Data Visualization Best Practices

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Questions and Answers

What is one of the 3Es of displaying data that emphasizes clarity in conveying a message?

Efficient

Ethical

Engaging

Effective (correct)

Which of the following tips aims to improve the viewer's understanding of data through placement?

Ensure visuals are placed near the corresponding text (correct)

Avoid any graphical elements

Limit the amount of data presented

Use multiple colors to attract attention

What should be avoided to maintain an ethical display of data?

Inflating results to create a stronger impact (correct)

Citing sources for data created by others

Representing quantities accurately

Including all relevant data

When using color in graphics, what is a recommended practice?

Limit usage to two or three colors for clarity Signup and view all the answers

What is a common mistake to avoid when using tables in data display?

Hiding obvious data points within tables Signup and view all the answers

Which movie had the highest weekend gross ticket sales during Christmas weekend of 2017?

Star Wars: The Last Jedi Signup and view all the answers

What is one important limitation of bar plots mentioned in the content?

They cannot start at zero. Signup and view all the answers

In a grouped bar plot, what determines the positions of the bars along the x-axis?

One categorical variable Signup and view all the answers

What is indicated about the bar order when representing unordered categories?

It should be arranged by data value. Signup and view all the answers

What might be a suitable alternative to represent data where bar length requires strict proportionality?

Dot plots Signup and view all the answers

What does the content imply regarding the visualization of median income levels categorized by age and race?

It is best represented using grouped bar plots. Signup and view all the answers

How should one interpret the age distribution of Titanic passengers?

By assessing the relative proportions of different ages. Signup and view all the answers

Why might a heatmap be considered effective for visualizing data values?

It presents data values through color representation. Signup and view all the answers

What is the primary objective during the data exploration phase?

Understanding key features of the dataset Signup and view all the answers

Which of the following is a characteristic of the data presentation phase?

Preparing high-quality, publication-ready figures Signup and view all the answers

In the data exploration phase, which of the following aspects is considered less important?

The aesthetic appeal of the figures Signup and view all the answers

What feature is crucial for effective data exploration tools?

Ability to adapt input data as required for different plots Signup and view all the answers

Which type of plotting is typically emphasized during the data exploration stage?

Quick iterations over various plots Signup and view all the answers

Why can some visualization tools hinder efficient data exploration?

They require unique input data for each plot type Signup and view all the answers

What should you prioritize when trying to understand a new dataset during exploration?

The efficiency of testing various visualizations Signup and view all the answers

What happens during the transition from data exploration to data presentation?

Focus shifts from understanding to visual appeal Signup and view all the answers

What is a common application for using 3D visualizations?

To show topographic relief of geographical features Signup and view all the answers

What is a choropleth map primarily used for?

Representing data values as differently colored spatial areas Signup and view all the answers

Which is a significant challenge when designing maps?

Determining how to accurately represent angles and areas Signup and view all the answers

What distinguishes a cartogram from a traditional map?

It distorts areas or represents them in a stylized way Signup and view all the answers

How is the earth's structure described?

As a slightly flattened spheroid along its axis Signup and view all the answers

What does the equator represent on the earth?

The line that separates the northern and southern hemispheres Signup and view all the answers

In what scenarios are 3D visualizations particularly effective?

When visualizing actual 3D objects and data mapped onto them Signup and view all the answers

What aspect of geospatial datasets is essential in ecological studies?

Locations of specific plants or animals Signup and view all the answers

What was the trend observed in submissions to bioRxiv soon after its launch?

Rapid, exponential growth in submissions Signup and view all the answers

What does the author suggest about depicting figures in presentations?

Simplified figures help audiences understand key points Signup and view all the answers

What is meant by 'making a figure for the generals'?

Simplifying figures to highlight only the important points Signup and view all the answers

Why might a single figure be more suitable for certain mediums, like social media?

It accommodates audiences with shorter attention spans Signup and view all the answers

What is a recommended approach before presenting complex figures?

Show a simplified version first to ease understanding Signup and view all the answers

What was the observed relationship between q-bio and bioRxiv submissions?

q-bio submissions decreased as bioRxiv submissions increased Signup and view all the answers

What is a potential issue when audiences view complex visualizations?

They may struggle to grasp the overall trends Signup and view all the answers

Why is it important to help readers understand visualizations?

To assist them in inferring the intended messages Signup and view all the answers

Which of the following statements about longitude is true?

The prime meridian is located at 0° longitude. Signup and view all the answers

What do lines of equal latitude represent?

Distance from the equator. Signup and view all the answers

What is the significance of the World Geodetic System (WGS) 84?

It serves as a datum for GPS technology. Signup and view all the answers

Which of the following describes altitude in the context of geospatial data?

It indicates how far the location is from the Earth's center. Signup and view all the answers

What happens during the process of map projection?

Distortions occur because it is impossible to flatten a sphere without losing some properties. Signup and view all the answers

What is the latitude of the South Pole?

-90° Signup and view all the answers

Which projection property can be preserved when mapping the Earth's surface?

Either angles or areas, but not both. Signup and view all the answers

How is the meridian opposite the prime meridian designated?

180°W Signup and view all the answers

What do meridians terminate at?

The poles. Signup and view all the answers

Study Notes

STAT 288: Data Visualization

Course taught by Dr. Abdulla Eid
Focuses on data visualization concepts and techniques
Covers course overview and key concepts, syllabus, data science cycle, data collection examples, open data sources, principles of effective and ethical data visualization, tips for effective and efficient display, and conclusion/next steps.

Welcome & Course Syllabus

Introduction to Data Visualization
Overview of course content and structure
Learning objectives and expectations

Data Science Cycle

Data Collection (Open-Source data, large sample representation)
Data Cleaning (Simple Data Mining)
Data Visualization (Focus of this course, data extraction, story telling)

Data Collection Examples

Students results
Stock markets & financial institutions
Demographics
Sport Data (Olympics, France 2024, American Football)
Election Data (2024 US Presidential Election)

Open Data Sources

Data.gov: Science, research, manufacturing, climate
Census.gov: US demographic
OpenDataNetwork.com: Robust data search engine
NCES.ed.gov: Education data
HealthData.gov: Health-related databases
DBpedia.org: Databases similar to Wikipedia
Kaggle.com: Community-published datasets

What is Data Visualization?

Data visualization is part art and science.
The challenge is to get the art right without losing the science.
Accurately conveying the data is paramount.
It must not mislead or distort the data.
Visualizations should be aesthetically pleasing and enhance the message.
Visual elements, colors, and design should not be distracting.

Ugly, Bad, and Wrong...

Examples of poor data visualizations.
Visualizations that do not accurately reflect the data
Bad visual design (e.g., inappropriate colors, unbalanced elements)

3Es of Displaying Data

Effective: Clearly convey the message
Efficient: Minimal use of resources
Ethical: Represent data truthfully

Tips for Effective Display

Visuals should be placed close to the relevant text.
Visuals should allow readers to pause and consider the ideas.
Graphics visually reinforce arguments.
A simple data story is essential for good delivery.

Effective Display:

Human-induced change: Certain naturally occurring gases (CO2 & H2O) trap atmospheric heat, leading to a greenhouse effect. Burning fossil fuels contribute CO₂ to the atmosphere, increasing to levels not seen in 650,000 years.
The greenhouse effect: Solar radiation passes through the atmosphere while greenhouse gases absorb and re-emit infrared radiation, warming the Earth's surface, and lower atmosphere.

Tips for Efficient Display

Use color carefully and sparingly.
Avoid color if black and white is sufficient.
Use color to establish visual patterns but do not over use it. Limit color interpretation to 2-3 colors at a time.

Which of these looks better?

Emphasis on strong contrast, best for visual annotations or charts on screen
Using black and white is better than various colors
Avoid misrepresenting data with the use of color

Tips for Ethical Display

Be honest in data presentation; do not exaggerate.
Cite sources for graphics or data not created by the presenter.
Obtain the proper permissions for use of any graphics or data.
Include all relevant data even if unexplained.
Accurately represent quantities/magnitudes.
Avoid using tables to hide obvious data points
Do not use color to mislead or misrepresent the data.

Conclusion & Next Steps

Review key points
Upcoming assignments and topics
Resources for further study

Chapter 2: Data Visualization: Mapping Data Onto Aesthetics

Data values converted to visual elements

Aesthetics and types of data

position, shape, size, color, line width and line type
data type classification (continuous or discrete)

Data Types

Quantitative/numerical (continuous, discrete)
Qualitative/categorical (unordered, ordered)
Date or Time
Text

Example of data type

Table of data with month, day, location, station ID, and temperature values for different locations.

Scales map data values onto aesthetics

Mapping values to positions, shapes or colors.

Example: Map Data to Aesthetics

Graph showing temperatures of different US locations over time.

3.2 Non-Linear Scale

original data, linear scale
log-transformed data, linear scale

3.3 Coordinate systems with curved axes

Cartesian Coordinates

Example of Curves Coordinate System

Graph with circular data for temperature, location, and time

Different System (Subject specific)

Cartesian longitude and latitude
Interrupted Goode homolosine
Robinson, Winkler triple

Chapter 4: Color Scales

Fundamental use cases: distinguish data groups, represent data values and highlight.

Qualitative Color Scale

Color choice for visually distinct but equivalent categories or groups

Chapter 5: Directory of Visualizations

Charts, plots, and graphs.

5.1 Amounts

Grouped Bars
Stacked Bars
Heatmap

5.2 Distribution

Histogram
Density Plot
Cumulative Density
Quantile-Quantile Plot

Distribution (Cont):

Boxplots
Violins
Strip Charts
Sina Plots
Stacked Histograms
Overlapping Densities
Ridgeline Plots

5.3 Proportions

Pie Chart
Bars
Stacked Bars
Multiple Pie Charts
Grouped Bars
Stacked Densities
Mosaic Plot
Treemap
Parallel Sets

5.4 X-Y Relationships

Scatterplot
Bubble Chart
Paired Scatterplot
Slopegraph
Density Contours
2D Bins
Hex Bins
Correlogram

5.5 Geospatial Data

Map
Choropleth
Cartograms
Cartogram Heatmap

5.6 Uncertainty

Error bars indicate the range of likely values for an estimate or measurement. Also, reference points can visualized as dots or bars.

Uncertainty (Cont):

Confidence strips provide a visual sense of uncertainty but are difficult to read accurately. Eyes, half-eyes combine error bars for visualizing distributions.

Uncertainty (Cont):

Shows different ways to visualize uncertainty with confidence bands, graded confidence bands, and fitted draws.

Chapter 6 Visualizing amounts

In many scenarios, we are interested in the magnitude of a set of numbers, such as the total sales of products, the number of people in a city, or the age of athletes from around the world.
Bar plots are the standard approach in this case, though there are other types like Dot plots, and Heatmaps

Bar Plot (Chart)

A common visualization for amounts and counts.

Ugly (Why?)

Examples of poorly designed bar plots.

Better Solution

A better alternative to poorly designed bar plots

Bad (Why?)

Showing examples of bad design choices, such as incorrect or misleading plotting choices

Order Matter (Of Some Sense)

The order of data in a bar plot is important when the categories are not inherently ordered

6.2 Grouped and stacked bars:

Visualizing amounts broken down by categories grouped (grouped bar plots), stacked (stacked bar plots).

Chapter 7: Visualizing Distributions:

Histograms, and Density plots
How many passengers of what ages were on the Titanic broken down by age groups
We call the counts for each age group is called Age Distribution
Visualizing single distribution, like how often certain ages appear
Using histograms and density plots

Histogram

Visually representing frequencies by using rectangles of varying heights proportional to different age bins
Width of the bins are all the same for a histogram

Density Plots

Drawing an underlying probability distribution for data.
Kernel estimation.

Bandwidth (Appearance)

Illustrating how the bandwidth/width affects the visualization.

What is the area under the curve in density plot?

Understanding the relationship to probability.

Warning!

Realizing that histograms and density plots are estimates.
There's a large difference in choice when visualizing data distribution using histograms or density plots and other options.

7.2 Visualizing multiple distributions at the same time

Visualizing overlapping distributions with stacked histograms.
Comparing age distribution between male and female passengers.

Why?

Illustrating why a particular visualization is bad.

Better Solution

Showing examples of visualizations that are better than the previous examples.
Illustrating the comparison between stacked bar charts and better data visualizations, and how one visualization is more suitable than the other. Data visualization should be clear and informative for the intended audience.
Visualizations need to be easily interpreted, and show the data in a clear and understandable manner rather than a complex 3-D appearance.
The presentation of data in simple terms will effectively capture the target population's attention and understanding

Chapter 8 Visualizing distributions: Empirical Cumulative Distribution Functions and Q-Q plots

How many students received equal to or less points on a certain exam
Shows the quantile-quantile of data in ascending or descending order

8.1 Empirical Cumulative Distribution Functions (Ecdfs)

Ranking and plotting the data points in order to visualize the cumulative proportion at a given point in the data.

8.3 Quantile-Quantile Plots (QQ)

Visually determine how well a dataset follows a particular distribution, such as a normal distribution.

Methodologies

Using ranked data points to visually see the distribution following a normal distribution.

Example

Showing how data points would look on a distribution based on a theoretical normal distribution.

Regression Line

Illustrating how a regression line is different from the line that represents a theoretical normal distribution

Chapter 9 Visualizing many distributions at once

Visualizing many distributions (e.g., monthly temperatures) at once with various plots such as boxplots, violin plots, and ridgeline plots.

9.1 Visualizing distributions along the vertical axis

Showing mean or median values with indication of variation using error bars.

But why Bad?

Discussing what is incorrect or misleading about a visualization.

Box Plot

Displaying summary statistics like the median, quartiles, minimum value.
Showing outliers in a dataset.

Temperature Example

Plots showing box plots comparing temperatures across the months.

Violin Plots

Showing similar data as box plots, but visualizing the distribution shape of the data.

Strip Chart

Showing scatterplot-like visualizations.
Displaying the frequency of different points for particular months.

Better (More points) Jettering

Showing the improvement to the strip chart

Sina Plots

Similar visualizations to strip charts, but the points are spread out to visualize variability across months

9.2 Visualizing Distributions along the horizontal axis

Displaying distributions along the horizontal axis(e.g. temperatures) with a specific dataset from multiple categories

Chapter 10: Visualizing Proportions

How to visually represent how different categories/groups compare to each other

Pie Charts

Visualizing proportions as slices of a circle

Rectangular Pie Charts (Stacked Bars)

Visualizing proportions using stacked bars

Side-by-Side Bars

Visualizing proportions using side-by-side bars

Table 10.1: Pros and cons of common approaches to visualizing proportions.

Summary of different approaches to visualize proportions

A case for side-by-side bars

Showing an example of an improved/more appropriate visual representation of proportions.

A Better way

Providing a better visualization

10.3 A case for stacked bars and stacked densities

Illustrating the use of stacked plots

Heat Maps

Representing proportions in grid form with gradients or colors

Chapter 11: Nested Proportions

Visualizing how one variable is broken down by other variables using nested visualizations.

11.1 Nested proportions gone wrong

Providing examples of incorrect visualizations about the proportions of different variables

Why Wrong?

Problems with the incorrect visualizations of proportions

11.2 Mosaic plots and treemaps

Mosaic plots and treemaps are used for displaying proportions within multiple categories.

Treemaps

Representing hierarchical data with nested boxes to show the proportion sizes

11.3 Nested pies

Visualizing proportions with nested pie charts.

Chapter 12: Visualizing Associations among Two or More Quantitative Variables

Scatter plots and other options.

12.1 Scatter plots

Visualizing the relationship between two quantitative variables using scatter plots.

12.2 Correlograms

Visualizing correlation between different variables.

Correlograms

Showing correlation of different variables against each other.

12.4 Paired Data

Plotting paired quantitative data through time on scatter plots.

Chapter 14: Visualizing Trends

Overall trends in data over time, and how to visualizes these trends

14.1 Smoothing

Using moving averages to smooth out data and see overall trends.

Moving Average (After or at)

Showing example plots comparing different moving averages and the use or limitations of the different types.

Observations from the previous figure

Observation of what kind of smoothing is a better choice against time, given a particular dataset.

Another Moving Average (LOESS)

Showing an alternative way to smooth data by using loess smoother.

14.2 Showing trends with a defined functional form

Fitting trends to data using linear or exponential functions

Log scale and Exp

Showing log scales for comparison

Chapter 26: Don't go 3D

3-D visualizations are often inappropriate

26.1 Avoid gratuitous 3D

Avoid turning standard 2-D visualizations into 3-D objects, as the extra dimensions do not typically convey any meaningful information.

Focus on the size of 25% in these four graphs

Demonstrating how 3-D projections distort the impression of the data