Report components should use the style that best visualizes the underlying data. Data tables are flexible and versatile for displaying data but can in a positive way be augmented by visualizations that show data in a more natural form such as a map, chart or tree.
The table is a versatile component that support multi column filtering, vertical row combining, pivoting and more. The table is also the default visualization of any new report component.
Bar and line charts¶
Line charts should be used only for time series (chronological) or when there is some other sequence to the dimensions on the x-axis, e.g. dates, months, sequence of stages of a project, sequence of meters along on a gas pipeline, and they should be used to detect trends and patterns, not to give people exact quantitative readings.
As line charts are not really intended to give people exact numbers, forcing zero scaling is not necessary and can make it considerably more difficult to detect said trends and patterns.
Bar charts should be used for comparing specific x-axis values, though they can certainly be used for time series, like line charts. They can also be used to display parts of a whole in favor of pie charts, in which case, the space between the bars should be reduced. Orientation: Do not use vertical or diagonal text to label the axis of a bar chart. If the x-axis has longer text descriptions, use horizontal bar chart, so the text can read left-to-right, horizontally (the way we normally read).
As the area of bars implies volume, it can be deceptive to use dynamic scaling with bar charts.
There should be some logical order to the dimensions on the x-axis. In the case of a line chart, it should follow the chronological, process, or stage order that caused you to select a line chart in the first place. In the case of bar charts, the order should have some rhyme and reason to it: sorted by y-axis value, alphabetical, etc., depending on the content of the chart and what its intended use is, e.g. ranking, distribution.
The pie chart is a circular graph that shows the relative contribution that different categories contribute to an overall total. A wedge of the circle represents each category’s contribution, such that the graph resembles a pie that has been cut into different sized slices. Every 1% contribution that a category contributes to the total corresponds to a slice with an angle of 3.6 degrees.
- Pie charts are a visual way of displaying data that might otherwise be given in a small table.
- Pie charts are useful for displaying data that are classified into nominal or ordinal categories. Nominal data are categorised according to descriptive or qualitative information such as county of birth or type of pet owned. Ordinal data are similar but the different categories can also be ranked, for example in a survey people may be asked to say whether they classed something as very poor, poor, fair, good, very good.
- Pie charts are generally used to show percentage or proportional data and usually the percentage represented by each category is provided next to the corresponding slice of pie.
- Pie charts are good for displaying data for around 6 categories or fewer. When there are more categories it is difficult for the eye to distinguish between the relative sizes of the different sectors and so the chart becomes difficult to interpret.
Summarize any remaining values to accurately represent the full dataset in a pie chart.
The pie chart is mainly used for showing proportion of different categories. Each arc length represents the proportion of data quantity.
If you need to be able to accurately discern differences between categories of data, the bar graph chart may be a better choice since it is easier to distinguish between the difference in length between bars than it is to judge potentially the minor radian differences of categories in a pie chart.
The term 'funnel analysis' comes from the analogy with a physical kitchen or garage funnel, which gets narrower along its length, allowing less volume to pass through it. Similarly, an analytics funnel helps visualize how a large number of individuals enter the funnel, yet only a small proportion of them will perform the intended actions and reach the end goal.
Funnel visualization is used to visualize falloff towards a goal and are often used in Ecommerce type situations, where there is a shopping cart and a checkout process involved. Marketers and analysts usually set up a Goal Funnel that starts at a landing page of a pay-per-click or email marketing campaign, and that ends at the Goal, which is usually the “Thank You” page or “Receipt” page that a user sees after they complete a purchase. After some data has been collected, marketers and analysts will take a look at each page, or “step” in the Funnel, and see where users are abandoning the shopping process.
A bubble chart is a type of chart that displays three dimensions of data. Each data point is plotted as a disk that expresses two of the vi values through the disk's xy location and the third through its size. Bubble charts can facilitate the understanding of social, economical, medical, and other scientific relationships.
Bubble charts can be considered a variation of the scatter plot, in which the data points are replaced with bubbles.
You can use a bubble chart instead of a scatter chart if your data has three data series that each contain a set of values.
The sizes of the bubbles are determined by the values in the third data series.
KPIs and Gauges¶
KPIs and guages display single values as a text or a text and gauge combination. They are named after Key Performance Indicators, a measurable value that demonstrates how effectively a company is achieving key business objectives.
The treemap is a data visualization that is used to display hierarchical data using nested rectangles; the treemap chart is created based on this technique of data visualization.
The treemap chart is used for representing hierarchical data in a tree-like structure. Data, organized as branches and sub-branches, is represented using rectangles, the dimensions and plot colors of which are calculated based on the quantitative variables associated with each rectangle—each rectangle represents two numerical values. You can drill down within the data to, theoretically, an unlimited number of levels. This makes the at-a-glance distinguishing between categories and data values easy.
The map displays data on a world map with a marker for each data point.