Captain Dash

The Ultimate Data Visualizations with Your iPad Reports

Captain Dash grants you the key to perfect data visualizations in The Report. From modest line charts to complex hierarchical Sankey diagrams, our chart gallery exhibits a variety of ready-to-use chart templates.

Our gallery offers you a multitude of charts that we’re ready to tailor to your individual data visualization needs. All of them are interactive, and many are editable and explorable.

Some concepts

Captain Dash's data model was designed after the Online Analytical Processing Theory (OLAP). At the core of OLAP lies the concept of a multidimensional cube composed of metrics and dimensions. All of our visualizations take after this model and accept only specific combinations of metrics and dimensions.

This Cheat Sheet may help you out with definitions:

Metric: A metric is any type of measurement used to gauge some quantifiable component of a company’s performance. It also defines a way to aggregate the data among the following operations: SUM, AVERAGE, MIN, MAX, COUNT. Examples: Sales, Visits, Bounce Rate, Facebook Fans, etc.

Dimensions: Dimensions provide context for a fact. They’re used for selecting and aggregating data at a desired level. Examples: Country, Time, Languages, etc. Dimensions can be hierarchical: Continent > Country > State > City.

Dimension Values: Values of dimensions are all the possible values that a dimension might take. For instance, the list of dimension values for the dimension Country is: “USA”, “France”, “UK”, “Germany”, “Spain”, “China”, “Tunisia”, etc.

Filters: A filter is used to restrict the data returned to a specific subset of data. It is a combination of one dimension and one dimension value. For example the filter Country==“France” will discard all information that did not occur in France.

Time Range: A time range is a specific filter operating over a time dimension. It is composed of a start date and an end date. All the data that does not fall within the time range will be discarded.

In order to request data from different data sources uniformly, Captain Dash is adding a normalization layer. It implies normalizing all the concepts above mentioned. We call this the unified API, because it provides a unified interface to all kinds of data sources. This means that the input data of each visualization has to fit this normalization and respect the following JSON format:

              
var data = {
  "headers":[
    {"id":"geo:country","type":"DIMENSION","display":"Countries","hierarchical":true},
    {"id":"ga:visits","type":"METRIC","display":"Visits","format_string":"%2d"},
    {"id":"ga:avgTimeOnSite","type":"METRIC","display":"Time spent","format_string":"time_seconds"},
    {"id":"ga:visitBounceRate","invert_growth":true,"type":"METRIC","display":"Bounce rate","format_string":"%.2f%"}
  ],
  "rows":[
    ["Africa,Eastern Africa,Burundi",20.0,64.0,65.0],
    ["Europe,Southern Europe,Portugal",2601.0,37.39061,82.27],
    ["Oceania,Australasia,Australia",264.0,63.72,77.65]
  ]
};
              
            

Headers describe values inside each row, which are in the same order. type indicates if the value is a dimension value or a metric. display defines how that dimension or metric should be named on screen to be human-readable. format_string defines how the metric's values should be encoded, following the sprintf(); function formats: the three values of Burundi, 20.0, 64.0, 65.0, within their respective format_string, %2d, time_seconds, %.2f%, will be displayed on screen as 20, 01:04 and 65.00%.
hierarchical and invert_growth can be specified to respectively define if the dimension is hierarchical or not and if the metric should be higher or lower for best results.

If you want to try our visualizations with your own data, you must respect those formats.

Bars

1 dimension + 1 metric
Example: Unique visitors over time

Bars cumulated

1 dimension + 1 metric
Example: Community growth

Bars with objective

1 dimension + 2 metric
Example: Sales over time

Bars Year-over-Year

1 dimension (time:monthly) + 1 metric
Example: Sales over time

Grouped/Stacked Bars

2 dimensions + 1 metric
Example: Visits by type of referral over time

Bar & Lines

1 dimension + 2 metrics
Example: Paid visits & Bounce rate over time

Lines

1 to 2 dimensions + 1 metric
1 dimension + 2 metrics (specific case)
Example: Likes & dislikes over time

Line Year-over-Year

1 dimension (time:monthly) + 1 metric
Example: growth over time

Calendar

1 dimension (time:daily) + 1 metric
Example: Visits by day

Punchcard

2 dimensions (day of week + hour of day) + 1 metric
Example: Temporal distribution of visits

Leaderboard

1 dimension + 1 metric
Example: Results per point of sale

Two dimensions Leaderboards

2 dimension + 1 metric
Example: Results per town and point of sale

Battle

1 dimension + 2 to 6 metrics
Example: Audience & engagement by country

Bubbles

1 dimension + 2 to 3 metrics
Example: Quantity & Quality of visits by country

Flow

1 hierarchical dimension + 1 metric
Example: Behavior flow

Pyramid

2 dimensions + 1 metric
Example: Customers by age and gender

Radar

1 dimension + 3 to 6 metrics
Example: Community by social network

Sankey

n dimensions + 1 metric
Example: Visitor journey

Scorecard

1 dimension + 3 to 6 metrics
2 dimensions + 1 metric
Example: Performance by videos

Sunburst

1 dimension (hierachical or not) + 1 metric
Example: Vistors localisation

Treemap

1 dimension (hierachical or not) + 1 metric
Example: Visitors localisation

Tags

1 dimension (keywords) + 1 metric
Example: Number of searches per keyword

Versus

1 dimension (with only two values) + 1 metric
Example: Are your visits from desktop or mobile ?

Parallel Coordinates

1 dimension + n metrics
Example: Quality of visits per browser

Choropleth Map

1 dimension (geo:region) + 1 metric
Example: Visits by state

Heatmap

2 dimensions (latitude & longitude) + 1 metric
Example: Visits by location