40 lines
3.6 KiB
Markdown
40 lines
3.6 KiB
Markdown
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# 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 **behavioral features** (a.k.a. digital biomarkers/phenotypes).
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RAPIDS is open source, documented, modular, tested, and reproducible. At the moment we support smartphone data collected with [AWARE](awareframework.com/) and wearable data from Fitbit devices.
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:material-slack: Questions or feedback can be posted on \#rapids in AWARE Framework\'s [slack](http://awareframework.com:3000/).
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:material-github: Bugs should be reported 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/issues?q=is%3Aissue+is%3Aopen+label%3Adiscussion).
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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 per 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 exact 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. **Extensible code**. You can easily add your own behavioral features in R or Python and keep authorship and citations.
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3. **Timezone aware**. Your data is adjusted to the specified timezone (multiple timezones suport *coming soon*).
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4. **Flexible day 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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7. **Tested code**. We are constantly adding tests to make sure our behavioral features are correct.
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8. **Reproducible code**. 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 your publications without any overhead.
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9. **Private**. All your data is processed locally.
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## How is it organized?
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The `config.yaml` file is the only file that you will have to modify. It includes parameters to manage participants, data sources, sensor data, visualizations and more.
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All data is saved in `data/`. The `data/external/` folder stores any data imported by the user, `data/raw/` stores sensor data as imported from your database, `data/interim/` has intermediate files necessary to compute behavioral features from raw data, and `data/processed/` has all the final files with the behavioral features per sensor and participant.
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All the source code is saved in `src/`. The `src/data/` folder stores scripts to download, clean and pre-process sensor data, `src/features` has scripts to extract behavioral features organized in their respective subfolders , `src/models/` can host any script to create models or statistical analyses with the behavioral features you extract, and `src/visualization/` has scripts to create plots of the raw and processed data.
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There are other important files and folders but only relevant if you are interested in extending RAPIDS (e.g. virtual env files, docs, tests, Dockerfile, the Snakefile, etc.).
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