Data viz

Introductory books

Data visualization is essential for exploratory analysis, model diagnostics, and communication of results. Some important introductory books on this topic are:

• Fundamentals of Data Visualization (Claus O. Wilke): Emphasizes core principles like color coding, effective chart types, and visual redundancy. While it uses R for examples, its lessons are tool-agnostic.

• The Visual Display of Quantitative Information (Edward R. Tufte): A timeless classic that emphasizes minimalist, honest, and data-centric design. Its principles have shaped the foundations of information visualization.

• Data Visualization: A Practical Introduction (Kieran Healy): A hands‑on guide that bridges visualization theory with implementation using R and ggplot2. Highly accessible, focusing on both conceptual clarity and practical plotting—including maps and model outputs.

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Overview of Data Visualization Tools (R, Python, and Linux)

This section provides a quick overview of common visualization libraries used in scientific computing, organized by programming environment.

Python

Python offers a wide range of libraries for both static and interactive plotting:

• Matplotlib: The foundational 2D plotting library for Python. Offers granular control and supports high-quality export for publication.
  • Website: https://matplotlib.org
• Seaborn: Built on top of Matplotlib, Seaborn simplifies statistical plotting with intuitive syntax and attractive defaults.
  • Website: https://seaborn.pydata.org
• Plotly: Enables interactive plots in the browser. Supports scatter plots, choropleths, 3D plots, and more.
  • Website: https://plotly.com/python
• Altair: A declarative library based on Vega-Lite. Best suited for tidy datasets and exploratory visualization.
  • Website: https://altair-viz.github.io
• Bokeh: Ideal for building interactive visualizations and dashboards. Integrates well with web applications.
  • Website: https://bokeh.org
• PyVista / Mayavi: Used for advanced 3D and volumetric visualizations, often in engineering and geoscience contexts.
  • Website: https://pyvista.org

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R

R is known for its expressive plotting ecosystem and is particularly well suited for statistical graphics:

• ggplot2: The most popular R visualization package. Based on the Grammar of Graphics and part of the tidyverse.
  • Website: https://ggplot2.tidyverse.org
• lattice: Offers multi-panel plots using a formula-based syntax. Preceded ggplot2 and still widely used in legacy workflows.
  • Website: https://cran.r-project.org/web/packages/lattice/index.html
• plotly (R): Provides interactive visualizations in R. Can convert static ggplot2 plots into interactive versions with minimal changes.
  • Website: https://plotly.com
• shiny: Enables the development of interactive web applications in R that can include dynamic plots and user inputs.
  • Website: https://shiny.posit.co
• leaflet (R): For creating interactive maps with markers, polygons, and raster overlays.
  • Website: https://rstudio.github.io/leaflet

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Linux & Command-Line Tools

Command-line tools are efficient for automation and quick diagnostics, especially in headless or remote environments:

• gnuplot: A terminal-based plotting tool that supports 2D/3D plotting. Scriptable and highly customizable.
  • Website: http://www.gnuplot.info
• Graphviz: Visualizes graphs from .dot files. Excellent for network diagrams, flowcharts, and dependency trees.
  • Website: https://graphviz.org
• ImageMagick: Not a plotting tool per se, but widely used for converting, resizing, and compositing images (e.g., building figures programmatically).
  • Website: https://imagemagick.org

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