37 lines
3.9 KiB
Markdown
37 lines
3.9 KiB
Markdown
# Welcome to RAPIDS documentation
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Reproducible Analysis Pipeline for Data Streams (RAPIDS) allows you to process smartphone and wearable data to [extract](features/feature-introduction.md) and [create](features/add-new-features.md) **behavioral features** (a.k.a. digital biomarkers), [visualize](visualizations/data-quality-visualizations.md) mobile sensor data, and [structure](workflow-examples/analysis.md) your analysis into reproducible workflows.
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RAPIDS is open source, documented, modular, tested, and reproducible. At the moment, we support data streams logged by smartphones, Fitbit wearables, and, in collaboration with the [DBDP](https://dbdp.org/), Empatica wearables. Read the [introduction to data streams](../../datastreams/data-streams-introduction) for more information on what specific data streams RAPIDS can process, and this tutorial if you want to [add support for new data streams](../../datastreams/add-new-data-streams).
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!!! tip
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:material-slack: Questions or feedback can be posted on the \#rapids channel in AWARE Framework\'s [slack](http://awareframework.com:3000/).
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:material-github: Bugs and feature requests should be posted on [Github](https://github.com/carissalow/rapids/issues).
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:fontawesome-solid-tasks: Join our discussions on our algorithms and assumptions for feature [processing](https://github.com/carissalow/rapids/discussions).
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:fontawesome-solid-play: Ready to start? Go to [Installation](setup/installation/), then to [Configuration](setup/configuration/), and then to [Execution](setup/execution/)
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:fontawesome-solid-sync-alt: Are you upgrading from RAPIDS [beta](https://rapidspitt.readthedocs.io/en/latest/)? Follow this [guide](migrating-from-old-versions)
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## How does it work?
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RAPIDS is formed by R and Python scripts orchestrated by [Snakemake](https://snakemake.readthedocs.io/en/stable/). We suggest you read Snakemake's docs but in short: every link in the analysis chain is atomic and has files as input and output. Behavioral features are processed per sensor and participant.
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## What are the benefits of using RAPIDS?
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1. **Consistent analysis**. Every participant sensor dataset is analyzed in the same way and isolated from each other.
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2. **Efficient analysis**. Every analysis step is executed only once. Whenever your data or configuration changes, only the affected files are updated.
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5. **Parallel execution**. Thanks to Snakemake, your analysis can be executed over multiple cores without changing your code.
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6. **Code-free features**. Extract any of the behavioral features offered by RAPIDS without writing any code.
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7. **Extensible code**. You can easily add your own data streams or behavioral features in R or Python, share them with the community, and keep authorship and citations.
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8. **Timezone aware**. Your data is adjusted to one or more time zones per participant.
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9. **Flexible time segments**. You can extract behavioral features on time windows of any length (e.g., 5 minutes, 3 hours, 2 days), on every day or particular days (e.g., weekends, Mondays, the 1st of each month, etc.), or around events of interest (e.g., surveys or clinical relapses).
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10. **Tested code**. We are continually adding tests to make sure our behavioral features are correct.
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11. **Reproducible code**. If you structure your analysis within RAPIDS, you can be sure your code will run in other computers as intended, thanks to R and Python virtual environments. You can share your analysis code along with your publications without any overhead.
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12. **Private**. All your data is processed locally.
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## How is it organized?
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In broad terms the `config.yaml`, [`.env` file](setup/configuration/#database-credentials), [participants files](setup/configuration/#participant-files), and [time segment files](setup/configuration/#time-segments) are the only ones that you will have to modify. All data is stored in `data/` and all scripts are stored in `src/`. For more information see RAPIDS' [File Structure](file-structure.md). |