Version 3 (categorical & numerical types) - Time Series Analysis
Analyze data in a time series. The time series explore pages in this website use this API method to get the results of the analysis.
URL
To call this method, use an HTTP GET request to the following URL:
where:
- [locale] = the locale of the language you want the data to be returned in (currently ka for Georgian or en for English)
Required Parameters
The following parameters must be included in the request:
Parameter | Description |
---|---|
access_token | All requests must include an access_token. You can obtain an access token easily, and for free, by going here. |
time_series_id | The ID of the time series. |
question_code | The code of a question in the time series. |
Optional Parameters
There following parameters are optional for this call.
Parameter | Description |
---|---|
language | Code of the language to return the time series information in (e.g., 'en' for English). If language is not provided, the default language of the time series will be used. |
filter_by_code | Code of question to filter the analysis by |
weighted_by_code | Code of question to weight the analysis by. If no value is given and the time series has a default weight, the weight will automatically be used. If you want an unweighted analysis, use the value 'unweighted'. |
can_exclude | Boolean flag indicating if answers that can be excluded should be excluded (default value is false) |
with_title | Boolean flag indicating if titles summarizing the data should be included (default value is false) |
with_chart_data | Boolean flag indicating if the results should include data formatted to be put into a Highcharts chart (default value is false) |
What You Get
The return object is a JSON object of the time series analysis with the following information:
Parameter | Description |
---|---|
time_series |
An object with the following values:
|
datasets |
An array of the datasets that are in the time series with the following values:
|
question |
An object with the following values:
|
filtered_by |
Only if filtered_by_code was provided. An object with the following values:
|
weighted_by |
Only if weighted_by_code we provided or the time series has a default weight and no weight was provided. An object with the following values:
|
analysis_type |
Indicates what type of analysis was performed:
|
results |
An object containing the results of the analysis with the following information:
If filtered_by_code was provided, the analysis results will have the following information:
|
chart |
Only if with_chart_data was true. An object with the following values:
|
Examples
Example 1
Here is an example of analyzing the results of Gender with the following url:
{ time_series: { id: "1111111111", title: "This is a time series!" }, datasets:[ { id: "11112009", title: "Dataset from 2009" label: "2009" }, { id: "11112011", title: "Dataset from 2011" label: "2011" }, { id: "11112013", title: "Dataset from 2013" label: "2013" } ], question: { code: "gender", original_code: "GENDER", text: "What is your gender?", is_mappable: false, answers:[ { value: "1", text: "Male", can_exclude: false, sort_order: 1 }, { value: "2", text: "Female", can_exclude: false, sort_order: 2 }, { value: "3", text: "Refuse to Answer", can_exclude: true, sort_order: 3 } ] }, analysis_type: "time_series", results: { total_responses:[ { dataset_label: '2009', dataset_title: 'Dataset from 2009', count: 150 }, { dataset_label: '2011', dataset_title: 'Dataset from 2011', count: 160 }, { dataset_label: '2013', dataset_title: 'Dataset from 2013', count: 200 } ], analysis: [ { answer_value: '1', answer_text: 'Male', dataset_results:[ { dataset_label: '2009', dataset_title: 'Dataset from 2009', count: 80, percent: 53.33 }, { dataset_label: '2011', dataset_title: 'Dataset from 2011', count: 85, percent: 53.13 }, { dataset_label: '2013', dataset_title: 'Dataset from 2013', count: 100, percent: 50 } ] }, { answer_value: '2', answer_text: 'Female', dataset_results:[ { dataset_label: '2009', dataset_title: 'Dataset from 2009', count: 68, percent: 45.33 }, { dataset_label: '2011', dataset_title: 'Dataset from 2011', count: 75, percent: 46.88 }, { dataset_label: '2013', dataset_title: 'Dataset from 2013', count: 95, percent: 47.5 } ] }, { answer_value: '3', answer_text: 'Refuse to Answer', dataset_results:[ { dataset_label: '2009', dataset_title: 'Dataset from 2009', count: 2, percent: 1.33 }, { dataset_label: '2011', dataset_title: 'Dataset from 2011', count: 0, percent: 0 }, { dataset_label: '2013', dataset_title: 'Dataset from 2013', count: 5, percent: 2.5 } ] } ] } }
Example 2
Here is an example of analyzing the results of Gender, filtered by Live:
{ time_series: { id: "1111111111", title: "This is a time series!" }, datasets:[ { id: "11112009", title: "Dataset from 2009" label: "2009" }, { id: "11112011", title: "Dataset from 2011" label: "2011" }, { id: "11112013", title: "Dataset from 2013" label: "2013" } ], question: { code: "gender", original_code: "GENDER", text: "What is your gender?", is_mappable: false, answers:[ { value: "1", text: "Male", can_exclude: false, sort_order: 1 }, { value: "2", text: "Female", can_exclude: false, sort_order: 2 }, { value: "3", text: "Refuse to Answer", can_exclude: true, sort_order: 3 } ] }, filtered_by: { code: "live", original_code: "LIVE", text: "Where do you live?", is_mappable: false, answers:[ { value: "1", text: "Tbilisi", can_exclude: false, sort_order: 1 }, { value: "2", text: "London", can_exclude: false, sort_order: 2 }, { value: "3", text: "New York City", can_exclude: false, sort_order: 3 } ] }, analysis_type: "time_series", results: { filter_analysis: [ { filter_answer_value: '1', fitler_answer_text: 'Tbilisi', filter_results: { total_responses:[ { dataset_label: '2009', dataset_title: 'Dataset from 2009', count: 15 }, { dataset_label: '2011', dataset_title: 'Dataset from 2011', count: 16 }, { dataset_label: '2013', dataset_title: 'Dataset from 2013', count: 20 } ], analysis: [ { answer_value: '1', answer_text: 'Male', dataset_results:[ { dataset_label: '2009', dataset_title: 'Dataset from 2009', count: 8, percent: 53.33 }, { dataset_label: '2011', dataset_title: 'Dataset from 2011', count: 9, percent: 56.25 }, { dataset_label: '2013', dataset_title: 'Dataset from 2013', count: 10, percent: 50 } ] }, { answer_value: '2', answer_text: 'Female', dataset_results:[ { dataset_label: '2009', dataset_title: 'Dataset from 2009', count: 7, percent: 46.67 }, { dataset_label: '2011', dataset_title: 'Dataset from 2011', count: 7, percent: 43.75 }, { dataset_label: '2013', dataset_title: 'Dataset from 2013', count: 10, percent: 50 } ] }, { answer_value: '3', answer_text: 'Refuse to Answer', dataset_results:[ { dataset_label: '2009', dataset_title: 'Dataset from 2009', count: 0, percent: 0 }, { dataset_label: '2011', dataset_title: 'Dataset from 2011', count: 0, percent: 0 }, { dataset_label: '2013', dataset_title: 'Dataset from 2013', count: 0, percent: 0 } ] } ] } }, { filter_answer_value: '2', fitler_answer_text: 'London', filter_results: { total_responses:[ { dataset_label: '2009', dataset_title: 'Dataset from 2009', count: 60 }, { dataset_label: '2011', dataset_title: 'Dataset from 2011', count: 80 }, { dataset_label: '2013', dataset_title: 'Dataset from 2013', count: 75 } ], analysis: [ { answer_value: '1', answer_text: 'Male', dataset_results:[ { dataset_label: '2009', dataset_title: 'Dataset from 2009', count: 35, percent: 58.33 }, { dataset_label: '2011', dataset_title: 'Dataset from 2011', count: 42, percent: 52.5 }, { dataset_label: '2013', dataset_title: 'Dataset from 2013', count: 40, percent: 53.33 } ] }, { answer_value: '2', answer_text: 'Female', dataset_results:[ { dataset_label: '2009', dataset_title: 'Dataset from 2009', count: 25, percent: 41.67 }, { dataset_label: '2011', dataset_title: 'Dataset from 2011', count: 38, percent: 47.5 }, { dataset_label: '2013', dataset_title: 'Dataset from 2013', count: 35, percent: 46.67 } ] }, { answer_value: '3', answer_text: 'Refuse to Answer', dataset_results:[ { dataset_label: '2009', dataset_title: 'Dataset from 2009', count: 0, percent: 0 }, { dataset_label: '2011', dataset_title: 'Dataset from 2011', count: 0, percent: 0 }, { dataset_label: '2013', dataset_title: 'Dataset from 2013', count: 0, percent: 0 } ] } ] } }, { filter_answer_value: '3', fitler_answer_text: 'New York City', filter_results: { total_responses:[ { dataset_label: '2009', dataset_title: 'Dataset from 2009', count: 75 }, { dataset_label: '2011', dataset_title: 'Dataset from 2011', count: 80 }, { dataset_label: '2013', dataset_title: 'Dataset from 2013', count: 100 } ], analysis: [ { answer_value: '1', answer_text: 'Male', dataset_results:[ { dataset_label: '2009', dataset_title: 'Dataset from 2009', count: 37, percent: 49.33 }, { dataset_label: '2011', dataset_title: 'Dataset from 2011', count: 40, percent: 50 }, { dataset_label: '2013', dataset_title: 'Dataset from 2013', count: 50, percent: 50 } ] }, { answer_value: '2', answer_text: 'Female', dataset_results:[ { dataset_label: '2009', dataset_title: 'Dataset from 2009', count: 36, percent: 48 }, { dataset_label: '2011', dataset_title: 'Dataset from 2011', count: 40, percent: 50 }, { dataset_label: '2013', dataset_title: 'Dataset from 2013', count: 45, percent: 45 } ] }, { answer_value: '3', answer_text: 'Refuse to Answer', dataset_results:[ { dataset_label: '2009', dataset_title: 'Dataset from 2009', count: 2, percent: 2.67 }, { dataset_label: '2011', dataset_title: 'Dataset from 2011', count: 0, percent: 0 }, { dataset_label: '2013', dataset_title: 'Dataset from 2013', count: 5, percent: 5 } ] } ] } } ] } }
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