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Features Overview

Everything included in DataSci Homelab, organized by category.

Base System

Component Version Notes
Operating System Ubuntu 22.04 LTS Long-term support, stable base
Architecture AMD64 + ARM64 Native support, no emulation
Locales en_US.UTF-8 Full Unicode support
Timezone America/Los_Angeles Configurable via env

R Environment

Core Installation

Component Version
R 4.5.2
RStudio Server 2025.12.0

Pre-installed R Packages

Package Description
tidyverse Complete tidyverse ecosystem (dplyr, ggplot2, tidyr, etc.)
data.table High-performance data manipulation
duckdb Embedded analytical database
Package Description
ggthemes Additional ggplot2 themes
wesanderson Wes Anderson color palettes
patchwork Combine multiple plots
scales Scale functions for visualization
showtext Custom fonts in plots
formattable Formatted tables
Package Description
lme4 Linear mixed-effects models
lmerTest Tests for lmer models
emmeans Estimated marginal means
performance Model performance metrics
rstatix Pipe-friendly statistics
broom Tidy model outputs
broom.mixed Tidy mixed model outputs
tidymodels Tidyverse modeling framework
Package Description
DBI Database interface
RPostgres PostgreSQL driver
RMySQL MySQL driver
odbc ODBC connections
dbplyr Database backend for dplyr
Package Description
devtools Package development tools
usethis Workflow automation
renv Project environments
styler Code formatting
lintr Code linting
conflicted Handle function conflicts
Package Description
quarto Next-gen R Markdown
tinytex Lightweight LaTeX
rticles Article templates
Package Description
pacman Package management
glue String interpolation
here Project-relative paths
janitor Data cleaning
skimr Data summaries
eyeris Eye-tracking data processing

Python Environment

Core Installation

Component Version
Python 3.10
pip Latest
JupyterLab 4.3+

Pre-installed Python Packages

Package Description
numpy Numerical computing
pandas Data manipulation
scipy Scientific algorithms
scikit-learn Machine learning
Package Description
pyarrow Apache Arrow interface
polars Fast DataFrame library
duckdb Embedded analytics
Package Description
xgboost Gradient boosting
lightgbm Fast gradient boosting
statsmodels Statistical models
Package Description
matplotlib Core plotting
seaborn Statistical visualization
plotly Interactive plots
pillow Image processing
Package Description
jupyterlab Modern Jupyter interface
notebook Classic notebook
ipykernel IPython kernel
ipywidgets Interactive widgets
jupyterlab-git Git integration
nbdime Notebook diffing
Package Description
psycopg2-binary PostgreSQL driver
pymysql MySQL driver
sqlalchemy SQL toolkit
Package Description
rpy2 R-Python bridge
python-dotenv Environment variables
tqdm Progress bars
requests HTTP library
beautifulsoup4 HTML parsing

Publishing Tools

Tool Version Description
Quarto 1.8.26 Universal publishing system
Pandoc 3.5 Document converter
TinyTeX Latest Minimal LaTeX distribution

Quarto Features

  • Render to HTML, PDF, Word, presentations
  • Works with both R and Python
  • Integrated with RStudio and Jupyter
  • Cross-reference support
  • Citation management

System Tools

Editors

Tool Description
vim Terminal text editor
nano Simple text editor

Development

Tool Description
git Version control
build-essential C/C++ compiler toolchain
gfortran Fortran compiler
cmake Build system

Fonts

Font Use Case
Liberation fonts Document rendering
DejaVu fonts Terminal/code
Lato Custom documents
SF Mono RStudio editor (via preferences)

RStudio Configuration

Default Preferences

The following preferences are applied automatically:

{
  "editor_keybindings": "vim",
  "editor_theme": "Xcode",
  "font_size_points": 13,
  "server_editor_font": "SF Mono",
  "relative_line_numbers": true,
  "rainbow_parentheses": true,
  "reformat_on_save": true,
  "auto_save_on_blur": true
}

See Customization for how to modify these.

JupyterLab Configuration

Default Settings

  • Token-based authentication
  • Git extension enabled
  • Notebook diffing enabled
  • Interactive widgets support

Available Kernels

Kernel Language
Python 3 Python
R R (via IRkernel)

Volume Persistence

Path Purpose Persisted
/home/rstudio User home directory ✓
/usr/local/lib/R/site-library R packages ✓
/home/rstudio/.local Python user packages ✓
/data Shared data ✓

Networking

Port Service
8787 RStudio Server
8888 JupyterLab

Both services are available on 0.0.0.0 (all interfaces) within the container.

Health Checks

The container includes health checks:

healthcheck:
  test: ["CMD", "curl", "-f", "http://localhost:8787"]
  interval: 30s
  timeout: 10s
  retries: 3
  start_period: 60s

This ensures:

  • Automatic restart on failure
  • Docker Compose health status
  • Proper startup sequencing

What's NOT Included

Intentionally excluded to keep the image focused:

  • GPU/CUDA support — Use cloud instances for GPU workloads
  • Database servers — Connect to external databases
  • Apache Spark — Use dedicated Spark clusters
  • Deep learning frameworks — Install as needed (TensorFlow, PyTorch)

These can be added via:

  1. Installing at runtime (packages persist in volumes)
  2. Building a custom image based on this one
  3. Using Docker Compose to add additional services