How to Visualize Categorical Data (With Examples)

Multivariate data visualization is the presentation of more than two variables in a graphical format. This type of data can be difficult to interpret if not displayed correctly. Categorical data, specifically, can be challenging to visualize because it is often non-numeric.

In this article, we’ll give you a quick overview of categorical data, how to visualize it using the most popular methods, and the best tools your business can use to visualize this data.

 

What Is Categorical Data?

Categorical data can be:

• Nominal: Nominal data are those that can only be classified, but not ranked in any way. For example, gender (male/female) is a type of categorical data that is nominal. We can only say that males and females are different, but we can’t say that one is better or worse than the other. Similarly, eye color (brown, blue, green) is a type of categorical data that is also nominal. 
• Ordinal: Ordinal data are those that can be classified and ranked. For example, if we were to ask people to rate their satisfaction with a new product on a scale from 1 to 5, with 1 being very unsatisfied and 5 being very satisfied, we would be collecting ordinal data. In this case, we could say that a rating of 3 is better than a rating of 2, but we couldn’t say that a rating of 3 is twice as good as a rating of 2. 

For visualization, the main difference between the two types of categorical data is that ordinal data can be sorted from greatest to least (or vice versa), while nominal data must be kept in the order in which it was collected.

Examples of categorical data include:

• Raw data (e.g. survey responses, individual observations)
• Aggregated data (e.g. means, totals, counts)
• Cross-tabulated data (e.g. contingency tables)

 

Raw Data

Raw data from a survey or individual observations can be difficult to visualize because it is often non-numeric. Here is what raw data about people’s features would look like:

##features(raw)

##    Hair   Eye    Sex

## 1 Blonde  Hazel Female

## 2 Brown Brown Female

## 3 Red  Brown Female

## 4 Brown Blue Female

## 5 Brown Green   Male

## 6 Blonde Blue   Male

 

Aggregated Data

You can aggregate raw categorical data into counts or proportions. This can be helpful if you want to compare how common different categories are.

library(dplyr)

agg <- count(raw, Hair, Eye, Sex)

features(agg)

## # A tibble: 6 x 4

##   Hair  Eye   Sex        n

##   <fct> <fct> <fct>  <int>

## 1 Blonde  Hazel Female 31

## 2 Brown Brown Female   32

## 3 Red  Brown Female   12

## 4 Brown Blue Female   8

## 5 Brown Hazel   Male  10

## 6 Blonde Blue   Male   7

Using the dplyr package, we can aggregate our raw data into counts. The features(agg) function shows us what the aggregated data looks like.

 

Cross-Tabulated Data

Another way to visualize categorical data is to cross-tabulate it. This means that you create a table showing how two or more variables are related.

Using xtabs, we can cross-tabulate the Hair and Eye colors of our aggregate data:

xtabs(n ~ Hair + Eye, data = agg)

##              Eye

## Hair        Blue Brown Hazel

##  Blonde       7     0     31

##  Brown       8    32     10

##  Red         0    12      0

The table shows us that more there are seven people with blonde hair and blue eyes, 31 people with blonde hair and hazel eyes, etc.

A few other commands that might be helpful when working with categorical data are:

• table(): This command will create a table showing how many observations there are for each category.
• tapply(): This command will apply a function to each level of a factor.
• split(): This command will split a data frame or matrix into groups based on the levels of a factor.

And while this list is not very comprehensive, it should give you a quick understanding of how categorical data works and what it looks like. The same logic and methodology can be applied to much larger data sets at the enterprise level.

 

What is the Best Visualization for Categorical Data? 

Most of us aren’t analysts. But we still need to use categorical data to make data-driven decisions for our businesses. Since looking at numbers, tables, and spreadsheets is unappealing–and impossible to read unless you’re an analyst–you need to find another way to make sense of all that data.

That’s where data visualization comes in.

There are many ways to visualize categorical data, but not all of them are created equal. The best examples of categorical data visualization include:

• Bar charts: The easiest way to read multivariate data
• Pie charts or donut charts: The best way to read data with fewer than five categories
• Line charts: For tracking how data changes over time
• Stacked bar charts: When you want to compare two or more variables
• Scatterplots, heat grids, and scorecards: When you need to get more granular

For businesses that deal with a lot of categorical data, (e.g. customer demographics, product data, survey responses, etc.), the best visualization is usually a bar chart or a pie chart.

Bar charts are simple and easy to understand, even for people who don’t have a lot of experience working with data. And they can be customized to show as much–or as little–detail as you want. 

Pie charts are also easy to understand, but they’re best used for data sets that have a limited number of categories (usually no more than five).

And if you want to show changes in categorical data over time, a line chart is usually the best option.

Scatterplots, heat grids, and scorecards are other options, but they’re typically used by analysts and data scientists to find trends and patterns in large data sets.

 

The Worst Ways to Visualize Categorical Data

There are also some methods for visualizing categorical data that you should avoid at all cost. These include:

• Tables: Too much information, not enough context
• Radar charts: Only use if you have three or fewer categories
• Bubble charts: Unless you’re an experienced data visualization expert, stay away from these

While these forms of visualization have their place, they’re not very user-friendly for people who don’t work with data on a daily basis.

 

What Visualization Tool Can Be Used for Categorical Data?

Appending your data into Excel spreadsheets is a free and straightforward method for visualizing data, but it’s practically impossible to create anything other than the most basic charts and graphs.

You’ll need a dedicated data visualization tool to create more advanced visualizations. Many different options are available, including Qlik, Tableau, and Power BI.

Let’s say you run a large ecommerce brand and want to visualize how your customers find you. Those ways could be:

• Organic search
• Social media
• Paid advertising
• Referral traffic
• Email marketing

You would first gather the data from each of these sources and warehouse it in Snowflake. Then, you would extract that data and load it into your visualization tool of choice. From there, you would be able to create a bar chart that shows how each of these channels contributes to your overall traffic.