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layout: default
title: User Guide
parent: Shiny App
nav_order: 1
---
# Shiny App User Guide
Step-by-step instructions for using the Polyphenol Estimation Pipeline Shiny application.
## Quick Start
1. Open the app (hosted version or run locally)
2. Go to the **Input** tab
3. Click **Load Demo Data** to try with sample data, or upload your own file
4. Click **Run Pipeline**
5. View results in the **Results** tab
6. Use Chart/Table buttons to switch between visualizations and data tables
7. Download results as CSV files
## Data Requirements
### ASA24 Data
Use the **Items Analysis File** from the ASA24 Researcher Site.
**Required columns:**
- `UserName` - Participant identifier
- `FoodCode` - WWEIA food code
- `RecallNo` - Recall number (1, 2, etc.)
- `FoodAmt` - Amount consumed in grams
**Recommended columns for DII calculation:**
- `KCAL`, `PROT`, `TFAT`, `CARB`, `FIBE` and other nutrient columns
**Important:** Each participant must have at least 2 completed recalls.
### NHANES Data
Use the **Individual Foods** file from NHANES dietary data.
**Required columns:**
- `SEQN` - Participant sequence number
- `DRXIFDCD` - Food code
- `RecallNo` - Recall day
**Recommended columns:**
- `DRXIGRMS` - Amount consumed in grams
- `DRXIKCAL`, `DRXIPROT`, `DRXITFAT` and other nutrient columns
## Using the Application
### Step 1: Get Started Tab
The Get Started tab provides:
- Overview of what the pipeline does
- Three-step guide to using the app
- Pipeline workflow diagram
Click **Go to Input** to proceed.
### Step 2: Input Tab
**Select Data Source**
Choose your data format:
- **ASA24** for ASA24 Items files
- **NHANES** for NHANES Individual Foods files
**Upload Data**
Click the upload area or drag and drop your file. Accepted formats:
- CSV (.csv)
- Excel (.xlsx, .xls)
Click "Data format help" for column requirements.
The app validates your data and shows a preview on the right.
**Or Load Demo Data**
Click **Load Demo Data** to test with included sample ASA24 data.
**Configure Options**
- **Include DII Calculation** - Check to compute the 42-component Dietary Inflammatory Index
### Step 3: Run Pipeline
Click **Run Pipeline** to process your data.
A progress indicator shows each step:
1. Preparing dietary data
2. Calculating nutrient totals
3. Disaggregating foods (FDA FDD)
4. Mapping to FooDB
5. Calculating polyphenol content
6. Summarizing total intake
7. Calculating class-level intake
8. Identifying food contributors
9. Calculating DII scores (if enabled)
10. Generating QA/QC report
The app navigates to Results when complete.
### Step 4: View Results
**Summary Cards**
Four cards at the top show:
- Subjects Analyzed
- Mean Polyphenol Intake (mg/day)
- Polyphenol Classes
- Unmapped Foods
**Results Tabs**
Each tab has **Chart** and **Table** buttons in the header. Click to switch views.
**Total Intake**
- Bar chart: Mean intake by subject (top 30)
- Histogram: Distribution of intake across subjects
- Table: Subject-level values
**By Polyphenol Class**
- Bar chart: Mean intake by polyphenol class
- Table: Class-level breakdown per subject
**Food Contributors**
- Treemap: Top 50 foods by polyphenol contribution
- Table: Food contribution details
**DII Scores** (if enabled)
- Histogram: Distribution of DII scores
- Bar chart: Per-subject scores (green = anti-inflammatory, red = pro-inflammatory)
- Table: DII scores with all/no-alcohol variants
### Step 5: QA/QC Tab
The QA/QC tab reports foods that could not be mapped to FooDB.
These items do not contribute to polyphenol estimates. Review the list to assess data quality.
Use Chart/Table buttons to switch between:
- Chart: Distribution of unmapped food percentage by recall
- Table: List of unmapped food items
### Step 6: Download Results
**Individual Downloads**
Each results tab has a download button (download icon) to export that specific table.
**Download All**
Click **Download All Results** at the bottom of the Results tab to get a ZIP file containing:
| File | Contents |
|------|----------|
| `total_intake_by_subject.csv` | Mean polyphenol intake per subject |
| `total_intake_by_recall.csv` | Polyphenol intake per recall |
| `class_intake_by_subject.csv` | Class-level intake per subject |
| `class_intake_by_recall.csv` | Class-level intake per recall |
| `food_contributors.csv` | Food contribution rankings |
| `dii_scores_by_subject.csv` | DII scores per subject (if enabled) |
| `dii_scores_by_recall.csv` | DII scores per recall (if enabled) |
| `unmapped_foods.csv` | Foods not mapped to FooDB (if any) |
## Tips
- **Test first:** Use "Load Demo Data" to explore the app before uploading your data
- **Check QA/QC:** Review unmapped foods to understand data coverage
- **Large files:** Allow extra processing time for datasets with many subjects
- **Reset:** Click "Reset" to clear all data and start over
- **Full screen:** Click the expand icon on any card for full-screen view
## About Page
The About tab contains:
- Pipeline methodology explanation
- Three-step process description
- Information about the 42-component DII calculation
- Credits for pipeline and application development
- Citation information for DII methodology
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