Understanding plots and graphs

There are many types of visual expression available to aid in visualization. We are going to briefly discuss the most common and useful ones, and then demonstrate several Java techniques for achieving these types of expression. The choice of graph, or other visualization tool will depend upon the dataset and application needs and constraints.

A bar chart is a very common technique for displaying relationships in data. In this type of graph, data is represented in either vertical or horizontal bars placed along an X and Y axis. The data is scaled so the values represented by each bar can be compared to one another. The following is a simple example of a bar chart we will create in the Using country as the category section:

A pie chart is most useful when you want to demonstrate a value in relation to a larger set. Think of this as a way to visualize how large the piece of pie is in relation to the entire pie. The following is a simple example of a pie chart showing the distribution of population for selected European countries:

Time series graphs are a special type of graph used for displaying time-related values. These are most appropriate when the data analysis requires an understanding of how data changes over a period of time. In these graphs, the vertical axis corresponds to the values and the horizontal axis corresponds to particular points in time. In particular, this type of graph can be useful for identifying trends across time,or suggesting correlations between data values and particular events within a given time period.

For example, stock prices and home prices will change, but their rate of change varies. Pollution levels and crime rates also change over time. There are several techniques that visualize this type of data. Often, specific values are not as important as their trend over time.

An index chart is also called a line chart. Line charts use the X and Y axis to plot points on a grid. They can be used to represent time series data. These points are connected by lines, and these lines are used to compare values of multiple data at one time. This comparison is usually achieved by plotting independent variables, such as time, along the X axis, and independent variables, such as frequency or percentages, along the Y axis.

The following is a simple example of an index chart showing the distribution of population for selected European countries:

When we wish to arrange larger amounts of data in a compact and useful manner, we may opt for a stem and leaf plot. This type of visual expression allows you to demonstrate the correlation of one value to many values in a readable manner. The stem refers to a data value, and the leaves are the corresponding data points. One common example of this is a train timetable. In the following table, the departure times for a train are listed:

However, this table can be hard to read. Instead, in the following partial stem and leaf plot, the stem represents the hours at which a train may depart, while the leaves represent the minutes within each hour:

This is much easier to read and process. A very popular form of visualization in statistical analysis is the histogram. Histograms allow you to display frequencies within data using bars, similar to a bar chart. The main difference is that histograms are used to identify frequencies and trends within a dataset while bar charts are used to compare specific data values within a dataset. The following is an example of a histogram we will create in the Creating histograms section:

A scatter plot is simply collections of points, and analysis techniques, such as correlation or regression, can be used to identify trends within these types of graph. In the following scatter chart, as developed in Creating scatter charts, the population along the X axis is plotted against the decade along the Y axis: