Create page for stats

[xmnlab]

It would be great to have a page with the stats.

• It could be in the /stats endpoint, and we don't need to have it listed in the menu.
• It could be generated by a github action with schedule.
• It should have tables and charts about all the end point accesed in the last month (from day 1 to the last day of that month).
• it should have data about access grouped by country.
• The charts could be created in plotly. And the final result should be a html file

I will add an initial code (from gpt) for that in the next comment.

[xmnlab]

import pandas as pd
import plotly.express as px
from googleapiclient.discovery import build
from oauth2client.service_account import ServiceAccountCredentials
from typing import Any, Dict, List

def initialize_analyticsreporting() -> Any:
    """Initializes and returns the Google Analytics Reporting API V4 service object.

    Returns
    -------
    Any
        An authorized Analytics Reporting API V4 service object.
    """
    # Replace with your own credentials.json file
    credentials = ServiceAccountCredentials.from_json_keyfile_name(
        'YOUR_CREDENTIALS_FILE.json', 
        ['https://www.googleapis.com/auth/analytics.readonly']
    )
    analytics = build('analyticsreporting', 'v4', credentials=credentials)
    return analytics

def get_report(analytics: Any, start_date: str, end_date: str) -> Dict:
    """Queries the Analytics Reporting API V4 for specified metrics and dimensions.

    Parameters
    ----------
    analytics : Any
        An authorized Analytics Reporting API V4 service object.
    start_date : str
        Start date for fetching data (format: YYYY-MM-DD).
    end_date : str
        End date for fetching data (format: YYYY-MM-DD).

    Returns
    -------
    Dict
        The response from the API query.
    """
    return analytics.reports().batchGet(
        body={
            'reportRequests': [
                {
                    'viewId': 'YOUR_VIEW_ID',
                    'dateRanges': [{'startDate': start_date, 'endDate': end_date}],
                    'metrics': [{'expression': 'ga:sessions'}],  # Example metric
                    'dimensions': [{'name': 'ga:country'}, {'name': 'ga:pagePath'}]  # Example dimensions
                }
            ]
        }
    ).execute()

def convert_to_dataframe(response: Dict) -> pd.DataFrame:
    """Parses and converts the API response into a pandas DataFrame.

    Parameters
    ----------
    response : Dict
        The response object from the API query.

    Returns
    -------
    pd.DataFrame
        A pandas DataFrame containing the response data.
    """
    list_of_dicts = []
    for report in response.get('reports', []):
        columnHeader = report['columnHeader']
        dimensionHeaders = columnHeader['dimensions']
        metricHeaders = [i['name'] for i in columnHeader['metricHeader']['metricHeaderEntries']]
        rows = report['data']['rows']

        for row in rows:
            dict_row = {}
            dimensions = row['dimensions']
            dateRangeValues = row['metrics']

            for header, dimension in zip(dimensionHeaders, dimensions):
                dict_row[header] = dimension

            for i, values in enumerate(dateRangeValues):
                for metricHeader, value in zip(metricHeaders, values['values']):
                    if i == 0:
                        dict_row[metricHeader] = value
                    else:
                        dict_row[metricHeader + str(i)] = value

            list_of_dicts.append(dict_row)

    return pd.DataFrame(list_of_dicts)

def create_charts(df: pd.DataFrame) -> List[px.Figure]:
    """Creates charts using Plotly based on the DataFrame.

    Parameters
    ----------
    df : pd.DataFrame
        The data to plot.

    Returns
    -------
    List[px.Figure]
        A list of Plotly chart objects.
    """
    charts = []
    if not df.empty:
        # Example chart: Sessions per Country
        fig1 = px.bar(df, x='ga:country', y='ga:sessions', title='Sessions per Country')
        charts.append(fig1)

        # Example chart: Sessions per PagePath
        fig2 = px.bar(df, x='ga:pagePath', y='ga:sessions', title='Sessions per Page')
        charts.append(fig2)

    return charts

def generate_html(charts: List[px.Figure], file_name: str = "report.html") -> None:
    """Generates an HTML file with the given charts.

    Parameters
    ----------
    charts : List[px.Figure]
        A list of Plotly chart objects.
    file_name : str, optional
        Name of the output HTML file, by default "report.html".
    """
    with open(file_name, 'w') as f:
        for chart in charts:
            f.write(chart.to_html(full_html=False, include_plotlyjs='cdn'))
            f.write('<br><hr><br>')  # Add separators between charts

def main() -> None:
    """Main function to handle the workflow of fetching, processing,
    and visualizing Google Analytics data.
    """
    analytics = initialize_analyticsreporting()
    start_date = '2023-01-01'  # Adjust the date range as needed
    end_date = '2023-01-31'
    response = get_report(analytics, start_date, end_date)
    df = convert_to_dataframe(response)
    charts = create_charts(df)
    generate_html(charts)

if __name__ == "__main__":
    main()

[xmnlab]

@DanielaIgRo do you have time this week to work on this issue?

[xmnlab]

Creating a GitHub Actions workflow to run a Python script on the first day of every month and store the output in a specific directory involves several steps. Below is a YAML configuration for a GitHub Actions workflow that achieves this. You'll need to add this configuration to your GitHub repository in the .github/workflows directory.

GitHub Actions Workflow File

Create a file named .github/workflows/monthly_stats_report.yml with the following content:

name: Monthly Stats Report

on:
  schedule:
    # Runs at 00:00 on the first day of every month
    - cron: '0 0 1 * *'

jobs:
  generate_report:
    runs-on: ubuntu-latest

    steps:
    - name: Check out repository
      uses: actions/checkout@v2

    - name: Set up Python
      uses: actions/setup-python@v2
      with:
        python-version: '3.x' # Specify the Python version

    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install pandas plotly google-auth-oauthlib google-auth-httplib2 google-api-python-client

    - name: Run the script
      run: python path/to/your/script.py

    - name: Commit and push if there are changes
      run: |
        git config --global user.email "your-email@example.com"
        git config --global user.name "Your Name"
        git add docs/stats/index.html
        git commit -m "Update monthly stats" || exit 0  # This will exit with 0 if there's nothing to commit
        git push

Steps to Set Up:

1. Script Path: Replace path/to/your/script.py with the actual path to your Python script within the repository.

2. Python Dependencies: Ensure all required Python packages are listed in the Install dependencies step.

3. Git Configuration: Replace your-email@example.com and Your Name with your email and name used for Git commits.

4. Output Directory: Modify the script to output index.html to docs/stats/. If necessary, create these directories in your repository.

5. Commit and Push: The workflow commits and pushes the updated index.html file back to your repository. Ensure this is the desired behavior.

Notes:

• GitHub Actions Permissions: Make sure your repository has GitHub Actions enabled and the necessary permissions to commit and push changes.
• Secrets and Credentials: If your script requires credentials or secrets (like API keys), use GitHub Secrets to securely pass them to the workflow.
• Cron Schedule: The cron job is set to 0 0 1 * *, which means 00:00 on the first day of every month. You can adjust this if needed.

After setting up this workflow, it will automatically run on the first day of every month, executing your script and updating the index.html file in the specified directory.