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Introduction to Data Visualization with R using ggplot (online)


Course Information


Course start date: Wednesday 10 May 2023 to Thursday 11 May 2023

Duration: 2 day online course

Course Module: Non-accredited

Level: CPD, Advanced / Professional

In this two-day course, you will gain a comprehensive introduction to data visualization in R using ggplot.

We will cover all the major data visualization techniques and use them to explore and illustrate the major patterns in data.  

This course is aimed at anyone who is interested in doing data visualization using R.

Data visualization is a major part of data science and statistical data analysis, and R is the most widely used program for data science and statistics.

Data visualization using R is widely used throughout academic scientific research, as well as widely throughout the public and private sectors. 

You can sign up for this course at any point within the live period until 3 working days prior to the course commencing.

Course Code

Course Description

The course will cover these key topics: 

  • utilising data visualisation techniques and tools particularly R’s `ggplot` to explore patterns in datasets and to present them in an accessible manner appropriate for the intended audience and/or publications 
  • visualizing univariate, bivariate and multivariate data through the widely used graphical techniques such as, scatterplots, histograms, density plots, barplots, and Tukey boxplots 
  • introducing a range of less familiar plot types such as frequency polygons, area plots, line plots, uncertainty plots, violin plots, and geospatial mappings 
  • modifying and refining plots styles and layouts by applying specific functionalities / controls like the limits and scales on axes, positions and nature of the axis ticks, colour palettes, and available ggplot themes 

During the course you’ll: 

  • understand the general principles behind `ggplot` for the purposes of data visualization  
  • recognise the major types of plots for visualizing distributions of univariate data and presenting multiple distributions simultaneously on the same plot using different colours and "facet" plots. 
  • learn how to visualise bivariate data using scatterplots, and how to apply linear and nonlinear smoothing functions to the data, add marginal histograms and labels to points, and scale each point by the value of a third variable 
  • expand your knowledge and application of less familiar plot types that are often related but not identical to those major types covered in earlier topics. 
  • learn about specific controls / functionalities of the plot to present the data in a visually appealing and accessible manner 
  • explore how to make plots for presentations and publications and insert them into documents using RMarkdown 

What will I gain?   

By the end of the course, you’ll have gained knowledge and understanding of the general purpose and principles of data visualisation and the fundamental graphical tools for visualising data. 

You’ll also be able to plot complex, multivariate datasets using a wide variety of fundamental graphical tools, and have the know-how to effectively explore and interrogate datasets visually. 

On completion of at least 80% of the course, you’ll receive a certificate of attendance. 

Where you'll learn: The course is delivered through interactive online workshops via Zoom.

It will be practical, hands-on, and workshop based.

There will be some brief lecture style presentations throughout, i.e., using slides or blackboard, to introduce and explain key concepts and theories.

Throughout the course, and we will use real-world data sets and coding examples. 

Tutor Profile: Mark Andrews is an Associate Professor at Nottingham Trent University whose research and teaching is focused on statistical methodology in research in the social and biological sciences.

He is the author of 2021 textbook on data science using R that is aimed at scientific researchers, and has a forthcoming new textbook on statistics and data science that is aimed at undergraduates in science courses.

His background is in computational cognitive science and mathematical psychology.  

Any questions?  Contact [email protected], Commercial Manager, School of Social Sciences 

Other available online CPD courses in this series include 

Introduction to statistics using R and Rstudio 

Introduction to Data Wrangling using R and tidyverse  

Introduction to Generalized Linear Models in R 

Introduction to Multilevel (hierarchical, or mixed effects) Models in R 

Introduction to Bayesian Data Analysis with R 

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