EKIO Theme | Color Palettes | Palette types | Scale Functions | Discrete scales | Continuous scales | Recipe Functions | Histogram | Bar plot | Scatter plot | Area plot | Smart aesthetic detection | Color Scales | GT Tables

Overview | Data producers | METRO SP transparency portal | Insper Dataverse | GeoSampa | Core datasets | passengers_entrance | Columns and definitions | Metrics | Time coverage by line | passengers_transported | station_averages | station_daily | Spatial datasets | lines | stations | Auxiliary datasets | Data notes and caveats | Entrance vs. transported | Station-level transfer counting | Line 5 ownership change | Station openings during the data window | Line 15 Sunday closures | Rounding in station averages | Lines 4 and 5: station codes | 2017 partial year | Trailing months and NA values | Source attribution

Trend extraction vs decomposition and detrending | A first trend | Multiple time series | Multiple trend methods | Finer control | How does augment_trends() compare to the traditional workflow? | What are the alternatives to trendseries? | Summary

Trend extraction vs decomposition | A first decomposition | STL decomposition | Regression decomposition | Grouped decomposition | Multiplicative seasonality | Other methods | Classical decomposition | Basic structural model | X-13ARIMA-SEATS (seasonal) | Summary

Trend extraction vs detrending | A first detrended series | Detrending does not deseasonalize | Percentage deviations from trend | Comparing detrending methods | Keeping the fitted trend | Grouped detrending | Summary

What is trendseries? | Why trendseries? | The four functions | augment_trends(): fit a trend | decompose_series(): split into trend, seasonal, and remainder | deseason_series(): remove seasonality | detrend_series(): extract the cycle | extract_trends(): the ts-native interface | Where to go next | Acknowledgements | Getting Help

What worked well and caveats | Goodbye to Fable 5

The theme | Color scales | Rounded bars | Palettes | Fonts

Agentic Coding benchmark | The data | Colors | Left panel | Right panel | Combining the panels | Prediction

Introduction | Core Interface | The source Argument | Example: Housing Credit Cycle | Example: Real Estate Credit Portfolio | Reference (all datasets)

Introduction | Recommended Workflow | The best indices | Working with realestatebr | Sale Indices | IVG-R | IGMI-R | FipeZap (sale) | Comparing indices | Rent Indices | IVAR | IQA and IQAIW | FipeZap (rent) | SECOVI-SP | Comparing Indices | Stacked tables | Normalising to a common base | International: BIS | Index Reference

Overview | The three datasets | Reading data | Processed vs. raw data | A peek at the structure without downloading | A short analysis | Managing the cache | The official data dictionary | Citing the data

metrosp | The datasets | Entrance and Transported | Entrance | Transported | Station Averages | Station Daily

Henderson and Spencer Moving Averages | The Henderson Filter | The weights | Basic usage | Choosing the window | The Spencer Moving Average | Bandpass Filters: Baxter-King and Christiano-Fitzgerald | Baxter-King Filter | Christiano-Fitzgerald Filter | The Hodrick-Prescott Filter | The smoothing parameter $\lambda$ | The Hamilton Filter | Hamilton vs. HP | All Filters Together | Quick Reference | References

Moving Averages | Simple example | Grouped series | Related methods

The two interfaces | The unified parameter system | Method catalogue | Moving averages | Smoothing methods | Econometric filters | Bandpass filters | Decomposition vs. trend extraction | References

Introduction | Installation | Font Setup (Optional) | Quick Start | Color Palettes | Browsing palettes | Accessing palette colors | Using ggplot2 Scales | Discrete scales | Continuous scales | Plot Helper Functions | Line chart | Bar chart | Scatter plot | Adding layers | Base R | Getting Help