Different Types of Charts and Graphs for Visualizing Data
Data visualization refers to the representation of information and data using pictorials, graphs, and charts to provide an easy way to see trends, outliers, and patterns in data, or grasp difficult concepts.
Below is a description of each type of chart/graph and design best practice for each.
- Bar Chart (horizontal bar graph)
A bar graph or a bar chart is a pictograph that uses bars instead of pictures to display information. Bar graphs can be vertical or horizontal. In this article, graphs with vertical bars are called column graphs or column charts. Bar graphs are used to plot numeric values for levels of a categorical feature as bars.
In bar graphs, the values of the categorical data are represented along the x-axis of the graph, and the length of the bars shows the values.
2. Stacked Bar Chart
A stacked bar chart is a bar graph with multiple data series stacked end-to-end with the far-right end of the bar representing the totals of all the components in the bar. The x-axis represents quantitative data while the y-axis categorical data. Stacked bar graphs are used to show how a larger component is divided into its various entities, and the relative effect each entity has on the total entity i.e., part -to-whole relationship. Each data series takes a different shade or color explained using a legend.
3. 100% Stacked Bar Chart
This variation of the stacked bar chart plots the percent of the values instead of the actual values. The total of each stacked bar always equals 100%.
4. Column Charts (Vertical Bar graphs)
Colum charts or column graphs are bar charts with vertical bars. In column charts, the values of the categorical data is shown along the y-axis of the graph, and the height of the bars denote the values.
5. Stacked Column Charts
Like stacked bar graphs, stacked column charts have each bar representing the whole and each segment denotes the various parts of the whole. The y-axis represents quantitative data while the x-axis categorical data.
6. 100% Stacked Column Graph
This variation of the stacked column chart plots the percent of the values instead of the actual values. The total of each stacked bar always equals 100%.
7. Grouped Bar Charts and Grouped Column Charts
A grouped bar/column chart (clustered bar/column graph) is another variation of the bar/column chart that compares different categories of two or more groups. The categories are grouped and arranged side-by-side making interpretation easy inside the groups and even between the same categories. They are useful in making comparisons across different categories of data.
Best practices for designing Bar and Column charts:
·All bars must be plotted against a zero-value baseline and have a consistent scale.
Avoid 3-d effects on your bars or rounding of the bar caps.
· Sort the bars from longest to shortest.
· Use consistent colors throughout the chart.
· Always make use of data labels for easy readability.
· Use bar charts if you have more than 10 items to compare to help avoid clutter.
· For stacked bar charts or stacked column charts, always limit data series and categories to less than five.
8. Line Graphs
Line graphs track changes over short and long periods of time to reveal trends or change over time. Line charts show the relationships within continuous data and use line segments connected by points from left to right to show the changes in value.
9. Area Charts
Area charts are generated by shading the region between the line and x-axis in the line chart and can be thought of as a combination of the line chart and bar chart. They also track changes over time for one or more entities, or two or more related groups that make up one whole category.
10. Stacked Area Charts
Stacked Area charts work by showing how the constituent parts of a whole change over time. The multiple data series start each point from the point left by the previous data series. The entire chart represents the total of all the data plotted.
11. 100% Stacked Area Chart
This variation of the stacked area chart plots the percent of the constituent parts of a whole instead of the actual values. The y-axis scale must always be 100%. The parts are always stacked up vertically, with the height of each stack representing the percentage proportion of that category at a given point in time.
Best practices for designing Line charts and Area charts:
Again, do not use 3-D charts.
Label the axes clearly.
Avoid distracting chart elements such as varying colors, grids, or bulky legends.
Start axis at zero, unless your data sets start way above zero.
Do not plot more than five lines as this will make the chart cluttered or hard to read.
12. Dual Axis (Combo) Charts
A dual axis chart provides a great way of illustrating the relationship between two different variables as it allows you to plot using a shared x-axis and two y-axes. It helps combine multiple kind of data into one chart using the column graph and the line graph in the same chart.
Best practices for designing Dual Axis Charts:
Always display variables with different numerical units.
Avoid overwhelming the chart with too many variables. Instead, display the relationship between two variables only.
Observe other best charting practices outlined above, such as using line charts to show trends.
13. Pie Chart
Pie charts consist of a circle divided into wedge-shaped segments used to show the relative sizes of components to one another and to the whole. The sum of all segments should equal 100%.
14. Doughnut Charts
The doughnut chart is a variant of the pie chart with a hole in the center. They display categories as arcs and not slices.
Best practices for designing Pie charts and Doughnut Charts:
Make sure your segments add up to 100% (recommended 2-decimal places).
Avoid using the 3-d version of the pie-chart
Avoid more than three categories to keep it clean and consistent.
15. Scatter Plots
Scatter plots, also known as X-Y Plots, are chart types used to show correlation, that is, the relationships between two numeric variables. Each point’s position on the x-and-y axes indicate value on the associated variable. If both variables increase together, they have a positive relationship or positive correlation. If one variable increase while the other decreases, they have a negative relationship or negative correlation. Sometimes both variables do not follow any pattern and hence, have no relationship.
Best practices for designing Scatter Plots:
Start y-axis value at 0.
Avoid overplotting — having lots of data points to plot makes it difficult to observe relationships between points and variables.
Always ensure you interpret the plot accordingly. Sometimes, the pattern is simply coincidental as correlation does not always imply causation.
Add trendlines to show the strength of the relationship between the two variables.
Include more variables and encode using colors to give the chart some nuance.
16. Bubble Chart
A bubble chart is a variant of the scatter chart that examines the relationships between three numerical variables. The chart displays bubbles (multiple circles) in a y-and-x-axis plot, with the bubbles replacing the dots in the scatter plots. The bubble chart allows the addition of marker size as a dimension to provide comparison between three variables rather than just two.
Best practices for designing Bubble Charts:
Always limit the number of points to plot to avoid clutter.
Include a legend or tick marks for the third variable to show how the different bubble sizes correspond with the third variable.
Do not use bubble chart for representation of zero or negative values since there are no areas to denote such values.
17. Treemap
Treemap chart is a visualization technique used to display hierarchical data using nested rectangles of sizes proportional to the corresponding data value. The rectangles represent the quantitative values of the categories and sub-categories, with each rectangle representing two numeric values. Drilling down from the category to the sub-category will make it easy distinguishing between categories and data values at-a-glance.
Best practices for designing a Treemap:
Avoid including many data points on a single level.
18. The Waterfall Chart
A waterfall chart displays the cumulative effect as values are added or subtracted. Waterfall charts have connecting lines that show how an initial value is affected by a series of positive and negative values. Waterfall charts excel at showing how positive and negative data points contribute to the total.
Best practices for designing a Waterfall Chart:
Use color-coding to display the change as the values increase or decrease.
19. Funnel Charts
Funnel charts are used to show values across multiple stages in a process. The values reduce as they pass from one phase to another and each phase is represented as a different portion of the whole process (100%). The values reduce gradually from the first phase to the last phase of the process, allowing the bars to resemble a funnel.
Best practices for designing a Funnel Chart:
Do not use a funnel chart for processes with less than three phases or if the stages are roughly of the same size.
Use proper labeling to avoid clutter.
Use different colors to make it easier to tell the different stages in the process.
