# Graphs

Graphs show the relationship between two or more variables, generally along an x- and y-axis. Graph types that are generally used in scientific articles are bar graphs, line graphs, and scatter plots, but many more exist all with their own characteristics. The type of graph most suitable for your data depends on the message you want to convey (*e.g.*, the relationship you want to show^{10}) and what type of data and variables you have^{11}. For more information about different types of variables, see the information box below.

This chapter gives you some general information on designing graphs and discusses some of the most common graph types in Earth sciences. If you want to learn more on data visualisation in graphs, we recommend you to also take a look at this website, an open-source e-book on the fundamentals of data visualisation. Although it is specifically written for creating graphs in R, it contains a lot of relevant information on general graph design.

A variable is any factor that can take on different values, *e.g.*, age, species, or concentration. Different types of variables can be distinguished, based on the type of data they contain:

**Quantitative variables** contain numerical data, i.e. amounts, and can be split into **discrete **and **continuous **variables.

**Discrete variables**consist of only whole numbers. This type of data is obtained by counting,*g.*, species counts.**Continuous variables**can consist of any real number. This type of data is obtained by numeric measurements,*g.*, temperature, age etc.

**Categorical variables **contain qualitative data and represents groupings. This group can be split into **nominal**, **ordinal**, and **binary** variables.

**Nominal variables**consist of groups without a rank or order in them**Ordinal variables**consist of groups ranked in a specific order**Binary variables**represent yes/no outcomes

Note that categorical variables can appear numerical when groups are given specific numbers, but the numbers represent groups.