In this part of the data visualization project, we will review charts that help find similarities and differences between various categories of data and discuss their purposes and specifics. But firstly, let’s sort out what a discrete data is. What is discrete data? Data is discrete if you can answer affirmatively on the following questions about it: Is it countable? Is it possible to divide the data into smaller parts? (i.e., to categorize it) Discrete data can contain only a finite number of values. One of its notable properties is that, unlike continuous data, it can’t be measured, only counted. : the number of players in a team, the number of planets in the Solar System. (continuous) : height, weight, length, income, temperature. Examples of discrete data Examples of non-discrete data Bar chart The simplest and the most popular type of chart. It displays grouped data using rectangular bars with lengths that are proportional to the values. Bar charts are broadly used in marketing and finance. Purpose Use it to compare data points that are spread across categories with each other. Pros Makes data more readable if the categories have long names or if you have more than 10 categories Recommendation Arrange bars on the chart in a logical ordering: ascending or descending one Examples and variations A chart in which the values are compared inside one category. single-series This is an example of a (grouped) . Use it for comparing multiple series inside the same category. clustered bar chart Stacked bar chart A stacked bar chart is composed of multiple bar series stacked horizontally one after the other. This modification of the bar chart makes it easier to track the variation in the individual values and their total value. Purpose Use it if you are interested in the proportional contributions either of categories to the total or of values within each category. Pros If designed wisely, this type of chart is the best option for comparing multiple categories by measuring the bar lengths Recommendation Limit the chart to six-seven series, otherwise, a chart will be cluttered and difficult to interpret Example Column chart It’s constructed the same way as a bar chart but with a vertical axis for categories and a horizontal — for their values. Purpose It helps track changes in values over time by comparing total column lengths. Pros Highly readable when comparing values between a small number of categories (less than 12) Recommendation In case your dataset has negative values, use the column chart instead of the bar chart as the negative values are associated with a downward direction Examples and variations A column chart: single-series (grouped) column chart: Clustered Stacked column chart A stacked column chart is composed of multiple column data series stacked on top of one another. Purpose The stacked column chart is designed to compare totals and notice changes at the item level that are likely to have the most impact on changes in totals. Pros Best for part-to-whole comparisons over time or across categories. It helps to get an understanding of the integral picture in a quick glance without a focus on the details. Examples Bullet chart A bullet chart is a modification of a bar chart adapted by modern business needs. It can be displayed both vertical and horizontal. The chart consists of a that represents the target value, an that represents the current value of a metric and a . target marker achievement bar comparison range Purpose It was designed by Stephen Few for tracking progress toward a goal and measuring how far you are from the target. Pros It removes the necessity to use circular/linear gauges and meters on a dashboard Recommendation Use custom background colors to encode the interpretation of values that lie within the comparison range Example Usual meaning: the lighter the background, the better the result. However, in the case of “Expenses”, according to its negative sense, you can reverse the quantitative scale. Spider chart In this chart, each variable’s axis starts from the center point. Axes are arranged radially around it. The value is presented by an anchor on the axis. This data point is connected with the axis by a line. It’s the process of plotting a polygon. As you may notice, it reminds a spider web; hence the name. Equivalent names are ‘ ’, ‘ ’, ‘ ’, and ‘ ’. polar chart web chat radar chart star plot Purpose It’s designed for comparing multivariate data that has three or more quantitative variables. Pros Works best for comparing products by highlighting their strengths (features) and weaknesses Recommendations Use color coding and labels to distinguish multiple items when comparing them. Don’t be misled by the area of the polygons: it is increased by the squares of values. Thus, one may think that tiny changes are more important than they really are. Take into account that the area and the shape of the polygons may vary significantly depending on the position of axes. Examples Summary We truly hope this overview will help you decide which charts are best suited for your data and get a general idea about how you can compare discrete groups of data graphically. Stay tuned for further updates! Useful links Recommendations on how to use the charts in your presentation How to use WebDataRocks Pivot Table with Google Charts Demos Column chart Bar chart Stacked column chart Stacked bar chart Originally published at www.webdatarocks.com .