Graph Design: The Data-ink Ratio and Expert Users

Kevin McGurgan

, Elena Fedoroksaya

, Tina M. Sutton

and Andrew M. Herbert

Department of Psychology, Rochester Institute of Technology, Rochester, NY, U.S.A.

Integrated Science Academy, Rochester Institute of Technology, Rochester, NY, U.S.A.

Keywords: Graph Design, Data-ink-Ratio, Data Visualization.

Abstract: Graphical depictions of data are common but there is little empirical work that has examined how graph design

principles are instantiated by graph makers. The data-ink ratio is one popular measure of graphical information

content, where the “ink” related to data is divided by the total amount of “ink” in the graph. Expert interviews

were conducted to examine graph use, creation, and opinions about the data-ink ratio concept. Interviewees

had a variety of opinions and preferences with regard to graph design, many of which were dependent upon

the specific circumstances of presentation. Most interviewees did not believe that high data-ink graph designs

were superior. The results suggest that arguments regarding the data-ink ratio deal with the subjective issue

of graph aesthetics.

1 INTRODUCTION

Graphical depictions of data are common in

publications of all types (from websites to journal

articles). Graph designers need to present information

in a way that graph users can understand (Katz, 2012).

Graphs provide a means of communicating

quantitative information in an easily-comprehensible

format and can make complex information visually

salient (Lohse, 1997; Shah, Freedman, & Vekiri,

2005; Wickens & Holland, 2000). Their usefulness

derives in part from grouping information for easy

search and reducing demands on memory, thereby

decreasing the complexity of tasks by imposing

structure on data (Tory & Moller, 2004). However,

poorly designed graphs can lead to difficulty in

understanding information and ultimately to negative

consequences (Freedman & Shah, 2002; Tufte, 1997).

2 THE DATA-INK RATIO

Most graphs are now generated using software and

the starting point for graph design is often determined

by the presets in such software. We are taught how to

make graphs in various courses, but most in the

sciences and social sciences do not take design

courses. Edward Tufte has written extensively on

graph design and proposed one design guideline in

particular called the data-ink ratio (Tufte, 1983).

Tufte argues that because the purpose of a graph is to

help people draw conclusions from data, graphs

should comprise data and little else. Tufte proposed

that there are two types of information in a graph –

data-ink and non-data-ink. Data-ink is “the non-

erasable core of a graphic” and “the non-redundant

ink arranged in response to variation in the numbers

represented” (Tufte, 1983, p. 93). According to Tufte,

all ink that does not depict statistical information, or

“chartjunk,” should be removed.

Data-ink ratios range between zero and one and

can be calculated by dividing data-ink by the total

amount of ink (or equivalent) in a graph (Tufte,

1983). Figure 1 provides an example of published bar

graphs and boxplots that have had the data-ink ratio

varied according to Tufte’s guidelines. It is unclear

how a data-ink ratio can be accurately calculated in

practice, and Tufte makes estimations rather than

numerical calculations.

The data-ink ratio is an influential concept in the field

of design (Zhu, 2007; Fry, 2008), and it is believed

that higher data-ink ratios will result in faster

judgments and increased accuracy in graph reading

tasks (Wickens & Holland, 2000). However, some

have characterized the data-ink ratio as having its

basis in Tufte’s design intuitions and lacking

experimental validation with behavioral data

(Carswell, 1992). For example, Tufte notes that

188

McGurgan, K., Fedoroksaya, E., Sutton, T. and Herbert, A.

Graph Design: The Data-ink Ratio and Expert Users.

DOI: 10.5220/0010263801880194

In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 3: IVAPP, pages

188-194

ISBN: 978-989-758-488-6

Figure 1: Example bar graphs and boxplots varying in data-ink ratio per Tufte’s guidelines. These are adapted from published

studies to create three levels of data-ink ratio. These were provided as examples to the participants interviewed. Bar graphs

from Lellis et al., 2013 and boxplots from Romoser & Fisher, 2009.

chartjunk should only be removed “within reason”

(Tufte, 1983, p. 96). This lack of specificity reflects

subjectivity in graph design choices.

2.1 Responses to the Data-ink Ratio

The data-ink ratio and Tufte’s design

recommendations have met with mixed reactions in

the literature. Some argue that the data-ink ratio is a

convenient way to measure the extent to which

chartjunk is used (Wainer, 1984). In contrast, Tukey

(1990) describes the data-ink ratio as a “dangerous

idea” and argues that overreliance on it can be

destructive and result in graphs that are both busy and

distracting. Removal of the box portion of a boxplot

results in three distinct perceptual groupings that are

from unrelated samples. Although the underlying idea

behind maximizing the data-ink ratio is to avoid

busyness and distraction in graph design, both

Kosslyn (2006) and Tukey (1990) suggest that those

recommendations alone would not produce the type

of graphs which Tufte advocates. There is some

evidence that chartjunk may benefit graph users

(Hullman, Adar, & Shah, 2011).

Empirical tests of the data-ink ratio have yielded

mixed results. When data-ink ratio has been varied,

high data-ink ratio graphs were not preferred over

lower ratio graphs (Kulla-Mader, 2007; Tractinsky &

Meyer, 2007) and did not produce consistent

differences in graph interpretation performance

(Gillan & Sorensen, 2009). Similarly, recall of

information from low and high data-ink ratio graphs

has not been found to differ, with some evidence that

low data-ink ratio graphs with embellishments are

better remembered (Bateman et al. 2010; Kelly,

1989). Other findings suggest better performance for

subjects viewing medium data-ink ratio graphs

(Blasio & Bizantz, 2002; Gillan & Richman, 1994).

2.2 Graph Comprehension

A variety of cognitive processes are associated with

graph comprehension, with most research focusing on

perception of graph components (Carswell, 1992;

Cleveland & McGill, 1984, 1985; Pinker, 1990).

Perceptual grouping of graph elements has been

emphasized as important for graph comprehension

(Kosslyn, 2006) and users find effective graphs allow

users to group information by colour, shape and so on

(Shah et al., 1999).

2.3 Rationale

Tufte (2015) has disparaged research on the data-ink

ratio concept for using undergraduate students as

participants. Models of graph comprehension include

graph literacy skills, or graph schemas, as an

important factor, so Tufte’s criticism may have some

merits. An interview method was used to gather

qualitative data from experts who produce and use

graphs.

A semi-structured interview method was used

(Carpendale, 2008). A discussion guide was created

to provide the necessary structure for the interviews,

including introductory information, potential

interview questions, and a rough outline for the

interview. The goal was to have some structure but to

allow for flexibility during the interviews (Portigal,

2013). The qualitative interview data were analyzed

using thematic analysis, a flexible method in which

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interviewee opinions and interviewer observations

are grouped into common themes (Carpendale, 2008).

Themes represent patterns in responses which relate

to the research questions and researcher judgment is

inherent in thematic analysis (Braun & Clarke, 2006).

A list of codes is generated from the transcribed

interviews, and the codes are further organized into

themes.

3 METHODS

Interviews were conducted with 7 faculty members

from the Rochester Institute of Technology (RIT)

with a variety of academic backgrounds. Five

interviewees held doctorate degrees. Three of those

were in psychology, one was in psychophysiology

and one was in industrial engineering, but taught

courses in applied statistics. The other two

interviewees held a master’s degree (the terminal

degree in their field) – one in graphic design and the

other in visual and verbal communication.

Participants were solicited based on a preference for

faculty who were likely to have opinions regarding

graph design (e.g., faculty in design, human factors

and statistics) and/or those with frequent graph use.

Participants were found through recommendations

from faculty members and departmental web pages.

3.1 Materials and Procedure

After agreeing to participate, interviewees were sent

a common set of nine pre-interview questions via e-

mail (e.g., What type(s) of graphs do you create most

frequently? What are the most important factors in the

design of graphs you create?). These questions were

focused on graph use and creation, and participants’

responses were used to create discussion guides

tailored to each person interviewed. Two

interviewees had prior knowledge of the study and

interview methodology (EF & TMS), but it was

determined that their responses were not

substantively different from those of other

interviewees.

Each interview lasted roughly one hour and

focused on the use and creation of graphs, context of

graph use, the importance of aesthetics in graph

design, knowledge of and opinions about the data-ink

ratio concept, and feedback on example graphs with

varying data-ink ratios. The example graphs were bar

graphs and boxplots that were systematically edited

to increase or decrease the data-ink ratio. Thus, a low,

medium and high data-ink ratio version of a bar graph

and boxplot were shown as part of the interview.

Interviews were conducted in participants’ offices to

allow access to personal materials, research

publications, graph-making software, or any other

work artifact that the interviewee wished to reference.

Audio recordings of the interviews (recorded with a

Sony ICD-PX312) were summarized and synthesized

using thematic analysis. Interviewees were given a

gift certificate ($10 value) for their participation in

the interview, but were not aware of any remuneration

at the time they agreed to participate. Gift certificate

funding was provided by RIT’s College of Liberal

Arts.

4 RESULTS

Interviewees reported using graphs for a variety of

reasons, including publishing empirical results,

understanding research, teaching courses, measuring

student progress in courses, evaluating the

effectiveness of interventions, and more. Frequency

of graph use ranged from daily usage to a few times

over the course of a semester, and heavy usage was

reported when involved in research projects.

Bar graphs, scatterplots and line graphs were

mentioned most often by the interviewees, with

others such as radial graphs, boxplots, ISOTYPE and

histograms mentioned infrequently. Some

interviewees preferred to use particular types of

graphs, such as bar graphs, because of ease of

interpretation, or boxplots because they show

complete distributions. One interviewee had a

preference for graphs that plotted every data point.

Others didn’t have preferences for particular types of

graphs, and instead preferred whichever graph was

most appropriate for the particular situation.

4.1 Data-ink Ratio and Example

Graphs

Three interviewees were familiar with the data-ink

ratio concept and provided opinions about it. One of

those three had a background in design. Two owned

copies of Tufte’s book. One of the three described the

data-ink ratio as a “neat idea” and agreed that graph

features with no relevance should be removed.

However, like Carswell (1992), that individual

expressed doubts as to whether data-ink ratios can

actually be measured and did not believe that the data-

ink ratio should be maximized, but rather that there is

a “sweet spot” for data-ink levels which is lower than

the maximum. This interviewee reported that he did

not apply the data-ink ratio to the design of graphs he

creates. The interviewee with an imaging science

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background described the data-ink concept as a

design argument that didn’t result in more usable

graphs. The third interviewee, with a background in

design, felt much more positively about the data-ink

concept and followed and taught many of Tufte’s

recommendations for graph creation. The remaining

four interviewees were either unfamiliar or only

vaguely familiar with the data-ink ratio.

Feedback regarding the low data-ink bar graph

tended to be negative or neutral. It was described as

both “fat” and “chunky” by different interviewees.

One interviewee described it as heavy handed, not

due to the size of the bars, but because of the “noise”

in the form of gridlines, tick marks, and other

elements that could be described as non-data-ink. On

the other hand, the graph was also described as having

“some nice elements” – the T-intersections on the

error bars were seen as helpful and the gridlines were

not “too heavy,” but could have been fainter. Another

interviewee identified this as their favorite bar graph

version, as the gridlines were helpful due to width of

the graph. That interviewee also found T-

intersections at the end of error bars to be helpful.

One participant described the medium data-ink

graph as “more pleasing” than the low data-ink bar

graph due to the increased white space and thinner

bars, but would have added faint gridlines and T-

intersections to the error bars. On the other hand, a

different participant felt that the bars should have

been closer together to facilitate comparisons, but

identified the graph as their favorite bar graph

version.

Two participants believed that the high data-ink

ratio bar graph would take longer to interpret than the

other versions, although one did note that familiarity

with the high data-ink style might make it easier to

use. An additional interviewee described the graph as

“horrible.” Another interviewee found this graph to

be elegant and minimal, but unnecessarily wide given

the increase in white space created by the thin bars.

One interviewee felt that there was “less in the way”

in the high data-ink bar graph, and that it could be

improved further by removing the “bar” portions of

the graph. This interviewee saw bars in general as a

waste of ink which might not add anything, as the

error bars are the key information. Another

interviewee felt that the high data-ink bar graph had

been “cleaned up” compared to the others, but that the

bars could be thicker to make it easier to differentiate

between their colors.

The low data-ink boxplot was generally described

as too busy. More interviewees gave negative

comments about the gridlines in this graph than about

the bar graph gridlines. Although they were the same

size and color as the gridlines in the bar graph, there

were a greater number in the boxplot (4 vs. 9,

respectively), suggesting that opinions regarding the

inclusion of gridlines are dependent upon the specific

graph. The medium data-ink boxplot received more

positive feedback than the low data-ink boxplot,

though many interviewees suggested varied

alterations to the design which they felt would

improve it.

The high data-ink boxplot was widely disliked –

all but one interviewee found it hard to read. It was

noted that the box portion, present in the low and

medium data-ink boxplots, helps to make each

distribution cohesive. This is similar to Kosslyn’s

(1985) argument that completing forms results in

fewer perceptual units. One interviewee commented

that the graph required too many “mental

gymnastics,” and wasn’t sure that she would have

known it was a boxplot in a different context. A

different interviewee felt that the high data-ink

boxplot “says the same thing as the others,” but does

so more efficiently. Additionally, that interviewee felt

that the high data-ink design would be accepted with

time, and that the other designs may eventually look

archaic. Finally, two interviewees who gave negative

feedback about this graph commented that it does

highlight the trend of median values in the graph

given the large amount of white space around them.

4.2 Graph Creation

A number of salient themes emerged on the topic of

graph creation goals. Nearly all interviewees named

clarity as a design goal, which was defined as

readability or “ease of use,” as well as avoiding

clutter. Interviewees wanted their graphs to be

understood by others with little effort. Accuracy was

also mentioned frequently as a design goal – graphs

should show the data as they actually are without

obscuring phenomena. The use of truncated axes was

the typical example of inaccuracy or dishonesty in

graph design.

Interviewees reported using a variety of software

packages to create graphs, including Excel, SPSS, R

Statistics, Adobe Illustrator, InDesign, MATLAB,

and JMP. Some interviewees used multiple programs

for graph creation, choosing whichever is more

appropriate (or easier) for a given graph creation task.

Two interviewees reported sketching graphs by hand

when early in the graph design process, which was

described as a way to avoid the limitations of software

and find the best way to display the data. The

importance of matching graph type to data type was

emphasized by three interviewees. For example, bar

Graph Design: The Data-ink Ratio and Expert Users

191

charts were listed as appropriate for comparing

categorical data, and scatterplots or line graphs for

trend data. This was seen as an aspect of graph

creation and design requiring particular skills and

knowledge.

Aside from an emphasis on accuracy, there were

four other graph design factors mentioned: aesthetics,

good labeling, Gestalt (notably grouping), and a

consistent hierarchy. All interviewees were conscious

of the aesthetics of graphs they create, but had a

variety of definitions for this concept. Some used

words like “clean” or “elegant” to describe their goals

with regard to aesthetics. Both of the interviewees

with a design background mentioned “balance” as a

graph design goal – the idea that a graph creator must

make trade-offs between simplicity, visual interest,

clarity, and completeness. Some interviewees

described graph-making conventions as “heavy-

handed” or even ugly, and nearly all interviewees

expressed some level of dissatisfaction with the look

of default designs offered in software packages.

Effective labeling was critical to a number of

interviewees – three reported that labels are among

the first features of graphs that they read, and that they

are helpful for identifying the variables or conditions

in an experiment. Gestalt principles were mentioned

in multiple interviews by those with both psychology

and design backgrounds. Features such as color and

grouping via proximity were seen as important to

good graph design. The principle of closure was

explicitly discussed – one interviewee noted that the

“box” portion of a boxplot helps each element to look

like a cohesive unit. Hierarchical structure in graph

design was explicitly mentioned by two interviewees.

One reported that the data should always be primary

in visual emphasis. The other interviewee reported

that the “most important things” in a graph should be

emphasized in the design, and that the designer

should know what the hierarchy of their graph is. For

example, if a line graph is being used to show trend

data, the line portion is most important, and that

element should be bolder than elements such as axes

or tick marks.

Interviewees had few absolute rules with regard to

graph creation – the majority of design choices

described during the interviews were dependent upon

the specific features of the data and context of

presentation. Interviewees did not want graphs to be

“busy” or to include features such as gridlines or T-

intersections, but definitions of what constitutes

superfluous varied between participants and

situations. It is notable that interviewees did not

explicitly focus on or mention the data-ink ratio in

their graph design factors.

5 DISCUSSION

The interviews suggest that if there is an optimal

design, it may be a medium data-ink level, as most

interviewees preferred and used such designs.

With regard to Tufte’s claim that his high data-ink

designs would be accepted with time, interview

feedback indicated that high data-ink designs are not

encountered or accepted by frequent users of graphs.

Although models of graph comprehension and the

results of the present study do seem to support the

claim that viewers would be accustomed to high data-

ink ratio designs, it does not seem that are “catching

on” given Tufte first published the data-ink concept

in 1983.

There may be several reasons for this. First,

Tufte’s designs disrupt the grouping of elements in a

graph. Although Tufte’s boxplot design allows for the

medians to be grouped continuously this may not be

useful if the x-axis doesn’t represent a continuous

variable. And experienced users find the lack of boxes

and empty space to disrupt their understanding of

what the boxplot is designed to show, namely the

distribution of scores for a sample. In boxplots, the

vertical grouping of elements is more important than

seeing how the medians relate horizontally.

As noted previously, instantiated graph schemas –

knowledge regarding specific graph types – have

been identified as an important factor in graph

comprehension (Pinker, 1990). Adding elements may

have acted to reduce visual complexity by facilitating

grouping elements or interpreting the data (Donderi,

2003). One interviewee commented that she would

not have been able to identify Tufte’s high data-ink

boxplot as a boxplot without the context provided by

the interview. This suggests that the high data-ink

ratio graph did not activate the boxplot schema.

6 CONCLUSIONS

The results suggest that the data-ink ratio concept

relates to the subjective issue of graph aesthetics.

Arguments about the aesthetics of graphs are worth

having – interview data showed that graph creators

care about the appearance of graphs and make efforts

to ensure that their graphs meet their aesthetic

standards. Our results indicate a graph creator who

prefers the look of Tufte’s high data-ink graphs

should feel free to use them, but graph creators should

not feel that maximizing data-ink ratio will result in

more usable graphs. In defending his ideas, Tufte

argued that it would be a mistake to underestimate the

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audiences of graphical information. With regard to

graph designs with different data-ink ratios, this

sentiment seems to be appropriate – graph users with

varying levels of experience can extract complex

information from high data-ink ratio designs.

ACKNOWLEDGEMENTS

Thanks to the faculty who agreed to participate in our

study. Thanks also to funding provided by the College

or Liberal Arts at RIT.

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