# Fundamental Graphs

When we are given a dataset, we must determine:

1. **What** to visualize i.e. *select and transform*
2. **How** to visualize it i.e. *choose/design a representation*

This section describes various fundamental graphs that can be used to visualize data, including:

* Bar Charts
* Scatter Plots
* Matrix
* Line Charts
* Symbol Maps

These fundamental graphs are widely adopted, effective and useful. They solve a very large percentage of visualization problems, and serve as a good training ground for more sophisticated graphs.

## Bar Chart

A bar chart is used to represent how a **quantity** distributes across a set of **categories**.

Every bar represents a category and the height of each bar denotes the quantity.

![](/files/-M5-0TYNtFaUh7oKqvkd)

Therefore, it is suited for **1 Categorical**/**Ordinal** attribute (on the X axis) and **1 Quantitative** attribute (on the Y axis).

## Line Chart

A line chart is used to visualize how a **quantity** changes in relation to another **quantity** (typically ***time***).

![](/files/-M5-0TYPXnJIVRSO8WLl)

Therefore, it is suited for **Temporal** and **Quantitative** attributes on the X and Y axes respectively.

## Scatter Plot

A scatter plot is used to visualize how a **quantity** relates to another **quantity**.

![](/files/-M5-0TYRUnPBL3BniKtx)

Therefore, it is suited for **Quantitative** and **Quantitative** attributes on the X and Y axes respectively.

## Matrix

A matrix can be used to visualize how a **quantity** distributes across *two* **categories**. Note that the sizes of the squares are proportional to the quantities.

![](/files/-M5-0TYTi8FMA43J7p00)

Therefore, it is suited for **2** **Categorical** (or **Ordinal**) attributes and **1 Quantitative** attribute (in the cells).

## Symbol Map

A symbol map is used to visualize how a **quantity** distributes across *two* **spatial coordinates**. Note that the sizes of the symbols (colored areas) are proportional to the quantities.

![](/files/-M5-0TYV1_ohdE8bTEle)

Therefore, it is suited for **Spatial** and **Quantitative** attributes.


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