Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

Histogram and Frequency Polygon

A histogram is a bar chart that displays a frequency distribution. It is constructed as follows:

• The class frequencies are shown on the vertical (y) axis (by the heights of bars drawn next to each other).
• The classes (intervals) are shown on the horizontal (x) axis.
• There is no space between the bars.

From a histogram, we can see quickly where most of the observations lie. The shapes of histograms will vary, depending on the choice of the size of the intervals.

The frequency polygon is another means of graphically displaying data. It is similar to a histogram but the bars are replaced by a line joined together. It is constructed in the following manner:

• Absolute frequency for each interval is plotted on the vertical (y) axis.
• The midpoint of each class (interval) is shown on the horizontal (x) axis.
• Neighboring points are connected with a straight line.

Unlike a histogram, a frequency polygon adds a degree of continuity to the presentation of the distribution.

It is helpful, when drawing a frequency polygon, first to draw a histogram in pencil, then to plot the points and join the lines, and finally to rub out the histogram. In this way, the histogram can be used as an initial guide to drawing the polygon.

A cumulative frequency distribution chartis the sum of the class and all classes below it in a frequency distribution.

Bar Chart

A bar chart is a way of summarizing a set of categorical data. The height of each bar is proportional to a specific aggregation (for example the sum of the values in the category it represents). The categories could be something like an age group or a geographical location. A bar chart usually compares different categories. It is useful for looking at a set of data and making comparisons.

Although they look the same, bar charts and histograms have one important difference: they plot different types of data. Plot discrete data on a bar chart, and plot continuous data on a histogram.

A bar chart is used for when you have categories of data: Types of movies, music genres, or dog breeds. It's also a good choice when you want to compare things between different groups. You could use a bar graph if you want to track change over time as long as the changes are significant (for example, decades or centuries). If you have continuous data, like people's weights or IQ scores, a histogram is best.

Grouped bar charts or stacked bar charts can present the frequency distribution of multiple categorical variable simultaneously.

Tree-Map

Tree-maps are an alternative way of visualizing the hierarchical structure of a tree diagram while also displaying quantities for each category via area size. Each category is assigned a rectangle area with their subcategory rectangles nested inside of it.

The main advantages:

• identify the relationship between two elements in a hierarchical data structure;
• accurately display multiple elements together;
• show ratios of each part to the whole;
• visualize attributes by size and color coding.

The downside to a tree-map is that it doesn't show the hierarchal levels as clearly as other charts.

Word Cloud

A word cloud is a novelty visual representation of text data, typically used to depict keyword metadata on websites, or to visualize free form text. Tags are usually single words, and the importance of each tag is shown with font size or color.

Line Chart

A line chart is generally used to show trend of a measure (or a variable) over time. Using a line chart, one can see the pattern of any dependent variable over time like share price, EPS of a company, weather recordings (like temperature, precipitation or humidity), etc.

Why do you need a Line Chart?

1. To see changes of a dependent variable over time.

2. To identify trends and spot spikes and dips

3. To compare patterns of multiple sections

Scatter Plot

A scatter plot (aka scatter chart, scatter graph) uses dots to represent values for two different numeric variables. The position of each dot on the horizontal and vertical axis indicates values for an individual data point. Scatter plots are used to observe relationships between variables.

Relationships between variables can be described in many ways: positive or negative, strong or weak, linear or nonlinear.

Scatter plots are a powerful tool for finding patterns between two variables, for assessing data range, and for spotting extreme values.

Through the use of a "Line of Best Fit" or a trend line, scatter plots to help identify trends.

Two common issues have been identified with the use of scatter plots - over-plotting and the interpretation of causation as correlation.

Over-plotting occurs when there are too many data points to plot, which results in the overlapping of different data points. It can make relationship identification between variables challenging.

Concerning correlation, it is important to remember that correlation does not mean that the changes observed in one variable are responsible for the changes observed in another variable. Correlation should not be interpreted as causation. Causation implies that an event occurring will have an impact on an outcome.

Heat Map

A heat map is data analysis tool that uses color the way a bar graph uses height and width. It organizes and summarizes data in a tabular format and represents it using a color spectrum. It is often used in displaying frequency distributions or visualizing the degree of correlation among different variables.

Guide to Selecting among Visualization Types

The key consideration when selecting among chart types is the intended purpose of visualizing data. That is, whether it is for exploring/presenting distributions or relationships or for making comparisons. Exhibit 34 in the textbook is a great summary for the purpose.