chartmaking

Chartmaking is the practice of creating graphical representations of data to communicate patterns, trends, and relationships. It involves preparing data, selecting an appropriate chart type, and applying visual design that makes the information clear and accurate.

The modern practice began in the late 18th century with William Playfair, who introduced the bar chart, line chart, and pie chart. Since then charts have become central in statistics, business, journalism, and science as tools for analysis and communication.

Common chart types include bar charts for comparisons, line charts for trends over time, pie charts for parts of a whole, histograms and box plots for distributions, scatter plots for relationships, and heatmaps for values across two variables. More specialized forms such as area charts, violin plots, and radar charts are used in specific contexts.

Good chartmaking adheres to design principles: choose a chart type that matches the data, use appropriate scales, label axes with units, and keep legends clear. Color should be used accessibly for colorblind users, and decoration should be minimized to avoid chartjunk. The data-ink ratio concept encourages presenting data efficiently.

Workflow typically includes data cleaning and transformation, selecting encodings (position for most accuracy, then length or color), and producing captions and source notes. Tools range from spreadsheet programs to programming libraries and visualization platforms, such as Excel, Python’s Matplotlib or Seaborn, R’s ggplot2, and Tableau.

Ethical chartmaking emphasizes honesty: avoid truncating axes, cherry-picking data, or misleading scales; provide sources and, when possible, underlying data for reproducibility.

Applications span business analytics, scientific research, journalism, and public policy.