Vibecode 1of10

You are building a lean indie version of 1of10.

Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt.

===== README.md =====
# 1of10 indie build

## Goal

Build the smallest trustworthy replacement for the core 1of10 workflow for one developer or a tiny team.

## Scope

Tracks a list of YouTube channels, computes each video's view multiple against its channel baseline, and shows a sortable outlier feed with thumbnails and titles.

## Quick start

1. Install the documented dependencies.
2. Copy `.env.example` to `.env`.
3. Run the development command chosen during implementation.
4. Complete the acceptance checks in `BUILD_PLAN.md`.

## Honest limits

This build deliberately does not replace:
- Cross-channel discovery: you can only find outliers in channels you already listed
- Historical depth, their index has view curves from before you started collecting
- Quota headroom, refreshing thousands of channels daily needs paid access or many keys
- Curated niche collections, thumbnail galleries and saved-idea workflows
- Shorts vs long-form normalization and view-velocity nuance that took them iterations to get right

If those capabilities are essential, use 1of10 instead of pretending the gap is solved.

===== AGENTS.md =====
# Agent instructions

- Optimize for a working, understandable weekend build.
- Prefer the fewest moving parts that satisfy the brief.
- Do not invent cryptography, security guarantees, APIs, or compliance claims.
- Keep secrets out of source control and logs.
- Add focused tests for destructive, security-sensitive, and data-loss paths.
- Run the project checks before declaring the build complete.
- Record any deliberate shortcut in the README under "Tradeoffs".

===== BUILD_PLAN.md =====
# Build plan

## Original build brief

Build a local YouTube outlier tracker: a self-hosted dashboard that flags videos massively outperforming their own channel's baseline.

Stack, no substitutions:
- Python 3.11, FastAPI, Jinja2 templates, plain CSS, no JS framework
- SQLite via sqlite3 stdlib, file at ./data/outliers.db
- httpx for YouTube Data API v3 calls
- API key from .env (YOUTUBE_API_KEY), python-dotenv, ship .env.example, never commit .env

Data model:
- channels(id, handle, title, added_at)
- videos(id, channel_id, title, published_at, thumbnail_url, duration_seconds, is_short, view_count, fetched_at)
- snapshots(video_id, view_count, taken_at) for view velocity

Ingest (CLI: python -m app.sync):
- For each tracked channel, resolve the uploads playlist, page playlistItems for up to the 200 most recent videos, then batch videos.list (50 ids per call) for statistics and contentDetails
- Treat anything under 180 seconds as a Short and baseline Shorts separately from long-form
- Baseline = median view_count of that channel's previous 10 videos of the same format that are at least 30 days old; outlier_score = views / baseline
- Log estimated quota units used per run and stop cleanly before 9500

Web UI on localhost:8000:
- Table sorted by outlier_score: thumbnail, title, channel, published date, views, baseline, score, link to the video
- Filters: format (short/long), minimum score, published within N days, channel
- A form to add or remove a tracked channel by handle or URL
- A page per channel showing its videos and baseline curve as a simple inline SVG

In scope: sync CLI, dashboard, filters, CSV export of the current view.
Out of scope: user accounts, auth, hosted deployment, telemetry, payments, cross-channel keyword search, thumbnail downloads, AI title suggestions.

Include a README with setup, how to get an API key, quota math, and a cron line for a daily sync. Handle API errors and deleted videos without crashing the run.

## Required capabilities

- YouTube Data API v3 key (free tier, 10k units/day)
- Python 3.11 and a machine or cheap VPS to run a daily refresh
- A hand-curated list of channels to track

## Delivery order

1. Scaffold the smallest runnable application and document its commands.
2. Implement the primary data model and core workflow.
3. Add validation, safe failure states, and persistence.
4. Cover the critical path with automated tests.
5. Exercise a clean install from the README and fix every missing step.

## Done when

- A new user can go from clone to first successful workflow using only the README.
- The core workflow works without paid infrastructure unless the brief requires it.
- Tests cover the highest-risk behavior.
- Known limitations are explicit rather than hidden.

===== .env.example =====
# Copy to .env and document every variable when it is introduced.
# Never put real credentials in this file.

APP_ENV=development
# Add only values required by the selected implementation.

You are building a lean indie version of 1of10.

Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt.

===== README.md =====
# 1of10 indie build

## Goal

Build the smallest trustworthy replacement for the core 1of10 workflow for one developer or a tiny team.

## Scope

Tracks a list of YouTube channels, computes each video's view multiple against its channel baseline, and shows a sortable outlier feed with thumbnails and titles.

## Quick start

1. Install the documented dependencies.
2. Copy `.env.example` to `.env`.
3. Run the development command chosen during implementation.
4. Complete the acceptance checks in `BUILD_PLAN.md`.

## Honest limits

This build deliberately does not replace:
- Cross-channel discovery: you can only find outliers in channels you already listed
- Historical depth, their index has view curves from before you started collecting
- Quota headroom, refreshing thousands of channels daily needs paid access or many keys
- Curated niche collections, thumbnail galleries and saved-idea workflows
- Shorts vs long-form normalization and view-velocity nuance that took them iterations to get right

If those capabilities are essential, use 1of10 instead of pretending the gap is solved.

===== AGENTS.md =====
# Agent instructions

- Optimize for a working, understandable weekend build.
- Prefer the fewest moving parts that satisfy the brief.
- Do not invent cryptography, security guarantees, APIs, or compliance claims.
- Keep secrets out of source control and logs.
- Add focused tests for destructive, security-sensitive, and data-loss paths.
- Run the project checks before declaring the build complete.
- Record any deliberate shortcut in the README under "Tradeoffs".

===== BUILD_PLAN.md =====
# Build plan

## Original build brief

Build a local YouTube outlier tracker: a self-hosted dashboard that flags videos massively outperforming their own channel's baseline.

Stack, no substitutions:
- Python 3.11, FastAPI, Jinja2 templates, plain CSS, no JS framework
- SQLite via sqlite3 stdlib, file at ./data/outliers.db
- httpx for YouTube Data API v3 calls
- API key from .env (YOUTUBE_API_KEY), python-dotenv, ship .env.example, never commit .env

Data model:
- channels(id, handle, title, added_at)
- videos(id, channel_id, title, published_at, thumbnail_url, duration_seconds, is_short, view_count, fetched_at)
- snapshots(video_id, view_count, taken_at) for view velocity

Ingest (CLI: python -m app.sync):
- For each tracked channel, resolve the uploads playlist, page playlistItems for up to the 200 most recent videos, then batch videos.list (50 ids per call) for statistics and contentDetails
- Treat anything under 180 seconds as a Short and baseline Shorts separately from long-form
- Baseline = median view_count of that channel's previous 10 videos of the same format that are at least 30 days old; outlier_score = views / baseline
- Log estimated quota units used per run and stop cleanly before 9500

Web UI on localhost:8000:
- Table sorted by outlier_score: thumbnail, title, channel, published date, views, baseline, score, link to the video
- Filters: format (short/long), minimum score, published within N days, channel
- A form to add or remove a tracked channel by handle or URL
- A page per channel showing its videos and baseline curve as a simple inline SVG

In scope: sync CLI, dashboard, filters, CSV export of the current view.
Out of scope: user accounts, auth, hosted deployment, telemetry, payments, cross-channel keyword search, thumbnail downloads, AI title suggestions.

Include a README with setup, how to get an API key, quota math, and a cron line for a daily sync. Handle API errors and deleted videos without crashing the run.

## Required capabilities

- YouTube Data API v3 key (free tier, 10k units/day)
- Python 3.11 and a machine or cheap VPS to run a daily refresh
- A hand-curated list of channels to track

## Delivery order

1. Scaffold the smallest runnable application and document its commands.
2. Implement the primary data model and core workflow.
3. Add validation, safe failure states, and persistence.
4. Cover the critical path with automated tests.
5. Exercise a clean install from the README and fix every missing step.

## Done when

- A new user can go from clone to first successful workflow using only the README.
- The core workflow works without paid infrastructure unless the brief requires it.
- Tests cover the highest-risk behavior.
- Known limitations are explicit rather than hidden.

===== .env.example =====
# Copy to .env and document every variable when it is introduced.
# Never put real credentials in this file.

APP_ENV=development
# Add only values required by the selected implementation.

You are building a production product version of 1of10.

Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt.

===== PRODUCT.md =====
# 1of10 product brief

## Problem

The core math is embarrassingly simple: pull a channel's uploads, compute a median or trailing baseline, divide each video's views by it, sort descending. The YouTube Data API hands you everything you need for that, and an agent can wire up a local outlier dashboard for a list of channels you care about in one sitting. Where it breaks down is scale: 1of10's real product is a pre-indexed corpus of millions of videos you can search across niches you have never heard of, with history that predates your interest. Your build can only see channels you thought to track, and the free API quota caps how many you can refresh per day. Good enough for watching 50 competitors, useless for open-ended idea mining.

## Product outcome

Tracks a list of YouTube channels, computes each video's view multiple against its channel baseline, and shows a sortable outlier feed with thumbnails and titles.

## Target user

A serious builder who needs a maintainable product foundation rather than a one-off demo.

## Required capabilities

- YouTube Data API v3 key (free tier, 10k units/day)
- Python 3.11 and a machine or cheap VPS to run a daily refresh
- A hand-curated list of channels to track

## Explicit non-goals for v1

- Cross-channel discovery: you can only find outliers in channels you already listed
- Historical depth, their index has view curves from before you started collecting
- Quota headroom, refreshing thousands of channels daily needs paid access or many keys
- Curated niche collections, thumbnail galleries and saved-idea workflows
- Shorts vs long-form normalization and view-velocity nuance that took them iterations to get right

## Success criteria

- The primary workflow is measurable end to end.
- Setup is reproducible in a clean environment.
- Failure, recovery, and support paths are documented.
- Product claims match what the implementation actually guarantees.

===== ARCHITECTURE.md =====
# Architecture

## Starting brief

Build a local YouTube outlier tracker: a self-hosted dashboard that flags videos massively outperforming their own channel's baseline.

Stack, no substitutions:
- Python 3.11, FastAPI, Jinja2 templates, plain CSS, no JS framework
- SQLite via sqlite3 stdlib, file at ./data/outliers.db
- httpx for YouTube Data API v3 calls
- API key from .env (YOUTUBE_API_KEY), python-dotenv, ship .env.example, never commit .env

Data model:
- channels(id, handle, title, added_at)
- videos(id, channel_id, title, published_at, thumbnail_url, duration_seconds, is_short, view_count, fetched_at)
- snapshots(video_id, view_count, taken_at) for view velocity

Ingest (CLI: python -m app.sync):
- For each tracked channel, resolve the uploads playlist, page playlistItems for up to the 200 most recent videos, then batch videos.list (50 ids per call) for statistics and contentDetails
- Treat anything under 180 seconds as a Short and baseline Shorts separately from long-form
- Baseline = median view_count of that channel's previous 10 videos of the same format that are at least 30 days old; outlier_score = views / baseline
- Log estimated quota units used per run and stop cleanly before 9500

Web UI on localhost:8000:
- Table sorted by outlier_score: thumbnail, title, channel, published date, views, baseline, score, link to the video
- Filters: format (short/long), minimum score, published within N days, channel
- A form to add or remove a tracked channel by handle or URL
- A page per channel showing its videos and baseline curve as a simple inline SVG

In scope: sync CLI, dashboard, filters, CSV export of the current view.
Out of scope: user accounts, auth, hosted deployment, telemetry, payments, cross-channel keyword search, thumbnail downloads, AI title suggestions.

Include a README with setup, how to get an API key, quota math, and a cron line for a daily sync. Handle API errors and deleted videos without crashing the run.

## Boundaries

Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors.

## Production baseline

- Configuration: validated at startup with safe local defaults where possible.
- Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives.
- Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions.
- Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts.
- Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists.
- Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test.

## Decision records

For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it.

===== AGENTS.md =====
# Agent instructions

- Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code.
- Implement milestone by milestone; keep each change reviewable and leave the application runnable.
- Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present.
- Never invent cryptography or silently weaken a requirement to make a test pass.
- Use provider interfaces for external services and deterministic fakes in tests.
- Add migrations and rollback or recovery notes for persistent data changes.
- Log useful operational context without credentials, tokens, passwords, or personal data.
- Update documentation and run all checks before completing a milestone.

===== MILESTONES.md =====
# Delivery milestones

## M0 — Decisions and scaffold

- Confirm the runtime, persistence model, threat boundaries, and deployment target.
- Create a reproducible local environment and continuous checks.

## M1 — Core workflow

- Implement the smallest end-to-end product path with validation and tests.
- Keep integrations behind interfaces.

## M2 — Trust layer

- Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards.
- Test abuse cases and destructive operations.

## M3 — Operability

- Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration.

## M4 — Release gate

- Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise.
- Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations.

===== OPERATIONS.md =====
# Operations

## Before release

- Validate configuration and secrets at startup.
- Define backup, restore, and rollback procedures and test them.
- Document logs, error tracking, health signals, and alert ownership.
- Set dependency update and vulnerability review expectations.

## Incident checklist

1. Contain the issue without destroying evidence or user data.
2. Record the timeline and affected scope.
3. Rotate exposed secrets and revoke compromised sessions or credentials.
4. Restore from a verified source when needed.
5. Document the root cause, remediation, and regression test.

## Launch constraint

Do not market omitted 1of10 capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.

# 1of10 indie build

## Goal

Build the smallest trustworthy replacement for the core 1of10 workflow for one developer or a tiny team.

## Scope

Tracks a list of YouTube channels, computes each video's view multiple against its channel baseline, and shows a sortable outlier feed with thumbnails and titles.

## Quick start

1. Install the documented dependencies.
2. Copy `.env.example` to `.env`.
3. Run the development command chosen during implementation.
4. Complete the acceptance checks in `BUILD_PLAN.md`.

## Honest limits

This build deliberately does not replace:
- Cross-channel discovery: you can only find outliers in channels you already listed
- Historical depth, their index has view curves from before you started collecting
- Quota headroom, refreshing thousands of channels daily needs paid access or many keys
- Curated niche collections, thumbnail galleries and saved-idea workflows
- Shorts vs long-form normalization and view-velocity nuance that took them iterations to get right

If those capabilities are essential, use 1of10 instead of pretending the gap is solved.

# Agent instructions

- Optimize for a working, understandable weekend build.
- Prefer the fewest moving parts that satisfy the brief.
- Do not invent cryptography, security guarantees, APIs, or compliance claims.
- Keep secrets out of source control and logs.
- Add focused tests for destructive, security-sensitive, and data-loss paths.
- Run the project checks before declaring the build complete.
- Record any deliberate shortcut in the README under "Tradeoffs".

# Build plan

## Original build brief

Build a local YouTube outlier tracker: a self-hosted dashboard that flags videos massively outperforming their own channel's baseline.

Stack, no substitutions:
- Python 3.11, FastAPI, Jinja2 templates, plain CSS, no JS framework
- SQLite via sqlite3 stdlib, file at ./data/outliers.db
- httpx for YouTube Data API v3 calls
- API key from .env (YOUTUBE_API_KEY), python-dotenv, ship .env.example, never commit .env

Data model:
- channels(id, handle, title, added_at)
- videos(id, channel_id, title, published_at, thumbnail_url, duration_seconds, is_short, view_count, fetched_at)
- snapshots(video_id, view_count, taken_at) for view velocity

Ingest (CLI: python -m app.sync):
- For each tracked channel, resolve the uploads playlist, page playlistItems for up to the 200 most recent videos, then batch videos.list (50 ids per call) for statistics and contentDetails
- Treat anything under 180 seconds as a Short and baseline Shorts separately from long-form
- Baseline = median view_count of that channel's previous 10 videos of the same format that are at least 30 days old; outlier_score = views / baseline
- Log estimated quota units used per run and stop cleanly before 9500

Web UI on localhost:8000:
- Table sorted by outlier_score: thumbnail, title, channel, published date, views, baseline, score, link to the video
- Filters: format (short/long), minimum score, published within N days, channel
- A form to add or remove a tracked channel by handle or URL
- A page per channel showing its videos and baseline curve as a simple inline SVG

In scope: sync CLI, dashboard, filters, CSV export of the current view.
Out of scope: user accounts, auth, hosted deployment, telemetry, payments, cross-channel keyword search, thumbnail downloads, AI title suggestions.

Include a README with setup, how to get an API key, quota math, and a cron line for a daily sync. Handle API errors and deleted videos without crashing the run.

## Required capabilities

- YouTube Data API v3 key (free tier, 10k units/day)
- Python 3.11 and a machine or cheap VPS to run a daily refresh
- A hand-curated list of channels to track

## Delivery order

1. Scaffold the smallest runnable application and document its commands.
2. Implement the primary data model and core workflow.
3. Add validation, safe failure states, and persistence.
4. Cover the critical path with automated tests.
5. Exercise a clean install from the README and fix every missing step.

## Done when

- A new user can go from clone to first successful workflow using only the README.
- The core workflow works without paid infrastructure unless the brief requires it.
- Tests cover the highest-risk behavior.
- Known limitations are explicit rather than hidden.

# Copy to .env and document every variable when it is introduced.
# Never put real credentials in this file.

APP_ENV=development
# Add only values required by the selected implementation.

# 1of10 product brief

## Problem

The core math is embarrassingly simple: pull a channel's uploads, compute a median or trailing baseline, divide each video's views by it, sort descending. The YouTube Data API hands you everything you need for that, and an agent can wire up a local outlier dashboard for a list of channels you care about in one sitting. Where it breaks down is scale: 1of10's real product is a pre-indexed corpus of millions of videos you can search across niches you have never heard of, with history that predates your interest. Your build can only see channels you thought to track, and the free API quota caps how many you can refresh per day. Good enough for watching 50 competitors, useless for open-ended idea mining.

## Product outcome

Tracks a list of YouTube channels, computes each video's view multiple against its channel baseline, and shows a sortable outlier feed with thumbnails and titles.

## Target user

A serious builder who needs a maintainable product foundation rather than a one-off demo.

## Required capabilities

- YouTube Data API v3 key (free tier, 10k units/day)
- Python 3.11 and a machine or cheap VPS to run a daily refresh
- A hand-curated list of channels to track

## Explicit non-goals for v1

- Cross-channel discovery: you can only find outliers in channels you already listed
- Historical depth, their index has view curves from before you started collecting
- Quota headroom, refreshing thousands of channels daily needs paid access or many keys
- Curated niche collections, thumbnail galleries and saved-idea workflows
- Shorts vs long-form normalization and view-velocity nuance that took them iterations to get right

## Success criteria

- The primary workflow is measurable end to end.
- Setup is reproducible in a clean environment.
- Failure, recovery, and support paths are documented.
- Product claims match what the implementation actually guarantees.

# Architecture

## Starting brief

Build a local YouTube outlier tracker: a self-hosted dashboard that flags videos massively outperforming their own channel's baseline.

Stack, no substitutions:
- Python 3.11, FastAPI, Jinja2 templates, plain CSS, no JS framework
- SQLite via sqlite3 stdlib, file at ./data/outliers.db
- httpx for YouTube Data API v3 calls
- API key from .env (YOUTUBE_API_KEY), python-dotenv, ship .env.example, never commit .env

Data model:
- channels(id, handle, title, added_at)
- videos(id, channel_id, title, published_at, thumbnail_url, duration_seconds, is_short, view_count, fetched_at)
- snapshots(video_id, view_count, taken_at) for view velocity

Ingest (CLI: python -m app.sync):
- For each tracked channel, resolve the uploads playlist, page playlistItems for up to the 200 most recent videos, then batch videos.list (50 ids per call) for statistics and contentDetails
- Treat anything under 180 seconds as a Short and baseline Shorts separately from long-form
- Baseline = median view_count of that channel's previous 10 videos of the same format that are at least 30 days old; outlier_score = views / baseline
- Log estimated quota units used per run and stop cleanly before 9500

Web UI on localhost:8000:
- Table sorted by outlier_score: thumbnail, title, channel, published date, views, baseline, score, link to the video
- Filters: format (short/long), minimum score, published within N days, channel
- A form to add or remove a tracked channel by handle or URL
- A page per channel showing its videos and baseline curve as a simple inline SVG

In scope: sync CLI, dashboard, filters, CSV export of the current view.
Out of scope: user accounts, auth, hosted deployment, telemetry, payments, cross-channel keyword search, thumbnail downloads, AI title suggestions.

Include a README with setup, how to get an API key, quota math, and a cron line for a daily sync. Handle API errors and deleted videos without crashing the run.

## Boundaries

Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors.

## Production baseline

- Configuration: validated at startup with safe local defaults where possible.
- Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives.
- Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions.
- Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts.
- Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists.
- Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test.

## Decision records

For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it.

# Agent instructions

- Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code.
- Implement milestone by milestone; keep each change reviewable and leave the application runnable.
- Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present.
- Never invent cryptography or silently weaken a requirement to make a test pass.
- Use provider interfaces for external services and deterministic fakes in tests.
- Add migrations and rollback or recovery notes for persistent data changes.
- Log useful operational context without credentials, tokens, passwords, or personal data.
- Update documentation and run all checks before completing a milestone.

# Delivery milestones

## M0 — Decisions and scaffold

- Confirm the runtime, persistence model, threat boundaries, and deployment target.
- Create a reproducible local environment and continuous checks.

## M1 — Core workflow

- Implement the smallest end-to-end product path with validation and tests.
- Keep integrations behind interfaces.

## M2 — Trust layer

- Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards.
- Test abuse cases and destructive operations.

## M3 — Operability

- Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration.

## M4 — Release gate

- Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise.
- Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations.

# Operations

## Before release

- Validate configuration and secrets at startup.
- Define backup, restore, and rollback procedures and test them.
- Document logs, error tracking, health signals, and alert ownership.
- Set dependency update and vulnerability review expectations.

## Incident checklist

1. Contain the issue without destroying evidence or user data.
2. Record the timeline and affected scope.
3. Rotate exposed secrets and revoke compromised sessions or credentials.
4. Restore from a verified source when needed.
5. Document the root cause, remediation, and regression test.

## Launch constraint

Do not market omitted 1of10 capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.

$ choose a build depth, inspect the files, then open the complete pack in your agent · this prompt is generated from the build plan · improve it via PR