In this guide, we will walk through different data and chart types to help you find the best ZingChart to use with your data!

Data Types

When translating your data into a visualization, the first step is to analyze the type of data you've gathered. There are multiple data types to consider:

• Numeric: Data that consists of one or many numeric variables, ordered or unordered (e.g. the number of pets for sale in a store)
• Category: Data that consists of one or many categorical variables (e.g. the number of pets for sale in a store, by species)
• Number and category: Data that consists of a mixture of the two above data types. Can be a one:one, one:many, or many:one relationship. (e.g. the number of pets sold per month, by species)
• Geographic: Data that is tied to geographic location (e.g. population maps, choropleth maps)
• Network/Hierarchy: Relational data (e.g. file system, org chart)
• Time series: Data that is measured over time (e.g. page hits per day)

While this is not an exhaustive list, this should cover most of the data type bases. Determining the type of data you have is a great guiding tool for what chart type you should choose - if you have time series data, a pie chart will not be an effective way to communicate any message!

If you're still unsure what kind of data you have, don't worry! Let's keep going, and we can come back to the data types later.

Chart Types

The best visualizations consist of reliable data and a clear message. Before we get into the different types of charts that ZingChart offers, let's look at the different messages that charts can provide. To help find the best chart type, ask yourself: what am I trying to communicate with my data?

Comparison

As the name implies, a comparison chart is useful for comparing two or more values. This is useful for quickly identifying minimum and maximum values or trends over time. Before deciding on a comparison chart, decide if your data is static or if it will change over time. Numeric, category, and time series data are best for comparison charts.

The bar chart above is a good example of a comparison chart. It has numerical data (the economic growth measured on the y axis), category data (Euro Zone and Greece), and time series data (measured from 1996-2006). The user can easily identify minimum and maximum growth years for both categories, as well as the steadier cyclical trend exhibited by the Euro Zone data.

Composition

Also known as "part of a whole," a composition chart is useful for showing how individual data points contribute to the dataset and how the dataset is divided. Composition charts can be helpful for demonstrating relative values as well as highlighting differences between data sets. Again, decide if your data is static or will be changing over time. Number and category and network/hierarchy data are best for composition charts.

The trellis pie chart above demonstrates a couple of composition uses. Each pie chart individually uses number and category data to show how the current shares are split between different countries. The charts together highlight changes to the distribution over time, so the user can see how the shares have moved.

Distribution

As the name implies, a distribution chart is useful for showing the distribution of data along a common axis from highest to lowest. Distribution charts can be used to highlight trend data, range, and outliers. Numeric data is best for distribution charts.

A population pyramid is a good example of a distribution graph, as it shows how a country's population is distributed by age category. The population pyramid shown above is using only numerical data - the total population count, and the count by age group.

Deviation

Similar to a distribution chart, a deviation chart is useful for highlighting outlier and trend data. Deviation charts should be used to demonstrate how all of the data points relate to a reference value. Numeric data is best for deviation charts.

The box plot above is an example of a deviation chart. Box plots are useful for specifically highlighting outlier data, as you can see with the round markers in the chart. This chart is using both numerical data (the ages of the athletes) and category data (the event the athlete participated in).

Relationship

Also known as "correlation," a relationship chart is useful for showing correlations, outliers, and data clusters across two or more variables. Numeric data or number and category data are best for relationship charts

The scatterplot shown above is using numerical data (the distance travelled, in meters, for each minute of a game) and category data (the different football games). With the scatterplot, you are able to easily identify data clusters at under 100m per minute, regardless of the teams playing. You can also easily identify outliers.

Geospatial

Geospatial charts are usually maps but can be any spatial visualization overlaid with data. Geospatial charts are useful for geographic data.

In the choropleth map above, the data being measured is the number of elections, out of 12, for which each state voted for the eventual winner. Since the data is separated by US state, it is easily visualized in the choropleth map, which uses darker colors to show larger values. This chart makes it easy to identify states that usually vote for the winner (Ohio, Florida) and states that don't (Minnesota).

Again, this is not an exhaustive list, but most charts will fall into one of the above categories. Each chart category listed above should be thought of as the "message" of your chart.

Choosing a ZingChart

By now, hopefully you have your data type(s) and chart category based on the data you are charting and the message you're trying to communicate. Now, use the following table to help you decide which ZingChart to choose for your visualization!

If you still have any questions, reach out to our team at any time using the chat window and we'd be happy to help you find the best ZingChart for your viz!