A standardised workflow from raw data to submission-quality figures, covering R/Python plotting and post-processing in Inkscape.
Remove missing values, outliers and duplicate records, and reshape the data into long format for easier plotting.
Long format usually works best for grouped plots in ggplot2 and seaborn/matplotlib.
Choose charts according to the research question: bar charts/box plots for comparison, line charts for trends, scatter plots for correlation, stacked charts for composition.
Avoid pointless 3D charts, too many colours and unnecessary decoration.
Draw the base figure with ggplot2, matplotlib or seaborn and export SVG, PDF or high-resolution PNG.
Make sure the data are represented correctly first, then polish.
Adjust fonts, line widths, colours, panel labels and combined layouts in Inkscape.
Keep an editable SVG file, then export the PDF/TIFF for submission.
Check figure dimensions, resolution, fonts, colour mode and file format against the journal's requirements.
Common requirements include 300–600 DPI, single/double column widths and TIFF/PDF/EPS formats.
