data-intake - SKILL.md Agent Skill

name: data-intake description: Skill for data intake, cleaning, and standardization using Epiverse-TRACE tools.

Epiverse-TRACE Data Intake & Cleaning

[!IMPORTANT] Use the Introspection Protocol: See epiverse-overview skill for the protocol. Before generating code, verify package APIs and functions using R introspection commands.

[!NOTE] Workflow Context: This skill's code should be included in Quarto document code chunks (not separate R scripts). See the reporting skill for the complete workflow structure.

This skill handles the first stage of the outbreak analysis pipeline: importing data from Health Information Systems and preparing it for analysis.

Packages

`readepi`

Purpose: Import data from Health Information Systems (DHIS2, SORMAS, SQL databases).

Key Functions:

login(): Authenticate with HIS
read_dhis2(): Import from DHIS2
read_sormas(): Import from SORMAS
read_rdbms(): Import from SQL databases
show_tables(): List available tables

`cleanepi`

Purpose: Standardize and clean epidemiological data.

Key Functions:

clean_data(): Master wrapper for common cleaning tasks
standardize_dates(): Convert dates to ISO 8601
standardize_column_names(): Convert to snake_case
check_subject_ids(): Verify unique identifiers
remove_duplicates(): Deduplicate records
replace_missing_values(): Handle missing data

`linelist`

Purpose: Tag epidemiological metadata for robust downstream analysis.

Key Functions:

make_linelist(): Convert data.frame to linelist object
set_tags(): Assign semantic tags (e.g., date_onset, outcome)
validate_linelist(): Check data integrity based on tags
tags_df(): Retrieve tagged data frame
tags_names(): List available tag names

`numberize`

Purpose: Convert written number words to numeric digits.

Key Functions:

numberize(): Convert "twenty-one" → 21. Supports English, French, Spanish.

Typical Workflow

Import: Use readepi to connect to HIS and import data
Clean: Use cleanepi::clean_data() to standardize and validate
Tag: Use linelist::make_linelist() to add epidemiological metadata
Validate: Use linelist::validate_linelist() to check integrity

Best Practices

Always use here::here() for file paths
Set seeds for reproducibility
Use ISO 8601 date format (YYYY-MM-DD)
Document data sources and processing steps
Check vignette(package = "pkg_name") for detailed workflows