The following guide shows instructions for running multiple level breakdowns using the /reports endpoint. Running multiple level breakdowns with the Reports API is helpful for visualizing the cross-product of values from two different dimensions.
For example, if you create an interactive map to explore product sales by campaigns and regions, the Reports API can dynamically update the map as new data becomes available. This allows team members to select different map locations from which they can view the current campaigns and product sales for the region.
In the following example, we show a simpler multiple breakdown report with the following levels:
- Top level: Visualize the
Daydimension filtered by thePage ViewsandVisitsmetrics. - Second level: Visualize the previous level of data after a specifying a
Dateand applying thePagedimension. - Third level: Visualize the previous two levels of data after applying the
Citiesdimension to a specified page.
For each level of the breakdown report below, we show a sample JSON request and response. We also show how each step is presented in Analysis Workspace to provide a visual representation. For more information about how to use Analysis Workspace to build and validate report requests, see the Reporting Tricks Guide. For information on using the /reports endpoint, see the Reports User Guide.
The top level report in the following example shows the dimension Day and includes the metrics Page Views and Visits. The date range is set to the week of November 18, 2018.
The JSON message request body for this report request follows:
{
"rsid": "[REPORT SUITE ID HERE]",
"globalFilters": [
{
"type": "dateRange",
"dateRange": "2018-11-18T00:00:00.000/2018-11-25T00:00:00.000"
}
],
"metricContainer": {
"metrics": [
{
"columnId": "0",
"id": "metrics/pageviews"
},
{
"columnId": "1",
"id": "metrics/visits"
}
]
},
"dimension": "variables/daterangeday",
"settings": {
"countRepeatInstances": true,
"limit": 400,
"page": 0,
"dimensionSort": "asc"
}
}
Running the report request results in the following response:
{
"totalPages": 1,
"firstPage": true,
"lastPage": true,
"numberOfElements": 7,
"number": 0,
"totalElements": 7,
"columns": {
"dimension": {
"id": "variables/daterangeday",
"type": "time"
},
"columnIds": [
"0",
"1"
]
},
"rows": [
{
"itemId": "1181018",
"value": "Nov 18, 2018",
"data": [
63,
30
]
},
{
"itemId": "1181019",
"value": "Nov 19, 2018",
"data": [
73,
37
]
},
{
"itemId": "1181020",
"value": "Nov 20, 2018",
"data": [
96,
42
]
},
{
"itemId": "1181021",
"value": "Nov 21, 2018",
"data": [
135,
57
]
},
{
"itemId": "1181022",
"value": "Nov 22, 2018",
"data": [
87,
45
]
},
{
"itemId": "1181023",
"value": "Nov 23, 2018",
"data": [
77,
50
]
},
{
"itemId": "1181024",
"value": "Nov 24, 2018",
"data": [
112,
49
]
}
],
"summaryData": {
"totals": [
643,
310
]
}
}
For the second level, you can break down a specific date by the Page dimension. In order to do this, note the previous JSON response for the Nov 19, 2018 date:
"itemId": "1181019",
"value": "Nov 19, 2018",
"data": [
73,
37
]
The response shows itemId 1181019, so we will use that in our report request below.
The JSON message request body for this report request looks like this:
{
"rsid": "[REPORT SUITE ID HERE]",
"globalFilters": [
{
"type": "dateRange",
"dateRange": "2018-11-18T00:00:00.000/2018-11-25T00:00:00.000"
}
],
"metricContainer": {
"metrics": [
{
"columnId": "0",
"id": "metrics/pageviews",
"filters": [
"0"
]
},
{
"columnId": "1",
"id": "metrics/visits",
"filters": [
"1"
]
}
],
"metricFilters": [
{
"id": "0",
"type": "breakdown",
"dimension": "variables/daterangeday",
"itemId": "1181019"
},
{
"id": "1",
"type": "breakdown",
"dimension": "variables/daterangeday",
"itemId": "1181019"
}
]
},
"dimension": "variables/page",
"settings": {
"countRepeatInstances": true,
"limit": 5,
"page": 0
}
}
Note The metricFilters attribute in the request contains a metric filter that corresponds to a metric in the metrics attribute of the request. Each metric has a filters array that applies metric filters to the metric column. In this example, we are applying metric filter 0 to the Page Views metric column and metric filter 1 to the Visits metric column.
Running the report request results in a response like the following:
{
"totalPages": 1,
"firstPage": true,
"lastPage": true,
"numberOfElements": 5,
"number": 0,
"totalElements": 5,
"columns": {
"dimension": {
"id": "variables/page",
"type": "string"
},
"columnIds": [
"0",
"1"
]
},
"rows": [
{
"itemId": "364325780",
"value": "videoPage3",
"data": [
22,
22
]
},
{
"itemId": "2095855582",
"value": "videoPage2",
"data": [
19,
19
]
},
{
"itemId": "2770622699",
"value": "videoPage1",
"data": [
15,
15
]
},
{
"itemId": "2519319590",
"value": "videoPage4",
"data": [
15,
15
]
},
{
"itemId": "4240790753",
"value": "videoPage5",
"data": [
2,
2
]
}
],
"summaryData": {
"totals": [
73,
37
]
}
}
The third level of our example shows how to further break down the report by applying the Cities dimension to the page videoPage3. To do this, we use the itemId of the row containing VideoPage3 in the second level response, which is 364325780. Note the value in the request below:
The JSON message request body for this report request looks like this:
{
"rsid": "[REPORT SUITE ID HERE]",
"globalFilters": [
{
"type": "dateRange",
"dateRange": "2018-11-18T00:00:00.000/2018-11-25T00:00:00.000"
}
],
"metricContainer": {
"metrics": [
{
"columnId": "0",
"id": "metrics/pageviews",
"filters": [
"0",
"2"
]
},
{
"columnId": "1",
"id": "metrics/visits",
"filters": [
"1",
"3"
]
}
],
"metricFilters": [
{
"id": "0",
"type": "breakdown",
"dimension": "variables/daterangeday",
"itemId": "1181019"
},
{
"id": "1",
"type": "breakdown",
"dimension": "variables/daterangeday",
"itemId": "1181019"
},
{
"id": "2",
"type": "breakdown",
"dimension": "variables/page",
"itemId": "364325780"
},
{
"id": "3",
"type": "breakdown",
"dimension": "variables/page",
"itemId": "364325780"
}
]
},
"dimension": "variables/geocity",
"settings": {
"countRepeatInstances": true,
"limit": 5,
"page": 0
}
}
Note: We now have 4 metric filters in the metricFilters array of our request. Filters 0 and 2 are applied to the Page Views metric column and filters 1 and 3 are applied to the Visits metric column.
Running the report request results in a response like the following:
{
"totalPages": 1,
"firstPage": true,
"lastPage": true,
"numberOfElements": 1,
"number": 0,
"totalElements": 1,
"columns": {
"dimension": {
"id": "variables/geocity",
"type": "enum"
},
"columnIds": [
"0",
"1"
]
},
"rows": [
{
"itemId": "3113581109",
"value": "Lehi (Utah, United States)",
"data": [
22,
22
]
}
],
"summaryData": {
"totals": [
22,
22
]
}
}