A **Scatterplot** displays the value of **2 sets of data** on 2 dimensions. Each dot represents an observation. The position on the X (horizontal) and Y (vertical) axis represents the values of the 2

variables. It is really useful to study the relationship between both variables. It is common to provide even more information using colors or shapes (to show groups, or a third variable). It is also

possible to map another variable to the size of each dot, what makes a bubble plot. If you have many dots and struggle with **overplotting**, consider using 2D density plot.

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**Ggplot2 Scatterplots**

Use the **geom_point()** function of ggplot2 to build scatter plot in R. It allows to create basic scatterplots, and offers a lot of different customisations

**Base R scatterplots**

Of course baseR offers a really good option to build a scatterplot as well.

**Manhattan plot**

A Manhattan plot is a particular type of scatterplot used in genomics. The X axis displays the position of a genetic variant on the genome. Each chromosome is usually represented using a different color. The Y axis shows p-value of the association test with a phenotypic trait.

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