About me: Dr Jens Roeser

  • Associate Professor in Psycholinguistics
  • Module leader for PSYC40940
  • Research interests:
    • language processing in production, acquisition, and comprehension,
    • writing and spelling (Roeser et al., 2019),
    • understudied languages (Garcia et al., 2023),
    • Bayesian modelling (Roeser et al., 2024, 2025), keystroke logging, and eye tracking.
  • Teaching experience across data science, statistics, cognitive psychology, and language acquisition (Andrews & Roeser, 2021; Roeser & Wood, 2019).

For Today

Part 1:

  • module overview,
  • assessment expectations,
  • schedule and dataset planning.

Part 2:

  • install/check R and RStudio,
  • render an RMarkdown file to HTML,
  • complete the individual RMarkdown exercise.

What this module is about

This module is about using R to communicate behavioural data clearly.

You will learn how to:

  • choose suitable visualisations for different data structures,
  • build publication-quality static figures,
  • write reproducible reports in RMarkdown,
  • build interactive dashboards in Shiny,
  • explain and defend visualisation design choices.

Why this matters

Good visualisation is not decoration.

It helps people:

  • understand patterns in complex behavioural data,
  • notice uncertainty, variation, and outliers,
  • avoid misleading summaries,
  • explore data interactively,
  • connect evidence to a clear argument.

Module learning outcomes

By the end of the module, you should be able to:

  • demonstrate a critical understanding of effective data visualisation,
  • use R to produce high-quality visualisations of behavioural data,
  • produce clear, reproducible visualisation reports,
  • build interactive visualisations or dashboards using Shiny,
  • make independent design decisions and explain their practical constraints.

Schedule

Session Date Title
1 24/09/26 Introduction to Visualisation of Behavioural Data and Data Dashboards
2 01/10/26 Foundations of Data Visualisation and ggplot2
3 08/10/26 Exploring Aesthetics and Geometries
4 15/10/26 Major Visualisation Tools
5 22/10/26 Customising Visualisations
6 29/10/26 Shiny Basics and Reactivity
7 05/11/26 Layout and Observers
8 12/11/26 Dynamic User Interfaces
9 19/11/26 Dashboard Workshop with Optional Plotly
10 26/11/26 Wrap Up and Assessment Support

Assessment Overview

The summative assessment is a portfolio project.

You will choose one complex behavioural dataset and produce:

  1. Static visualisations: four different visualisation types with publication-quality formatting.
  2. Interactive dashboard: a Shiny dashboard with user input and at least two interactive plots.
  3. Reflection: short commentary on design choices, constraints, and links to effective visualisation principles.

Deadline: Friday 8 January 2027, before 2 pm.

Submission Format

Submit one zip archive containing:

  • one RMarkdown file,
  • the rendered HTML file,
  • the RStudio project file,
  • the relevant data file,
  • any supporting files, such as bibliography files, images, or helper scripts,
  • the GenAI Usage Declaration Form.

Your work must be reproducible by someone else from the submitted files.

Formative Assessment

The formative assessment is a mini-portfolio project.

You will:

  • select a behavioural dataset,
  • create 3-4 visualisations using different techniques,
  • write brief commentary on what each visualisation shows,
  • explain why each visualisation was chosen,
  • reflect on clarity, limitations, and possible misleading features.

Deadline: Friday 30 October 2026, before 2 pm.

Dataset Support

Finding a suitable dataset is part of the module.

Before the formative assessment, you should start by looking for possible open behavioural datasets and checking whether they have clear documentation, usable variables, and a source you can cite.

There will be a tutorial checkpoint on 21 October 2026, before the formative deadline, where you can bring candidate datasets, links, codebooks, or early ideas for feedback.

After the formative assessment, dataset ideas can still be refined for the final portfolio:

  • you can bring revised candidate datasets for feedback,
  • I can help you judge whether a dataset is suitable in scope and complexity,
  • early ideas can be refined into a workable final portfolio dataset.

Places to Search for Data

Useful starting points include:

Search Strategy

Search with a topic plus terms such as open data, dataset, CSV, codebook, OSF, or reaction time.

Examples:

  • sleep questionnaire open data
  • memory reaction time dataset OSF
  • anxiety wellbeing csv codebook
  • attention experiment trial-level data

Then check whether the dataset has a usable behavioural outcome, meaningful predictors, documentation, and a licence or source that allows reuse.

Using AI Tools for Dataset Search

How to Engage

To do well in this module:

  • attend the weekly sessions,
  • complete the individual exercises,
  • keep your RMarkdown files, data, and outputs organised,
  • ask questions in class, or MS Teams,
  • use feedback from the formative assessment to improve the final portfolio,
  • engage with recommended reading (I’ll refer you to specific chapters).

Recommended Reading

References

Andrews, M. (2021). Doing data science in R: An introduction for Social Scientists. SAGE Publications Ltd.

Andrews, M., & Roeser, J. (2021). psyntur: Helper tools for teaching statistical data analysis. https://CRAN.R-project.org/package=psyntur

Garcia, R., Roeser, J., & Kidd, E. (2023). Finding your voice: Voice-specific effects in Tagalog reveal the limits of word order priming. Cognition, 236, 105424.

Roeser, J., Conijn, R., Chukharev, E., Ofstad, G. H., & Torrance, M. (2025). Typing in tandem: Language planning in multisentence text production is fundamentally parallel. Journal of Experimental Psychology: General, 154(7), 1824–1854. https://doi.org/10.1037/xge0001759

Roeser, J., De Maeyer, S., Leijten, M., & VaWaes, L. (2024). Modelling typing disfluencies as finite mixture process. Reading and Writing, 37(2), 359–384. https://doi.org/10.1007/s11145-023-10489-4

Roeser, J., Torrance, M., & Baguley, T. (2019). Advance planning in written and spoken sentence production. Journal of Experimental Psychology: Learning, Memory, and Cognition, 45(11), 1983–2009.

Roeser, J., & Wood, C. (2019). Language and literacy. In P. Banyard, C. Norman, G. Dillon, & B. Winder (Eds.), Essential psychology (Vol. 3, pp. 197–226). Sage.

Wickham, H. (2016). ggplot2: Elegant graphics for data analysis. Springer.

Wickham, H. (2021). Mastering shiny. O’Reilly Media.