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Fundamentals of data analysis with R
Your data gains value when you can explore it and explain what it reveals. Use programming to structure processing tasks and make your analyses reproducible. Strengthen your ability to turn a business question into verifiable, understandable results.
- Duration
- 2 days 14 hours
- Code
- DEV023FR Code
Presentation
The R language is an essential tool in research and data science, enabling robust, reproducible statistical analysis. This 2-day course is designed to help you work independently with R, from basic syntax to report automation.
The course takes a practical approach to demystifying R programming. Learn to configure your environment with RStudio, structure and clean datasets, then explore them visually using the established ggplot2 package. Rather than focusing on dry theory, you will work with real data to produce immediately usable results.
The course concludes with automation best practices. Discover how to turn repetitive tasks into efficient scripts and generate dynamic documents with RMarkdown, ensuring your analyses remain traceable.
Objectives
By the end of this course, you will be able to:
- use R and RStudio to run scripts and manage projects;
- manipulate and prepare data structures, including vectors, data frames and lists;
- create professional data visualisations with ggplot2;
- apply fundamental statistical functions, including tests, correlations and regressions;
- automate analytical report production with RMarkdown.
Program
Day 1: mastering syntax and data preparation
- Installing and getting started with the RStudio interface.
- Managing data structures: vectors, matrices and data frames.
- Importing CSV and Excel files, and cleaning data through missing-value handling and filtering.
Hands-on exercises
- Explore RStudio, create a dataset and clean a real file, such as a healthcare dataset.
Day 2: producing visual and automated analyses
- Creating advanced charts with ggplot2's grammar, including bar charts, line charts and scatter plots.
- Applying descriptive statistics and simple tests, including t-tests and chi-squared tests.
- Automating processing: creating functions, using loops and generating RMarkdown reports.
Hands-on exercises
- Design a visual dashboard, conduct a complete statistical analysis and generate the final automated report.
Audience
This course is intended for professionals seeking to strengthen their data processing practices, including:
- data analysts and research analysts who want to move beyond Excel's limitations and build repeatable analytical processes;
- researchers, doctoral candidates and students in science, economics or healthcare who need a standard tool for scientific publications;
- developers moving into data roles who want to add statistical skills to their technical expertise;
- anyone seeking to automate data processing, visualisation and reporting.
Prerequisites
The following background is recommended:
- Basic knowledge: confidence using software development tools and basic statistical knowledge will help you benefit fully from the exercises.
Teaching and assessment methods
- Initial skills assessment
- Training materials provided to participants
- Continuous assessment throughout the course
- End-of-course feedback questionnaire
- Combination of theory and practical application
- Attendance records
- Post-course follow-up evaluation
- Practical exercises
Course highlights
- Established tools: learn directly with RStudio and ggplot2, the tools most widely used by the R community.
- Reproducible approach: incorporate scripting and automated reporting with RMarkdown from the outset, supporting traceability.
- Business-relevant practice: workshops use realistic sales and healthcare data scenarios for immediate application in your work.
- Rapid skills development: progress from installation to producing a complete report in two days.
Dates and sessions
Choose the date and delivery format that suit you.
No upcoming sessions are currently available.
Any brand names and logos mentioned in this course description (such as R, RStudio, ggplot2 and RMarkdown) belong to their respective owners. Their mention for educational purposes does not imply any commitment or partnership.