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WID Codes Dictionary2 months ago
1. General Presentation | 2. General Structure | 2.1 WID Code Examples | 2.1.1 Series Type (one-letter code) | 2.1.2 Concept (five-letter code) | 2.1.3 Age Group (three-digit code) | 2.1.4 Population Unit (one-letter code) | 2.1.5 Series Type x Population Unit Availability | 2.2 Country Codes | Country Codes | World Region Codes | Historical Country Codes | Country Subregion Codes | 2.3 Percentile Codes | 3. Aggregate Income Variables | 3.1.1 GDP and Net National Income | 3.1.2 Decomposition of Foreign Income | 3.1.3 Decomposition of Foreign Wealth | 3.1.4 Decomposition of National Income between Sectors | 3.1.5 Labor and Capital Share of National Income | 3.1.6 Income Decomposition by Institutional Sector | 3.2 Income of Households and NPISH | 3.2.1 Income of the Sectors Combined | 3.2.1.1 Primary Incomes of Households and NPISH | 3.2.1.2 Secondary Incomes of Households and NPISH | 3.2.1.3 Consumption and Savings of Households and NPISH | 3.2.1.4 Relation between Net and Gross Variables | 3.2.2 Income of Households | 3.2.2.1 Primary Incomes of the Household Sector | 3.2.2.2 Secondary Incomes of the Household Sector | 3.2.2.3 Consumption and Savings of the Household Sector | 3.2.2.4 Relation between Net and Gross Variables | 3.2.3 Income of NPISH | 3.2.3.1 Primary Incomes of NPISH | 3.2.3.2 Secondary Incomes of NPISH | 3.2.3.3 Consumption and Savings of NPISH | 3.2.3.4 Relation between Net and Gross Variables | 3.3 Income of the Corporate Sector | 3.3.1 Income of the Sectors Combined | 3.3.1.1 Primary Income of Corporations | 3.3.1.2 Secondary Income of Corporations | 3.3.1.3 Relation between Net and Gross Variables | 3.3.2 Income of Non-financial Corporations | 3.3.2.1 Primary Income | 3.3.2.2 Secondary Income | 3.3.2.3 Relation between Net and Gross Variables | 3.3.3 Income of Financial Corporations | 3.3.3.1 Primary Income | 3.3.3.2 Secondary Income | 3.3.3.3 Relation between Net and Gross Variables | 3.4 Income of the Government Sector | 3.4.1 Primary Income of the Government | 3.4.2 Secondary Income of the Government | 3.4.3 Consumption and Savings of the Government | 3.5 Current Account | 3.5.1 Trade Balance | 3.5.2 Trade Balance of Services | 3.5.3 Trade Balance of Goods | 3.5.4 Capital Account | 3.5.5 Net Foreign Transfers | 4. Distributed Income Variables | 4.1 DINA Income | 4.1.1 Pretax Income | 4.1.2 Post-tax Income | 4.1.3 Factor Income | 4.2 "Fiscal" Income | 5. Aggregate Wealth Variables | 5.1 National Economy | 5.2 Households and NPISH | 5.2.1 Combined Sector (Private Wealth) | 5.2.2 Household Sector | 5.2.3 NPISH | 5.3 Corporate Sector | 5.4 General Government Sector | 6. Distributed Wealth Variables | 7. Price Index, Exchange Rates, Populations, etc. | 7.1 Price Index | 7.2 Exchange Rates | 7.3 Population | 7.4 Wealth/Income Ratios and Labor/Capital Shares | 7.5 Inequality Transparency | 8. Aggregate Carbon Variables | 8.1 National Territorial Emissions | 8.2 National Imported Emissions | 8.3 National Exported Emissions | 8.4 National Net Imports of Emissions | 8.5 National Carbon Footprint | 9. Distributed Carbon Variables | 10. Quick Reference
widr3 months ago
Installation | Variable codes | Downloading data | Multiple countries and percentiles | Excluding interpolated points | Source metadata | Key parameters | Tidyverse integration | Reusable query objects | Caching | Currency conversion | Inequality measures | Gini coefficient | Top fractile share | Percentile ratio | Plotting | Example | Quick reference
Advanced Querying6 months ago
Large Data Downloads | Caching for Reproducibility | Parallel Downloads | Working with Multiple Datasets
Data Visualisation6 months ago
Time Series Plots | Regional Comparisons | Gender Comparisons | Spatial Visualisation
Getting Started with nomisdata6 months ago
Introduction | Installation | Basic Workflow | 1. Search for Datasets | 2. Explore Dataset Structure | 3. Get Code Options | 4. Download Data | Using Example Data | API Key Setup | Next Steps
Spatial Analysis with nomisdata6 months ago
Introduction | Getting Spatial Data | Fetch with Boundaries | Basic Mapping | Advanced Spatial Analysis | Joining Non-Spatial and Spatial Data | Spatial Aggregation | Distance Analysis | Interactive Maps | Limitations | Resources
Working with Geography7 months ago
Understanding Geography Types | Looking Up Geographies | Fetching Data for Specific Areas | Geography Hierarchies
Advanced Leakage Detection with leakr8 months ago
Introduction | Understanding leakr's Detection Capabilities | Advanced Target Leakage Scenarios | Medical Diagnosis Example | Financial Data with Temporal Issues | Advanced Duplication Detection | Near-Duplicate Detection in Customer Data | Configuration and Customisation | Custom Configuration Options | Working with Large Datasets | Stratified Sampling for Balanced Analysis | Advanced Reporting and Analysis | Detailed Report Analysis | Best Practices for Advanced Usage | 1. Multi-Stage Validation | 2. Domain-Specific Validation | Summary
Framework Integration with leakr8 months ago
Introduction | Integration with caret | Basic caret Integration | Advanced caret Integration with Preprocessing | Integration with mlr3 | Basic mlr3 Integration | Advanced mlr3 Integration with Pipelines | Integration with tidymodels | Basic tidymodels Integration | Advanced tidymodels Integration with Feature Engineering | Data Import and Export Integration | Import with Automatic Auditing | Export with Audit Reports | Snapshot and Version Control Integration | Creating Data Snapshots | Workflow Integration Patterns | Pattern 1: Pre-Training Validation | Pattern 2: Post-Training Audit | Pattern 3: Continuous Monitoring | Performance Considerations | Memory-Efficient Processing | Best Practices for Framework Integration | 1. Integration Timing | 2. Configuration Management | 3. Error Handling and Logging | Summary
Getting Started with leakr8 months ago
Introduction | Basic Usage: The leakr_audit() Function | Simple Example with iris Dataset | Understanding the Output | Working with Train/Test Splits | Detecting Specific Leakage Patterns | Target Leakage Detection | Duplication Detection | Configuration and Customisation | Visualising Results | Working with Large Datasets | Next Steps | Summary
Google Cloud Speech-to-Text API9 months ago
Returned structure | Demo for Google Cloud Speech-to-Text API | Word transcripts | Custom configurations | Asynchronous calls
Google Cloud Text-to-Speech API9 months ago
Returned structure | Talk Languages | Support for SSML | Effect Profiles | Browser Speech player | Using with Shiny
Google Cloud Translation API9 months ago
Language Translation | HTML support | Language Detection | Translation API limits
Google Natural Language API9 months ago
Demo for Entity Analysis
Introduction to googleLanguageR9 months ago
Google Natural Language API | Google Cloud Translation API | Google Cloud Speech API | Installation | Usage | Authentication
Exploring Van Gogh Palettes in R10 months ago
------------------------------------------------------------------------------ | 1. Display a single palette | Visualise a discrete palette | Visualise a colorblind-safe palette (\dontrun{} optional) | \dontrun{ | } | 2. Generate colorblind-safe versions | Standard palette | Colorblind-safe version (\dontrun{} optional) | Compare original vs colorblind palettes (\dontrun{} optional) | 3. Discrete vs Continuous palettes | Discrete palettes: fixed number of colors, best for categorical data | Continuous palettes: interpolate to n colors, ideal for gradients | 4. Working with ggplot2 | 4a. Discrete color scale | 4b. Continuous color scale | 4c. Fill scales | Continuous fill example | 5. Inspect all palettes programmatically | Get tidy data frame of all palettes | With colorblind adjustment (\dontrun{} optional) | List all palette names | 6. Multi-palette comparison visualisation | Compare multiple palettes side-by-side (\dontrun{} optional) | 7. Additional functions | View detailed palette info | Future features: suggested palettes | vangogh::vangogh_suggest(7, colorblind = TRUE) | Future features: export palettes | vangogh::vangogh_export("palettes.json", colorblind = TRUE) | vangogh::vangogh_export("palettes.csv")