Proportions can be visualized as pie charts, side-by-side bars, or stacked bars (Chapter 10), and as in the case for amounts, bars can be arranged either vertically or horizontally.

When proportions are specified according to multiple grouping variables, then mosaic plots, treemaps, or parallel sets are useful visualization approaches

Scatterplots represent the archetypical visualization when we want to show one quantitative variable relative to another. If we have three quantitative variables, we can map one onto the dot size, creating a variant of the scatterplot called bubble chart. For paired data, where the variables along the x and the y axes are measured in the same units, it is generally helpful to add a line indicating x = y

For large numbers of points, regular scatterplots can become uninformative due to overplotting. In this case, contour lines, 2D bins, or hex bins may provide an alternative. When we want to visualize more than two quantities, on the other hand, we may choose to plot correlation coefficients in the form of a correlogram instead of the underlying raw data

When the x axis represents time or a strictly increasing quantity such as a treatment dose, we commonly draw line graphs. If we have a temporal sequence of two response variables, we can draw a connected scatterplot where we first plot the two response variables in a scatterplot and then connect dots corresponding to adjacent time points. We can use smooth lines to represent trends in a larger dataset.

The primary mode of showing geospatial data is in the form of a map. In addition, we can show data values in different regions by coloring those regions in the map according to the data.(Choropleth). In some cases, it may be helpful to distort the different regions according to some other quantity (e.g., population number) or simplify each region into a square. Such visualizations are called cartograms.