ETC5523: Communicating with Data

Basic communication theory and practice

Lecturer: Michael Lydeamore

Department of Econometrics and Business Statistics

• michael.lydeamore@monash.edu

• Week 1

• cwd.numbat.space

👩🏻‍🏫 ETC5523 Teaching Team

Dr. Michael Lydeamore

Lecturer & Chief Examiner

Dr. Fonti Kar

Tutor

Contacting the teaching team

• For private matters, contact michael.lydeamore@monash.edu using your Monash student email and citing the unit name.
• For non-private matters, you should post this on the Ed discussion board.

🎯 ETC5523 Learning Objectives

Learning objectives

1. Effectively communicate data analysis, using a blog, reports and presentation.
2. Learn how to build a web app to provide an interactive data analysis.
3. Learn to construct a data story.

Specific outcomes

After this unit, you should be able to:

• understand and apply the elements of effective communication,
• host Quarto HTML outputs online for blogging, reports or other purposes,
• be more confident with web technologies (HTML/CSS),
• make a Shiny web app

🏛️ ETC5523 unit structure

• 2 hour lectures are interactive sessions
• 1 hour workshops → practice skills, ask questions in small groups
• 1 hour tutorial → only go to the one you are assigned to!

🪵 Materials

Unit website

cwd.numbat.space

• Lecture slides and tutorial materials are available on the unit website
• Lecture videos and assessments will be available on Moodle

Note

Materials are designed to develop your hard and soft skills.

✋ Consultation hours

• A total of 2 hours of consultation each week

• See Moodle announcement for the Zoom links for the consultations

• Seek help early and often!

💯 Course assessments

The assessments build one communication practice in stages:

• Week 4 — inspect: identify how published articles, blogs and presentations are structured.
• Week 7 — translate: turn academic research into an executive-focused technical report.
• Week 10 — publish and present: adapt the report into a blog and recorded data-story briefing.
• Week 14 — make it reusable and explorable: package the work, document it and build an interactive app.

Each assessment is worth 25%.

🏁 Expectations Part 1

• Attend lectures, workshops and assigned tutorials
• Minimum total expected workload is 144 hours, that’s 12 hours each week or 8.5 hours of self study per week
• Check the unit homepage and unit Moodle page
• ETC5513 (or equivalent) is a prerequisite → you need to catch up fast if you’re not confident
• Install the latest R and Positron (or your chosen IDE that has the ability to run R code)

🏁 Expectations Part 2

Be an active learner!

An active learner asks questions, considers alternatives, questions assumptions, and even questions the trustworthiness of the author or speaker. An active learner tries to generalize specific examples, and devise specific examples for generalities.

An active learner doesn’t passively sponge up information — that doesn’t work! — but uses the readings and lecturer’s argument as a springboard for critical thought and deep understanding.

– Spencer (2022) Data in Wonderland

Communication time

Aim

• Explain how basic communication theory applies to communicating with data
• Demonstrate communication competence by selecting appropriate behaviour for an audience and monitoring its effect
• Identify and apply rhetorical elements to improve data storytelling
• Clearly articulate and express technical problems for others to help you

Why

• Analysis is only useful when another person can understand, trust or use it.
• Communication choices are embedded in technical work: tables, plots, reports, documentation and interfaces.
• The course develops one practice across many different forms.

The model we will use

Audience + purpose
Who is this for, and what should change?

Claim → evidence → implication
What should they understand, why should they believe it, and why does it matter?

Form + delivery → feedback + revision
How can they use it, and what tells us whether it got through?

Preview the model here. We will return to it after communication theory, rhetoric and the reproducible-example case.

Communicating

To effectively communicate, we must realize that we are all different in the way we perceive the world and use this understanding as a guide to our communication with others.

– Anthony Robbins

Communication is a skill

If you practice, it will get better.

Why should you listen to me though?

I’ve had a lot of practice.

To scientists:

• Dozens of presentations at conferences and seminars,
• Broad collaborations in a wide range of fields

To less technical audiences:

• Hundreds of media interviews,
• Probably a hundred ministerial briefings,
• Many more high level meetings with government and industry stakeholders

What did I just do?

I selected evidence about my experience for a particular audience.

• Purpose: establish why this lecture may be worth your attention.
• Ethos: build credibility using relevant experience.
• Selection: omit experience that does not help that purpose.
• Risk: credibility claims still need to be judged critically.

Communication is a skill—and this was a communication choice.

Communicating with data

The two words ‘information’ and ‘communication’ are often used interchangeably, but they signify quite different things. Information is giving out; communication is getting through.

– Sydney J. Harris

The Basics of
Communication Theory

Communication here refers to human communication

In this section, communication refers to human communication.

Communication is symbolic

• Arbitrary nature of symbols is overcome with linguistic rules
• Agreement among people about these rules is required to effectively communicate
• Meanings rest in people, not words

Communication is a process

Communication is often thought of as discrete, independent acts but in fact it is a continuous, ongoing process.

Linear communication model

Transactional communication

Communication competence

• There is no single ideal way to communicate: competence is situational and relational.
• Select behaviour appropriate to the audience, purpose and context.
• Be able to perform that behaviour, not merely describe it.
• Use perspective taking and cognitive complexity to see plausible alternatives.
• Self-monitor the response and use feedback to adapt.

Types of communication

• Intrapersonal – communicating with one-self
• Dyadic/interpersonal – two people interacting
• Small group – two or more people interacting with group membership
• Public – a group too large for all to contribute
• Mass – messages transmitted to large, wide-spread audiences via media

Tutorial

How does your communication strategy change for different types of communication?

Effective communication

• Communication doesn’t always require complete understanding
• We notice some messages more and ignore others, e.g. we tend to notice messages that are:
  • intense,
  • repetitious, and
  • contrastive.
• Motives also determine what information we select from environment

• We are influenced by what is most obvious

Rhetoric

The art of effective or persuasive speaking or writing

Three rhetorical appeals

Ethos appeals through credibility and trustworthiness. Pathos appeals through values, stakes and emotion. Logos appeals through reasoning and evidence.

These appeals can overlap. They are choices within a rhetorical situation, not fixed labels for the writer, audience and context.

The rhetorical situation

Before choosing words, visuals or software, ask:

• Speaker/source: what establishes responsibility and credibility?
• Audience: what do they know, value and have power to do?
• Purpose: what should change after the communication?

• Message: what is the central claim and supporting evidence?
• Context: what constraints, conventions and competing messages matter?
• Form: what medium makes an appropriate response possible?

Watch like a communicator

As you watch, identify:

1. the intended audience and purpose;
2. the central claim;
3. the evidence selected to support it;
4. how voice and visuals divide the work; and
5. the response the speaker is trying to create.

Hans Rosling

Debrief the choices

• What could the audience understand from the visual before hearing the explanation?
• What did the speaker add that the visual could not?
• Where did credibility, reasoning and stakes appear?
• What was simplified—and did that simplification preserve the evidence?

A compelling form does not replace an honest claim. It helps the claim get through.

A communication masterclass

Communicating your problem

• Asking for help, requires you to communicate what your problem is to another party.

• How you communicate your problem, can assist you greatly in getting the answer to your problem.

🆘 Asking for help 1 Part 1

Exercise: Come up with a solution to this problem.

🆘 Asking for help 1 Part 2

I am looking to adjust the size of two separate ggplots within the same R chunk in Rmarkdown. These plots must be different when outputted as a pdf, so defining the dimensions at the beginning of the chunk doesn't work. Does anyone have any ideas? My code is below.

```{r, fig.height = 3, fig.width = 3}
ggplot(df, aes(weight, height)) +
  geom_point()

ggplot(df, aes(height, volume)) +
  geom_point()
```

• could not find function “ggplot”
• the package needs to be loaded

🆘 Asking for help 1 Part 3

I am looking to adjust the size of two separate ggplots within the same R chunk in Rmarkdown. These plots must be different when outputted as a pdf, so defining the dimensions at the beginning of the chunk doesn't work. Does anyone have any ideas? My code is below.

```{r, fig.height = 3, fig.width = 3}
library(ggplot2)
ggplot(df, aes(weight, height)) +
  geom_point()

ggplot(df, aes(height, volume)) +
  geom_point()
```

• Data df is not defined!

🆘 Asking for help 1 Part 4

I am looking to adjust the size of two separate ggplots within the same R chunk in Rmarkdown. These plots must be different when outputted as a pdf, so defining the dimensions at the beginning of the chunk doesn't work. Does anyone have any ideas? My code is below.

```{r, fig.height = 3, fig.width = 3}
library(ggplot2)
df <- read.csv("mydata.csv")
ggplot(df, aes(weight, height)) +
  geom_point()

ggplot(df, aes(height, volume)) +
  geom_point()
```

• Is the data used in this question necessarily for the question?
• Do we even have a copy of mydata.csv??

🆘 Asking for help 1 Part 5

I am looking to adjust the size of two separate ggplots within the same R chunk in Rmarkdown. These plots must be different when outputted as a pdf, so defining the dimensions at the beginning of the chunk doesn't work. Does anyone have any ideas? My code is below.

```{r, fig.height = 3, fig.width = 3}
library(ggplot2)
ggplot(trees, aes(Girth, Height)) +
  geom_point()

ggplot(trees, aes(Height, Volume)) +
  geom_point()
```

• Author is using the built-in dataset trees here

❓ How to ask questions?

Checklist (note: not an exhaustive checklist)

Is the problem clearly and succinctly described? Is the expected solution or behaviour outlined? Are you asking the right people at the right place?

If the question is asked in a public forum or similar:

Can people who can answer your question find your question? E.g. does the post have appropriate tags or keywords to reach the right experts?

If the problem is computer system related…

Can the problem be easily reproduced on other people’s system? Is the minimum reproducible code or steps supplied?

If the problem is based on data …

Is the data supplied? If the data is big, could you cull your data further to communicate or reproduce the problem?

Session Information

You can easily get the session information in R using sessioninfo::session_info().
Scroll to see the packages used to make these slides.

sessioninfo::session_info()

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.6.1 (2026-06-24)
 os       Ubuntu 24.04.4 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  C.UTF-8
 ctype    C.UTF-8
 tz       UTC
 date     2026-08-12
 pandoc   3.6.3 @ /opt/quarto/bin/tools/ (via rmarkdown)
 quarto   1.7.32 @ /usr/local/bin/quarto

─ Packages ───────────────────────────────────────────────────────────────────
 ! package     * version date (UTC) lib source
 P cli           3.6.6   2026-04-09 [?] RSPM (R 4.6.0)
 P digest        0.6.39  2025-11-19 [?] RSPM (R 4.6.0)
 P evaluate      1.0.5   2025-08-27 [?] RSPM (R 4.6.0)
 P fastmap       1.2.0   2024-05-15 [?] RSPM (R 4.6.0)
 P htmltools     0.5.9   2025-12-04 [?] RSPM (R 4.6.0)
 P jsonlite      2.0.0   2025-03-27 [?] RSPM (R 4.6.0)
 P knitr         1.51    2025-12-20 [?] RSPM (R 4.6.0)
 P otel          0.2.0   2025-08-29 [?] RSPM (R 4.6.0)
   renv          1.0.7   2024-04-11 [1] RSPM (R 4.6.1)
 P rlang         1.3.0   2026-07-05 [?] RSPM (R 4.6.0)
 P rmarkdown     2.31    2026-03-26 [?] RSPM (R 4.6.0)
 P sessioninfo   1.2.4   2026-06-04 [?] RSPM (R 4.6.0)
 P xfun          0.60    2026-07-09 [?] RSPM (R 4.6.0)
 P yaml          2.3.12  2025-12-10 [?] RSPM (R 4.6.0)

 [1] /home/runner/work/cwd/cwd/renv/library/linux-ubuntu-noble/R-4.6/x86_64-pc-linux-gnu
 [2] /home/runner/.cache/R/renv/sandbox/linux-ubuntu-noble/R-4.6/x86_64-pc-linux-gnu/e7c0fad7

 P ── Loaded and on-disk path mismatch.

──────────────────────────────────────────────────────────────────────────────

🎁 Reproducible Example with reprex LIVE DEMO

• Copy your minimum reproducible example then run

reprex::reprex(session_info = TRUE)

• Once you run the above command, your clipboard contains the formatted code and output for you to paste into places like GitHub issues, Stack Overflow and forums powered by Discourse, e.g. RStudio Community.
• For general code questions, I suggest that you post to the community forums rather than Moodle.

Communicating with Data

A map for the course

Communication is a cycle of choices

Audience + purpose
Who is it for, and what should change?

Claim → evidence → implication
What, why believe it, and why does it matter?

Form + delivery
What helps people access and use it?

Feedback + revision
What got through, and what should change next?

This is the spine of the unit. Feedback returns us to the audience, purpose or any other earlier choice.

The medium changes; the problem does not

Shape the message

• presentations and data stories
• articles and reports
• tables and visualisations

Make the message usable

• websites and styling
• packages and documentation
• interactive apps and dashboards

Each output is an interface between your analysis and an audience.

One analysis, many legitimate forms

Suppose your analysis finds that a program improved an outcome, with substantial uncertainty.

• A manager may need the decision, likely benefit and risk.
• An analyst may need assumptions, diagnostics and reproducible code.
• The public may need context, consequences and plain language.
• Future you may need documentation explaining what was done and why.

The evidence stays honest; the selection, order and form change.

Audience-centred does not mean audience-pleasing

Adapt

• language and level of detail
• order and emphasis
• examples and medium
• the action you make possible

Do not distort

• uncertainty or limitations
• relevant context
• scale or comparisons
• what the evidence can support

Asking for help was not a detour

A reproducible example makes the same communication choices:

• Audience: someone who does not share your computer or context
• Purpose: enable them to diagnose one problem
• Message: expected behaviour versus observed behaviour
• Evidence: minimal code, data, output and session information
• Form and delivery: a self-contained example in a place they can access

The question to carry through the course

What should this audience
understand, believe or do—
and what form will help them get there?

Week 1 Lesson

Summary

• Communication is not the transfer of information; it succeeds when a message gets through to a particular audience.
• There is no single ideal form. Competence means selecting and performing an appropriate response to the audience, purpose and context.
• Across this course, we will move through audience and purpose → claim, evidence and implication → form and delivery → feedback and revision.
• Adapting a message must not distort the evidence: the form may change, but the analysis must remain honest.
• A reproducible request for help is our first example of making technical work understandable and usable by someone else.

Week 1 Lesson

Resources

• See more at Learn R Chapter 3: Troubleshooting and asking for help
• Watch more about storytelling with data at:
  • Why storytelling is so powerful in the digital era
  • Why storytelling is more trustworthy than presenting data
  • Making data mean more through storytelling