, 2]]` returns `[1, 2]`. # Amazon Quick expressions Amazon Quick offers additional expressions to enhance the functionality of Highcharts visuals. Use the following sections to learn more about common Quick expressions for highcharts visuals. For more information about JSON expression language in Amazon Quick, see the [Highcharts Visual QuickStart Guide](https://democentral.learnquicksight.online/#Dashboard-FeatureDemo-Highcharts-Visual) in [DemoCentral](https://democentral.learnquicksight.online/#). **Topics** + [ ## `getColumn` ](#highcharts-expressions-getcolumn) + [ ## `formatValue` ](#highcharts-expressions-formatvalue) ## `getColumn` Use the `getColumn` expressions to return values from specified column indices. For example, the following table shows a list of products alongside their category, and price. | Product name | Category | Price | | --- | --- | --- | | Product A | Technology | 100 | | Product B | Retail | 50 | | Product C | Retail | 75 | The following `getColumn` query generates an array that shows all product names alongside their price. ``` { product name: ["getColumn", 0], price: ["getColumn", 2] } ``` The follwing JSON is returned: ``` { product name: ["Product A", "Product B", "Product C"], price: [100, 50, 75] } ``` You can also pass multiple columns at once to generate an array of arrays, shown in the following example. **Input** ``` { values: ["getColumn", 0, 2] } ``` **Output** ``` { values: [["Product A", 100], ["Product B", 50], ["Product C", 75]] } ``` Similar to `getColumn`, the following expressions can be used to return column values from field wells or themes: + `getColumnFromGroupBy` returns columns from the group by field. The second argument is the index of the column to return. For example, `["getColumnFromGroupBy", 0]` returns values of the first field as an array. You can pass multiple indices to get an array of arrays where each element corresponds to the field in the group by field well. + `getColumnFromValue` returns columns from the value field well. You can pass multiple indices to get an array of arrays where each element corresponds to the field in the values field well. + `getColorTheme` returns the current color pallete of a Quick theme, shown in the following example. ``` { "color": ["getColorTheme"] } ``` ``` { "color": ["getPaletteColor", "secondaryBackground"] } ``` **Example** ![\[alt text not found\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/get-column-example.png) `getColumn` can access any column from the table: + `["getColumn", 0]` - returns array `[1, 2, 3, 4, 5, ...]` + `["getColumn", 1]` - returns array `[1, 1, 1, 1, 1, ...]` + `["getColumn", 2]` - returns array `[1674, 7425, 4371, ...]` `getColumnFromGroupBy` works similarly, but its index is limited to the columns in the group by field well: + `["getColumnFromGroupBy", 0]` - returns array `[1, 2, 3, 4, 5, ...]` + `["getColumnFromGroupBy", 1]` - returns array `[1, 1, 1, 1, 1, ...]` + `["getColumnFromGroupBy", 2]` - does not work, since there are only two columns in the group by field well `getColumnFromValue` works similarly, but its index is limited to the columns in the value field well: + `["getColumnFromValue", 0]` - returns array `[1, 2, 3, 4, 5, ...]` + `["getColumnFromValue", 1]` - does not work, since there is only one column in the value field well + `["getColumnFromValue", 2]` - does not work, since there is only one column in the value field well ## `formatValue` Use the `formatValue` expression to apply Quick formatting to your values. For example, the following expression formats the x-axis label with the format value that is specified in the first field of Quick field wells. ``` "xAxis": { "categories": ["getColumn", 0], "labels": { "formatter": ["formatValue", "value", 0] } } ``` # Using histograms Use a histogram chart in Amazon Quick to display the distribution of continuous numerical values in your data. Amazon Quick uses un-normalized histograms, which use an absolute count of the data points or events in each bin. To create a histogram, you use one measure. A new histogram initially displays ten *bins* (also called *buckets*) across the X-axis. These appear as bars on the chart. You can customize the bins to suit your dataset. The Y-axis displays the absolute count of the values in each bin. Make sure that you adjust the format settings so that you have a clearly identifiable shape. If your data contains outliers, this becomes clear if you spot one or more values off to the side of the X-axis. For information about how Amazon Quick handles data that falls outside display limits, see [Display limits](working-with-visual-types.md#display-limits). ## Histogram features To understand the features supported by histograms, use the following table. **** | Feature | Supported? | Comments | For more information | | --- | --- | --- | --- | | Changing the legend display | No | | [Legends on visual types in Quick](customizing-visual-legend.md) | | Changing the title display | Yes | | [Titles and subtitles on visual types in Quick](customizing-a-visual-title.md) | | Changing the axis range | No | However, you can change the bin count or the bin interval width (range of distribution). | | | Showing or hiding axis lines, grid lines, axis labels, and axis sort icons | Yes | | [Axes and grid lines on visual types in Quick](showing-hiding-axis-grid-tick.md) | | Changing the visual colors | Yes | | [Colors in visual types in Quick](changing-visual-colors.md) | | Focusing on or excluding elements | No | | | | Sorting | No | | | | Performing field aggregation | No | Histograms use only the count aggregation. | | | Adding drill-downs | No | | | ## Creating a histogram Use the following procedure to create a histogram. **To create a histogram** 1. On the analysis page, choose **Visualize** on the tool bar. 1. Choose **Add** on the application bar, and then choose **Add visual**. 1. On the **Visual types** pane, choose the histogram icon. 1. On the **Fields list** pane, choose the field that you want to use in the **Value** field well. A **Count** aggregate is automatically applied to the value. The resulting histogram shows the following: + The X-axis displays 10 bins by default, representing the intervals in the measure that you choose. You can customize the bins in the next step. + The Y-axis displays the absolute count of individual values in each bin. 1. (Optional) Choose **Format** on the visual control to change the histogram format. You can format the bins either by count or width, not both together. The count setting changes how many bins display. The width setting changes how wide or long of an interval each bin contains. ## Formatting a histogram Use the following procedure to format a histogram. **To format a histogram** 1. Choose the histogram chart that you want to work with. It should be the highlighted selection. The visual controls display on the top right of the histogram. 1. Choose the cog icon on the visual control menu to view the **Format visual** options. 1. On the **Properties** pane, set the following options to control the display of the histogram: + **Histogram** settings. Chose *one* of the following settings: + Bin count (option 1): The number of bins that display on the X-axis. + Bin width (option 1): The width (or length) of each interval. This setting controls the number of items or events to include in each bin. For example, if your data is in minutes, you can set this to 10 to show 10-minute intervals. + With the following settings, you can explore the best way to format the histogram for your dataset. For example, in some cases, you might have a tall peak in one bin, while most of the other bins look sparse. This isn't a useful view. You can use the following settings individually or together: + Change the **Number of data points displayed** in the **X-axis** settings. Amazon Quick displays up to 100 bins (buckets) by default. If you want to display more (up to 1,000), change the X-axis setting for **Number of data points displayed**. + Enable **Logarithmic scale** in the **Y-axis** settings. Sometimes your data doesn't fit the shape that you want and this can provide misleading results. For example, if the shape is skewed so far to the right that you can't read it properly, you can apply a log scale to it. Doing this doesn't normalize your data; however, it does reduce the skew. + Display **Data labels**. You can enable the display of data labels to see the absolute counts in the chart. Even if you don't want to display these in most cases, you can enable them while you're developing an analysis. The labels can help you decide on formatting and filtering options because they reveal counts in bins that are too small to stand out. To see all the data labels, even if they overlap, enable **Allow labels to overlap**. 1. (Optional) Change other visual settings. For more information, see [Formatting in Amazon Quick](formatting-a-visual.md). ## Understanding histograms Although histograms look similar to bar charts, they are very different. In fact, the only similarity is their appearance because they use bars. On a histogram, each bar is called a *bin* or a *bucket*. Each bin contains a range of values called an *interval*. When you pause on one of the bins, details about the interval appear in a tooltip that shows two numbers enclosed in glyphs. The type of enclosing glyphs indicates if the numbers inside them are part of the interval that's inside the selected bin, as follows: + A square bracket next to a number means that the number is included. + A parenthesis next to a number means that the number is excluded. For example, let's say that the first bar in a histogram displays the following notation. ``` [1, 10) ``` The square bracket means that the number 1 is included in the first interval. The parenthesis means that the number 10 is excluded. In the same histogram, a second bar displays the following notation. ``` [10, 20) ``` In this case, 10 is included in the second interval, and 20 is excluded. The number 10 can't exist in both intervals, so the notation shows us which one includes it. **Note** The pattern used for marking intervals in a histogram comes from standard mathematical notation. The following examples show the possible patterns, using a set of numbers that includes 10, 20, and every number in between. [10, 20] – This set is closed. It has hard boundaries on both ends. [10, 21) – This set is half open. It has a hard boundary on the left and a soft boundary on the right. (9, 20] – This set is half open. It has a soft boundary on the left and a hard boundary on the right. (9, 21) – This set is open. It has soft boundaries on both ends. Because the histogram uses quantitative data (numbers) rather than qualitative data, there's a logical order to the distribution of the data. This is called a *shape*. The shape is often described the qualities the shape possesses, based on the count in each bin. Bins that contain a higher number of values form a *peak*. Bins that contain a lower number of values form a *tail* on the edge of a chart, and a *valley* between peaks. Most histograms fall into one of the following shapes: + Asymmetrical or *skewed* distributions have values that cluster near the left or the right—the low or high end of the X-axis. The direction of skewness is defined by where the longer tail of the data is, not by where the peak is. It's defined this way because this direction also describes the location of the mean (average). In skewed distributions, the mean and the median are two different numbers. The different types of skewed distribution are as follows: + *Negatively* skewed or *left* skewed – A chart that has the mean to the left of the peak. It has a longer tail to the left and a peak to the right, sometimes followed by a shorter tail. + *Positively* skewed or *right* skewed – A chart that has the mean to the right of the peak. It has a longer tail to the right and a peak to the left, sometimes preceded by a shorter tail. + Symmetrical or *normal* distributions have a shape that's mirrored on each side of a center point (for example, a bell curve). In a normal distribution, the mean and the median are the same value. The different types of normal distribution are as follows: + Normal distribution, or *unimodal* – A chart that has one central peak representing the most common value. This is commonly called a bell curve, or a Gaussian distribution. + Bimodal – A chart that has two peaks representing the most common values. + Multimodal – A chart that has three or more peaks representing the most common values. + Uniform – A chart that has no peaks or valleys, with a relatively equal distribution of data. The following table shows how a histogram differs from a bar chart. | Histogram | Bar chart | | --- | --- | | A histogram displays the distribution of values in one field. | A bar chart compares the values in one field, grouped by dimension. | | A histogram sorts values into bins that represent a range of values, for example 1–10, 10–20, and so on. | A bar chart plots values that are grouped into categories. | | The sum of all bins equals exactly 100% of the values in the filtered data. | A bar chart isn't required to display all of the available data. You can change display settings at the visual level. For example, a bar chart might show only the top 10 categories of data. | | Rearranging bars detracts from the meaning of the chart as a whole. | Bars can be in any order without changing the meaning of the chart as a whole. | | There are no spaces between the bars, to represent the fact this is continuous data. | There are spaces between the bars, to represent the fact that this is categorical data. | | If a line is included in a histogram, it represents the general shape of the data. | If a line is included in a bar chart, it's called a combo chart, and the line represents a different measure than the bars. | # Using image components Use image components to upload static images from your desktop to a Quick analysis. Each Quick analysis sheet supports up to 10 image components. Image components are not included in the 50 visual per sheet limit. The file size of an image component can't exceed 1MB. The following file formats are supported for image components: + `.bmp` + `.jpg/.jpeg` + `.png` + `.tiff` + `.webp` Use the following procedure to add an image component to a Quick analysis: **To add an image component to a Quick analysis** 1. Open the [Quick console](https://quicksight.aws.amazon.com/). 1. Open the Quick analysis that you want to add an image to. 1. Choose the **Add image** button in the toolbar at the top of the analysis. 1. The file selection of your desktop opens. Choose the file that you want to upload, and then choose **Open**. The file size of the image component can't exceed 1MB. 1. The image is uploaded to Quick and appears in the analysis. 1. (Optional) To add alt text or update the image scaling options, choose the **Properties** icon at the top right of the image to open the **Properties** pane. 1. (Optional) To add a [custom tooltip](https://docs.amazonaws.cn/quicksuite/latest/userguide/customizing-visual-tooltips) to the image, open the **Properties** pane, choose **Interactions**, and then choose **Add action**. Filter actions are not supported for image components. You can also use the **Interactions** section to add custom navigation and URL actions to the image component. 1. (Optional) To duplicate or replace the image, choose the **More options** ellipsis (three dots) icon at the top right of the image, and then choose the action that you want to perform. # Using KPIs Use a key performance indicator (KPI) to visualize a comparison between a key value and its target value. A KPI displays a value comparison, the two values being compared, and a visual that provides context to the data that's displayed. You can choose from a set of predesigned layouts to suit your business needs. The following image shows an example of a KPI visual that uses a sparkline. 1. Choose **Add (\$1)** drop down in the **Visuals pane.** 1. Choose the KPI icon from Visual types menu. ## KPI features To understand the features supported by the KPI visual type in Amazon Quick, use the following table. | Feature | Supported? | Comments | For more information | | --- | --- | --- | --- | | Changing the title display | Yes | | [Titles and subtitles on visual types in Quick](customizing-a-visual-title.md) | | Removing the title | Yes | You can choose not to display a title. | | | Changing comparison method | Yes | By default, Amazon Quick automatically chooses a method. The settings are auto, difference, percent, and difference as percent. | | | Changing the primary value displayed | Yes | You can choose comparison (default) or actual. | | | Displaying or removing the progress bar | Yes | You can format the visual to either display (default) or not display a progress bar. | | For more information on KPI formatting options, see [KPI options](KPI-options.md). ## Creating a KPI Use the following procedure to create a KPI. **To create a KPI** 1. Create a new analysis for your dataset. 1. In the **Visual types** pane, choose the KPI icon. 1. From the **Fields list** pane, drag the fields that you want to use to the appropriate field wells. You must use measure fields as indicated by the target field well. If you choose to use a dimension field as a measure, the **Count** aggregate function is automatically applied to it to create a numeric value. To create a KPI, drag a measure to the **Value** field well. To compare that value to a target value, drag a different measure to the **Target value** field well. 1. (Optional) Choose formatting options by selecting the on-visual menu at the upper-right corner of the visual, then choosing **Format visual**. ## Changing a KPI's layout Use the following procedure to change the layout for a KPI. **To change the layout of a KPI** 1. Navigate to the KPI visual that you want to change and choose **KPI layouts**. 1. In the **KPI Layouts** pane, choose the KPI layout that you want to use. # Using layer maps Use layer maps to visualize data with custom geographic boundaries, such as congressional districts, sales territories, or user-defined regions. With layer maps, Quick authors upload GeoJSON files to Amazon Quick that shape layers over a base map and join with Quick data to visualize associated metrics and dimensions. Shape layers can be styled by color, border, and opacity. Quick authors can also add interactivity to layer maps through tooltips and custom actions. **Note** Amazon Quick layer map visuals only support polygon shapes. Line and point geometries are not supported. The following image shows a layer map visual in Amazon Quick. ![\[alt text not found\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/layer-map.png) ## Creating a shape layer with layered maps Use the procedure below to create a shape layer with layer map visuals in Amazon Quick. 1. Open the [Quick console](https://quicksight.aws.amazon.com/). 1. Open the Quick analysis that you want to add a layer map to. 1. Choose **Add** on the application bar, and then choose **Add visual**. 1. On the **Visual types** pane, choose one of the layer map icon. 1. An empty map visual appears in the analysis and prompts you to continue configuring the layer. Choose **CONFIGURE LAYER** to continue configuring the layer map. 1. The **Layer properties** pane opens to the right. Navigate to the **Shape file** section, and then choose **UPLOAD SHAPE FILE**. 1. Choose the GeoJSON file that you want to visualize. The file must be in `.geojson` format and must not exceed 100 MB. 1. Navigate to the **Data** section. 1. For **Shape file key field**, choose the field that you want the shape to visualize. 1. (Optional) For **Dataset key field**, choose the dataset field that you want the shape to visualize. To assign color to the shapes, add a color field. If the color field is a measure, the shape uses gradient coloring. If the color field is a dimension, the shape uses categorical coloring. If a color field is not assigned to the shape, use the fill color option in the **Styling** section of the **Layer properties** pane to set a common color for all shapes. 1. (Optional) To change the layer name, navigate to the **Layer options** section and enter a name in the **Layer name** input. 1. (Optional) To change the fill or border colors, navigate to the **Styling** section and choose the color switch next to the object that you want to change. To adjust the opacity of the color, enter a percentage amount in the input located next to the eye icon. If you do not assign a color field to the **Dataset key field**, the fill color can be used to set a common color for all shapes. # Using line charts Use line charts to compare changes in measure values over period of time, for the following scenarios: + One measure over a period of time, for example gross sales by month. + Multiple measures over a period of time, for example gross sales and net sales by month. + One measure for a dimension over a period of time, for example number of flight delays per day by airline. Line charts show the individual values of a set of measures or dimensions against the range displayed by the Y axis. Area line charts differ from regular line charts in that each value is represented by a colored area of the chart instead of just a line, to make it easier to evaluate item values relative to each other. Because a stacked area line chart works differently than other line charts, simplify it if you can. Then the audience won't try to interpret the numbers. Instead, they can focus on the relationships of each set of values to the whole. One way to simplify is to remove the numbers down the left side of the screen by reducing the step size for the axis. To do this, choose the **Options** icon from the on-visual menu. In **Format Options** under **Y-axis,** enter **2** as the **Step size**. Each line on the chart represents a measure value over a period of time. You can interactively view the values on the chart. Hover over any line to see a pop-up legend that shows the values for each line on the **X axis**. If you hover over a data point, you can see the **Value** for that specific point on the **X axis**. Use line charts to compare changes in values for one or more measures or dimensions over a period of time. In regular line charts, each value is represented by a line, and in area line charts each value is represented by a colored area of the chart. Use stacked area line charts to compare changes in values for one or more groups of measures or dimensions over a period of time. Stacked area line charts show the total value for each group on the x-axis. They use color segments to show the values of each measure or dimension in the group. Line charts show up to 10,000 data points on the x-axis when no color field is selected. When color is populated, line charts show up to 400 data points on the x-axis and up to 25 data points for color. For more information about data that falls outside the display limit for this visual type, see [Display limits](working-with-visual-types.md#display-limits). ## Line chart features To understand the features supported by line charts, use the following table. | Feature | Supported? | Comments | For more information | | --- | --- | --- | --- | | Changing the legend display | Yes | | [Legends on visual types in Quick](customizing-visual-legend.md) | | Changing the title display | Yes | | [Titles and subtitles on visual types in Quick](customizing-a-visual-title.md) | | Changing the axis range | Yes | You can set the range for the Y axis. | [Range and scale on visual types in Quick](changing-visual-scale-axis-range.md) | | Showing or hiding axis lines, grid lines, axis labels, and axis sort icons | Yes | | [Axes and grid lines on visual types in Quick](showing-hiding-axis-grid-tick.md) | | Adding a second Y-axis | Yes | | [Creating a dual-axis line chart](#dual-axis-chart) | | Changing the visual colors | Yes | | [Colors in visual types in Quick](changing-visual-colors.md) | | Focusing on or excluding elements | Yes, with exceptions | You can focus on or exclude any line on the chart, except in the following cases: [\[See the AWS documentation website for more details\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/line-charts.html) In these cases, you can only focus on a line, not exclude it. | [Focusing on visual elements](focusing-on-visual-elements.md) [Excluding visual elements](excluding-visual-elements.md) | | Sorting | Yes, with exceptions | You can sort data for numeric measures in the X axis and Value field wells. Other data is automatically sorted in ascending order. | [Sorting visual data in Amazon Quick](sorting-visual-data.md) | | Performing field aggregation | Yes | You must apply aggregation to the field that you choose for the value, and can't apply aggregation to the fields you choose for the X axis and color. | [Changing field aggregation](changing-field-aggregation.md) | | Adding drill-downs | Yes | You can add drill-down levels to the X axis and Color field wells. | [Adding drill-downs to visual data in Quick Sight](adding-drill-downs.md) | ## Creating a line chart Use the following procedure to create a line chart. **To create a line chart** 1. On the analysis page, choose **Visualize** on the tool bar. 1. Choose **Add** on the application bar, and then choose **Add visual**. 1. On the **Visual types** pane, choose one of the line chart icons. 1. From the **Fields list** pane, drag the fields that you want to use to the appropriate field wells. Typically, you want to use dimension or measure fields as indicated by the target field well. If you choose to use a dimension field as a measure, the **Count** aggregate function is automatically applied to it to create a numeric value. + To create a single-measure line chart, drag a dimension to the **X axis** field well and one measure to the **Value** field well. + To create a multi-measure line chart, drag a dimension to the **X axis** field well and two or more measures to the **Value** field well. Leave the **Color** field well empty. + To create a multi-dimension line chart, drag a dimension to the **X axis** field well, one measure to the **Value** field well, and one dimension to the **Color** field well. 1. (Optional) Add drill-down layers by dragging one or more additional fields to the **X axis** or **Color** field wells. For more information about adding drill-downs, see [Adding drill-downs to visual data in Quick Sight](adding-drill-downs.md). ## Creating a dual-axis line chart If you have two or more metrics that you want to display in the same line chart, you can create a dual-axis line chart. A *dual-axis chart* is a chart with two Y-axes (one axis at the left of the chart, and one axis at the right of the chart). For example, let's say you create a line chart. It shows the number of visitors who signed up for a mailing list and for a free service over a period of time. If the scale between those two measures varies widely over time, your chart might look something like the following line chart. Because the scale between measures varies so greatly, the measure with the smaller scale appears nearly flat at zero. ![\[Image of a line chart with two lines and one axis. One line is flat at zero.\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/dual-axis-chart1.png) If you want to show these measures in the same chart, you can create a dual-axis line chart. The following is an example of the same line chart with two Y-axes. ![\[Image of the previous line chart with dual axes. Both lines are now visible.\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/dual-axis-chart2.png) **To create a dual-axis line chart** 1. In your analysis, create a line chart. For more information about creating line charts, see [Creating a line chart](#create-measure-line-chart). 1. In the **Value field well**, choose a field drop-down menu, choose **Show on: Left Y-axis**, and then choose **Right Y-axis**. Or you can create a dual-axis line chart using the **Properties** pane: 1. On the menu in the upper-right corner of the line chart, choose the **Format visual** icon. 1. In the **Properties** pane that opens, choose **Data series**. 1. In the **Data series** section, choose the **Show on right axis** icon for the value that you want to place on a separate axis. Use the search bar to quickly find a value if you need to. ![\[Image of Data series section of the Format visual pane with the Show on right axis icon circled in red.\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/dual-axis-chart3.png) The icon updates to indicate that the value is being shown on the right axis. The chart updates with two axes. The **Properties** pane updates with the following options: + To synchronize the Y-axes for both lines back into a single axis, choose **Single Y-axis** at the top of the **Properties** pane. + To format the axis at the left of the chart, choose **Left Y-axis**. + To format the axis at the right of the chart, choose **Right Y-axis**. For more information about formatting axis lines, see [Axes and grid lines](showing-hiding-axis-grid-tick.md). For more information about adjusting the range and scale of an axis, see [Range and scale](changing-visual-scale-axis-range.md). # Creating maps and geospatial charts You can create two types of maps in Quick: point maps and filled maps. *Point maps* show the difference between data values for each location by size. *Filled maps* show the difference between data values for each location by varying shades of color. **Important** Geospatial charts in Quick currently aren't supported in some Amazon Web Services Regions, including in China. For help with geospatial issues, see [Geospatial troubleshooting](geospatial-troubleshooting.md). Before you get started creating maps, do the following: + Make sure that your dataset contains location data. *Location data* is data that corresponds to latitudinal and longitudinal values. Location data can include a column for latitude and a column for longitude in your dataset. It can also include a column with city names. Quick can chart latitude and longitude coordinates. It also recognizes geographic components such as country, state or region, county or district, city, and ZIP code or postal code. + Make sure that your location data fields are marked as geospatial data types. + Consider creating geographic hierarchies. For more information about working with geospatial data, including changing field data types and creating geospatial hierarchies, see [Adding geospatial data](geospatial-data-prep.md). To learn more about creating maps in Quick, see the following. **Topics** + [ # Creating point maps ](point-maps.md) + [ # Creating filled maps ](filled-maps.md) + [ # Interacting with maps ](maps-interacting.md) # Creating point maps You can create point maps in Quick to show the difference between data values for each location by size. Each point on this type of map corresponds to a geographic location in your data, such as a country, state or province, or city. The size of the points on the map represents the magnitude of the field in the **Size** field well, in relation to other values in the same field. The color of the points represents the values in the **Color** field well. The field values in the **Color** field well display in the legend, if you choose a field for color. Use the following procedure to create a point map in Quick. To create point maps in Quick, make sure that you have the following: + One geospatial field (such as country, state or region, county or district, city, or ZIP code or postal code). Or you can use one latitude field and one longitude field. + One numeric field (measure) for size. + (Optional) A categorical field (dimension) for color. For information on formatting geospatial maps, see [Map and geospatial chart formatting options](https://docs.amazonaws.cn/quicksight/latest/user/geospatial-formatting). ## Creating point maps **To create a point map** 1. Add a new visual to your analysis. For more information about starting analyses, see [Starting an analysis in Quick Sight](creating-an-analysis.md). For more information about adding visuals to analyses, see [Adding a visual](creating-a-visual.md#create-a-visual). 1. For **Visual type**, choose the **Points on map** icon. It looks like a globe with a point on it. 1. Drag a geographic field from the **Fields list** pane to the **Geospatial** field well, for example `Country`. You can also choose a latitude or longitude field. A point map appears with a point for each location in your data. If the field is part of a geographic hierarchy, the hierarchy displays in the field well. 1. Drag a measure from the **Fields list** pane to the **Size** field well. The points on the map update to show the magnitude of values for each location. 1. (Optional) Drag a dimension from the **Fields list** pane to the **Color** field well. Each point updates to show a point for each categorical value in the dimension. # Creating filled maps You can create filled maps in Quick to show the difference between data values for each location by varying shades of color. Use the following procedure to create a filled map in Quick. To create filled maps in Quick, make sure that you have the following: + One geospatial field (such as country, state or region, county or district, or ZIP code or postal code). + (Optional) A numeric field (measure) for color. ## Creating filled maps **To create a filled map** 1. Add a new visual to your analysis. For more information about starting analyses, see [Starting an analysis in Quick Sight](creating-an-analysis.md). For more information about adding visuals to analyses, see [Adding a visual](creating-a-visual.md#create-a-visual). 1. For **Visual type**, choose the **Filled map** icon. 1. Drag a geographic field from the **Fields list** pane to the **Location** field well, for example `Country`. A filled map appears with each location in your data filled in by the number of times they appear in your dataset (the count). If the field is part of a geographic hierarchy, the hierarchy displays in the field well. 1. (Optional) Drag a measure from the **Fields list** pane to the **Color** field well, for example `Sales`. Each location updates to show the sum of sales. # Interacting with maps When you view a map visual in an Quick analysis or published dashboard, you can interact with it to explore your data. You can pan, zoom in and out, and autozoom to all the data. By default, map visuals are always zoomed based on the underlying data. When you pan around in the map or zoom to a different level, the zoom to data icon appears above the zoom in and out icons at bottom right of the map. Using this option, you can quickly zoom back to the underlying data. **To pan in a map visual** + Click anywhere on the map visual and drag your cursor in the direction that you want to pan the map. **To zoom in or out in a map visual** + On the map visual, choose the plus or minus icons at bottom right. Or you can double-click the map to zoom in, and shift-double-click to zoom out. **To zoom back to all the data** + On the map visual, choose the zoom to data icon. This icon appears when you pan or zoom in on a map. # Using small multiples Use this feature when you need to set multiple comparative visuals in a row. When you activate the *small multiples* feature, Amazon Quick creates a container or shelf of small visuals, presented side-by-side. Each copy of the visual contains a one view of the data. Using small multiples is a way to get a holistic view of your business, in an efficient and interactive way. Small multiples aren't listed in the palette visualization icons. Instead, the option to create small multiples appears as a field well, in the visuals that support it. **To add small visuals to your analysis** 1. On a line, bar, or pie charts, add a field to the **Small multiples** field well. 1. To see your small multiples, you need to enlarge the container that holds them, so you can see all of them at once. 1. To format the set of small multiples, choose Format visual (the pencil icon) from the menu on the visual. You can adjust the following settings: + **Layout** + **Visible rows** + **Visible columns** + **Number of panels** + Panel title options (toggle) + Font size and color + Font weight + Text alignment + **Panel order options (toggle)** Line thickness, style, and color + **Panel gutter** (toggle) **Spacing** + **Panel background** (toggle) **Background color** # Using pie charts Use pie charts to compare values for items in a dimension. The best use for this type of chart is to show a percentage of a total amount. Each wedge in a pie chart represents one item in the dimension. Wedge size represents the proportion of the value for the selected measure that the item represents compared to the whole for the dimension. Pie charts are best when precision isn't important and there are few items in the dimension. To create a donut chart, use one dimension in the **Group/Color** field well. With only one field, the chart displays the division of values by row count. To display the division of dimension values by a metric value, you can add a metric field to the **Value** field well. Pie charts show up to 20 data points for group or color. For more information about how Amazon Quick handles data that falls outside display limits, see [Display limits](working-with-visual-types.md#display-limits). ## Pie chart features To understand the features supported by pie charts, use the following table. | Feature | Supported? | Comments | For more information | | --- | --- | --- | --- | | Changing the legend display | Yes | | [Legends on visual types in Quick](customizing-visual-legend.md) | | Changing the title display | Yes | | [Titles and subtitles on visual types in Quick](customizing-a-visual-title.md) | | Changing the axis range | Not applicable | | [Range and scale on visual types in Quick](changing-visual-scale-axis-range.md) | | Showing or hiding axis labels. | Yes | | [Axes and grid lines on visual types in Quick](showing-hiding-axis-grid-tick.md) | | Changing the visual colors | Yes | | [Colors in visual types in Quick](changing-visual-colors.md) | | Focusing on or excluding elements | Yes, with exceptions | You can focus on or exclude a wedge in a pie chart, except when you are using a date field as a dimension. In that case, you can only focus on a wedge, not exclude it. | [Focusing on visual elements](focusing-on-visual-elements.md) [Excluding visual elements](excluding-visual-elements.md) | | Sorting | Yes | You can sort on the field that you choose for the value or the group or color. | [Sorting visual data in Amazon Quick](sorting-visual-data.md) | | Performing field aggregation | Yes | You must apply aggregation to the field that you choose for the value, and can't apply aggregation to the field that you choose for group or color. | [Changing field aggregation](changing-field-aggregation.md) | | Adding drill-downs | Yes | You can add drill-down levels to the Group/Color field well. | [Adding drill-downs to visual data in Quick Sight](adding-drill-downs.md) | ## Creating a pie chart Use the following procedure to create a pie chart. **To create a pie chart** 1. On the analysis page, choose **Visualize** on the tool bar. 1. Choose **Add** on the application bar, and then choose **Add visual**. 1. On the **Visual types** pane, choose the pie chart icon. 1. From the **Fields list** pane, drag the fields that you want to use to the appropriate field wells. Typically, you want to use dimension or measure fields as indicated by the target field well. If you choose to use a dimension field as a measure, the **Count** aggregate function is automatically applied to it to create a numeric value. To create a pie chart, drag a dimension to the **Group/Color** field well. Optionally, drag a measure to the **Value** field well. 1. (Optional) Add drill-down layers by dragging one or more additional fields to the **Group/Color** field well. For more information about adding drill-downs, see [Adding drill-downs to visual data in Quick Sight](adding-drill-downs.md). # Using pivot tables Use pivot tables to show measure values for the intersection of two dimensions. Heat maps and pivot tables display data in a similar tabular fashion. Use a heat map if you want to identify trends and outliers, because the use of color makes these easier to spot. Use a pivot table if you want to analyze data on the visual. To create a pivot table, choose at least one field of any data type, and choose the pivot table icon. Amazon Quick creates the table and populates the cell values with the count of the column value for the intersecting row value. Typically, you choose a measure and two dimensions measurable by that measure. Pivot tables support scroll down and right. You can add up to 20 fields as rows and 20 fields as columns. Up to 500,000 records are supported. Using a pivot table, you can do the following: + Specify multiple measures to populate the cell values of the table, so that you can see a range of data + Cluster pivot table columns and rows to show values for subcategories grouped by related dimension + Sort values in pivot table rows or columns + Apply statistical functions + Add totals and subtotals to rows and columns + Use infinite scroll + Transpose fields used by rows and columns + Create custom total aggregations To easily transpose the fields used by the rows and columns of the pivot table, choose the orientation icon (![\[alt text not found\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/pivot-orientation.png)) near the top right of the visual. To see options for showing and hiding totals and subtotals, formatting the visual, or exporting data to a CSV file, choose the Menu items icon at top right. As with all visual types, you can add and remove fields. You can also change the field associated with a visual element, change field aggregation, and change date field granularity. In addition, you can focus on or exclude rows or columns. For more information about how to make these changes to a pivot table, see [Changing fields used by a visual in Amazon Quick](changing-visual-fields.md). For information on formatting pivot tables, see [Formatting in Amazon Quick](formatting-a-visual.md). For information on custom total aggregations for pivot tables, see [Custom total values](tables-pivot-tables-custom-totals.md). **Topics** + [ ## Pivot table features ](#pivot-table-features) + [ # Creating a pivot table ](create-pivot-table.md) + [ # Orienting pivot table values ](pivot-table-value-orientation.md) + [ # Expanding and collapsing pivot table clusters ](expanding-and-collapsing-clusters.md) + [ # Showing and hiding pivot table columns in Quick ](hiding-pivot-table-columns.md) + [ # Sorting pivot tables in Quick ](sorting-pivot-tables.md) + [ # Using table calculations in pivot tables ](working-with-calculations.md) + [ # Pivot table limitations ](pivot-table-limitations.md) + [ # Pivot table best practices ](pivot-table-best-practices.md) ## Pivot table features Pivot tables don't display a legend. To understand the features supported by pivot tables, use the following table. | Feature | Supported? | Comments | For more information | | --- | --- | --- | --- | | Changing the legend display | No | | [Legends on visual types in Quick](customizing-visual-legend.md) | | Changing the title display | Yes | | [Titles and subtitles on visual types in Quick](customizing-a-visual-title.md) | | Changing the axis range | Not applicable | | [Range and scale on visual types in Quick](changing-visual-scale-axis-range.md) | | Changing the visual colors | No | | [Colors in visual types in Quick](changing-visual-colors.md) | | Focusing on or excluding elements | Yes, with exceptions | You can focus on or exclude any column or row, except when you are using a date field as one of the dimensions. In that case, you can only focus on the column or row that uses the date dimension, not exclude it. | [Focusing on visual elements](focusing-on-visual-elements.md) [Excluding visual elements](excluding-visual-elements.md) | | Sorting | Yes | You can sort fields in the Rows or Columns field wells alphabetically or by a metric in ascending or descending order. | [Sorting visual data in Amazon Quick](sorting-visual-data.md) [Sorting pivot tables in Quick](sorting-pivot-tables.md) | | Performing field aggregation | Yes | You must apply aggregation to the field or fields you choose for the value. You can't apply aggregation to the fields that you choose for the rows or columns. If you choose to create a multi-measure pivot table, you can apply different types of aggregation to the different measures. For example, you can show the sum of the sales amount and the maximum discount amount. | [Changing field aggregation](changing-field-aggregation.md) | | Adding drill-downs | No | | [Adding drill-downs to visual data in Quick Sight](adding-drill-downs.md) | | Showing and hiding totals and subtotals | Yes | You can show or hide totals and subtotals for rows and columns. Metrics automatically roll up to show subtotals when you collapse a row or column. If you use a table calculation, use aggregates to display roll-ups. | | | Exporting or copying data | Yes | You can export all of the data to a CSV file. You can select and copy the content of the cells. | [Exporting data from visuals](exporting-data.md) | | Conditional formatting | Yes | You can add conditional formatting for values, subtotals and totals. | [Conditional formatting on visual types in Quick](conditional-formatting-for-visuals.md) | **Topics** # Creating a pivot table Use the following procedure to create a pivot table. **To create a pivot table** 1. On the analysis page, choose the **Visualize** icon on the tool bar. 1. On the **Visuals** pane, choose **\$1 Add**, and then choose the pivot table icon. 1. From the **Fields list** pane, choose the fields that you want to include. Amazon Quick automatically places these into the field wells. To change the placement of a field, drag it to the appropriate field wells. Typically, you use dimension or measure fields as indicated by the target field well. If you choose to use a dimension field as a measure, the **Count** aggregate function is automatically applied to it to create a numeric value. + To create a single-measure pivot table, drag a dimension to the **Rows** field well, a dimension to the **Columns** field well, and a measure to the **Values** field well. + To create a multi-measure pivot table, drag a dimension to the **Rows** field well, a dimension to the **Columns** field well, and two or more measures to the **Values** field well. + To create a clustered pivot table, drag one or more dimensions to the **Rows** field well, one or more dimensions to the **Columns** field well, and a measure to the **Values** field well. You can also select multiple fields for all of the pivot table field wells if you want to. Doing this combines the multi-measure and clustered pivot table approaches. **Note** To view roll-ups for calculated fields, make sure that you are using aggregates. For example, a calculated field with `field-1 / field-2 `doesn't display a summary when rolled up. However, `sum(field-1) / sum(field-2) `does display a roll-up summary. ## Choosing a layout When you create a pivot table in Amazon Quick, you can further customize the way your data is presented with Tabular and Hierarchy layout options. For pivot tables that use a tabular layout, each row field is displayed in its own column. For pivot tables that use a hierarchy layout, all row fields are displayed in a single column. Indentation is used to differentiate row headers of different fields. To change the layout of a pivot table visual, open the **Format visual** menu of the pivot table that you want to change and choose the layout option that you want from the **Pivot options** section. Depending on the layout that you choose for your pivot table visual, different formatting options are available. For more information about formatting differences between tabular and hierarchy pivot tables, see [Table and pivot table formatting options in Quick](format-tables-pivot-tables.md). # Orienting pivot table values Display orientation You can choose to display a pivot table in a columnar or row-based format. Columnar is the default. When you change to a row-based format, a column with the value name is added to the right of the row header column. **To change a pivot table format** 1. On the analysis page, choose the pivot table visual that you want to edit. 1. Expand the **Field wells** pane by choosing the field wells at the top of the visual. 1. On the **Values** field well, choose one of the following options: + Choose **Column** for a columnar format. + Choose **Row** for a row format. **Note** If you use only one metric, you can eliminate the repeated header by formatting the visual and styling it with the **Hide single metric** option. # Expanding and collapsing pivot table clusters If you are using grouped columns or rows in a pivot table, you can expand or collapse a group to show or hide its data in the visual. **To expand or collapse a pivot table group** 1. On the analysis page, choose the pivot table visual that you want to edit. 1. Choose one of the following: + To collapse a group, choose the collapse icon near the name of the field. + To expand a group, choose the expand icon near the name of the field. The collapse icon shows a minus sign. The expand icon shows a plus sign. In the following screenshot, `Customer Region` and the `Enterprise` segment are expanded, and `SMB` and `Startup` are collapsed. When a group is collapsed, its data is summarized in the row or column. ![\[alt text not found\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/pivot-table-collapse.png) # Showing and hiding pivot table columns in Quick Showing and hiding pivot table columns By default, all columns, rows, and their field values appear when you create a pivot table. You can hide columns and rows that you don't want to appear in the pivot table without changing the pivot table values. When you have more than one measure in the pivot table, you can also hide values. At any time, you can choose to show any hidden fields in the pivot table. When you publish the visual as part of a dashboard, anyone who subscribes to the dashboard can export the pivot table to a comma-separated value (CSV) or Microsoft Excel file. They can choose to export only the visible fields, or all fields. For more information, see [Exporting data from a dashboard to a CSV](export-or-print-dashboard.md#export-dashboard-to-csv). **To hide a column or row in a pivot table** 1. In your analysis, select the pivot table visual that you want to work with. 1. Choose the three-dot menu in the **Rows**, **Columns** or **Values** field wells, and then choose **Hide**. **To show all hidden fields in a pivot table** 1. In your analysis, select the pivot table visual that you want to work with. 1. Choose any field in the **Fields well** and choose **Show all hidden fields**. # Sorting pivot tables in Quick Sorting pivot tables In Amazon Quick, you can sort values in a pivot table by fields in the **Rows** and **Columns** field wells or quickly by column headers in the pivot table. In pivot tables, you can sort rows and columns independently of each other in alphabetical order, or by a measure. **Note** You can't run Total, Difference, and Percent Difference table calculations when a pivot table is being sorted by a measure. For more information about using table calculations in pivot tables, see [Using table calculations in pivot tables](working-with-calculations.md). ## Understanding sorting in pivot tables When you have multiple panes in a pivot table, sorting is applied to each pane independently. For example, the `Segment` column in the pivot table on the left is being sorted in ascending order by `Cost`. Given that there are multiple panes, the sort starts over for each pane and the rows within each pane (for `Segment`) are ordered by lowest to highest cost. The table on the right has the same sort applied, but the sort is being applied across the entire table, as shown following. ![\[Image of a pivot table with a sort highlighted in red.\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/sorting-pivot-tables2.png) When you apply multiple sorts to a pivot table, sorting is applied from the outside dimension to the inside dimension. Consider the following example image of a pivot table. The `Customer Region` column is sorted by `Cost` in descending order (as shown in orange). The `Channel` column is sorted by Revenue Goal in ascending order (as shown in blue). ![\[Image of a pivot table showing two measure value columns sorted.\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/sorting-pivot-tables3.png) ## Sorting pivot tables using row or column headers Sorting pivot tables using row or column headers Use the following procedure to sort a pivot table using Row or Column headers. **To sort values in a tabular pivot table using table headers** 1. In a tabular pivot table chart, choose the header that you want to sort. 1. For **Sort by**, choose a field to sort by and a sort order. You can sort dimension fields alphabetically a–z or z–a, or you can sort them by a measure in ascending or descending order. ![\[Animated .gif file of sorting values in a pivot table using column headers.\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/sorting-pivot-table7.gif) ## Sorting pivot tables using value headers Sorting pivot tables using value headers Use the following procedure to sort a pivot table using value headers. **To sort a pivot table using value headers** 1. In a pivot table chart, choose the value header that you want to sort. 1. Choose **Ascending** or **Descending**. ![\[Animated .gif file of sorting values in a pivot table using value headers.\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/sorting-pivot-tables-value.gif) Sorting by value headers in a pivot table also works on subtotals. ## Sorting tabular pivot tables using the field wells Sorting tabular pivot tables using the field wells Use the following procedure to sort values in a tabular pivot table using the field wells. **To sort values in a tabular pivot table using the field wells** 1. On the analysis page, choose the tabular pivot table that you want to sort. 1. Expand the **Field wells**. 1. In the **Rows** or **Columns** field well, choose the field that you want to sort, and then choose how you want to sort the field for **Sort by**. You can sort dimension fields in the **Rows** or **Columns** field wells alphabetically from a–z or z–a, or you can sort them by a measure in ascending or descending order. You also have the option to collapse all or expand all rows or columns for the field you choose in the field well. You can also remove the field, or to replace it with another field. + To sort a dimension field alphabetically, hover your cursor over the field in the **Rows** or **Columns** field well, and then choose the a–z or z–a sort icon. ![\[Image of a field in the Rows field well with the sort by field and alphabetical sort icons indicated in red squares.\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/sorting-pivot-tables1.png) + To sort a dimension field by a measure, hover your cursor over the field in the **Rows** or **Columns** field well. Then choose a measure from the list, and then choose the ascending or descending sort icon. ![\[Image of a field in the Rows field well with the sort by field and sort icons indicated in red squares.\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/sorting-pivot-tables4.png) Or, if you want more control over how the sort is applied to the pivot table, customize the sort options. **To create a sort using the **sort options**** 1. On the analysis page, choose the pivot table that you want to sort. 1. Expand **Field wells**. 1. Choose the field that you want to sort in the **Rows** or **Columns** field well, and then choose **Sort options**. 1. In the **Sort options** pane that opens at left, specify the following options: 1. For **Sort by**, choose a field from the drop-down list. 1. For **Aggregation**, choose an aggregation from the list. 1. For **Sort order**, select **Ascending** or **Descending**. 1. Choose **Apply**. ## Sorting hierarchy pivot tables using the field wells For tabular pivot tables, each field in the **Rows ** field well has a separate title cell. For hierarchy pivot tables, all row fields are displayed in a single column. To sort, collapse, and expand these row fields, select the **Rows** label to open the **Combined row fields** menu and choose the option that you want. Each field in a hierarchy pivot table can be individually sorted from the **Combined row fields** menu. ![\[Image of the Combined row fields menu.\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/pivot-table-combined-row-fields-menu.png) More advanced formatting options such as **Hide** and **Remove** are available from the field well menus. # Using table calculations in pivot tables You can use table calculations to apply statistical functions to pivot table cells that contain measures (numeric values). Use the following sections to understand which functions you can use in calculations, and how to apply or remove them. The data type of the cell value automatically changes to work for your calculation. For example, say that you apply the **Rank** function to a currency data type. The values display as integers rather than currency, because rank isn't measured as currency. Similarly, if you apply the **Percent difference** function instead, the cell values display as percentages. **Topics** + [ # Adding and deleting pivot table calculations ](adding-a-calculation.md) + [ # Functions for pivot table calculations ](supported-functions.md) + [ # Ways to apply pivot table calculations ](supported-applications.md) # Adding and deleting pivot table calculations Use the following procedures to add, modify, and remove table calculation on a pivot table. **Topics** + [ # Adding a pivot table calculation ](add-a-calculation.md) + [ # Changing how a calculation is applied ](change-how-a-calculation-is-applied.md) + [ # Removing a calculation ](remove-a-calculation.md) # Adding a pivot table calculation Use the following procedure to add a table calculation to a pivot table. **To add a table calculation to a pivot table** 1. Expand the **Field wells** pane by choosing the field wells near the bottom of the visual. 1. Choose the field in the **Values** well that you want to apply a table calculation to, choose **Add table calculation**, and then choose the function to apply. **Note** You can't run Total, Difference, and Percent Difference table calculations when a pivot table is being sorted by a measure. To use these table calculations, remove the sort from the pivot table. # Changing how a calculation is applied Use the following procedure to change the way a table calculation is applied to a pivot table. **To change the way a table calculation is applied to a pivot table** 1. Expand the **Field wells** pane by choosing field wells at the top of the visual. 1. Choose the field in the **Values** well that has the table calculation that you want to change, choose **Calculate as**, and then choose the way that you want the calculation applied. # Removing a calculation Use the following procedure to remove a table calculation from a pivot table. **To remove a table calculation from a pivot table** 1. Expand the **Field wells** pane by choosing the field wells near the bottom of the visual. 1. Choose the field in the **Values** well that you want to remove the table calculation from, and then choose **Remove**. # Functions for pivot table calculations You can use the following functions in pivot table calculations. **Topics** + [ ## Running total ](#running-total) + [ ## Difference ](#difference) + [ ## Percentage difference ](#percent-difference) + [ ## Percent of total ](#percent-of-total) + [ ## Rank ](#rank) + [ ## Percentile ](#percentile) You can apply functions listed to the following data: ![\[alt text not found\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/running-total1.png) ![\[alt text not found\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/running-total1.png) ## Running total The **Running total** function calculates the sum of a given cell value and the values of all cells prior to it. This sum is calculated as `Cell1=Cell1, Cell2=Cell1+Cell2, Cell3=Cell1+Cell2+Cell3`, and so on. Applying the **Running total** function across the table rows, using **Table across** for **Calculate as**, gives you the following results. ![\[alt text not found\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/running-total2.png) ## Difference The **Difference** function calculates the difference between a cell value and value of the cell prior to it. This difference is calculated as `Cell1=Cell1-null, Cell2=Cell2-Cell1, Cell3=Cell3-Cell2,` and so on. Because `Cell1-null = null`, the Cell1 value is always empty. Applying the **Difference** function across the table rows, using **Table across** for **Calculate as**, gives you the following results. ![\[alt text not found\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/difference.png) ## Percentage difference The **Percentage Difference** function calculates the percent difference between a cell value and the value of the cell prior to it, divided by the value of the cell prior to it. This value is calculated as `Cell1=(Cell1-null)/null, Cell2=(Cell2-Cell1)/Cell1, Cell3=(Cell3-Cell2)/Cell2,` and so on. Because `(Cell1-null)/null = null`, the Cell1 value is always empty. Applying the **Percentage Difference** function across the table rows, using **Table across** for **Calculate as**, gives you the following results. ![\[alt text not found\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/percentage-difference.png) ## Percent of total The **Percent of Total** function calculates the percentage the given cell represents of the sum of all of the cells included in the calculation. This percentage is calculated as `Cell1=Cell1/(sum of all cells), Cell2=Cell2/(sum of all cells),` and so on. Applying the **Percent of Total** function across the table rows, using **Table across** for **Calculate as**, gives you the following results. ![\[alt text not found\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/percent-of-total.png) ## Rank The **Rank** function calculates the rank of the cell value compared to the values of the other cells included in the calculation. Rank always shows the highest value equal to 1 and lowest value equal to the count of cells included in the calculation. If there are two or more cells with equal values, they receive the same rank but are considered to take up their own spots in the ranking. Thus, the next highest value is pushed down in rank by the number of cells at the rank above it, minus one. For example, if you rank the values 5,3,3,4,3,2, their ranks are 1,3,3,2,3,6. For example, suppose that you have the following data. ![\[alt text not found\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/rank.png) Applying the **Rank** function across the table rows, using **Table across** for **Calculate as**, gives you the following results. ![\[alt text not found\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/rank2.png) ## Percentile The **Percentile** function calculates the percent of the values of the cells included in the calculation that are at or below the value for the given cell. This percent is calculated as follows. ``` percentile rank(x) = 100 * B / N Where: B = number of scores below x N = number of scores ``` Applying the **Percentile** function across the table rows, using **Table across** for **Calculate as**, gives you the following results. ![\[alt text not found\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/percentile.png) # Ways to apply pivot table calculations You can apply table calculations in the ways described following. Table calculations are applied to only one field at a time. Thus, if you have a pivot table with multiple values, calculations are only applied to the cells representing the field that you applied the calculation to. **Topics** + [ ## Table across ](#table-across) + [ ## Table down ](#table-down) + [ ## Table across down ](#table-across-down) + [ ## Table down across ](#table-down-across) + [ ## Group across ](#group-across) + [ ## Group down ](#group-down) + [ ## Group across down ](#group-across-down) + [ ## Group down across ](#group-down-across) ## Table across Using **Table across** applies the calculation across the rows of the pivot table, regardless of any grouping. This application is the default. For example, take the following pivot table. ![\[alt text not found\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/sample-pivot.png) Applying the **Running total** function using **Table across** gives you the following results, with row totals in the last column. ![\[alt text not found\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/table-across.png) ## Table down Using **Table down** applies the calculation down the columns of the pivot table, regardless of any grouping. Applying the **Running total** function using **Table down** gives you the following results, with column totals in the last row. ![\[alt text not found\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/table-down.png) ## Table across down Using **Table across down** applies the calculation across the rows of the pivot table, and then takes the results and reapplies the calculation down the columns of the pivot table. Applying the **Running total** function using **Table across down** gives you the following results. In this case, totals are summed both down and across, with the grand total in the lower-right cell. ![\[alt text not found\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/running-total-across-down.png) In this case, suppose that you apply the **Rank** function using **Table across down**. Doing so means that the initial ranks are determined across the table rows and then those ranks are in turn ranked down the columns. This approach gives you the following results. ![\[alt text not found\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/rank-table-across-down.png) ## Table down across Using **Table down across** applies the calculation down the columns of the pivot table. It then takes the results and reapplies the calculation across the rows of the pivot table. You can apply the **Running total** function using **Table down across** to get the following results. In this case, totals are summed both down and across, with the grand total in the lower-right cell. ![\[alt text not found\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/running-total-down-across.png) You can apply the **Rank** function using **Table down across** to get the following results. In this case, the initial ranks are determined down the table columns. Then those ranks are in turn ranked across the rows. ![\[alt text not found\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/rank-table-down-across.png) ## Group across Using **Group across** applies the calculation across the rows of the pivot table within group boundaries, as determined by the second level of grouping applied to the columns. For example, if you group by field-2 and then by field-1, grouping is applied at the field-2 level. If you group by field-3, field-2, and field-1, grouping is again applied at the field-2 level. When there is no grouping, **Group across** returns the same results as **Table across**. For example, take the following pivot table where columns are grouped by `Service Line` and then by `Consumption Channel`. ![\[alt text not found\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/sample-pivot.png) You can apply the **Running total** function using **Group across** to get the following results. In this case, the function is applied across the rows, bounded by the columns for each service category group. The `Mobile` columns display the total for both `Consumption Channel` values for the given `Service Line`, for the `Customer Region` and `Date` (year) represented by the given row. For example, the highlighted cell represents the total for the `APAC` region for `2012`, for all `Consumption Channel` values in the `Service Line` named `Billing`. ![\[alt text not found\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/group-across.png) ## Group down Using **Group down** applies the calculation down the columns of the pivot table within group boundaries, as determined by the second level of grouping applied to the rows. For example, if you group by field-2 and then by field-1, grouping is applied at the field-2 level. If you group by field-3, field-2, and field-1, grouping is again applied at the field-2 level. When there is no grouping, **Group down** returns the same results as **Table down**. For example, take the following pivot table where rows are grouped by `Customer Region` and then by `Date` (year). ![\[alt text not found\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/sample-pivot.png) You can apply the **Running total** function using **Group down** to get the following results. In this case, the function is applied down the columns, bounded by the rows for each `Customer Region` group. The `2014` rows display the total for all years for the given `Customer Region`, for the `Service Line` and `Consumption Channel` represented by the given column. For example, the highlighted cell represents the total the `APAC` region, for the `Billing` service for the `Mobile` channel, for all the `Date` values (years) that display in the report. ![\[alt text not found\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/group-down.png) ## Group across down Using **Group across down** applies the calculation across the rows within group boundaries, as determined by the second level of grouping applied to the columns. Then the function takes the results and reapplies the calculation down the columns of the pivot table. It does so within group boundaries as determined by the second level of grouping applied to the rows. For example, if you group a row or column by field-2 and then by field-1, grouping is applied at the field-2 level. If you group by field-3, field-2, and field-1, grouping is again applied at the field-2 level. When there is no grouping, **Group across down** returns the same results as **Table across down**. For example, take the following pivot table where columns are grouped by `Service Line` and then by `Consumption Channel`. Rows are grouped by `Customer Region` and then by `Date` (year). ![\[alt text not found\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/sample-pivot.png) You can apply the **Running total** function using **Group across down** to get the following results. In this case, totals are summed both down and across within the group boundaries. Here, these boundaries are `Service Line` for the columns and `Customer Region` for the rows. The grand total appears in the lower-right cell for the group. ![\[alt text not found\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/running-total-group-across-down.png) You can apply the **Rank** function using **Group across down** to get the following results. In this case, the function is first applied across the rows bounded by each `Service Line` group. The function is then applied again to the results of that first calculation, this time applied down the columns bounded by each `Customer Region` group. ![\[alt text not found\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/rank-group-across-down.png) ## Group down across Using **Group down across** applies a calculation down the columns within group boundaries, as determined by the second level of grouping applied to the rows. Then Amazon Quick takes the results and reapplies the calculation across the rows of the pivot table. Again, it reapplies the calculation within group boundaries as determined by the second level of grouping applied to the columns. For example, if you group a row or column by field-2 and then by field-1, grouping is applied at the field-2 level. If you group by field-3, field-2, and field-1, grouping is again applied at the field-2 level. When there is no grouping, **Group down across** returns the same results as **Table down across**. For example, take the following pivot table. Columns are grouped by `Service Line` and then by `Consumption Channel`. Rows are grouped by `Customer Region` and then by `Date` (year). ![\[alt text not found\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/sample-pivot.png) You can apply the **Running total** function using **Group down across** to get the following results. In this case, totals are summed both down and across within the group boundaries. In this case, these are `Service Category` for the columns and `Customer Region` for the rows. The grand total is in the lower-right cell for the group. ![\[alt text not found\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/running-total-group-across-down.png) You can apply the **Rank** function using **Group down across** to get the following results. In this case, the function is first applied down the columns bounded by each `Customer Region` group. The function is then applied again to the results of that first calculation, this time applied across the rows bounded by each `Service Line` group. ![\[alt text not found\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/rank-group-down-across.png) # Pivot table limitations The following limitations apply to pivot tables: + You can create pivot tables with up to 500,000 records. + You can add any combination of row and column field values that add up to 40. For example, if you have 10 row field values, then you can add up to 30 column field values. + You can create pivot table calculations only on nonaggregated values. For example, if you create a calculated field that is a sum of a measure, you can't also add a pivot table calculation to it. + If you are sorting by a custom metric, you can't add a table calculation until you remove the custom metric sort. + If you are using a table calculation and then add a custom metric, you can't sort by the custom metric. + Totals and subtotals are blank for table calculations on metrics aggregated by distinct count. # Pivot table best practices It's best to deploy a minimal set of rows, columns, metrics, and table calculations, rather than offering all possible combinations in one pivot table. If you include too many, you risk overwhelming the viewer and you can also run into the computational limitations of the underlying database. To reduce the level of complexity and reduce the potential for errors, you can take the following actions: + Apply filters to reduce the data included in for the visual. + Use fewer fields in the **Row** and **Column** field wells. + Use as few fields as possible in the **Values** field well. + Create additional pivot tables so that each displays fewer metrics. In some cases, there's a business need to examine many metrics in relation to each other. In these cases, it can be better to use multiple visuals on the same dashboard, each showing a single metric. You can reduce the size of the visuals on the dashboard, and colocate them to form a grouping. If a decision the viewer makes based on one visual creates the need for a different view, you can deploy custom URL actions to launch another dashboard according to the choices made by the user. It's best to think of visuals as building blocks. Rather than using one visual for multiple purposes, use each visual to facilitate one aspect of a larger business decision. The viewer should have enough data to make a well-informed decision, without being overwhelmed by the inclusion of all possibilities. # Using radar charts Using radar charts You can use radar charts, which are also known as spider charts, to visualize multivariate data in Amazon Quick. In a radar chart, one or more groups of values are plotted over multiple common variables. Each variable has its own axis, and each axis is arranged radially around a central point. The data points from a single observation are plotted on each axis and connected to each other to form a polygon. Multiple observations can be plotted in a single radar chart to display multiple polygons, which makes it easier to spot outlying values for multiple observations quickly. In Quick, you can organize a radar chart along its category, value, or color axes by dragging and dropping fields to the **Category**, **Value**, and **Color** field wells. How you choose to distribute fields among the field wells determines the axis that the data is plotted on. The following image shows an example of a radar chart. ![\[Radar chart plotting employee satisfaction variables by department.\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/radar-chart-example.png) ## Radar chart features To view the features supported by radar charts, use the following table. **** | Feature | Supported? | Comments | For more information | | --- | --- | --- | --- | | Changing the legend display | Yes | | [Legends on visual types in Quick](customizing-visual-legend.md) | | Changing the title display | Yes | | [Titles and subtitles on visual types in Quick](customizing-a-visual-title.md) | | Changing the axis range | Yes | | [Range and scale on visual types in Quick](changing-visual-scale-axis-range.md) | | Changing the visual colors | Yes | | [Colors in visual types in Quick](changing-visual-colors.md) | | Focusing on or excluding elements | Yes | | [Focusing on visual elements](focusing-on-visual-elements.md) [Excluding visual elements](excluding-visual-elements.md) | | Sorting | Limited | You can only sort data fields that are in the Category and Color field wells. | [Sorting visual data in Amazon Quick](sorting-visual-data.md) | | Performing field aggregation | Yes | | [Changing field aggregation](changing-field-aggregation.md) | | Adding drill-downs | Not supported | | [Adding drill-downs to visual data in Quick Sight](adding-drill-downs.md) | | Choosing size | Yes | | [Formatting in Amazon Quick](formatting-a-visual.md) | | Showing totals | Not supported | | [Formatting in Amazon Quick](formatting-a-visual.md) | ## Creating a radar chart Use the following procedure to create a radar chart. **To create a radar chart** 1. On the analysis page, choose **Visualize** on the tool bar. 1. Choose **Add** on the application bar, and then choose **Add visual**. 1. On the **Visual types** pane, choose the radar chart icon. 1. From the **Fields list** pane, drag the fields that you want to use to the appropriate field wells. In most cases, you want to use dimension or measure fields as indicated by the target field well. To create a radar chart, drag fields to the **Category**, **Value**, and **Group/Color** field wells. The axis that a radar chart is organized around is determined by the way that you organize fields into their respective field wells: + In a radar chart that uses a **value axis**, dimension values are shown as lines and axes represent value fields. To create a radar chart that uses a value axis, add one category field to the **Color** field well and one or more values to the **Value** field well. + In a radar chart that uses a **dimension axis**, group dimension values are shown as axes and value fields are shown as lines. All axes share a range and scale.To create a radar chart that uses a dimension axis, add one dimension to the **Group** field well and one or more values to the **Value** field well. + In a radar chart that uses a **dimension-color axis**, group dimension values are shown as axes and color dimension values are shown as lines. All axes share a range and scale. To create a radar chart that uses a dimension-color axis, add one dimension to the **Category** field well, one value to the **Value** field well, and one dimension to the **Color** field well. # Using Sankey diagrams Using Sankey diagrams Use Sankey diagrams to show flows from one category to another, or paths from one stage to the next. For example, a Sankey diagram can show the number of people migrating from one country to another. A Sankey diagram can also show the path a web visitor takes from one page to the next on a company website, with possible stops along the way. ## Data for Sankey diagrams To create Sankey diagrams in Quick, your dataset should contain a measure and two dimensions (one dimension containing source categories and another containing destination categories). The following table is a simple example of data for a Sankey diagram. | Dimension (Source) | Dimension (Destination) | Measure (Weight) | | --- | --- | --- | | A | W | 500 | | A | X | 23 | | A | Y | 147 | The following Sankey diagram is created when the dimensions and measure are added to the field well, with the A node on the left linking to the W, Y, and X nodes on the right. The width of each link between nodes is determined by the value in the Measure (Weight) column. The nodes are automatically ordered. To create multilevel Sankey diagrams in Amazon Quick, your dataset should still contain a measure and two dimensions (one for source and one for destination), but in this case your data values differ. The following table is a simple example of data for a multilevel Sankey diagram with two stages. | Dimension (Source) | Dimension (Destination) | Measure (Weight) | | --- | --- | --- | | A | W | 500 | | A | X | 23 | | A | Y | 147 | | W | Z | 300 | | X | Z | 5 | | Y | Z | 50 | The following Sankey diagram is created when the dimensions and measure are added to the field well. Here, the A node on the left links to the W, Y, and X nodes in the middle, and the W, Y, and X nodes then link to the Z node on the right. The width of each link between nodes is determined by the value in the Measure (Weight) column. ### Working with cyclical data Sometimes, the data that you use for a Sankey diagram contains cycles. For example, suppose that you're visualizing user traffic flows between pages on a website. You might discover that users who come to page A move to page E, and then come back to page A. An entire flow might look something like A-E-A-B-A-E-A. When your data contains cycles, the nodes in each cycle are repeated in Quick. For example, if your data contains the flow A-E-A-B-A-E-A, the following Sankey diagram is created. ## Preparing data for Sankey diagrams If your dataset doesn't contain Source or Destination columns, prepare your data to include them. You can prepare data when creating a new dataset, or when editing an existing dataset. For more information about creating a new dataset and preparing it, see [Creating datasets](creating-data-sets.md). For more information about opening an existing dataset for data preparation, see [Editing datasets](edit-a-data-set.md). The following procedure uses an example table (illustrated in following) to demonstrate how to prepare your data for Sankey diagrams in Quick. The table includes three columns: Customer ID, Time, and Action. | Customer ID | Time | Action | | --- | --- | --- | | 1 | 9:05 am | Step 1 | | 1 | 9:06 am | Step 2 | | 1 | 9:08 am | Step 3 | | 2 | 11:44 am | Step 1 | | 2 | 11:47 am | Step 2 | | 2 | 11:48 am | Step 3 | To create a Sankey diagram in Quick using this data, first add Source and Destination columns to the table. Use the following procedure to learn how. **To add Source and Destination columns to your table** 1. Add a Step Number column to the table to number or rank each row. There are multiple ways to compute the Step Number column. If your data source is compatible with SQL and your database supports `ROW_NUMBER` or `RANK` functions, you can use custom SQL in Quick to order the rows in the Step Number column. For more information about using custom SQL in Quick, see [Using SQL to customize data](adding-a-SQL-query.md). [\[See the AWS documentation website for more details\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/sankey-diagram.html) 1. Add a Next Row Number column to the table with values equal to Step Number plus one. For example, in the first data row of the table, the value for Step Number is 1. To compute the value for Next Step Number for that row, add 1 to that value. 1 \$1 1 = 2 The value for Step Number in the second data row of the table is 2; therefore, the value for Next Step Number is 3. 2 \$1 1 = 3 [\[See the AWS documentation website for more details\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/sankey-diagram.html) 1. Join the table with itself: 1. For **Join type**, choose **Inner**. 1. For **Join clauses**, do the following: 1. Choose **Customer ID** = **Customer ID** 1. Choose **Next Step Number** = **Step Number** Joining the two tables creates two columns for Customer ID, Time, Action, Step Number and Next Step Number. The columns from the table at the left of the join are Source columns. The columns from the table at the right of the join are Destination columns. For more information about joining data in Quick, see [Joining data](joining-data.md). 1. (Optional) Rename columns to indicate sources and destinations. The following is an example: 1. Rename the **Action** column on the left to **Source**. 1. Rename the **Action [copy]** column on the right to **Destination**. 1. Rename the **Time** column on the left to **Start Time**. 1. Rename the **Time [copy]** column on the right to **End Time**. Your data is now ready to visualize. ## Creating Sankey diagrams Use the following procedure to create a Sankey diagram. **To create a Sankey diagram** 1. On the analysis screen, choose **Visualize** on the left toolbar. 1. On the application bar, choose **Add**, and then choose **Add visual**. 1. On the **Visual types** pane, choose the Sankey diagram icon. 1. On the menu in the upper-right corner of the visual, choose the **Properties** icon. 1. In the **Properties pane**, choose either the **Source** or **Destination** section. ### Customizing the number of nodes Use the following procedure to customize the number of nodes that appear in a Sankey diagram. Quick supports up to 100 Source/Destination nodes. **To customize the number of nodes that appear in a Sankey diagram** 1. On the analysis page, choose the Sankey diagram visual that you want to format. 1. On the menu in the upper-right corner of the visual, select the **Format Visual** icon. 1. In the **Properties** pane that opens, choose either the **Source** or **Destination** tab. 1. For **Number of nodes displayed**, enter a number. The nodes in the diagram update to the number that you specified. The top nodes are automatically shown. All other nodes are placed in an **Other** category. **Note** Specifying the number of Source nodes controls how many Source nodes can appear overall in the diagram. Specifying the number of Destination nodes controls how many Destination nodes can appear per Source node. This means that if there is more than one Source node in your diagram, the overall number of Destination nodes will be higher than the number specified. Quick supports up to 100 Source/Destination nodes. For example, the following Sankey diagram has a limit of three source nodes (out of five), so the top three are shown in the diagram. The other two source nodes are placed in the Other category. To remove the **Other** category from the diagram, select it in the view and choose **Hide “other” categories**. ## Sankey diagram features To understand the features supported by Sankey diagrams, use the following table. | Feature | Supported? | For more information | | --- | --- | --- | | Changing the legend display | No | | | Changing the title display | Yes | [Titles and subtitles on visual types in Quick](customizing-a-visual-title.md) | | Changing the axis range | No | | | Changing the visual colors | No | | | Focusing on or excluding elements | Yes | [Focusing on visual elements](focusing-on-visual-elements.md) [Excluding visual elements](excluding-visual-elements.md) | | Sorting | No | | | Performing field aggregation | Yes | [Changing field aggregation](changing-field-aggregation.md) | | Adding drill-downs | No | | | Conditional formatting | No | | # Using scatter plots Use scatter plots to visualize two or three measures across two dimensions. Each bubble on the scatter plot represents one or two dimension values. The X and Y axes represent two different measures that apply to the dimension. A bubble appears on the chart at the point where the values for the two measures for an item in the dimension intersect. Optionally, you can also use bubble size to represent an additional measure. Scatter plots show up to 2500 datapoints in aggregated and unaggregated scenarios regardless of whether a color or label dimension is used in the visual. Due to the order of limit operations, there may be cases where fewer datapoints for a dataset are shown. For more information about how Amazon Quick handles data that falls outside display limits, see [Display limits](working-with-visual-types.md#display-limits). ## Scatter plot features To understand the features supported by scatter plots, use the following table. | Feature | Supported? | Comments | For more information | | --- | --- | --- | --- | | Changing the legend display | Yes, with exceptions | Scatter plots display a legend if you have the Group/Color field well populated. | [Legends on visual types in Quick](customizing-visual-legend.md) | | Changing the title display | Yes | | [Titles and subtitles on visual types in Quick](customizing-a-visual-title.md) | | Changing the axis range | Yes | You can set the range for both the X and Y axes. | [Range and scale on visual types in Quick](changing-visual-scale-axis-range.md) | | Showing or hiding axis lines, grid lines, axis labels, and axis sort icons | Yes | | [Axes and grid lines on visual types in Quick](showing-hiding-axis-grid-tick.md) | | Changing the visual colors | Yes | | [Colors in visual types in Quick](changing-visual-colors.md) | | Focusing on or excluding elements | Yes, with exceptions | You can focus on or exclude a bubble in a scatter plot, except when you are using a date field as a dimension. In that case, you can only focus on a bubble, not exclude it. | [Focusing on visual elements](focusing-on-visual-elements.md) [Excluding visual elements](excluding-visual-elements.md) | | Sorting | No | | [Sorting visual data in Amazon Quick](sorting-visual-data.md) | | Performing field aggregation | Yes | You must apply aggregation to the fields you choose for the X axis, Y axis, and size, and can't apply aggregation to the field that you choose for the group or color. | [Changing field aggregation](changing-field-aggregation.md) | | Displaying unaggregated fields | Yes | On the field context menu, choose None to display unaggregated X and Y axis values. If your scatter plot shows unaggregated fields, you can't apply aggregations to the field that is in the color or label field well. Mixed aggregation is not supported for scatter plots. | | | Adding drill-downs | Yes | You can add drill-down levels to the Group/Color field well. | [Adding drill-downs to visual data in Quick Sight](adding-drill-downs.md) | ## Creating a scatter plot Use the following procedure to create a scatter plot. **To create a scatter plot** 1. On the analysis page, choose **Visualize** on the tool bar. 1. Choose **Add** on the application bar, and then choose **Add visual**. 1. On the **Visual types** pane, choose the scatter plot icon. 1. From the **Fields list** pane, drag the fields that you want to use to the appropriate field wells. Typically, you want to use dimension or measure fields as indicated by the target field well. If you choose to use a dimension field as a measure, the **Count** aggregate function is automatically applied to it to create a numeric value. To create a scatter plot, drag a measure to the **X axis** field well, a measure to the **Y axis** field well, and a dimension to the **Color** or **Label** field well. To represent another measure with bubble size, drag that measure to the **Size** field well. 1. (Optional) Add drill-down layers by dragging one or more additional fields to the **Color** field well. For more information about adding drill-downs, see [Adding drill-downs to visual data in Quick Sight](adding-drill-downs.md). ## Scatter plot use cases You can choose to plot unaggregated values even if you are using a field on Color by using the aggregate option **none** on the field menu, which also contains aggregation options like **sum**, **min**, and **max**. If one value is set to be aggregated, the other value will be automatically set as aggregated. The same applies to unaggregated scenarios. Mixed aggregation scenarios are not supported, meaning that one value cannot be set as aggregated while the other is unaggregated. Note that the unaggregated scenario, which is the **none** option, is supported only for numerical values, while categorical values, such as dates or dimensions, will display only aggregate values, such as **count** and **count distinct**. Using the **none** option, you can choose to set both X and Y values to either aggregated or unaggregated from the **X axis** and **Y axis** field menus. This will define whether or not values will be aggregated by dimensions in the **Color** and **Label** field wells. To get started, add the required fields and choose the appropriate aggregation based on your use case,as shown in the following sections. ### Unaggregated use cases + Unaggregated X and Y values with Color ![\[unaggregated-color\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/unaggregated-color.png) + Unaggregated X and Y values with Label ![\[unaggregated-label\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/unaggregated-label.png) + Unaggregated X and Y values with Color and Label ![\[unaggregated-color-label\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/unaggregated-color-label.png) ### Aggregated use cases + Aggregated X and Y values with Color ![\[aaggregated-color\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/aggregated-color.png) + Aggregated X and Y values with Label ![\[aggregated-label\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/aggregated-label.png) + Aggregated X and Y values with Color and Label ![\[aggregated-color-label\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/aggregated-color-label.png) # Using tables as visuals Use a table visual to see a customized table view of your data. To create a table visual, choose at least one field of any data type. You can add as many columns as you need, up to 200. You can also add calculated columns. Table visuals don't display a legend. You can hide or display the title on a table. You can also hide or display totals, and choose to show totals at the top or the bottom of the table. For more information, see [Analytics formatting per type in Quick](analytics-format-options.md). **To create a table visual** 1. Open Amazon Quick and choose **Analyses** on the navigation pane at left. 1. Choose one of the following: + To create a new analysis, choose **New analysis** at upper right. For more information, see [Starting an analysis in Quick Sight](creating-an-analysis.md). + To use an existing analysis, choose the analysis that you want to edit. 1. Choose **Insert** from the file menu and then **Add Visual**. 1. At lower left, choose the table icon from **Visual types**. 1. On the **Fields** list pane, choose the fields that you want to use. If you want to add a calculated field, choose **Insert** on the file menu and then **Add Calculated Field**. To create a nonaggregated view of the data, add fields only to the **Value** field well. Doing this shows data without any aggregations. To create an aggregated view of the data, choose the fields that you want to aggregate by, and then add them to the **Group by** field well. **To show or hide columns on a table** 1. On your visual, choose the field that you want to hide, then choose **Hide column**. 1. To display hidden columns, choose any column, then choose **Show all hidden columns**. **To transpose columns to rows and rows to columns** + Choose the transpose icon ( ![\[alt text not found\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/transpose-icon.png)) near the top right of the visual. It has two arrows at a 90 degree angle. **To vertically align columns** 1. On your visual, choose the **Format visual** icon ( ![\[alt text not found\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/format-visual-icon.png)) near the top right of the visual. 1. In the **Properties** pane, choose **Table options**, and choose your table's vertical alignment. **To wrap the text for headers** 1. On your visual, choose the **Format visual** icon ( ![\[alt text not found\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/format-visual-icon.png)) near the top right of the visual. 1. In the **Properties** pane, choose **Table options**, and select **Wrap header text**. **To rearrange columns in a table chart** 1. Open the analysis with the visual that you want to sort. Visuals pane will be open by default. 1. Do one of the following: + Drag and drop one or more fields in **Field wells** to rearrange their order. + Select a field directly in the table and choose the left or right arrow on **Move column**. # Using field styling You can render URLs in a table as links by using the **Field styling** pane of the format visual menu. You can add up to 500 rows of links for each page in a table. Only https and mailto hyperlinks are supported. **To add links to your tables** 1. From the Quick homepage, choose **Analyses**, and then choose the analysis that you want to customize. 1. Choose the table that you want to change. 1. On the menu at the upper right of the table, choose **Format visual**. 1. For **Format visual**, choose **Field styling**. 1. On the **Field styling** pane, choose the field that you want to style from the menu. 1. In the **Url options** section of the **Field styling** menu, choose **Make URLs hyperlinks**. After you add links to your table, you can choose where you want the links to open when they're selected in the **Open in** section of the **Field style** pane. You can choose to have links open in a new tab, a new window, or in the same tab. You can also choose how you want to style the link in the **Style as** section of **Field style** pane. Your links can appear as hyperlinks, icons, or plain text, or you can set a custom link. To adjust the font size of a link icon or URL, change the **Font size** in the **Cells** section of the **Table options** pane of the **Format visual** menu. You can set any URLs in your table that point to images to render in the table as images. Doing this can be useful when you want to include an image of a product as a part of a table. **To show URLs as images** 1. From the Quick home page, choose **Analyses**, and then choose the analysis that you want to customize. 1. Choose the table that you want to change. 1. On the menu at the upper-right of the table, choose **Format visual**. 1. In the **Format visual** menu, choose **Field styling**. 1. In the **Field styling** pane, choose the field that you want to style from the menu. 1. In the **Url options** section of the **Field styling** menu, choose **Show URLs as images**. After rendering images in a table, you can choose how to size the images in the **Image sizing** section of the **Field style** pane. You can fit images to their cell's height or width, or you can choose not to scale the image. Images fit to a cell's height by default. # Freeze columns to table visuals Freeze columns You can freeze columns to your table visuals to lock specific columns in place on screen. This allows essential information to remian visible while readers scroll across the table. You can freeze columns one at a time, or you can freeze groups of columns in one action. All pinned columns are fixed to the far left side of the table and stay visible on screen at all times. This allows Quick readers to have a constant reference point for key data or information as they interact with other parts of the table. **To freeze columns to a table** 1. On the table that you want to freeze a column to, choose the column that you want to pin. 1. Choose one of the following options. + To freeze a single column, choose **Freeze column**. + To freeze all columns up to the column that you choose, choose **Freeze up to this column**. If your table has multiple pinned columns, you can reorder the columns in the order that you want. To adjust the order of the pinned columns on a table, choose the header of the column that you want to move, and then choose **Move** in the direction that you want. **To unfreeze columns from a table** 1. On the table that you want to change, choose the pinned column that you wnat to unpin. 1. Choose one of the following options. + To unfreeze a single column, choose **Unfreeze column**. + To unfreeze all frozen columns, choose **Unfreeze all columns**. # Custom total values Quick authors can define the total and subtotal aggregations for their table or pivot table visuals from the field wells. For tables,the custom total menu is only available if totals are toggled on for the visual. **To change the aggregation of a total or subtotal** 1. Navigate to the analysis that you want to change, and choose the table or pivot table visual whose total you want to define. 1. Choose the field that you want to change from the field wells. 1. Choose **Total**, and then choose the aggregation that you want. The following options are available. + **Default** – The total calculation uses the same aggregation as the metric field. + **Sum** – Calculates the sum of the data in the visual. + **Average** – Calculates the average of the data in the visual. + **Min** – Calculates the minimum value of the data in the visual. + **Max** – Calculates the maximum value of the data in the visual. + **None (HIDE)** – Totals are not calculated. When you choose this option, the total and subtotal cells in the visual are left blank. If the outer dimension is sorted with the metric field that calculates the total or subtotal, the dimension is sorted alphabetically. When you change the value from **None (HIDE)** to another value, the outer dimension is sorted by the subtotals that are calculated with the specified aggregation type. The following limitations apply to custom totals. + Conditional formatting is not supported for custom totals. + Total aggregations aren't supported for string columns. Total aggregations include **Min**, **Max**, **Sum**, and **Average**. + Date columns are incompatible with **Average** and **Sum** total aggregation functions. # Sorting tables In Amazon Quick, you can sort values in a table by fields in the columns headers of the table or with the **Sort visual** tool. You can sort up to 10 columns in a single table. Quick can also use an off-visual sort You can sort columns in an **Ascending** or a **Descending** order. The following image shows the **Sort visual** icon and pop over. ![\[The Sort visual icon and the Sort visual pop over that it opens.\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/table-sort-icon.png) ## Single column sort options Quick Authors can access single column sort options from the field wells, the column headers, or from the **Sort visual** menu. Use the procedure below to use set up a single column sort on a table in Quick. 1. Open the [Quick console](https://quicksight.aws.amazon.com/). 1. Open the analysis that you want to work in and navigate to the table that you want to sort. 1. Choose the header of the column that you want to sort. 1. For **Sort by**, choose the arrow icon, and then choose the field that you want to sort by. You can also set up a single column sort in the **Sort visual** menu. To access the sort visual menu, choose the **Sort visual** icon in the on-visual menu. In the **Sort visual** menu, choose the field that you want to sort by, and then choose if you want the sort in an ascending or descending order. By default, new sorts are sorted in an ascending order. When you are finished, choose **APPLY**. Tables that use single column sorting are sorted one column at a time. When a user chooses a new column to sort by, the previous sort order is overridden. To make changes to a single column sort, open the **Sort visual** menu annd use the dropdown menus to choose a new field or sort order. When you are finished with your changes, choose **APPLY**. To reset a table to its original state, open the **Sort visual** menu and choose **RESET**. ## Multi column sort options Quick authors can access multi column sort options from the **Sort visual** menu. Use the procedure below to set up a multi column sort for a table. 1. Open the [Quick console](https://quicksight.aws.amazon.com/). 1. Open the analysis that you want to work in and navigate to the table that you want to sort. 1. Choose the **Sort visual** icon to open the **Sort visual** menu. 1. Alternatively, choose a header that you want to sort. 1. For **Sort by**, choose the arrow icon, and then choose **Multiple fields**. 1. In the **Sort visual** menu that opens, choose a field from the **Sort by** dropdown, and then choose whether you want the field sorted in an ascending or descending order. 1. To add another sort, choose **ADD SORT**, and repeat the workflow from Step 4. You can add up to 10 sorts to each table. 1. When you are finished, choose **APPLY**. Columns are sorted in the order that they are added to the **Sort visual** menu. To change the order that columns are sorted by, open the **Sort visual** menu and use the **Sort by** dropdowns to reorder the sorts. When you are finished, choose **APPLY** to apply the new sort order to the table. To reset a table to its original state, open the **Sort visual** menu and choose **RESET**. ## Off visual sort options Quick authors can configure an off-visual sort to sort the values in a table by a field and aggregation that is a part of the dataset that the table uses but not in one of the table's field wells. One off-field sort can be configured to a single table at a time. Use the procedure below to configure an off-visual sort. **To add an off-visual sort to a table** 1. Open the [Quick console](https://quicksight.aws.amazon.com/). 1. Open the analysis that you want to work in and navigate to the table that you want to sort. 1. Choose the header of any column in the table. 1. For **Sort by**, choose the arrow icon, and then choose **Off-visual field**. 1. In the **Off-visual field** pane that appears, open the **Sort by** dropdown menu and choose the field that you want to sort. 1. For **Aggregation** open the dropdown menu and choose the aggregation that you want to use. 1. For **Sort order**, choose if you want the sort to be in an ascending or descending order. 1. When you are finished, choose **Apply**. After a off-visual sort is applied to a table, the sort is shown in the **Sort visual** menu. The sort order of a table that contains an off-visual sort depends on the sort configuration of the table when the off-visual sort is added. If an off-visual sort is added to a table that already has a single or multi column sort configured, the off-visual sort overrides all other sorts. If the off-visual sort is applied before single or multi column sorts, you can add and reorder more sorts to the table. # Using text boxes Add text to add context to sheets in an analysis by using a text box. Text can hold directions, descriptions, or even hyperlinks to external websites. The toolbar on the text box offers font settings so you can customize the font type, style, color, size, spacing, size in pixels, text highlights, and alignment. The text box itself has no format settings. To add text to a new text box, simply select it and begin typing. # Using tree maps To visualize one or two measures for a dimension, use tree maps. Each rectangle on the tree map represents one item in the dimension. Rectangle size represents the proportion of the value for the selected measure that the item represents compared to the whole for the dimension. You can optionally use rectangle color to represent another measure for the item. Rectangle color represents where the value for the item falls in the range for the measure, with darker colors indicating higher values and lighter colors indicating lower ones. Tree maps show up to 100 data points for the **Group by** field. For more information about how Amazon Quick handles data that falls outside display limits, see [Display limits](working-with-visual-types.md#display-limits). ## Tree map features To understand the features supported by tree maps, use the following table. **** | Feature | Supported? | Comments | For more information | | --- | --- | --- | --- | | Changing the legend display | Yes | | [Legends on visual types in Quick](customizing-visual-legend.md) | | Changing the title display | Yes | | [Titles and subtitles on visual types in Quick](customizing-a-visual-title.md) | | Changing the axis range | Not applicable | | [Range and scale on visual types in Quick](changing-visual-scale-axis-range.md) | | Changing the visual colors | No | | [Colors in visual types in Quick](changing-visual-colors.md) | | Focusing on or excluding elements | Yes, with exceptions | You can focus on or exclude a rectangle from a tree map, except when you are using a date field as the dimension. In that case, you can only focus on a rectangle, not exclude it. | [Focusing on visual elements](focusing-on-visual-elements.md) [Excluding visual elements](excluding-visual-elements.md) | | Sorting | No | Default sorting is in descending order by the measure in the Size column. | [Sorting visual data in Amazon Quick](sorting-visual-data.md) | | Performing field aggregation | Yes | You must apply aggregation to the fields you choose for size and color, and can't apply aggregation to the field that you choose to group by. | [Changing field aggregation](changing-field-aggregation.md) | | Adding drill-downs | Yes | You can add drill-down levels to the Group by field well. | [Adding drill-downs to visual data in Quick Sight](adding-drill-downs.md) | ## Creating a tree map Use the following procedure to create a tree map. **To create a tree map** 1. On the analysis page, choose **Visualize** on the tool bar. 1. Choose **Add** on the application bar, and then choose **Add visual**. 1. On the **Visual types** pane, choose the tree map icon. 1. From the **Fields list** pane, drag the fields that you want to use to the appropriate field wells. Typically, you want to use dimension or measure fields as indicated by the target field well. If you choose to use a dimension field as a measure, the **Count** aggregate function is automatically applied to it to create a numeric value. To create a tree map, drag a measure to the **Size** field well and a dimension to the **Group by** field well. Optionally, drag another measure to the **Color** field well. 1. (Optional) Add drill-down layers by dragging one or more additional fields to the **Group by** field well. For more information about adding drill-downs, see [Adding drill-downs to visual data in Quick Sight](adding-drill-downs.md). # Using waterfall charts Use a waterfall chart to visualize a sequential summation as values are added or subtracted. In a waterfall chart, the initial value goes through a (positive or negative) change, with each change represented as a bar. The final total is represented by the last bar. Waterfall charts are also known as *bridges* because the connectors between the bars bridge the bars together, showing that they visually belong to the same story. Waterfall charts are most commonly used to present financial data, because you can show change within one time period or from one time period to another. This way, you can visualize the different factors that have an impact your project cost. For example, you can use a waterfall chart to show gross sales to net income within the same month, or the difference in net income from last year to this year, and the factors that were responsible for this change. You can also use waterfall charts to present statistical data, for example how many new employees you hired and how many employees left your company within a year. The following screenshot shows a waterfall chart. ![\[alt text not found\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/waterfall-chart.png) **To create a basic waterfall chart visual** 1. Open Amazon Quick and choose **Analyses** on the navigation pane at left. 1. Choose one of the following: + To create a new analysis, choose **New analysis** at upper right. For more information, see [Starting an analysis in Quick Sight](creating-an-analysis.md). + To use an existing analysis, choose the analysis that you want to edit. 1. Choose **Add (\$1), Add Visual**. 1. At lower left, choose the waterfall chart icon from **Visual types**. 1. On the **Fields list** pane, choose the fields that you want to use for the appropriate field wells. Waterfall charts require one category or measure in **Value**. 1. (Optional) Add drill-down layers by dragging one or more additional fields to the **Group/Color** field well. For more information about adding drill-downs, see [Adding drill-downs to visual data in Quick Sight](adding-drill-downs.md). To understand the features supported by waterfall charts, see [Analytics formatting per type in Quick](analytics-format-options.md). For customization options, see [Formatting in Amazon Quick](formatting-a-visual.md). # Using word clouds As an engaging way to display how often a word is used in relation to other words in a dataset, use word clouds. The best use for this type of visual is to show word or phrase frequency. It can also make a fun addition to show trending items or actions. You can use a fixed dataset for creative purposes. For example, you might make one of team goals, motivational phrases, various translations of a specific word, or anything else that you want to draw attention to. Each word in a word cloud represents one or more values in a dimension. The size of the word represents the frequency of a value's occurrence in a selected dimension, in proportion to the occurrences of other values in the same dimension. Word clouds are best when precision isn't important and there aren't a large number of distinct values. The following screenshot shows an example of a word cloud. ![\[alt text not found\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/word-cloud.png) To create a word cloud, use one dimension in the **Group by** field well. Optionally, you can add a metric to the **Size** field well. Word clouds usually look better with 20–100 words or phrases, but the format settings offer a wide range of flexibility. If you choose too many words, they can become too small to be legible, depending on the size of your display. By default, word clouds display 100 distinct words. To show more, change the format setting for **Number of words**. Word clouds are limited to 500 unique values for **Group by**. To avoid displaying the word **Other**, format the visual to hide the **Other** category. For more information about how Amazon Quick handles data that falls outside display limits, see [Display limits](working-with-visual-types.md#display-limits). ## Word cloud features To understand the features supported by word clouds, see the following table. | Feature | Supported? | Comments | For more information | | --- | --- | --- | --- | | Changing the legend display | No | | [Legends on visual types in Quick](customizing-visual-legend.md) | | Changing the title display | Yes | | [Titles and subtitles on visual types in Quick](customizing-a-visual-title.md) | | Changing the axis range | Not applicable | | [Range and scale on visual types in Quick](changing-visual-scale-axis-range.md) | | Changing the visual colors | Yes | To change the color, choose a word and then choose a color. | [Colors in visual types in Quick](changing-visual-colors.md) | | Focusing on or excluding elements | Yes | | [Focusing on visual elements](focusing-on-visual-elements.md) [Excluding visual elements](excluding-visual-elements.md) | | Sorting | Yes | | [Sorting visual data in Amazon Quick](sorting-visual-data.md) | | Performing field aggregation | Yes | You can't apply aggregation to the field that you choose for Group by. You must apply an aggregation to the field that you choose for Size. | [Changing field aggregation](changing-field-aggregation.md) | | Adding drill-downs | Yes | You can add drill-down levels to the Group by field well. | [Adding drill-downs to visual data in Quick Sight](adding-drill-downs.md) | | Using format options | Yes | You can choose to allow vertical words, emphasize scale, use a fluid layout, use lowercase, and set the amount of padding between words. You can set the maximum string length for the word cloud (default is 40). You can also choose the number of words for the Group by field (default is 100; maximum is 500). | [Formatting in Amazon Quick](formatting-a-visual.md) | | Showing totals | No | | [Formatting in Amazon Quick](formatting-a-visual.md) | ## Creating a word cloud Use the following procedure to create a word cloud. **To create a word cloud** 1. On the analysis page, choose **Visualize** on the tool bar. 1. Choose **Add** on the application bar, and then choose **Add visual**. 1. On the **Visual types** pane, choose the word cloud icon. 1. From the **Fields list** pane, drag the fields that you want to use to the appropriate field wells. Typically, you want to use dimension or measure fields as indicated by the target field well. If you choose to use a dimension field as a measure, the **Count** aggregate function is applied by default. To create a word cloud, add a dimension to the **Group by** field well. Optionally, add a measure to the **Size** field well. 1. (Optional) Add drill-down layers by dragging one or more additional fields to the **Group by** field well. For more information about adding drill-downs, see [Adding drill-downs to visual data in Quick Sight](adding-drill-downs.md).