Introduction
ThedatefixR package provides a user-friendly Shiny appthat allows users to standardize messy date data using a graphical userinterface (GUI). This is particularly useful for researchers, dataanalysts, and anyone working with datasets containing inconsistentlyformatted dates who prefer not to use R code directly.
The app supports the same powerful date parsing capabilities as thecoredatefixR functions, including:
- Multiple date formats and separators
- International month names in 9 languages
- Custom imputation strategies for missing date components
- Excel and CSV file processing
- Batch processing of multiple date columns
Installation and Setup
Prerequisites
The Shiny app requires additional dependencies that are not installedautomatically withdatefixR. This design choice allows thecore package to be installed on secure systems where these packagesmight not be permitted.
Required dependencies: -DT - for interactive datatables -shiny - for the web application framework -readxl - for reading Excel files -htmltools -for HTML generation
Launching the App
To start the app, simply run:
If any required dependencies are missing, the app will detect thisand offer to install them automatically.
Theme Options
The app supports two visual themes:
# Default datefixR theme (recommended)fix_date_app(theme="datefixR")# Standard Shiny themefix_date_app(theme="none")Step-by-Step Usage Guide
1. File Upload
- Click theBrowse button in the left sidebar
- Select either a CSV (.csv) or Excel (.xlsx) file
- The file will be automatically uploaded and processed
- ClickRefresh to display the uploaded data
2. Column Selection
After uploading your file:
- Review the data in theResults tab
- In the left sidebar, check boxes will appear for each column
- Select the columns containing date data that need to bestandardized
- Multiple columns can be selected simultaneously
3. Imputation Settings
Configure how missing date components should be handled:
Day of Month Imputation
- Options: 1-28, or NA
- Default: 1 (first day of month)
- Purpose: What day to use when only month/year areprovided
Advanced Features
File Format Support
The app automatically detects and handles:
- CSV files: Comma-separated values with automaticencoding detection
- Excel files: Both .xlsx and .xls formats, reads thefirst worksheet
Example Workflow
Here’s a complete example of using the app:
Prepare your data: Create a CSV file with messydates
id,event_date,follow_up1,"02/03/21","April 2021"2,"15-Dec-2020","2021"3,"2020/05/01","May 15 2021"Launch and configure:
Upload and process:
- Upload your CSV file
- Select “event_date” and “follow_up” columns
- Set day imputation to 15, month imputation to 6
- Click Refresh
Download results: Clean, standardized date dataready for analysis
Troubleshooting
Common Issues
App won’t start: - Ensure all dependencies areinstalled - Try runninginstall.packages(c("DT", "shiny", "readxl", "htmltools"))
File won’t upload: - Check file format (only .csvand .xlsx supported) - Ensure file size is reasonable (< 100MBrecommended) - Verify file isn’t corrupted
Dates not parsing correctly: - Review your formatassumption (dmy vs mdy) - Check for unusual date formats in your data -Consider pre-cleaning obviously problematic entries
Download not working: - Ensure you’ve selected atleast one date column - Try refreshing the processed data first - Checkbrowser download settings
Performance Considerations
- File Size: The app handles files up to severalthousand rows efficiently
- Processing Time: Complex date parsing may take afew seconds for large datasets
- Memory Usage: Keep file sizes reasonable (<100MB) for optimal performance
For the latest updates and to report issues, visit thedatefixR GitHubrepository.