Charts Overview
Last updated
Last updated
ADVIZOR Charts are a collection of both familiar and novel ways of visualizing data that allow you to better understand your data and what it is trying to tell you.
Charts that can be defined by a single field:
Bar Chart: weighted categories as bars.
Bar Charts show the categories in a field and how data is distributed across these categories. Use this to understand which categories in a field are most significant.
Pie Chart: weighted categories as pie slices.
Each category in a field is represented by a slice of the pie. It is harder to make precise magnitude comparisons between pie slices than between bars in a Bar Chart. However, selected subsets of pie slices can be compared directly as percents of the whole (similar to "spine plot" mode in Bar Charts).
Line Chart: connected weighted categories.
A Line Chart shows a sequence of categories, connected by a line. The line emphasizes the progression of data, so Line Charts are appropriate for data where adjacent items have a relationship (e.g., time, location, etc.).
Histogram: distribution of a continuous field.
A Histogram shows the relative number of occurrences of values in a numeric (continuous) field. This shows the distribution of the field, with smoothing to emphasize the overall shape of the curve.
Charts that are defined by two fields:
Scatter Plot: interaction of 2 (usually numeric/continuous) fields.
A Scatter Plot shows the occurrences of combinations of values in 2 continuous fields. Look for patterns of relationship between the fields.
Multiscape: interaction of 2 categorical fields.
Plot the interaction of combinations of categories from 2 fields. This can be viewed as a 3D Bar Chart, weighted by sum or ratio of numeric fields.
Time Table: events over time.
A specialized Scatterplot for showing events over time. Time or a continuous numeric field is shown on the X axis, with values from a categorical field arranged on the Y, optionally grouped by categories in a 3rd field. Values from additional dimensions can be mapped to attributes of data points like size, angle, and extent.
Heat Map: plane-filling aggregation of two hierarchically-related fields.
Values from a detail field are shown nested within higher-level categories, with blocks sized by sum or average of a numeric measure, to form a plane-filling display. Blocks can be colored by a calculated measure, mapped to a custom color scale.
Multiple field displays:
Data Sheet: textual grid of data fields.
Displays data field values as text in a grid, sorted by any combination of fields. The raw, row-level data may be searched textually on any field. The grid may be zoomed out to a graphical array of bar charts to allow patterns between fields to be studied.
Parabox: parallel lines and box plots.
The Parabox shows an array of field distributions. For continuous data, the field is summarized as a box plot, marked for median, quartile, and outlier values. For categorical data, the data is summarized as a list of bubbles, sized by the relative number of rows in each category. Lines may also be plotted over the display, connecting the field values for individual data rows.
Data Constellations: a graph of relationships.
A Data Constellations chart shows related objects represented as a graph of nodes and links between pairs of nodes. It provides 2D and 3D graph viewers, plus placement algorithms for layout of the graph. Data is taken from two tables, one listing nodes and the other listing links between them.
Maps: data over geography.
Map charts show sized data points positioned over a geographical groundplane. The map may be an image or scaleable polygon outlines. A map provides the node linkage capabilities of the Data Constellations chart as well.
Counts: data field summaries.
Summarize fields via common statistics: mean, standard deviation, mode, etc.
Summary Sheet: aggregated measures for field categories.
Textual grid that summarizes categories in a primary field and aggregates a number of measures: sum, average, ratio, count, etc.
Controls:
Text Filter: filter data to analyze.
Filter the data that is being analyzed by removing rows in selected categories in a field from the analysis set.
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