Visualizing your data in Data Refinery

Visualizing information in graphical ways can give you insights into your data. By enabling you to look at and explore data from different perspectives, visualizations can help you identify patterns, connections, and relationships within that data as well as understand large amounts of information very quickly.

You can also visualize your data with these same charts in an SPSS Modeler flow. Right-click a node and select Profile.

Chart examples

To visualize your data:

  1. From Data Refinery, click the Visualizations tab.
  2. Start with a chart or select columns:
    • Click any of the available charts. Then add columns in the DETAILS panel that opens on the left side of the page.
    • Select the columns that you want to work with. Suggested charts will be indicated with a dot next to the chart name. Click a chart to visualize your data.

    Important: Available chart types are ordered from most relevant to least relevant, based on the selected columns. If there are no columns in the data set with a data type that is supported for a chart type, that chart will not be available. If a column’s data type is not supported for a chart, that column is not available for selection for that chart. Dots next to the charts’ names suggest the best charts for your data.


Data Refinery provides support for the following charts:

  • 3D charts display data in a 3-D coordinate system by drawing each column as a cuboid to create a 3D effect.

  • Bar charts are handy for displaying and comparing categories of data side-by-side. The bars can be in any order. You can also arrange them from high to low or from low to high.

  • Box plot charts compare distributions between many groups or data sets. They display the variation in groups of data: the spread and skew of that data as well as outliers.

  • Candlestick charts are a type of financial chart that displays price movements of a security, derivative, or currency.

  • Customized charts give you the ability to render charts based on JSON input.

  • Dual Y-axes charts use two Y-axis variables to show relationships between data.

  • Error bars indicate the error or uncertainty in a value, and they give a general idea of how precise a value is or conversely, how far a value might be from the true value.

  • Heat map charts display data as color to convey activity levels or density. Typically low values are displayed as cooler colors and high values are displayed as warmer colors.

  • Histogram charts show the frequency distribution of data.

  • Line charts show trends in data over time by calculating a summary statistic for one column for each value of another column and then drawing a line connecting the values.

  • Map charts show geographic point data, enabling you to compare values and show categories across geographical regions.

  • Multi-charts display up to four combinations of Bar, Line, Pie, and Scatter plot charts.

  • Multi-series charts display data from multiple data sets or multiple columns as a series of points connected by straight lines or bars.

  • Parallel coordinate charts display and compare rows of data (called profiles) to find similarities. Each row is a line and the value in each column of the row is represented by a point on that line.

  • Pie charts show proportion. Each value in a series is displayed as a proportional slice of the pie, with the pie representing the total sum of the values.

  • Population pyramid charts show the frequency distribution of a variable across categories. They are typically used to show changes in demographic data.

  • Quantile-quantile (Q-Q) plot charts compare the expected distribution values with the observed values by plotting their quantiles.

  • Radar charts integrate three or more quantitative variables represented on axes (radii) into a single radial figure. Data is plotted on each axis and joined to adjacent axes by connecting lines. Radar charts are useful to show correlations and compare categorized data.

  • Relationship charts show how columns of data relate to one another and what the strength of that relationship is by using varying types of lines.

  • Scatter plot charts show correlation (how much one variable is affected by another) by displaying and comparing the values in two columns.

  • Scatterplot matrix charts are scatter plot charts organized into a matrix so that it is easy to look at all pairwise correlations together.

  • t-SNE charts help you visualize high-dimensional data sets. They’re useful for embedding high-dimensional data into a space of two or three dimensions, which can then be visualized in a scatter plot.

  • Treemap charts display hierarchical data as a set of nested areas. Use to compare sizes between groups and single elements nested in the groups.

  • Word cloud charts display how frequently words appear in text by making the size of each word proportional to its frequency.


You can take any of the following actions:

  • Start over: Clears the visualization and the Details pane, and returns you to the starting page for visualizations

  • Download visualization:

    • Download chart image: Download a PNG file that contains an image of the current chart.

    • Download chart details: Download a JSON file that contains the details for the current chart.

  • Set global preferences that apply to all charts

Chart actions

Available chart actions depend on the chart. Chart actions include:

  • Zoom

  • Restore: View the chart at normal scale

  • Select data: Highlight data in the Data tab that you’ve selected in the chart

  • Clear selection: Remove highlighting from the data in the Data tab

Learn more

Data Visualization – How to Pick the Right Chart Type?