08: Data Visualization

Nowadays, data visualization is much more than just listing numbers in a table. Infographics and interactive charts perhaps best describe the need for the modern presentation of results. Such visualization is meant to make findings intuitively intelligible and easily understandable at a glance, even to the non-expert. At the same time, it should give the resources to interactively dig deeper into the details for those interested or those evaluating the merits. Let the science-based graphic design kick in the artist in you!

Table of contents

1. Introduction to Scientific Graphic Design

• 1.1 Raster Graphics Tools
• 1.2 Vector Graphics Tools
• 1.3 Adobe Creative Cloud
• 1.4 Template-based Web Tools

2. Introduction to Scientific Graphing

• 2.1 Gnuplot – Creating Plots in the UNIX Shell
• 2.2 Plotly-Dash – Data Processing & Interactive Plotting with Python
  • 2.2.1 Introduction to Plotly (Python library)
  • 2.2.2 Introduction to Dash (Python library)
  • 2.2.3 Plotly Graphing - Interactive Examples in the JupyterLab
    • Creating XY Scatter Plot
    • Creating 1D Volcano Plot
    • Creating Heatmap
    • Creating Dendrogram
    • Creating Clustergram (Heatmap with Dendrograms)
• 2.3 RStudio – Data Processing & Plotting with R
  • (start with section 4. Development Environment: R Environment: Setting Up RStudio ⤴)
    • Creating Boxplots in R
    • Creating Heatmaps in R
    • Creating Heatmaps in R using ComplexHeatmap

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