Coordinate Systems: Setting the Stage for Your Plot

🧭 Defining Data Placement

So far, you’ve used aesthetics and layers to shape your charts, with data placed based on the values mapped to x and y aesthetics — either specified explicitly or partly returned by ggplot2.

Without much thought, all your charts were built using a  Cartesian coordinate system  — a familiar grid of linear horizontal and vertical axes. By default, these axes have also been untransformed, meaning each tick mark always represented the same interval of change.

But ggplot2 provides a range of coordinate systems that go far beyond this default. Whether you want to flip coordinates, fix aspect ratios, apply transformations, or project data into circular or spatial space, the coordinate system plays a key role.

Some coordinate systems keep shapes intact but adjust orientation or scaling. Others reshape your entire grid — to turn bars into wedges, to display log transformations, or to control how spatial data is projected into a 2-dimensional space.

🎛️ Coordinate Systems at a Glance

Coordinate systems in ggplot2 determine how your data's x and y values are translated into positions on the plot. Understanding them is key to creating more advanced and specialized visualizations.

•  Linear Coordinates 
  
  These systems maintain the direct, proportional mapping of data values to visual positions, preserving the original geometric shapes and relationships.
  • coord_cartesian() 📈
    is the default system showing data relationships along linear axes that adjust to the available space.
  • coord_fixed() 🔒
    locks the aspect ratios, ensuring that a unit on the x-axis represents the same physical distance (or a multiple of it) on the y-axis.
  • coord_flip() 🔄
    swaps x and y axes for easier horizontal layouts (however, in many layers you can simply switch x and y nowadays).