[Explained] How to Create Histograms in Tableau: The Complete Guide

Understanding your data is key to informed decision making. But with massive datasets becoming the norm, traditional graphs can obscure valuable insights.

That‘s why you need histograms – they condense data to uncover patterns you never knew existed.

In this complete guide, you‘ll master histograms in Tableau through step-by-step instructions, visual examples, and actionable best practices.

You‘ll learn:

  • What histograms are and why they‘re invaluable for today‘s data
  • Common histogram myths and misconceptions
  • Setting up your data for accurate histograms
  • Building histograms automatically and manually in Tableau
  • Customizing histograms for max visual impact
  • Comparing Tableau‘s histograms to other tools
  • Real-world examples and use cases

So if you‘re struggling to analyze large datasets, this guide is for you. Let‘s start uncovering insights!

Histograms 101

Before we dive into Tableau specifics, let‘s ensure you have histograms down cold.

A histogram groups numbers into ranges called "bins," then plots the frequency of values in each bin as bars. The height or area of bars represents how common numbers in that group are.

For example, here‘s daily website traffic data visualized as a histogram:

Histogram example

Instead of plotting each traffic number, values are bucketed into ranges. We instantly see most days fall into the 501-1000 visitors bin.

Compared to individual data points or traditional graphs, patterns emerge more clearly.

Histograms help us:

  • Identify the overall shape of data distribution
  • Pinpoint central tendencies
  • Spot variations and anomalies
  • Make decisions by understanding common values

For large or complex datasets, histograms make identifying insights possible.

Now let‘s tackle some common histogram myths.

Histogram Myths and Misconceptions

There‘s some funky histogram folklore floating around. Before using them yourself, let‘s set the record straight.

Myth: Histograms are only for frequency data

Truth: While histograms traditionally plotted frequency, today they visualize both frequency and aggregated data like sums or averages across groups.

Myth: The bars must touch

Truth: It‘s fine for blank spaces to exist between bars in a histogram. The key is consistency in range sizes.

Myth: Histograms can‘t handle negative numbers

Truth: Histogram bins can include negative ranges, but may get cut off visually by the x-axis. Tableau remedies this with dual-axis histograms.

As you can see, don‘t let false assumptions limit your use of histograms!

Now let‘s prep your data…

Setting Up Your Data for Success

They say data preparation does the heavy lifting before analysis pays off. That applies doubly for histograms!

Follow these steps before hitting build:

Start with a relevant numeric field

Choose a field like sales, revenue or website clicks – histograms visualize the patterns in these values. Discrete and continuous fields both work.

Aggregate data (optional)

To simplify things, you can aggregate your data into sums, averages or medians per category before plotting. Common for temporal data.

Check distribution assumptions

Assess whether your data meets histogram assumptions: randomness and independent observations. Plot your data first to check.

Identify outliers upfront

Histogram bins can obscure outliers. Flag them now for special handling when building your histogram.

Choose one primary field initially****

Histogram newbies should start simple with one field rather than multidimensional questions. We‘ll get there!

Prepping your data takes effort but prevents major rework. Now the fun starts…

Building Histograms in Tableau

Tableau makes histogram creation simple through Show Me‘s one-click functionality or manual construction. Let‘s explore both approaches.

The Automatic Route with Show Me

Tableau‘s Show Me menu instantly visualizes data once connected. For histograms:

1. Connect to data

Open a saved data source or connect a new Excel, CSV or database file.

2. Select field

In the data pane, choose the numeric field for your histogram.

3. Show Me!

Click Show Me in the menu bar then choose Histogram. Done!

But simplicity has some tradeoffs: limited customizations and Tableau makes the binning choices.

For more complex needs, go manual…

Building Histograms Step-by-Step

The manual route involves more pointing and clicking but unlocks customization. Follow along:

1. Connect to data

Same first step as Show Me – have a dataset ready!

2. Create bins

Right click on your numeric field > Create > Bins. Set bin size.

3. Drag bin field to Columns

From the data pane, bins go into Columns. This forms the x-axis ranges.

4. Add count

Drag your original field again to Rows. Right click > Measure > Count. This counts occurrences per bin.

5. Format visualization

Style colors, labels, size etc. to your needs. We‘ll explore more formats next.

And voila! Your custom histogram is complete.

Now for the fun part…

Formatting for Maximum Visual Impact

Tableau offers endless formatting options from colors to trend lines. But with great power comes great responsibility.

Follow these best practices for polished, professional histograms:

Label Clearly

Well-labeled axes make or break understanding. Include units and descriptive titles.

Use Sparse Colors

Limit colors to highlight outliers or subsets. Default gray keeps focus on distribution shape.

Right-Size Bins

Too many bins overcomplicates. Too few hides detail. Shoot for 6-15 balanced ranges.

Shape Outliers Visibly

Consider bullets, dual axes or log scale to call out outliers without distortion.

Annotate Insights

Call out trends, anomalies and interesting quirks with annotations.

Maintain Subtle Elements

Gridlines, zero lines and borders enhance without distracting.

Finding the right balance takes experimentation. Now let‘s tweak to handle outliers and skew…

Tips for Handling Outliers and Skew

Tableau has built-in options when unusual data distorts histograms.

Strategies for Outliers

Dual axes – Plot bars against the primary left axis, outliers against the secondary right axis with a different scale.

Log scale – Switch to logarithmic axis scale instead of linear to visually compress outlier value heights.

Capping – Limit the upper bin range to constrain extreme values vs. squeezing all bars.

Labeling – Call out outliers with annotations, labels, reference lines or distinct formatting.

Data exclusion – Omit true anomalies but use caution – outliers still hold insights.

Tactics for Skew

Log scale – Compress the long tail of right or left-skewed distributions.

Multi-modal bins – Varying bin sizes can better fit skewed shapes.

Transformations– Apply log, exponential or other math transforms to unskew the field.

Don‘t let challenging data detract from insightful histograms. Now let‘s compare to bar charts.

Histograms vs Bar Charts

Histograms and bar charts both employ vertical bars – but express very different things.

Bars represent:

  • Histogram: Ranges of metric values
  • Bar chart: Discrete categories

Intent differs:

  • Histogram: Show distribution shape
  • Bar chart: Compare category amounts

Data works best:

  • Histograms: Large metric data
  • Bar charts: Category labels and counts

Common uses:

  • Histograms: View patterns in website traffic
  • Bar charts: Breakdown sales by product type

In short:

  • Continuous numeric data? Histogram.
  • Categorical data? Bar chart.

The chart type should match the data and question at hand.

Now that you‘re a histogram expert in Tableau, let‘s wrap up.

Conclusion and Next Steps

With huge datasets as the norm, histograms help cut through the noise to reveal insights. As we covered:

  • Histograms simplify distributions rather than plot all points
  • Tableau offers automation through Show Me and customization via manual builds
  • Formatting clearly, handling skew and labeling effectively take practice

Still have questions? Tableau‘s training goes deeper across beginner to advanced levels.

To complement your new histogram skills, consider these other Tableau chart types:

  • Box plots to compare groups and spot outliers
  • Scatter plots to assess relationships between variables
  • Heatmaps to uncover trends across geographical data

The key is matching the chart to your analytical question, then customizing for max insight.

Now put your new histogram superpowers to work! Import your freshest dataset and start uncovering trends. The visual stories in your data are waiting to be told.

I‘m excited to hear what you discover. Let me know how it goes!