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 design1.1 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
2.1.1 Gnuplot: variables, loops, conditionals
2.1.2 Gnuplot: filled curves
2.2 Plotly-Dash: interactive plotting with Python
2.2.1 Introduction to Plotly (python)
2.2.2 Introduction to Dash (python)
2.2.3 Plotly graphing - interactive examples in the JupyterLab
Creating XY scatter plot
Creating 1D volcano plot
Creating heatmap
Creating dendrogram
Creating clustergram
2.3 RStudio – data processing & plotting with R
Creating boxplots in R
Creating heatmaps in R
Creating heatmaps in R using ComplexHeatmap