RELATED APPLICATIONS
This application is a continuation of U.S. patent application Ser. No. 14/628,187, filed Feb. 20, 2015, entitled “Systems and Methods for Using Displayed Data Marks in a Dynamic Data Visualization Interface,” which claims priority to U.S. Provisional Application Ser. No. 62/047,579, filed Sep. 8, 2014, entitled “Systems and Methods for Providing Drag and Drop Analytics in a Data Visualization User Interface,” each of which is incorporated by reference herein in its entirety.
This application is related to U.S. patent application Ser. No. 14/628,170, filed Feb. 20, 2015, entitled “Systems and Methods for Providing Adaptive Analytics in a Dynamic Data Visualization Interface,” U.S. patent application Ser. No. 14/628,176, filed Feb. 20, 2015, entitled “Systems and Methods for Providing Drag and Drop Analytics in a Dynamic Data Visualization Interface,” and U.S. patent application Ser. No. 14/628,181, filed Feb. 20, 2015, entitled “Systems and Methods for Using Analytic Objects in a Dynamic Data Visualization Interface,” each of which is incorporated by reference herein in its entirety.
TECHNICAL FIELD
The disclosed implementations relate generally to data visualization and more specifically to systems, methods, and user interfaces that provide analytic functions for interactively exploring and investigating a data set.
BACKGROUND
Data visualization applications enable a user to understand a data set visually, including distribution, trends, outliers, and other factors that are important to making business decisions. Some data sets are very large or complex. Various analytic tools can be used to help understand the data, such as regression lines, average lines, and percentile bands. However, analytic functionality may be difficult to use or hard to find within a complex user interface. In addition, analysis sometimes requires using analytic functions on two or more subsets of data at the same time
SUMMARY
Disclosed implementations address the above deficiencies and other problems associated with data visualizations that use analytic functions. Some implementations simplify the complexity of using analytic functions by providing a palette of analytic options that may be dragged and dropped to display corresponding analytic data on a visual graphic. In some implementations, an analytic function has sub-options, which are displayed in a drop area, and the user selects a sub-option by dropping an icon for the analytic function onto the sub-option in the drop area. For example, a trend line (regression line) is an analytic function, which has several sub-options that may be displayed for user selection: a linear trend line, an exponential trend line, a logarithmic trend line, or a polynomial trend line.
Some implementations simplify the process of comparing analytic data for different subsets of data from a data source. When an analytic function has been selected (e.g., an average line, a trend line, or quartile bands), a user may select any subset of data points (or visual marks, which may represent more than a single data point), and the user interface displays that analytic function based on the selected subset, while still continuing to display the analytic data for the entire subset. This allows a user to quickly compare a subset to the whole set. In some implementations, the user may continue to modify the set of selected points or marks, and the analytic data for the selected subset adjusts according to the selection.
Disclosed implementations make experimenting with analytic techniques easier. Exemplary analytical operations or functions include summarizing the data, modeling the data, or performing custom operations specified by a user. For example, analytic functions may provide references lines, reference bands, statistical bands (e.g., averages, medians with quartiles, average with predefined confidence interval (e.g., 95%), box plots, trend lines, totals, subtotals, and forecasts).
Some implementations provide a drag and drop user interface for analytic icons. This functionality has various benefits for users, including: allowing users to easily experiment and iterate; drop spots where a user may drag an analytic icon show options that a user will most likely want to experiment with; it becomes easy to pick up and re-drop an object/analytic icon to try a different analytic function; analytic functions that are commonly used together are grouped as a single “analytic icon,” and thus can be selected in one step; and the analytic techniques are not buried in pull down menus.
Some implementations provide instant/adaptive analytics. This functionality has various benefits for users. For visualizations with a reference line, a reference band, a trend line, or other analytic function applied, a user may want to compare the analytic data for the set of data to an identified subset. When the user selects a subset of the marks, the user will see a new line or band corresponding to just the selected items. The user can instantly view the analytic data for just the selected marks (sample group), and compare the analytic data to the same analytic functionality applied to all marks (e.g., the “population”). This provides an interactive experience for comparing a sample group to the overall data set. In particular, implementations show an instant, selection-based reference line, band, trend line, or other analytic function alongside the original analytic line or band.
Some implementations with instant/adaptive analytics display the difference between the analytic data for the selected subset and the analytic data for the whole set in a tooltip when hovering over the selected subset or when the subset is selected. The instant/adaptive analytics are calculated and shown for each selection event, so as the user adds or removes marks from the selection, the analytic data updates on the fly, providing immediate feedback. The analytic data for the selected subset is displayed using the same formula or definition as the analytic data for the whole set of displayed data. For example, if an “average” line has been applied to the whole set, then an average line is created for the selected subset. In addition, the scope of the analytic data for the selected subset is inherited from the scope of the original line (e.g. table, pane, or cell).
In some implementations, the analytic data created for the selected subset is referred to as the “instant” line or band, and the analytic data for the entire set of data is referred to as the “original” line or band. In some instances, the instant and original items are close together on the display, and thus labels for some of the items may be obscured. In some implementations, the items are ordered in layers (e.g., like layers in a drawing program). In some implementations, the items are drawn from top to bottom as follows: (top) the instant label, the original label, the instant line, the original line, the instant band, and the original band (bottom). This layering helps users to understand the data visualization and the analytic data displayed in the data visualization. In particular, this allows the user to distinguish visually between the original and the instant line or band. Some implementations de-emphasize the original line, original band, and/or original label to distinguish them from the instant line, instant band, and/or instant label. This may be implemented by dimming, changing color, graying out, or other techniques.
In accordance with some implementations, a method executes at an electronic device with a display. For example, the electronic device can be a smart phone, a tablet, a notebook computer, or a desktop computer. The method concurrently displays a chart that displays visual marks that represent a set of data (e.g., bars in a bar chart or geometric shapes such as circles, squares, triangles, or other representations of data points in a scatter plot) and a plurality of analytic icons. In some implementations, the analytic icons are displayed in a panel that toggles between data that may be used to make the chart and analytic icons that correspond to analytical operations that may be performed on the data used to make the chart.
The method detects a first portion of a user input on a first analytic icon in the plurality of analytic icons (e.g., a mouse click down, finger down, or other selection of the first analytic icon and/or an initial mouse drag or finger drag on the first analytic icon) and in response, displays one or more option icons that correspond to options for performing a first analytical operation that corresponds to the first analytic icon.
The method also detects a second portion of the user input on the first analytic icon. For example, after a mouse click or finger down on the first analytic icon, a mouse drag or finger drag on the first analytic icon moves the first analytic icon across the display and over a respective option icon and/or a mouse up or finger up that “drops” the first analytic icon on the respective option icon. In some implementations, the option icons are “drop-targets” for the respective analytic icon. In response to detecting the second portion of the user input, the first analytic icon moves over a respective option icon in the one or more option icons that are displayed such that the first analytic icon is over the respective option icon immediately prior to ceasing to detect the input. The method then adds one or more graphics to the chart (e.g., analytic lines and/or bands) that correspond to the first analytical operation and a respective option that corresponds to the respective option icon.
In some implementations, the second portion of the input results in dropping the first analytic icon on the respective option icon and displays one or more graphics in the chart that correspond to the first analytical operation and the respective option in response to the dropping. In some implementations, the second portion of the input results in hovering the first analytic icon over the respective option icon and displaying one or more graphics (e.g., an average line) in the chart that corresponds to the first analytical operation and the respective option in response to the hovering (i.e., providing a preview of the analytic operation). In some implementations, if the input ends while the first analytic icon is hovering over the respective option icon, the first analytic icon is “dropped” on the respective option icon and the one or more graphics in the chart that correspond to the first analytical operation and the respective option remain displayed. In some implementations, the added graphics include reference lines, reference bands, statistical bands (e.g., averages, medians with quartiles, averages with predefined confidence intervals (e.g., 95%), box plots, trend lines, totals, subtotals, and/or forecasts).
In some implementations, the input comprises a drag and drop operation. For example, with a mouse or other pointing device, the user moves a pointer over the first analytic icon, presses and holds down a button on the pointing device to select the first analytic icon, “drags” the first analytic icon over the respective option icon by moving the pointer, and “drops” the first analytic icon by releasing the button. With a touch screen, the user can contact the first analytic icon with a finger (e.g., a long press), “drag” the first analytic icon over the respective option icon by moving the finger, and “drop” the first analytic icon by lifting off the finger from the touch screen.
In some implementations, the options that correspond to the one or more option icons are specific to the first analytical operation. That is, there is a different set of displayed option icons depending on the selected analytic icon.
In some implementations, in response to detecting the first portion of the input on the first analytic icon (e.g., when the first analytic icon is hovered over or selected), the method visually distinguishes the first analytic icon from other analytic icons in the plurality of analytic icons (e.g., by outlining or highlighting).
In some implementations, in response to detecting the first portion of the input on the first analytic icon, the method visually distinguishes the first analytic icon from other analytic icons in the plurality of analytic icons and concurrently dims the chart. In some implementations, the device visually deemphasizes the chart when the one or more options icons are displayed, to indicate to the user the need to select an option icon.
In some implementations, an image is displayed on a respective option icon that illustrates a type of analytic graphic that will be added to the chart if the respective option icon is selected.
In some implementations, in response to detecting the second portion of the input on the first analytic icon, the method performs the first analytical operation that corresponds to the first analytic icon on at least part of the data in the set of data in accordance with the respective option and displays the result. In some implementations, the analytical operation includes summarizing the data, modeling the data, and/or performing custom predefined operations specified by a user. In some implementations, the analytical operation includes determining averages, medians with quartiles, averages with predefined confidence intervals (e.g., 95%), box plots, trend lines, totals, subtotals, and/or forecasts.
In some implementations, the first analytical operation includes a plurality of analytical operations. For example, a single analytic icon may provide both a median and quartile bands, or a single analytic icon may provide both a mean average and a 95% confidence interval.
In some implementations, in response to detecting the second portion of the input on the first analytic icon, the method ceases to display the first analytic icon over the respective option icon and ceases to display the one or more option icons.
In some implementations, while displaying the chart with one or more added graphics, the method detects a first portion of a second input on a second analytic icon (e.g., a mouse click down, finger down, or other selection of the second analytic icon and/or an initial mouse drag or finger drag on the second analytic icon). In response, one or more option icons are displayed that correspond to options for performing a second analytical operation that corresponds to the second analytic icon. The method also detects a second portion of the second input on the second analytic icon and in response, moves the second analytic icon over a respective option icon in the one or more option icons such that the second analytic icon is over the respective option icon immediately prior to ceasing to detect the input. The method also adds one or more graphics to the chart that correspond to the second analytical operation and a respective option that corresponds to the respective option icon. In some implementations, the one or more added graphics that correspond to the second analytical operation replace the one or more added graphics that correspond to the first analytical operation. In some implementations the one or more added graphics that correspond to the second analytical operation are displayed concurrently with the one or more added graphics that correspond to the first analytical operation.
In accordance with some implementations, a method executes at an electronic device with a display. For example, the electronic device may be a smart phone, a tablet computer, a notebook computer, or a desktop computer. The method displays a chart, which includes visual marks that represent a set of data and a first line and/or first band (e.g., statistical lines or bands, such as averages, medians with quartiles, averages with predefined confidence intervals, box plots, trend lines, totals, subtotals, and/or forecasts) based on (e.g., calculated using) data in the set of data that corresponds to the displayed visual marks. The method detects one or more inputs that select a plurality (but less than all) of the displayed visual marks in the chart. In response to detecting the one or more inputs, the method displays a second line and/or second band (e.g., analogous statistical lines or bands to the first line and/or first band) based on (e.g., calculated using) data in the set of data that corresponds to the selected plurality of the displayed visual marks. The method maintains display of the chart and the first line and/or first band in the chart while the second line and/or second band are displayed.
In some implementations, the one or more inputs are detected on the displayed chart.
In some implementations, the one or more inputs include a separate input on each visual mark (e.g., a finger tap gesture or mouse click) in the plurality of the displayed visual marks.
In some implementations, the one or more inputs used to select the plurality of the displayed visual marks in the chart are made with a selection box or lasso tool.
In some implementations, the first line and/or first band displayed in the chart are calculated using data in the set of data that correspond the displayed visual marks, independent of whether or not a respective displayed visual mark is selected, and the second line and/or second band displayed in the chart are calculated in an analogous manner using just data in the set of data that correspond to the selected displayed visual marks. In some implementations, the second line and/or second band is based on an original formula (e.g. “average”) calculated for the selected marks, and the scope of the second line and/or second band is inherited from the scope of the first line and/or first band (e.g. table, pane, or cell, as illustrated in some of the figures).
In some implementations, while displaying the chart, the first line and/or first band, and the second line and/or second band, the method detects one or more inputs that modify the plurality of selected visual marks. For example, the inputs may select additional displayed visual marks and/or deselect displayed visual marks that were previously selected. In response to detecting the one or more inputs, the method modifies the second line and/or second band based on (e.g., calculated using) data in the set of data that corresponds to the modified plurality of the displayed visual marks in the chart that are selected and maintains display of the chart and the first line and/or first band in the chart. In some implementations, the second line and/or second band is recalculated and the updated second line and/or second band displays in response to each selection event.
In some implementations, in response to detecting the one or more inputs that select the plurality of the displayed visual marks in the chart, the method displays a third line and/or third band based on (e.g., calculated using) data in the set of data that corresponds to displayed visual marks other than the selected plurality of the displayed visual marks.
In some implementations, a third line and/or third band is calculated based on the data that corresponds to visual marks that are not selected. In some implementations, the third line and/or third band is displayed concurrently with the first line and/or first band and the second line and/or second band (not shown).
In some implementations, the third line and/or third band replaces the first line and/or first band, and is displayed concurrently with the second line and/or second band (not shown). For example, if the selected visual marks correspond to suspect data points or outliers, then the third line and/or third band (which excludes the suspect data points) may be more informative than the first line and/or first band (which includes the suspect data points).
In some implementations, in response to detecting the one or more inputs that select the plurality of the displayed visual marks in the chart, the method visually deemphasizes (e.g., by dimming) the first line and/or first band relative to the second line and/or second band. In some implementations, visually deemphasizing the first (original) line or band helps the user to distinguish visually between the first (original) line or band and the second (instant) line or band.
In some implementations, the second line is displayed above the first line in a z-height order on the display (e.g., the elements in the graphical user interface can be thought of as “layers” coming out from the display, and the layers for the z-height order).
In some implementations, the second band is displayed above the first band in a z-height order on the display (e.g., layer ordering). In some implementations, the graphics in the chart are drawn from top to bottom as follows: (top) instant label, original label, instant line, original line, instant band, original band (bottom).
Some implementations provide both drag and drop analytics as well as adaptive analytics. In accordance with some implementations, a method executes at an electronic device with a display, concurrently displaying a chart that displays visual marks (e.g., bars in a bar chart or geometric shapes such as circles, squares, triangles, or other representations of data points in a scatter plot) that represent a set of data and a plurality of analytic icons. The method detects a first portion of an input on a first analytic icon in the plurality of analytic icons and in response, displays one or more option icons that correspond to options for performing a first analytical operation that corresponds to the first analytic icon. The method also detects a second portion of the input on the first analytic icon and in response, moves the first analytic icon over a respective option icon in the one or more displayed option icons such that the first analytic icon is over the respective option icon immediately prior to ceasing to detect the input. The method then adds a first line and/or first band to the chart that correspond to the first analytical operation and a respective option that corresponds to the respective option icon. While displaying the chart and the first line and/or first band, the method detects one or more inputs that select a plurality of the displayed visual marks in the chart. In response to detecting the one or more inputs, the method displays a second line and/or second band based on data in the set of data that corresponds to the selected plurality of the displayed visual marks and maintains display of the chart and the first line and/or first band in the chart.
Implementations may provide drag and drop analytics, adaptive analytics, or both. The descriptions above for implementing these features individually apply as well when these features are combined. Furthermore, implementations may provide additional features, some of which are illustrated in the figures, including
In accordance with some implementations, a method executes at an electronic device with a display. The method concurrently displays a chart and a visual analytic object. In some implementations, the chart is a bar chart, a line chart, or a scatter plot. The chart displays visual marks representing a set of data, displayed in accordance with contents of a plurality of displayed shelf regions. For example, some implementations include a columns shelf region 120 and a rows shelf region 122 as illustrated in
The method displays the visual analytic object superimposed on the chart. For example, as illustrated in
The method detects a first portion of an input on top of the visual analytic object (e.g., clicking, performing a mouse down, touching the display, or tapping the display). In response, the method displays a moveable icon corresponding to the visual analytic object while maintaining display of the visual analytic object. For example, in
The method detects a second portion of the input on the moveable icon (e.g., a “dragging” input) and in response, moves the moveable icon over a first shelf region of the plurality of shelf regions such that the moveable icon is over the first shelf region immediately prior to ceasing to detect the input. For example, in
When the input ceases to be detected, the method updates the content of the first shelf region based on the first analytic operation corresponding to the visual analytic object. For example, after dragging the moveable icon 1350 to the filters shelf region 1348 (as shown in
The method then updates the chart in accordance with updated content of the first shelf region. For example, in
In some implementations, the input is a drag and drop operation.
In some implementations, an image is displayed on the moveable icon that identifies the type of the visual analytic object. For example, in
In some implementations, the visual analytic object is an average line, a trend line, a median line, a constant reference line, a distribution band, or a quartile band. Although many of the examples provided herein use average line, the same techniques apply to other types of lines (which may be straight lines or curved lines, such as an exponential curve), as well as some analytic bands (such as quartile bands or confidence bands). For example, when an analytic band is dropped on the filters shelf region, some implementations create a filter based on which marks are inside or outside of the band.
In some instances, updating the content of the first shelf region based on the first analytic operation modifies a formula for a data element in the first shelf region. This is illustrated in
In some implementations, updating the content of the first shelf region using the first analytic operation comprises placing in the first shelf region a data element whose formula is based on the first analytic operation. This is illustrated in
In some implementations, the first shelf region is a color encoding shelf, and updating the chart in accordance with updated content of the first shelf region includes displaying a first subset of the visual marks in a first color based on positioning of the first set of visual marks in the chart relative to the visual analytic object, and displaying the remaining visual marks in a second color distinct from the first color. This is illustrated in
In some implementations, the first shelf region is a label encoding shelf, and updating the chart in accordance with updated content of the first shelf region includes displaying labels for a first subset of the visual marks based on positioning of the first set of visual marks in the chart relative to the visual analytic object (e.g., similar to the labels 1400 shown in
In some implementations, the first shelf region is a filter shelf, and updating the chart in accordance with updated content of the first shelf region comprises displaying a first subset of the visual marks based on positioning of the first set of visual marks in the chart relative to the visual analytic object, and filtering out the remaining visual marks from the chart. This is illustrated in
In some implementations where the first shelf region is a filter shelf, the method displays a quick filter box that enables a user to select displaying display all of the visual marks, displaying only the first subset of visual marks, or displaying only visual marks not in the first subset. This is illustrated by the quick filter box 1354 in
In accordance with some implementations, a method executes at an electronic device with a display. The method displays a chart that includes visual marks representing a set of data, displayed in accordance with contents of a plurality of displayed shelf regions. Each shelf region determines a respective characteristic of the chart. The method detects selection of a plurality of visual marks, as illustrated by the selection 1382 in
The method detects a first portion of an input on one of the selected marks, and in response displays a moveable icon corresponding to the selected visual marks while maintaining display of the visual marks. This is illustrated by the moveable icon 1384 in
The method detects a second portion of the input on the moveable icon; and in response, moves the moveable icon over a first shelf region of the plurality of shelf regions such that the moveable icon is over the first shelf region immediately prior to ceasing to detect the input. This is illustrated by the moveable icon 1384 in
When the method ceases to detect the input, the method updates the content of the first shelf region based on the selected visual marks. This is analogous to the filter designation pill 1352 in
In some implementations, the input comprises a drag and drop operation.
In some implementations, an image is displayed on the moveable icon that identifies the selected visual marks, as illustrated by the moveable icon 1384 in
In some implementations, updating the content of the first shelf region based on the selected visual marks includes placing in the first shelf region a group data element whose elements are the selected visual marks. This is illustrated by the group data element (pill) 1412 in
In some implementations, the first shelf region is a color encoding shelf, and wherein updating the chart in accordance with updated content of the first shelf region comprises displaying the selected visual marks in a first color, and displaying the remaining visual marks in a second color distinct from the first color. This is illustrated in
In some implementations, the first shelf region is a label encoding shelf, and wherein updating the chart in accordance with updated content of the first shelf region comprises displaying labels for the selected visual marks and not displaying labels for visual marks not selected. This is illustrated in
In some implementations, the first shelf region is a filter shelf, and updating the chart in accordance with updated content of the first shelf region includes displaying only the selected visual marks and filtering out the remaining visual marks from the chart. This is analogous to the filtering example illustrated in
Thus methods, systems, and graphical user interfaces are disclosed that provide data visualization analytic functions, enabling a user to apply analytic functions quickly with a drag and drop interface, and to quickly compare analytic functions for a subset of data against analytic functions for the entire data set. When analytic objects are created, they can be dragged to other locations to create or modify other elements. Similarly, displayed visual marks can be selected and dragged to other locations to create or modify the display.
For a better understanding of the aforementioned systems, methods, and graphical user interfaces, as well as additional systems, methods, and graphical user interfaces that provide data visualization analytics, reference should be made to the Description of Implementations below, in conjunction with the following drawings in which like reference numerals refer to corresponding parts throughout the figures.
Reference will now be made to implementations, examples of which are illustrated in the accompanying drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be apparent to one of ordinary skill in the art that the present invention may be practiced without requiring these specific details.
DESCRIPTION OF IMPLEMENTATIONS
The graphical user interface 100 also includes a data visualization region 112. The data visualization region 112 includes a plurality of shelf regions, such as a columns shelf region 120 and a rows shelf region 122. These are also referred to as the column shelf 120 and the row shelf 122. As illustrated here, the data visualization region 112 also has a large space for displaying a visual graphic. Because no data elements have been selected yet, the space initially has no visual graphic. In some implementations, the data visualization region 112 has multiple layers that are referred to as sheets.
In some implementations, the memory 214 includes high-speed random access memory, such as DRAM, SRAM, DDR RAM or other random access solid state memory devices. In some implementations, the memory 214 includes non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid state storage devices. In some implementations, the memory 214 includes one or more storage devices remotely located from the CPU(s) 202. The memory 214, or alternately the non-volatile memory device(s) within the memory 214, comprises a non-transitory computer readable storage medium. In some implementations, the memory 214, or the computer readable storage medium of the memory 214, stores the following programs, modules, and data structures, or a subset thereof:
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- an operating system 216, which includes procedures for handling various basic system services and for performing hardware dependent tasks;
- a communications module 218, which is used for connecting the computing device 200 to other computers and devices via the one or more communication network interfaces 204 (wired or wireless) and one or more communication networks, such as the Internet, other wide area networks, local area networks, metropolitan area networks, and so on;
- a web browser 220 (or other client application), which enables a user to communicate over a network with remote computers or devices;
- a data visualization application 222, which provides a graphical user interface 100 for a user to construct visual graphics. A user selects one or more data sources 240 (which may be stored on the computing device 200 or stored remotely), selects data fields from the data source(s), and uses the selected fields to define a visual graphic. In some implementations, the information the user provides is stored as a visual specification 228. The data visualization application 222 includes a data visualization generation module 226, which takes the user input (e.g., the visual specification 228), and generates a corresponding visual graphic (also referred to as a “data visualization” or a “data viz”). The data visualization application 222 then displays the generated visual graphic in the user interface 100. In some implementations, the data visualization application 222 executes as a standalone application (e.g., a desktop application). In some implementations, the data visualization application 222 executes within the web browser 220 or another application; and
- zero or more databases or data sources 240 (e.g., a first data source 240-1 and a second data source 240-2), which are used by the data visualization application 222. In some implementations, the data sources can be stored as spreadsheet files, CSV files, XML files, or flat files, or stored in a relational database.
Each of the above identified executable modules, applications, or set of procedures may be stored in one or more of the previously mentioned memory devices, and corresponds to a set of instructions for performing a function described above. The above identified modules or programs (i.e., sets of instructions) need not be implemented as separate software programs, procedures, or modules, and thus various subsets of these modules may be combined or otherwise re-arranged in various implementations. In some implementations, the memory 214 may store a subset of the modules and data structures identified above. Furthermore, the memory 214 may store additional modules or data structures not described above.
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In some implementations, there is a second group 1014 of analytic operations that perform statistical modeling. In some implementations, the “Model” group 1014 includes an analytic operation to show both an average line and a 95% confidence interval, an analytic operation to compute a trend line (a regression line), and an analytic operation to compute a forecast line. In some implementations, a forecast line is implemented by extending a trend line on a temporal axis.
Some implementations also provide a third group 1016 of custom analytic operations, which may be reference lines, reference bands, or distribution bands. When used, the user can specify various parameters of the custom reference analytics.
In some implementations, analytic operations that are not currently applicable are dimmed, grayed out, displayed in a different color and/or otherwise de-emphasized.
The analytic operators available on the Analytics tab are displayed as selectable icons or “pills.” The term “pill” is sometimes used because of the pill shape displayed when an analytic operator icon is selected or dragged in some implementations.
In
In some instances, an analytic operation can be applied to the data visualization based on two or more different data elements, such as creating a horizontal average line for one data element or a vertical average line for a different data element. This is sometimes referred to as a multi-axis or multi-measure scenario. In
In some implementations, the list region 1029 illustrated in
The list region 1029 illustrated here applies to other analytic operations as well when they can apply to more than one axis and/or more than one data element. Analytic operations are generally available only for numeric data elements (e.g., measures), so the analytic operations that can be applied depend on the data types of the data elements placed in the columns shelf 120 and the rows shelf 122.
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Some implementations allow a user to drag a trend line 1042 (or other analytic object), as illustrated in
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Once the reference line icon 1080 is dropped, the reference line 1086 is created, as illustrated in
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The user notices that there are spikes at the end of each year, and wonders about the trend for just those year-end points. In
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In some implementations, a user can edit data elements to create ad hoc calculations or formulas. In
The user wants to compute a residual value for each country, which is the difference between the emissions for the country and the average for the ranked countries. In this case, the user is interested in the residuals within each ranked group. The averages are displayed visually in the screen as the average lines, so visually the user wants to subtract the average line from the bars.
Once the expression in the pill 1336 is saved or applied, the data visualization is regenerated and redisplayed as illustrated in
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In some implementations, various visual encodings can be specified on the Marks shelf 1367. Visual encodings can define what colors are used for the marks, the size of the marks, labels for the marks, or what data is included in tooltips for the marks. Analytic objects, such as average lines, can be dragged to the marks shelf to create various useful encodings.
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As illustrated above, analytic objects that are displayed in a data visualization may be dragged to various locations in the interface, and used to build formulas, create or modify encodings, and so on. Like analytic objects, visual marks can be dragged to various locations in the user interface. Rather than viewing visual marks as a just an output of a data visualization process, implementations enable a user to use visual marks as part of an interactive process to modify or refine what is displayed.
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The marks shelf includes icons or sub-shelves for color 1394, labels 1396, and tooltips 1398. As illustrated in
If the pill 1384 for the selected countries is dropped on the Tooltip icon 1398 on the Marks shelf, some implementations include the data for these selected countries in the tooltips, which can be useful for comparing the emissions of each country. This is illustrated in
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Some implementations provide the same preview functionality for each of the analytic operations. Some of the analytic operations take more time to generate and display than other analytic operations, and thus some implementations provide previews for the ones where the preview can be generated and displayed quickly enough (e.g., when the preview can be generated and displayed in less than half a second).
The analytic features provided by disclosed implementations bring “experimentation” to all aspects of data analysis. Analytics capabilities are grouped together in an Analytics pane. This includes some pre-built or pre-configured combinations of analytic features that are analytically useful together (such as a single option that adds two reference lines AND a trend line). Disclosed implementations provide immediate feedback so that users can see what they are building as they build it. In addition, implementations provide incremental building, which allows users to easily experiment and iterate through different perspectives as they successively add new data elements or analytic objects.
Drag and drop for analytics includes several aspects. As illustrated above, a user can drag an icon for an analytic operation to a drop area to create a corresponding analytic object in the data visualization. Going the other way, a user can drag an existing analytic object (e.g., a reference line or band) back to the drop area to place it on a different drop target, thus creating a different type of analytic object. The user can also drag an analytic object out of the data visualization region to remove it from the display, or drag an analytic object to a shelf as illustrated in
In some implementations, analytic options that are not appropriate for the current visualization are dimmed or otherwise de-emphasized, and thus unavailable for selection. In some implementations, if creating an analytic object would create a substantial delay (e.g., due to complex calculations on a large data set), the user interface provides feedback about the potential delay before the analytic object begins creation. In some implementations, the user interface provides tooltips for individual analytic operations in the Analytics pane and/or tooltips for the groupings.
The disclosed implementations typically provide instant or immediate updates or feedback based on user selection. In practice, “instant” means within a short period of time and without additional user input. For example, “instant” updates may occur within a tenth of a second, a half of a second, or a second. As computer processors become more powerful, instant updates can occur for even more complex operations.
The terminology used in the description of the invention herein is for the purpose of describing particular implementations only and is not intended to be limiting of the invention. As used in the description of the invention and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and/or groups thereof.
The foregoing description, for purpose of explanation, has been described with reference to specific implementations. However, the illustrative discussions above are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The implementations were chosen and described in order to best explain the principles of the invention and its practical applications, to thereby enable others skilled in the art to best utilize the invention and various implementations with various modifications as are suited to the particular use contemplated.