{"id":21653625,"url":"https://github.com/anlbora/lookerreport","last_synced_at":"2026-02-14T03:03:53.078Z","repository":{"id":245325395,"uuid":"817919651","full_name":"anlbora/LookerReport","owner":"anlbora","description":"This provides a comprehensive guide to understanding and using the Company Report Dashboard. 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This dashboard is designed to offer insights into the employee distribution, salary ranges, and departmental structures within the company.\n\n## Report Link\n- [Report](https://lookerstudio.google.com/s/lKz2T_3DCKc)\n\n## Dashboard Overview\n\n![1](https://github.com/anlbora/LookerReport/assets/100442507/9dfe2f90-7c03-40df-a62e-9d88c5f383b2)\n\nThe dashboard is divided into several sections, each providing valuable information through visualizations and data tables. Below is a breakdown of the components included in the dashboard:\n\n### 1. Filters and Navigation\n\n- **Department Dropdown**: Select a specific department to filter the data displayed in the report.\n- **Salary Slider**: Adjust the range to filter employees based on their salary.\n- **Name Search**: Enter a first name to filter the data by employee name.\n- **Date Range Selector**: Choose a date range to filter employees by their join date.\n- **Reset Filters Button**: Click this button to clear all filters and reset the dashboard to its default view.\n  \n![2](https://github.com/anlbora/LookerReport/assets/100442507/140e5b82-6c75-4276-8051-e5ad0180126d)\n\n![3](https://github.com/anlbora/LookerReport/assets/100442507/400554ff-9b9f-4dbb-9691-684f45c13be4)\n\n![4](https://github.com/anlbora/LookerReport/assets/100442507/b0c85ac7-960f-4086-af30-642b981e4c8c)\n\n![5](https://github.com/anlbora/LookerReport/assets/100442507/73eb7edf-c8f9-4a8e-b9d2-f4f66b7c0271)\n\n![6](https://github.com/anlbora/LookerReport/assets/100442507/4760c065-147c-4413-ab9f-5c49d6b87474)\n\n### 2. Key Metrics\n\n- **Record Count**: Displays the total number of employees in the filtered view.\n- **Average Salary**: Shows the average salary of the employees in the filtered view.\n\n### 3. Data Table\n\nThe data table provides detailed information about each employee, including:\n- **ID**: Employee identification number.\n- **First Name**: Employee's first name.\n- **Last Name**: Employee's last name.\n- **Title**: Job title of the employee.\n- **Email**: Employee's email address.\n- **Join Date**: Date the employee joined the company.\n- **Phone Number**: Contact number of the employee.\n- **Salary**: Employee's salary.\n\n### 4. Charts\n\n#### a. Record Count by Title\n\nThis bar chart shows the number of employees for each job title within the company. It helps in understanding the distribution of employees across different roles.\n\n#### b. Salary by Employee Title\n\nThis pie chart illustrates the percentage of total salary attributed to each job title. It's useful for visualizing how the company's payroll is distributed among different roles.\n\n#### c. Departmental Specific Insights\n\nFor each department, the dashboard provides:\n- **Record Count by Title**: Similar to the company-wide chart but filtered by the selected department.\n- **Salary by Employee Title**: Provides the salary distribution for the titles within the selected department.\n\n## Example Usage\n\n1. **Viewing All Employees**:\n   - Reset all filters to see the data for all employees in the company.\n   - Observe the total number of employees and the average salary in the company.\n\n2. **Analyzing a Specific Department**:\n   - Select a department from the dropdown to filter the data.\n   - Check the detailed records and charts related to that department to understand the structure and salary distribution.\n\n3. **Salary Analysis**:\n   - Use the salary slider to view employees within a specific salary range.\n   - Analyze which titles and departments have the highest or lowest average salaries.\n\n4. **Join Date Trends**:\n   - Adjust the date range selector to view employees hired during a specific period.\n   - This helps in analyzing hiring trends over time.\n\n## Files and Resources\n\n### Included Files\n\n- **company_employees_combined_realistic.csv**: The combined dataset containing employee details.\n\n### Generated Charts\n\n- **Employee Distribution by Department**: Visualizes the number of employees per department.\n- **Average Salary by Department**: Shows the average salary within each department.\n- **Employee Distribution by Title Level**: Illustrates the count of employees at different title levels (Entry-Level, Mid-Level, Managerial, Senior Management).\n- **Salary Distribution Across the Company**: Provides a histogram of the salary distribution.\n- **Join Date Trends**: Displays the number of employees hired over time.\n- **Employee Count by Department and Title Level**: Combines department and title level to show detailed employee counts.\n\n## How to Use the Dashboard\n\n1. **Open the CSV file**: Load `company_employees_combined_realistic.csv` into your data visualization tool (e.g., Excel, Power BI, Tableau).\n2. **Apply Filters**: Use the provided filters to narrow down the data based on your requirements.\n3. **Analyze Charts**: Examine the generated charts to get insights into employee distribution, salary ranges, and departmental breakdowns.\n4. **Use the Data Table**: For detailed analysis, refer to the data table which provides comprehensive employee information.\n\n## Data Creation\n\n### Organization Structure\n```json\n    \"Finance\": {\n        \"Entry-Level\": [\"Accountant\", \"Financial Analyst\"],\n        \"Mid-Level\": [\"Senior Financial Analyst\"],\n        \"Managerial\": [\"Finance Manager\"],\n        \"Senior Management\": [\"Finance Director\"]\n    },\n    \"HR\": {\n        \"Entry-Level\": [\"HR Specialist\", \"Recruiter\"],\n        \"Mid-Level\": [\"Senior HR Specialist\"],\n        \"Managerial\": [\"HR Manager\"],\n        \"Senior Management\": [\"HR Director\"]\n    },\n    \"Engineering\": {\n        \"Entry-Level\": [\"Software Engineer\", \"Mechanical Engineer\", \"Electrical Engineer\"],\n        \"Mid-Level\": [\"Senior Software Engineer\", \"Senior Mechanical Engineer\", \"Senior Electrical Engineer\"],\n        \"Managerial\": [\"Engineering Manager\"],\n        \"Senior Management\": [\"VP of Engineering\"]\n    },\n    \"Sales\": {\n        \"Entry-Level\": [\"Sales Associate\", \"Business Development Representative\"],\n        \"Mid-Level\": [\"Senior Sales Associate\"],\n        \"Managerial\": [\"Sales Manager\"],\n        \"Senior Management\": [\"Sales Director\"]\n    },\n    \"Marketing\": {\n        \"Entry-Level\": [\"Marketing Coordinator\", \"Content Creator\"],\n        \"Mid-Level\": [\"Senior Marketing Coordinator\"],\n        \"Managerial\": [\"Marketing Manager\"],\n        \"Senior Management\": [\"Marketing Director\"]\n    },\n    \"IT\": {\n        \"Entry-Level\": [\"IT Support\", \"Network Administrator\"],\n        \"Mid-Level\": [\"Senior IT Support\"],\n        \"Managerial\": [\"IT Manager\"],\n        \"Senior Management\": [\"IT Director\"]\n    },\n    \"Operations\": {\n        \"Entry-Level\": [\"Operations Coordinator\", \"Supply Chain Specialist\"],\n        \"Mid-Level\": [\"Senior Operations Coordinator\"],\n        \"Managerial\": [\"Operations Manager\"],\n        \"Senior Management\": [\"Operations Director\"]\n    },\n    \"Customer Service\": {\n        \"Entry-Level\": [\"Customer Service Representative\", \"Call Center Agent\"],\n        \"Mid-Level\": [\"Senior Customer Service Representative\"],\n        \"Managerial\": [\"Customer Service Manager\"],\n        \"Senior Management\": [\"Customer Service Director\"]\n    },\n    \"role_distribution\": {\n      \"Entry-Level\": 0.70,\n      \"Mid-Level\": 0.20,\n      \"Managerial\": 0.07,\n      \"Senior Management\": 0.03\n  }\n```\n### Data Creation Code\n\n```python\n# Calculate the number of each role type for 2600 entries\nnum_entries = 2600\nentries_distribution = {role: int(num_entries * pct) for role, pct in role_distribution.items()}\n\n# Ensure total is exactly 2600 due to rounding issues\nentries_distribution[\"Entry-Level\"] += num_entries - sum(entries_distribution.values())\n\n# Generate data based on the organizational structure\ndata = []\nid_counter = 1\n\nfor role, count in entries_distribution.items():\n    for _ in range(count):\n        department = random.choice(list(organizational_structure.keys()))\n        title = random.choice(organizational_structure[department][role])\n        salary = round(random.uniform(40000, 120000), 2)\n        join_date = faker.date_between(start_date='-10y', end_date='today')\n        \n        person = {\n            \"ID\": id_counter,\n            \"First Name\": faker.first_name(),\n            \"Last Name\": faker.last_name(),\n            \"Department\": department,\n            \"Title\": title,\n            \"Salary\": salary,\n            \"Join Date\": join_date,\n            \"Email\": faker.email(),\n            \"Phone Number\": faker.phone_number()\n        }\n        data.append(person)\n        id_counter += 1\n\n# Create DataFrame for additional 2600 entries\ndf_2600 = pd.DataFrame(additional_data)\n\n# Save the dataframe to a new CSV file\nfile_path = \"/mnt/data/company_employees.csv\"\ndf_2600.to_csv(file_path, index=False)\n\n```\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanlbora%2Flookerreport","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fanlbora%2Flookerreport","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanlbora%2Flookerreport/lists"}