About the course
R is a powerful open-source language and environment widely recognised as a leading tool for statistical computing, data analysis, and high-quality graphics. A.k.a "rlang", its extensive capabilities, supported by a vast and growing ecosystem of user-contributed packages, make it indispensable for professionals and researchers across numerous disciplines who need to perform sophisticated data manipulation, statistical modelling, and visualisation.
This offering is a flexible, modular training programme designed to provide participants with comprehensive skills in using R, from foundational concepts to advanced analytical and programming techniques. The programme is structured as a series of focused, one-day modules (F01-F09) that can be combined and delivered in various configurations to create a custom learning path perfectly aligned with an individual's or team's specific requirements, existing R knowledge, and data analysis goals.
The programme covers a wide spectrum of R usage, starting with foundational concepts necessary for navigating the R environment, understanding different types of R objects, loading, saving, and working effectively with data. Building upon these fundamentals, the modules delve into using R for statistical analysis, covering essential methods for describing data, understanding data distribution and sampling, performing basic hypothesis testing (such as tests for differences between samples, correlation, and association), and progressing to more advanced statistical techniques including Analysis of Variance (ANOVA) and various forms of regression analysis.
Furthermore, the programme includes modules dedicated to leveraging R's exceptional graphical capabilities, from producing standard plots and customising every aspect of their appearance (adding elements, working with colour) to creating professional-quality graphics for publication or presentation and employing advanced graphical methods for exploratory data analysis. Participants can also unlock the full potential of R by exploring core R programming tools to write custom functions, scripts, and control flow using loops and conditional statements, gain proficiency in using the popular Tidyverse suite of packages for efficient data manipulation and visualisation (specifically readr, tidyr, dplyr, ggplot2), and integrate their R analyses, results, and graphics seamlessly into dynamic reports, documents, and presentations using RMarkdown. Whether you are a complete beginner needing a foundation in R or an experienced user seeking to deepen specific analytical, graphical, or programming skills, this modular training programme offers the targeted modules you need to advance your R proficiency.
Instructor-led online and in-house face-to-face options are available - as part of a wider customised training programme, or as a standalone workshop, on-site at your offices or at one of many flexible meeting spaces in the UK and around the World.
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- Navigate the R environment, understand R objects and data types, and effectively load, save, and manage data in R.
- Perform basic data summary, aggregation, and cross-tabulation on R data objects.
- Conduct fundamental statistical analyses in R, including describing data, understanding distributions, and performing basic hypothesis tests (differences, correlation, association).
- Perform advanced statistical analyses such as Analysis of Variance (ANOVA) and regression modelling (including generalised linear modelling) in R.
- Produce standard and customised graphs using R, add various elements (lines, points, text, legends, special characters), work with colour, and export graphics for different uses, creating professional-quality visualisations.
- Apply advanced graphical methods and multivariate plots for visual data exploration and highlighting patterns.
- Write and save R scripts, create custom functions with parameters and results, and use control flow (conditional expressions, loops) for developing custom R solutions, including error trapping.
- Utilise key packages from the Tidyverse suite (readr, tidyr, dplyr, ggplot2) for efficient data import, cleaning, manipulation, and visualization.
- Integrate R code, results, and graphics into dynamic HTML, Word, PDF, and slide presentations using RMarkdown.
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This modular R training programme is designed for individuals and teams who need to acquire or enhance their skills in data analysis, statistics, and graphics using the R language. It is suitable for:
Data Analysts
Data Scientists
Statisticians and Biostatisticians
Researchers across various academic and industry disciplines
Students and academics
Anyone needing to perform data manipulation, statistical computing, or data visualization using R, regardless of their specific background.
The programme offers modules suitable for complete beginners as well as those with some prior experience using R who wish to gain more advanced or specialised skills.
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The prerequisites vary by module within this flexible training programme:
The "Beginning R - Foundation" module is specifically designed for complete beginners. No previous experience of statistics, graphics, or computer languages is needed.
Subsequent Modules: Most of these modules assume some previous experience of using R, as gained from the F01 Foundation module or equivalent prior learning. Experience with statistical analysis is generally not necessary unless the module description explicitly states otherwise; the focus is on performing the analysis in R.
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This menu of R programming learning modules is available for private / custom delivery for your team - as an in-house face-to-face workshop at your location of choice, or as online instructor-led training via MS Teams (or your own preferred platform).
Get in touch to find out how we can deliver tailored training which focuses on your project requirements and learning goals.
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Each of these chapters are designed to comprise a day's training - you can build a tailored syllabus, choosing the 1-day modules that meet your learning needs accordingly.
Beginning R - Foundation
Suitable for complete beginners. No previous experience of statistics, graphics or computer languages is needed.
Overview of the R environment.
Getting help in R.
Simple maths in R.
Data in R: Introduction to data types and structures.
Loading and saving data.
Types of R object.
Making R data objects.
Cross tabulation.
Working with data objects.
Data Summary and Aggregation.
Visualising Data: Introduction to producing basic graphs.
Statistical Hypothesis Testing with R
An introduction to the basics of hypothesis testing using R. This module assumes some knowledge of R but experience of statistical analysis is not necessary.
Statistical analyses using R: Introduction to statistical computing.
Describing data.
Data distribution and sampling.
Basic hypothesis testing: Principles and concepts.
Tests for differences in samples (e.g., t-tests).
Tests for correlation.
Tests of association (e.g., chi-squared tests).
Introduction to Analysis of Variance (ANOVA).
Introduction to regression.
Visualising and reporting results of statistical tests.
Advanced Graphics
This module builds on the foundation and extends your skills in graphical presentation. The module focusses on the skills you need to create professional quality graphics. Some knowledge of the graphical functions of R is required; our foundation module would be ideal preparation.
Using R for graphical summary: Advanced techniques.
Producing graphs using R: Beyond the basics.
Customising graphs.
Adding lines, points, symbols and text to graphs.
Adding Legends.
Adding lines and curves to plots.
Using colour effectively.
Using Special characters, e.g. superscript, maths symbols.
Exporting graphics to different formats (PDF, PNG, JPEG, etc.).
Unsupervised Machine Learning
Unsupervised machine learning is a collective term for methods of data analysis that seek to find and identify clusters in your data. Some prior knowledge of R is required but you don’t need any experience of cluster analysis or statistics.
Unsupervised Machine Learning: Concepts and applications.
Similarity & dissimilarity measures for data.
Hierarchical cluster analysis.
K-means analysis.
Introduction to Multivariate analysis (ordination) techniques.
Performing and interpreting cluster analysis in R.
Supervised Machine Learning
Supervised machine learning is a general term for methods of predictive data analysis. The most commonly used method is regression. Some prior knowledge of R is required but no especial knowledge or experience of statistics or regression are necessary.
Supervised Machine Learning: Concepts of predictive modelling.
Introduction to regression analysis in R.
Regression model building.
Curvilinear regression.
Calculating Best-fit lines.
Confidence Intervals for regression models.
Advanced Model building techniques.
Generalised linear modelling (GLM) for non-Gaussian data.
Performing and interpreting regression models in R.
Visual Data Exploration
This module focusses on graphical methods of data exploration and covers some advanced graphical techniques. Some previous knowledge of R is assumed; our foundation module would be ideal preparation.
Visual data exploration: Advanced graphical methods for exploring data.
Graphs of data distribution (e.g., histograms, density plots, box plots).
Graphs highlighting sample differences.
Graphs highlighting relationships (e.g., scatter plots, matrix plots).
Graphs of compositional data.
Multivariate plots (e.g., PCA plots, heatmaps).
Using R for visual data exploration.
R Programming Tools
This module explores the flexibility of R by exploring the programming tools that allow you to create your own custom routines. Some previous experience of using R is needed for this module; our foundation module would be ideal preparation.
Custom solutions using R: Introduction to R programming.
Writing and saving R Scripts.
Creating custom functions with function parameters.
Understanding and returning function results.
Handling User Intervention in scripts/functions.
Using Conditional expressions (if, else).
Error Trapping and handling.
Argument matching in functions.
Using Loops (for, while).
Creating and working with Custom class results.
Developing custom R solutions using programming tools.
Using the Tidyverse
The Tidyverse is a popular suite of R packages designed to help the data scientist. Some previous experience of using R is essential; our foundation module would be ideal preparation.
Using the Tidyverse: Tools for data scientists.
Introduction to the Tidyverse philosophy and packages.
Importing data with readr.
Cleaning data with tidyr.
Manipulating data with dplyr.
Graphics with ggplot2 (Grammar of Graphics).
Working with Tidy data principles.
Performing data analysis tasks using the Tidyverse workflow.
RMarkdown
Markdown is a popular way to encode text and graphics into HTML, PDF and other document formats. RMarkdown integrates the power and flexibility of R with Markdown. Some knowledge of using R is essential here, although you don’t need to be an advanced user. No previous experience of HTML or “regular” markdown is required.
RMarkdown: Integrating R results and graphics into documents.
Markdown syntax basics.
Integrating R code, results, and graphics into Markdown documents.
Outputting documents as HTML documents.
Outputting documents as Word documents.
Outputting documents as PDF documents (requires LaTeX installation).
Creating slide presentations using RMarkdown.
Creating dynamic and reproducible reports.
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The R Project for Statistical Computing: The official website for the R language and environment.
RStudio: A popular and powerful Integrated Development Environment (IDE) for R.
Official R Documentation: Manuals and reference materials for the R language (e.g., "An Introduction to R").
CRAN Task Views: Organised lists of R packages by topic, helpful for discovering tools.
Stack Overflow (tagged 'r'): A large community forum for asking and answering R programming questions.
RMarkdown Documentation: Official resources for getting started and using RMarkdown effectively.
ggplot2 Documentation: Comprehensive documentation for creating data visualizations using the ggplot2 package.
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