A viewer is a visual component associated with a table. Unlike other products, our viewers are superfast, completely interactive, and are capable of handling tens of millions of rows (or millions of columns).
Viewers belonging to the same view share the same row selection and filter. Viewers are saved as part of the project. Also, it is possible to save viewers and views individually and reuse them (or share with teammates) later on.
- Current rows
- Viewers as filters
- Common actions
- Group tooltips
- Statistical hypothesis testing
Once a table is open, click on the icons shown on the left pane to open the corresponding viewer.
Viewers are docked within a view. To rearrange it, start dragging viewer's header. Drop zone indicators will appear; move the mouse cursor to one of them and release the mouse button to dock the viewer at that spot. To resize the viewer, drag the viewer's border.
All viewers share the same row selection and filtered state, which can be manipulated in a consistent way across all viewers:
|ESC||Deselect all rows and reset filter|
|Ctrl+A||Select all rows|
|Ctrl+Shift+A||Deselect all rows|
|Ctrl+Click||Toggle selected state|
|Shift+Click||Select point or group|
|Ctrl+Shift+Click||Deselect point or group|
To select rows in the grid:
|Shift+Mouse Drag||Select rows|
|Ctrl+Shift+Mouse Drag||Deselect rows|
|Mouse Drag row headers||Select rows|
|Shift+drag column headers||Select columns|
|Ctrl+click column headers||Select columns|
|Ctrl+Shift+click column headers||Deselect columns|
|(Ctrl+) Shift + ↑↓||(Un)select rows|
|(Ctrl+) Shift + ←→||(Un)select columns|
|(Ctrl+) Shift + mouse-drag||(Un)select rows|
|(Ctrl+) Shift + ENTER||(Un)Select rows with the current value|
Rows in a grid can not only be selected or filtered, in addition to that, the grid keeps track of a current row and highlights it in green. This indication is a neat and lightweight way to update information related to the current value and lets users explore and compare rows with ease.
To make a row current, simply click on it, or navigate up and down the grid using the cursor up and down keys. Info panels in the context panel get synchronized with the current cell.
It is also integrated into Datagrok's visualizations and cheminformatics functionality, e.g., similarity search, so as you move from one row to another you immediately see where the row values belong on the chart or which molecules have the most similar structure to the reference. This also works the other way around: by first clicking on a visual element, you will see the row it represents in the grid.
To open filter group, click on the funnel icon in the toolbox:
Alternatively, click on the column's "hamburger icon" to filter by the individual column:
Viewers as filters
By default, clicking on a segment that represents multiple rows will select these rows. However, some viewers, such as Bar Chart and Pie Chart, could be also used for filtering of the underlying table. Such viewers are a popular choice for interactive dashboards.
To control that behavior, click on the viewer's hamburger icon, open "On click" and choose the desired mode. Internally, this sets two different properties of a viewer:
row source- specifies which rows should be visualized on the viewer (all | filtered | selected)
on click- specifies what happens when user click on a group of rows (select | filter).
By setting these properties manually, it is possible to achieve different combination of interactivity (for instance, a viewer that shows only selected rows)
Each viewer created in Datagrok can be embedded into an external site as an iframe. It remains fully interactive and will be bound to the data for which it was created inside the platform. To generate an iframe for a viewer, open its context menu, then go to the Viewer submenu and select Embed:
Now you can copy the generated iframe and use it in your site. The only thing you need to remember is that this feature works only for data uploaded as a project to the server.
All visualizations are tightly coupled. Hover, selection, filtering on one viewer is displayed on the rest:
Each viewer has a set of properties associated with it that define either the appearance
(such as "Back Color" or "Font"), or data (such as "Value" or "Split"). The most important data properties (usually
columns to visualize) are exposed as combo boxes on top of the viewer. To edit the rest of the properties, either click
on the "gear" icon on top of the viewer, or press F4 when the viewer has focus, or right-click and
Viewer | Properties.
Many viewers support the following:
All of the common actions are available from the context menu. To bring it up, either right-click, or click on the " hamburger" menu in the top left corner. The icons in the viewer header are only visible when the mouse is hovering over the viewer.
The following commands are the most common:
|Properties||Show viewer properties in the context panel|
|Reset View||Reset zoom level. Applies for: Scatter plot, Line chart, Bar chart, 3D scatter plot, and Box plot|
General commands are available under the General submenu:
|Clone||Create a copy of the viewer|
|Full Screen||Show in full screen. Alt+F|
|Close||Close the viewer|
|Use in Trellis||Add a Trellis plot, using this viewer as a renderer|
|Save to Gallery||Saves this viewer to a gallery|
|Embed||Create HTML code that can be embedded in an external site|
Style-related commands reside under the Style submenu:
|Pick up||Remember the style of the current viewer|
|Apply||Apply previously remembered style. This option, as well as the "Apply data settings" and "Apply Style Settings", is only enabled when the settings of the corresponding viewer type were picked up previously.|
|Apply Data Settings||Apply only "Data" section of the settings. Can be used for viewers belonging to different views as long as the data source remains the same|
|Apply Style Settings||Apply all settings, except for the "Data" section. You can use this option for viewers that have different data source|
|Set as Default||Use style settings for new viewers of that type. The viewer's properties will be changed automatically to match the ones from the remembered style|
|Reset Default||Clear default settings|
The commands from the To Script submenu produce code that can be used to build a similar visualization using R or Python:
|to R||Open the visualization preview and get the code snippet in R|
|to Python||Open the visualization preview and get the code snippet in Python|
Tooltip-related settings reside under the Tooltip submenu:
|Hide||Hide the tooltip|
|Use as Group Tooltip||Use this viewer in tooltips that correspond to groups of rows|
|Remove Group Tooltip||Stop using this viewer as a group tooltip|
|Set Default Tooltip...||Set row tooltip settings for all viewers associated with the data frame|
|Set ||Set a tooltip template for this specific viewer|
See also: setting tooltips programmatically
One of the unique features of the Datagrok platform is the ability to quickly visualize multiple rows in a tooltip, using the settings of another viewer.
Once the "Use as Group Tooltip" command is executed, the original viewer is no longer required, and it is safe to close it if you choose so.
The following picture illustrates the concept:
Trellis plots are useful for finding the structure and patterns in complex data. A Trellis plot is a layout of smaller charts in a grid with consistent scales. Each smaller chart represents rows that belong to a corresponding category. The grid layout looks similar to a garden trellis, hence the name Trellis Chart.
There are two ways to add a trellis plot:
- click on the "Trellis Plot" icon in the toolbox, and then customize the inner chart by clicking on the "gear" icon on the left
- create a viewer that you want to eventually become an inner chart, customize it the way you like, and then click
Viewer | Use in Trellis
See Trellis Plot for more details.
Statistical hypothesis testing
To help users analyze their data in depth, our visualizations include a number of statistical features:
- Box plots show p-value, which allows to determine whether the findings are statistically significant
- Scatter plots can display a regression line along with its equation; moreover, it is possible to plot multiple regression lines by encoding categories with color
- The values of Pearson's correlation coefficient computed for correlation plots are highlighted, which makes it easy to trace the strength of relationship between given variables
- Statistics viewer gives a concise summary of commonly used measures for selected columns
- The platform's viewers offer two commands,
To Script | To Pythonand
To Script | To R, that can be used to reproduce charts with Python or R code respectively
View Layout contains relative positions of viewers in a table view, along with the viewers' properties. By separating layouts from the actual data displayed, it's possible to save current layout (View | Layout | Save to Gallery) and later apply it to a different dataset (View | Layout | Open Gallery).
Saved layouts that are applicable to the current table are shown in the " Layouts" pane, see picture below.
To clone current view, either do View | Layout | Clone, or click on the plus sign on the view header strip, and choose Clone.
|A scatter plot (also called a scatter graph, scatter chart, scattergram, or scatter diagram) is a type of plot or mathematical diagram using Cartesian coordinates to display values for typically two variables for a set of data. If the points are color-coded you can increase the number of displayed variables to three. The data is displayed as a collection of points, each having the value of one variable determining the position on the horizontal axis and the value of the other variable determining the position on the vertical axis. |
3D scatter plot
|Use 3D scatter plot to plot data points on three axes to show the relationship between three variables. Each row in the data table is represented by a marker whose position depends on its values in the columns set on the X, Y, and Z axes. Additionally, you can color-code and size-code points, as well as display labels next to markers.|
3D Scatter Plot
|A histogram is a graphical representation of the distribution of numerical data. |
|A line chart or line graph is a type of chart which displays information as a series of data points called 'markers' connected by straight line segments. It is a basic type of chart common in many fields. It is similar to a scatter plot except that the measurement points are ordered (typically by their x-axis value) and joined with straight line segments.|
|A bar chart presents grouped data with rectangular bars with lengths proportional to the values that they represent. The bars can be plotted vertically or horizontally.|
|The box plot (a.k.a. box and whisker diagram) is a standardized way of displaying the distribution of data based on the five number summary: minimum, first quartile, median, third quartile, and maximum.|
|A set of controls for quick filtering, selection, and visual assessment of column values.|
|A Trellis Chart is a layout of smaller charts in a grid with consistent scales. Each smaller chart represents rows that belong to a corresponding category. Trellis Charts are useful for finding the structure and patterns in complex data. The grid layout looks similar to a garden trellis, hence the name Trellis Chart.|
|Tree maps display hierarchical (tree-structured) data as a set of nested rectangles. Each branch of the tree is given a rectangle, which is then tiled with smaller rectangles representing sub-branches. A leaf node's rectangle has an area proportional to a specified dimension of the data.|
|Form allows you to customize the appearance of the row by manually positioning the fields, and adding other visual elements, such as pictures or panels. A form can be used either as a stand-alone viewer, or as a row template of the Tile Viewer.|
|Calendar lets you analyze longitudinal data. It needs at least one column of type DateTime.|
|Google Map Viewer overlays latitude/longitude data from the corresponding table on top of the Google Map.|
| ||Shows a map that is applicable for the specified dataset. Typically, it would represent a geographical area (countries, states, counties, etc), but also it can show an arbitrary shapes (such as a store floor plan, brain regions, or EEG electrodes).|
|A default view for the interactive exploration of tables that might contain multiple different viewers all sharing the same row filter and row selection.|
|Use Matrix Plot to assess the relationship among many pairs of columns at the same time.|
|Network diagram is used to visualize graphs, where values of the specified two columns become nodes, and rows become edges. It is possible to color-code and size-code nodes and columns by choosing the aggregate function that would apply to the values that represent an edge or a Node.js.|
Parallel coordinates plot
|Parallel coordinates is a common way of visualizing high-dimensional geometry and analyzing multivariate data.|
Parallel Coordinates Plot
|Pie chart is useful for reflecting numerical proportions.|
|A word cloud is a graphical representation of word frequency. Any other aggregation function can be used as well for representing size or color of the particular word.|
|A quick way to assess correlations between all columns at once. Cells are color-coded by the Pearsson correlation coefficient. Histograms along the diagonal show the corresponding distribution. Hover over the cell to see the corresponding scatter plot. The grid is sortable. Select columns in the view by selecting corresponding rows.|
|Unlike scatter plot that visualizes each individual data point, density plot splits 2D area by bins, and color-codes it depending on the number of points that fall within this bin. The darker the color, the more points it contains.|
|A Heat Map is a graphical representation of table where each cell value is represented as color. It is based on grid, so all of the grid's features are applicable to the heat map as well.|
|Use Markup Viewer to host any text, arbitrary HTML content, or markdown-formatted text. In most cases, the viewer will auto-detect content type. Use the "Content Type" property to explicitly specify it.|
|Visualizes rows as a collection of forms that are positioned as tiles.|
Provides specified descriptive Statistics for the chosen columns.
Visualizes magnitude and color for data on 3D globe using: latitude, longitude. Details: Globe