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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.


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:

ESCDeselect all rows and reset filter
Ctrl+ASelect all rows
Ctrl+Shift+ADeselect all rows
Ctrl+ClickToggle selected state
Shift+ClickSelect point or group
Ctrl+Shift+ClickDeselect point or group

To select rows in the grid:

Shift+Mouse DragSelect rows
Ctrl+Shift+Mouse DragDeselect rows
Mouse Drag row headersSelect rows
Shift+drag column headersSelect columns
Ctrl+click column headersSelect columns
Ctrl+Shift+click column headersDeselect 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


Current rows

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:

Viewers Embedding

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:

Viewers Interaction

For example, filtering on a histogram affects the scatter plot:

Viewers Interaction 2


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 select Viewer | Properties.



Common actions

Many viewers support the following:

Double-clickReset View
Mouse dragPan

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:

PropertiesShow viewer properties in the context panel
Reset ViewReset zoom level. Applies for: Scatter plot, Line chart, Bar chart, 3D scatter plot, and Box plot

General commands are available under the General submenu:

CloneCreate a copy of the viewer
Full ScreenShow in full screen. Alt+F
CloseClose the viewer
Use in TrellisAdd a Trellis plot, using this viewer as a renderer
Save to GallerySaves this viewer to a gallery
EmbedCreate HTML code that can be embedded in an external site

Style-related commands reside under the Style submenu:

Pick upRemember the style of the current viewer
ApplyApply 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 SettingsApply 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 SettingsApply all settings, except for the "Data" section. You can use this option for viewers that have different data source
Set as DefaultUse 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 DefaultClear default settings

Pick up

The commands from the To Script submenu produce code that can be used to build a similar visualization using R or Python:

to ROpen the visualization preview and get the code snippet in R
to PythonOpen the visualization preview and get the code snippet in Python

Row tooltips

Tooltip-related settings reside under the Tooltip submenu:

HideHide the tooltip
Use as Group TooltipUse this viewer in tooltips that correspond to groups of rows
Remove Group TooltipStop using this viewer as a group tooltip
Set Default Tooltip...Set row tooltip settings for all viewers associated with the data frame
Set <Viewer> Tooltip...Set a tooltip template for this specific viewer

See also: setting tooltips programmatically

Group tooltips

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:

Group Tooltip


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 on 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 Python and To Script | To R, that can be used to reproduce charts with Python or R code respectively

Statistical hypothesis testing


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.


Scatter plot

Scatter PlotA 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.
Scatter Plot

3D scatter plot

3D Scatter PlotUse 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


HistogramA histogram is a graphical representation of the distribution of numerical data.

Line chart

Line ChartA 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.
Line Chart

Bar chart

Bar ChartA 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.
Bar Chart

Box plot

Box PlotThe 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.
Box Plot


FilterA set of controls for quick filtering, selection, and visual assessment of column values.

Trellis plot

Trellis PlotA 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.
Trellis Plot

Tree map

Tree MapTree 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.
Tree Map


FormForm 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.


CalendarCalendar lets you analyze longitudinal data. It needs at least one column of type DateTime.

Google map

Google MapGoogle Map Viewer overlays latitude/longitude data from the corresponding table on top of the Google Map.
Google Map

Shape map

Shape Map
Shape 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).
Shape Map


GridA default view for the interactive exploration of tables that might contain multiple different viewers all sharing the same row filter and row selection.

Matrix plot

Matrix PlotUse Matrix Plot to assess the relationship among many pairs of columns at the same time.
Matrix Plot

Network diagram

Network DiagramNetwork 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.
Network Diagram

Parallel coordinates plot

PC PlotParallel coordinates is a common way of visualizing high-dimensional geometry and analyzing multivariate data.
Parallel Coordinates Plot

Pie chart

Pie ChartPie chart is useful for reflecting numerical proportions.
Pie Chart

Word cloud

Word CloudA 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.
Word Cloud

Correlation plot

Correlation PlotA 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.
Correlation Plot

Density plot

Density PlotUnlike 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.
Density Plot

Heat map

Heat MapA 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.
Heat Map

Markup viewer

MarkupUse 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.

Tile viewer

Tile viewerVisualizes rows as a collection of forms that are positioned as tiles.
Tile Viewer


Provides specified descriptive Statistics for the chosen columns.


Visualizes magnitude and color for data on 3D globe using: latitude, longitude. Details: Globe



See also: