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gooseR πŸ¦†

Version Lifecycle: stable License: MIT CRAN Status

gooseR brings goose AI into R. It’s a comprehensive, intelligent R development assistant powered by the goose CLI.

πŸš€ What’s New in v0.1.1 (Feature Complete!)

πŸ” Intelligent Code Analysis

πŸ“Š Survey Data Tools

🎯 Essential Workflow Functions

πŸ’Ύ Enhanced Memory Management

🎨 Beautiful Formatting

✨ Core Features

About goose

goose is your friendly AI partner who can understand what you want to do and help you do it! The best part is that goose can learn from your preferences and remember them for next time!

Learn more about goose

Installation

# Install from GitHub (soon on CRAN!)
# install.packages("remotes")
remotes::install_github("blockbtheriault/gooseR")

Configuration

If Goose CLI is already working on your machine, you’re ready to go! No extra R-side setup needed.

library(gooseR)

# Test your setup
if (goose_test_cli()) message("Goose CLI is ready! πŸ¦†")

If you don’t have Goose CLI configured yet:

# Configure credentials (example for OpenAI)
goose_configure(provider = "openai", model = "gpt-4o", api_key = "your-key")

Quick Start

library(gooseR)

# Ask Goose a question
goose_ask("Summarize mtcars and suggest 2 visualizations")

# Get intelligent code review
goose_honk(severity = "moderate")  # Reviews your current script

# Clean survey data
survey_data <- read.csv("qualtrics_export.csv")
clean_data <- goose_rename_columns(survey_data)
goose_view_column_map(clean_data)  # See the mapping

# Save and load R objects with memory
model <- lm(mpg ~ wt + cyl, data = mtcars)
goose_save(model, category = "models", tags = c("mtcars", "regression"))
my_model <- goose_load("model")

# Create a branded visualization
library(ggplot2)
ggplot(mtcars, aes(wt, mpg)) +
  geom_point() +
  theme_brand("block") +
  labs(title = "Fuel Efficiency")

Real-World Workflows

Survey Researcher

# Load messy Qualtrics data
survey <- read.csv("survey_export.csv")

# Clean column names intelligently
clean <- goose_rename_columns(survey)
# "How satisfied are you with..." β†’ sat_overall
# "On a scale of 1-10..." β†’ nps

# Get analysis plan
goose_make_a_plan("exploratory")

# Save for tomorrow
goose_continuation_prompt()

Data Scientist

# Share data context
goose_give_sample(my_data)

# Get analysis plan
plan <- goose_make_a_plan("predictive")

# Write your model...
model <- glm(outcome ~ ., data = my_data, family = binomial)

# Get tough feedback
goose_honk(severity = "harsh")

# Create handoff document
goose_handoff()

Team Lead

# Backup team's work
goose_backup()

# Clean up test objects
goose_clear_tags(c("test", "temp", "draft"))

# Summarize sprint work
goose_summarize_session()

# Create continuation for next sprint
goose_continuation_prompt()

Key Functions by Category

πŸ€– AI Assistant

πŸ” Intelligent Analysis (NEW!)

πŸ“Š Data Tools (NEW!)

πŸ’Ύ Memory Management

🎨 Visualization & Branding

🎯 Formatting (NEW!)

⚑ Advanced Features

Visual Examples

More examples in docs/assets/ and inst/examples/.

Documentation & Examples

Version History

v0.1.1 (Current) - Feature Complete! πŸŽ‰

v0.1.0 (Initial Release)

Contributing

We welcome contributions! Please see the CONTRIBUTING.md and CODE_OF_CONDUCT.md files in the source repository at https://github.com/blockbtheriault/gooseR.

License

MIT License. See LICENSE for details.

Acknowledgments

Built with ❀️ by the Block People Analytics & Research team. Special thanks to the goose team for creating such an amazing AI platform!


gooseR: Making R development more intelligent, one honk at a time! πŸ¦†

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.