Charts have two primary functions: displaying data and inviting further exploration of a topic. Data can be illustrated visually by charts when a simple table cannot adequately depict relationships between data points or patterns in the data set.
Add a chart to a dashboard
Click on the Add insight button down the dropdown on the top right of any dashboard, then select the type of chart you wish to create (this can be changed later).
Select a data source for your chart
Use the Select dataset tab from the navigation menu on the left side of the screen.
Keypup grants you access to various datasets based on the applications and projects & repositories you connected.
You can identify on the right part all the fields you can query on each dataset by clicking on the See all available fields button.
Select the dataset containing the fields you are willing to use in your chart.
Select the chart type
Click on the visualization that will best represent your metric(s). You can choose from the following options :
Area chart: to show trends over time. Area charts are best used to illustrate coverage, especially with two metrics overlapping each other (e.g. count of issues and count of bug issues)
Bar chart: This graph uses horizontal bars to show a comparison between categories. This type of graph is best used to display a metric associated with a fixed set of categories (e.g. count per project).
Column chart: This graph uses vertical bars to show a comparison between categories. As for the bar chart, this type of graph is best used to display a metric associated with a fixed set of categories (e.g. count per issue state).
Cycle-time chart: This graph shows a collection of cells that each represent a value, generally the duration of each stage of a defined process.
Gauge chart: This graph shows a single value on a speedometer-like scale. This type of chart is best used to contextualize a metric over a defined range, and optionally some thresholds.
Heatmap chart: This graph uses cells that contain a value and/or color to represent a distribution (e.g. a count of commits per project per week) over 2 axes.
Line chart: Also known as a line plot or a line graph is a visualization in which lines connect individual data points. In line charts, quantitative values are displayed over a specified period.
Pie chart: This visual shows a parts-to-whole relationship between categorical or nominal data. Pie slices represent percentages of the whole. When categorical data are analyzed, they are often divided into groups and the responses are arranged in a specific order.
Query your selected dataset for your chart
Once you have selected your visualization type and data source, you can begin building your insight by querying your dataset. From the navigation menu on the left, select the Configure table tab.
The table will include two columns by default: Dimension and Metric.
Dimensions are field attributes of your datasets. For example, it could be a date of merging, an author or a label, etc.
Metrics are numerical values aggregated from a function applied to all records (e.g.
COUNT) or to a quantitative field (e.g.
SUM) on all records.
Depending on how comfortable you are with query building, you can execute simple or complex queries. Select the field(s) you wish to query
From the drop-down menu at the top of the Dimension column, or choose
Choose an operator, aggregator, or Custom formula from the drop-down menu at a Metric level.
Simple query (selected formula):
Advanced query (custom formula):
Apply a filter (recommended)
You can apply AND / OR filters to narrow down the data used in your chart.
To do so click on the Configure filter button at the top right corner of the insight builder interface.
Configure drilldown for your chart
The drilldown feature allows you to explore and analyze data from a particular insight in detail. It is accessed by clicking on specific data points in your insights.
By default, the drilldown table is disabled and must be activated thanks to the slider on the right part, from the Configure drilldown tab.
When enabled for the first time, a default configuration is applied:
A set of recommended columns is added
The filters are pre-configured to recover the clicked data point.
You can then apply any customization. Click here to learn more about drilldown configuration.
Customize your chart "look & feel"
Cosmetic customization is automatically suggested based on your chart type, from the Customize tab. For readability purposes, you can further customize charts by tweaking the colors but also by updating the title, labels and legend.
Depending on the nature of the chart, you can also toggle values in the cell (for heatmaps) or add visual thresholds (for all area charts, line charts, bar charts and column charts) to illustrate where your value goes below or beyond a chosen value.
Document the chart
Insight can be complex to understand by others, which is why having proper documentation alongside it is important.
The documentation should explain the purpose of the insight (“Why do we track this metric in the team?”), provide some highlights about its configuration (e.g. “Showing data labeled as bug over the last 12 months”), and explain the best way to read it (e.g. “More than 15 bugs raised per week would be considered a warning for the team”). Doing so will guarantee that the insight remains useful and readable to others at all times.
To do so, you can set the following elements:
Title (mandatory): a short and self-explanatory title
One-liner description (recommended): a short description that is displayed when browsing insights in collections.
Full documentation (recommended): exhaustive documentation regarding the goals, configurations, or reading tips about the insight. The full documentation can be written and formatted using markdown.
The full documentation can also be disabled, in which case the (?) tooltip near the insight title on the dashboard will remain hidden. Even when disabled, the documentation can still be accessed from the insight configuration.