Different Charts for Different Data

This workshop will introduce you to Tableau Software and the wide range of chart types it can create. We’ll focus on which visualizations work best for different types of data, progressing from bar graphs to combination charts.


Before we dive into building visualizations with Tableau, here’s a bit of information about how to use this guide and the building blocks that make up most visualizations you’ll see.

How to use this guide

We move pretty quickly during the workshop. There’s a lot to cover in just 90 minutes! If you miss a step, don’t worry. You can find all the materials we discuss here. Use this guide to solidify your skills or as a reference this year as you dive into your own Tableau projects.


This workshop uses Baltimore’s 311 data from 2016. Click here to download the Excel spreadsheet we use during the workshop. The original data as well as its documentation can be found here. Thank you to Open Data Baltimore for making this data available to the public.


The complete Tableau workbook we use during the workshop can be found here. Just go to the bottom right hand corner and download your own copy. Then you can deconstruct each visualization we built with a fresh version.


The guide that follows documents the key steps that go into building each of the different chart types we create during the workshop. If you get stuck, download a copy of the original workbook and compare your worksheet to the ones there. Good luck!


The basic pieces of nearly all data visualizations can be broken down into three kinds of marks: points, lines, and areas. From there, we use position, size, color, and shape to tell multiple marks apart. Check out how these fundamental components are leveraged in these great examples from Tableau Public.


Skyler Johnson


Curtis Harris


Ann Jackson and Josh Jackson


Jacob Olsufka


RJ Andrews


Let’s start with one of the most common data visualization tasks: comparing categories. We’ll dive into the categorical divisions in Baltimore’s 311 data by creating a basic bar chart, 100% stacked bar chart, and a treemap.

Bar Chart

The workhorses in the data viz world, bar charts do a great job comparing the small groups of values. The human eye does a great job of differentiating lengths, so we can glance at a well-sorted bar chart and immediately understand what’s going on. Here are the steps we used to make a bar chart with a filter.

100% Stacked Bar Chart

When the question shifts from how big or small a category is to how a given total splits out by percentages, 100% stacked bar charts become helpful. In our example below, it doesn’t matter how many service requests each agency received, only the different percentages of how they received them.


When bar charts start to have too many categories to be useful, it’s usually time to try a treemap. More precise than its word cloud and bubble chart cousins, the treemap does a great job of showing how diverse a variable can be without losing track of the heavy hitters. 


Now let’s move on to visualizing data as it relates to time. Date and time variables are super flexible in Tableau. Just keep an eye on what’s discrete and what’s continuous.

Line Chart

The most classic chart type for longitudinal data, the line chart is a great place to start when it comes to visualizing changes over time. The human eye can detect very subtle shifts in how a line moves up or down. 

Area Chart

Like line charts, area charts do a great job of displaying changes over time. We can add color to distinguish between different segments in a single area chart. Or, as we’ll do below, we can split out those segments into their own area charts using a tactic called small multiples.

Heat Map

The human eye sees color before it absorbs anything else. A heat map takes advantage of this by filling a typical grid with color instead of plain numbers or text. We’ll still offer our end user all that tabular information in the tooltip. At a glance, we can spot the most intense 311 day in 2016: January 25.

Combination Chart

Last but not least we’ll create a chart that combines a bar chart with a circle marker. Combination charts like this can be especially helpful when there are two variables that need to be considered at the same time. To make this chart, we will also create our first calculated field to determine the number of days between the service requests created date and due date.

The second workshop dives into prepping a data set for Tableau. Using just ten records, we’ll work through the most common problems you’ll encounter while Structuring Data for Tableau.


To help democratize quantitative knowledge by creating educational resources and experiences that make data visualization–and by extension, data itself–approachable, exciting, and meaningful.


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