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DVGG05

Data Visualisation in R using ggplot2 (COURSE FULL)

Data Visualisation in R using ggplot2 – online course covering scatterplots, histograms, bar plots, boxplots, density plots, line plots, heatmaps, maps, and advanced customisation for publication-quality graphics.

  • Duration: 3 Days, 4 hours per day
  • Next Date: December 3-5, 2025
  • Format: Live Online Format
TIME ZONE

UK (GMT) local time - All sessions will be recorded and made available to ensure accessibility for attendees across different time zones.

£250Registration Fee

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5.0

from 200+ reviews

  • Overview
  • Instructors
  • Schedule

Course Description

This 3-day course introduces participants to the theory and practice of data visualisation in R using ggplot2. Beginning with the history and principles of effective graphics, the course develops through the Grammar of Graphics, basic plots, and advanced customisation. Participants will learn to create and interpret a wide range of visualisations including scatterplots, histograms, bar plots, boxplots, density plots, line plots, heatmaps, and maps. Emphasis is placed on clarity, accuracy, and communication of data patterns. By the end of the course, participants will be confident in producing, customising, and exporting publication-quality figures in R.

What You’ll Learn

During the course will cover the following:

  • Understand the aims and principles of effective data visualisation.
  • Use the ggplot2 Grammar of Graphics framework to build plots layer by layer.
  • Produce core visualisation types: scatterplots, histograms, bar plots, boxplots, and line plots.
  • Add aesthetics (colour, shape, size), facets, and smoothing lines to enhance plots.
  • Work with distributions, density plots, heatmaps, and geospatial visualisations.
  • Customise themes, colours, and labels for clarity and communication.
  • Combine multiple plots into panels and export high-quality graphics.
  • Critically evaluate plots for accuracy, clarity, and effectiveness.

Course Format

Interactive Learning Format

Each day features a well-balanced combination of lectures and hands-on practical exercises, with dedicated time for discussing participants’ own data, time permitting.

Global Accessibility

All live sessions are recorded and made available on the same day, ensuring accessibility for participants across different time zones.

Collaborative Discussions

Open discussion sessions provide an opportunity for participants to explore specific research questions and engage with instructors and peers.

Comprehensive Course Materials

All code, datasets, and presentation slides used during the course will be shared with participants by the instructor.

Personalized Data Engagement

Participants are encouraged to bring their own data for discussion and practical application during the course.

Post-Course Support

Participants will receive continued support via email for 30 days following the course, along with on-demand access to session recordings for the same period.

Who Should Attend / Intended Audiences

This course is designed for, data analysts, postgraduate students, and early-career researchers. It assumes participants have basic experience using R and RStudio, such as importing data and running simple functions, as well as an understanding of fundamental statistical concepts like mean, variance, and correlation. No prior statistical experience is required, since the focus is on visualization rather than modelling. While not essential, familiarity with data wrangling tools such as dplyr and tidyr, along with some basic plotting in R, will be helpful.

Equipment and Software requirements

A laptop or desktop computer with a functioning installation of R and RStudio is required. Both R and RStudio are free, open-source programs compatible with Windows, macOS, and Linux systems.

A working webcam is recommended to support interactive elements of the course. We encourage participants to keep their cameras on during live Zoom sessions to foster a more engaging and collaborative environment.

While not essential, using a large monitor – or ideally a dual-monitor setup – can significantly enhance your learning experience by allowing you to view course materials and work in R simultaneously.

All necessary R packages will be introduced and installed during the workshop. A comprehensive list of required packages will also be shared with participants ahead of the course to allow for optional pre-installation.

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Dr. Niamh Mimnagh

Dr. Niamh Mimnagh

Niamh is a statistician working at the interface of ecology, epidemiology, and data science. Her research focuses on applying and developing statistical and machine learning methods to address real-world challenges such as estimating species population sizes from count and trace data and predicting livestock disease re-emergence using sparse or imbalanced datasets. She works with a wide array of statistical approaches, including Bayesian hierarchical models, N-mixture models, anomaly detection algorithms, and spatial analysis techniques.

 

Niamh earned her PhD in Statistics, with a focus on multispecies abundance modelling, and holds a first-class MSc in Data Science. Alongside her research, she is actively engaged in science communication and education, running a popular blog on applied statistics for non-specialists, and regularly delivering workshops and guest lectures on topics such as GLMs and machine learning with imbalanced data.

 

Education & Career

  • PhD in Statistics (Multispecies Abundance Modelling)
  • MSc in Data Science (First Class Honours)
  • Instructor, consultant, and science communicator in statistical ecology and epidemiology

 

Research Focus

Niamh’s work centres on extracting meaningful insights from complex ecological and epidemiological data. She is particularly interested in population estimation techniques and predictive modelling for conservation and disease management, using advanced statistical tools and reproducible workflows.

 

Current Projects

  • Development of Bayesian and ML approaches for estimating species abundance from imperfect data
  • Modelling livestock disease risk using spatial and temporal predictors
  • Creating accessible educational materials for teaching applied statistics in R

 

Professional Consultancy

Niamh provides expert statistical support to academic and applied research projects, with a focus on ecological monitoring, conservation planning, and disease modelling. She also advises on study design and data workflows for interdisciplinary teams.

 

Teaching & Skills

  • Teaches topics including GLMs, Bayesian statistics, machine learning for imbalanced data, and spatial statistics in R
  • Advocates for reproducibility, open science, and accessible statistical training
  • Experienced in communicating complex methods to broad audiences

 

Links

 

Session 1- 01:15:00 – Why Visualisation Matters

Historical and modern motivations: Anscombe’s quartet, Datasaurus, John Snow’s cholera map, Nightingale’s polar plots, Minard’s map of Napoleon’s march. Principles of effective visualisation: clarity, scepticism, openness (Tukey, Tufte).

Session 2 – 01:15:00  – Getting Started with R, RStudio, and ggplot2

RStudio interface and workflow, tidyverse overview: tibble, read_csv, and data wrangling basics. Grammar of Graphics: data, aesthetics, geoms, layers, and themes.

Session 3 – 01:15:00 – First Plots with ggplot2

Scatterplots and line plots. Adding aesthetics (colour, shape, size). Building plots layer by layer.

Session 4 – – 01:15:00 – Distributions: Histograms and Density Plots
Histograms with binwidths and bins. Comparing groups with fill, dodge, transparency.m Density plots, rug plots, and kernel smoothing.

Session 5 – 01:15:00 – Bar Plots and Boxplots
Bar charts for frequencies and proportions. Stacked, dodged, and filled bar plots (Titanic dataset example). Boxplots, jitter plots, violin plots, and grouped comparisons.

Session 6 – 01:15:00 – Scatterplots in Depth
Advanced scatterplots: aesthetics, transparency, and smoothing lines (lm, loess, GAM). Labelling points and avoiding overlap (ggrepel). Faceting by variables (facet_wrap, facet_grid).

Session 7 – 01:15:00 – Line Plots and Time Series
Line plots with grouping variables. Multiple time series and colouring by factor. Nottingham temperature dataset case study.

Session 8 – Advanced Plot Types
Heatmaps for two-dimensional data. Geospatial visualisation: choropleth maps, polygons, and scaling fills. Working with colour scales and palettes (manual, Brewer, distiller).

Session 9 – 01:15:00 – Putting It All Together
Fine-tuning: themes, labels, legends, and scales. Combining plots into panels (ggpubr, patchwork). Exporting publication-quality figures (ggsave). Course summary and best practices.

Testimonials

PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.

Rolando O. Santos

PhD Assistant Professor, Florida International University

Courses attended

SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07

Testimonials

PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.

Rolando O. Santos

PhD Assistant Professor, Florida International University

Courses attended

SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07

Testimonials

PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.

Rolando O. Santos

PhD Assistant Professor, Florida International University

Courses attended

SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07

Frequently asked questions

Everything you need to know about the product and billing.

When will I receive instructions on how to join?

You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.

Do I need administrator rights on my computer?

Yes — administrator access is recommended, as you may need to install software during the course. If you don’t have admin rights, please contact us before the course begins and we’ll provide a list of software to install manually.

I’m attending the course live — will I also get access to the session recordings?

Yes. All participants will receive access to the recordings for 30 days after the course ends.

I can’t attend every live session — can I join some sessions live and catch up on others later?

Absolutely. You’re welcome to join the live sessions you can and use the recordings for those you miss. We do encourage attending live if possible, as it gives you the chance to ask questions and interact with the instructor. You’re also welcome to send questions by email after the sessions.

I’m in a different time zone and plan to follow the course via recordings. When will these be available?

We aim to upload recordings on the same day, but occasionally they may be available the following day.

I can’t attend live — how can I ask questions?

You can email the instructor with any questions. For more complex topics, we’re happy to arrange a short Zoom call at a time that works for both of you.

Will I receive a certificate?

Yes. All participants receive a digital certificate of attendance, which includes the course title, number of hours, course dates, and the instructor’s name.

When will I receive instructions on how to join?

You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.

Do I need administrator rights on my computer?

Yes — administrator access is recommended, as you may need to install software during the course. If you don’t have admin rights, please contact us before the course begins and we’ll provide a list of software to install manually.

I’m attending the course live — will I also get access to the session recordings?

Yes. All participants will receive access to the recordings for 30 days after the course ends.

I can’t attend every live session — can I join some sessions live and catch up on others later?

Absolutely. You’re welcome to join the live sessions you can and use the recordings for those you miss. We do encourage attending live if possible, as it gives you the chance to ask questions and interact with the instructor. You’re also welcome to send questions by email after the sessions.

I’m in a different time zone and plan to follow the course via recordings. When will these be available?

We aim to upload recordings on the same day, but occasionally they may be available the following day.

I can’t attend live — how can I ask questions?

You can email the instructor with any questions. For more complex topics, we’re happy to arrange a short Zoom call at a time that works for both of you.

Will I receive a certificate?

Yes. All participants receive a digital certificate of attendance, which includes the course title, number of hours, course dates, and the instructor’s name.

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DVGG05 ONLINE
DVGG05 ONLINE
£ 250.00
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£250.00
3 December 2025 - 5 December 2025
Delivered remotely (United Kingdom), Western European Time Zone, United Kingdom
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