De-Cluttering Exercise

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En provenance du cours de University of California, Davis

Essential Design Principles for Tableau

586 notes

University of California, Davis

Essential Design Principles for Tableau

586 notes

Cours 2 sur 5 dans Specialization Data Visualization with Tableau

In this course, you will analyze and apply essential design principles to your Tableau visualizations. This course assumes you understand the tools within Tableau and have some knowledge of the fundamental concepts of data visualization. You will define and examine the similarities and differences of exploratory and explanatory analysis as well as begin to ask the right questions about what’s needed in a visualization. You will assess how data and design work together, including how to choose the appropriate visual representation for your data, and the difference between effective and ineffective visuals. You will apply effective best practice design principles to your data visualizations and be able to illustrate examples of strategic use of contrast to highlight important elements. You will evaluate pre-attentive attributes and why they are important in visualizations. You will exam the importance of using the "right" amount of color and in the right place and be able to apply design principles to de-clutter your data visualization.

À partir de la leçon

Visual Perception and Cognitive Load

Welcome to this second module. This module will explore specific data visualization concepts that apply the concepts you learned about how the human brain works from the last module. In this module, you will be able to define cognitive load and what clutter means from a visualization perspective. You will be able to visually illustrate the principles of visual perception and use contrast to enhance your visualizations. You will be able to define and use pre-attentive attributes like color to make effective visualizations.

Visual Perception and Cognitive Principles Introduction1:20

Cognitive Load and Clutter8:43

Principles of Visual Perception4:35

Strategic Use of Contrast10:46

Pre-Attentive Attributes of Visualizations4:37

Color as a Pre-Attentive Attribute9:54

De-Cluttering Exercise7:30

Rencontrer les enseignants

Govind Acharya
Principal Analyst
Budget and Institutional Analysis
Hunter Whitney
Sr. Consultant, Author, Instructor
Design Strategy and Data Visualization

[MUSIC]

Hey there and welcome to this final lesson within this module.

I'm very excited to be at this point because what we're going to do

is a deep dive into one visualization.

We're going to go from having a not so beautiful visualization

to a de-cluttered one, and hopefully one that actually looks kind of nice too.

The purpose of this lesson is to demonstrate how you would effectively

de-clutter visualization, taking into account various elements of the topics

that we discussed throughout this module.

The visualization that we're going to de-clutter is from

the United States Department of Agriculture or USDA.

And we've seen this visualization before from earlier in this module.

Because the information that the USDA has is generally public,

we happen to have the underlying data set for this visualization.

So let's dive in.

[MUSIC]

Now what I've been showing you is actually an image from the US Department of

Agriculture.

I do have the data.

So I attempted to faithfully replicate their

rather interesting cylindrical 3D type of graph that they have

on their website in Excel, and you can see that here.

You can see the underlying data which I'm showing you here and

the before visualization.

And we're going to take this, and make it more effective.

Because the information that the USDA has is generally public.

Luckily we have the underlying data set that we have from this visualization.

Which is great it makes our efforts to go through this exercise much easier,

much cleaner.

So let's talk about what we need to do here.

The first thing I'm going to do is remove the 3D effect.

And I've said this several times, and

I'm going to say it several more times I'm sure, don't do 3D charts.

A 3D chart showing 2D data doesn't add any value to your charts,

but does add a lot of noise, and makes the chart much harder to read.

So I'm just going to do that by creating a whole new chart to start over, and

then we can compare how it's looking with the one I want to de-clutter.

The next thing we are going to do is,

we're going to make the colors much less intense.

We have already a fairly mellow blue.

And I'm going to change it to a gray.

And so the idea is that we want to just take it down a notch or several notches.

Bright colors are frankly tiring to look at,

the USDA graph is very tiring to look at.

Here's a, in a very intense blue, that clashes with the gold or yellow, or

whatever color that is.

So, I'm just going to tone it down by making it a nice shade of grey for now.

The bar widths seem a little off to me.

I actually think that the USDA graph did a good job of

having the correct bar width, assuming that the 2D, if you infer the 2D,

it seems like it's the right, appropriate bar width.

So we want to, what I'm going to do is just make them just wide enough so

that the white space, so there's enough white space so

that the bars are slightly thicker than the white space between them.

When the bars are too close together,

your brand will naturally try to evaluate area versus length.

So we want to have that right balance.

And that's what I'm doing here.

My next suggestion is pretty counter-intuitive, but

it's well in line with what we're getting to.

Which is always sort of de-cluttering,

making everything every clean and easy for people to read.

So what I'm going to do is I want to remove the x axis and

actually add that data labels directly.

So that we can just compare the numbers directly instead of

people having to go down the bottom to try to infer it.

We could actually just tell them what that number is.

Final changes actually, to improve readability quite a bit,

is to sort the bars in descending order.

So what we're going to do is make it so that people can easily see

without having to figure out the order,

where the price inflation is the highest and

the lowest, instead of having to guess it.

Just give that information.

And we can do it by sorting it.

Plus we have the data labeled.

That combination will be very helpful.

So I'm actually going to dispense with the reference line.

I actually find that the reference line is not very effective.

Instead I'm going to put some text and

say that the national average is 25.5, and just leave it at that.

And then people will then have that information.

I'll put it near the 25 line, but

I think it's just better to not provide that piece of information.

Sometimes those reference lines are helpful, I think here as I was thinking

about it before presenting, I think it's just a distraction to the data.

Our final point is that,

a very good practice after creating a chart is to take a step back.

Notice, I breathe right before saying that.

You take a step back and identify necessary elements.

Just eliminate that clutter and just remove them.

Repeat this process until nothing else can be removed.

Because every element of the visualization has a purpose and

supports the objective of the visualization.

And that is fundamentally what it's all about.

What is the objective of the visualization?

And, I think that the after is a much better representation of

an explanation of retail food price inflation, than

the original screencast.

And I'll do that once I get the okay to do the official screencast.

And then, that's it.

So this concludes this lesson and the module.

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