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{unhcrreports}: AI-Powered Humanitarian Reporting

[!IMPORTANT] Reporting Engine: This package functions as the “Reporting Engine” for UNHCR statistics. It focuses on orchestrating automated reports by combining data, visualization, and AI narratives.

Graphics Source: The standardized charts and maps used in these reports are built upon the unhcrviz package, which provides a high-level API over unhcrthemes.

unhcrreports automates the production of analytical reports (Country and Regional) by: 1. Retrieving Data: Fetching the latest official statistics via the refugees package. 2. Visualizing Trends: Generating standardized plots using unhcrviz. 3. Generating Narratives: Using Large Language Models (LLMs) to interpret data and write context-aware insights. 4. Publishing: Compiling everything into polished Quarto reports.

Installation

This package is built with the help of {fusen}. You can install it from GitHub with:

# Install from GitHub
devtools::install_github("matheus-hardt/unhcrreports")

# Install from local source
devtools::install(".")

Quick Start

Generate a full report with a single command:

library(unhcrreports)

# Generate a Country Report (e.g., Brazil)
generate_report(
  type = "country", 
  name = "BRA", 
  year = 2024,
  gp_provider = "openai", # or "azure", "gemini", "ollama"
  gp_model = "gpt-4o-mini"
)

AI Configuration

To enable AI-generated narratives, you need to set up an API key for a supported LLM provider.

Environment Setup

Set your API key as an environment variable in your .Renviron file or session:

# For Google Gemini
Sys.setenv(GEMINI_API_KEY = "your_api_key_here")

# For OpenAI
Sys.setenv(OPENAI_API_KEY = "your_api_key_here")

# For Anthropic
Sys.setenv(ANTHROPIC_API_KEY = "your_api_key_here")

# For Azure OpenAI
# Sys.setenv(AZURE_OPENAI_ENDPOINT = "...")
# Sys.setenv(AZURE_OPENAI_API_KEY = "...")

Local LLM (Ollama)

If you prefer to run a local model using Ollama:

  1. Install Ollama: Download from ollama.com.
  2. Pull a Model: bash ollama pull qwen2.5:32b
  3. Run Report: Passing gp_provider = "ollama" and gp_model = "qwen2.5:32b".