Syllabus 2022
Statistics in R for Postgraduate Students
Organisers: Dr Nicolaas Puts and Dr Lucas França
Email: nicolaas.puts@kcl.ac.uk, lucas.franca@kcl.ac.uk
Lecture: 10:00-12:00, Monday
📜 Course Description
This is pilot edition for an optional R/RStudio module for the "Introduction to Applied Statistical Methods" course. This is optional and you will not be tested or be able to get credits for it. However, this will provide you with knowledge in data science that will be pivotal for a prospective academic career and improve your CV.
⚠️
The SPSS practicals are still mandatory if you enroll on this module.
🗝 Enrolment
Prerequisite(s): Basic mathematics and computation knowledge Concurrent Enrolment: Introduction to Applied Statistical Methods 2022-2023 Recommended Preparation: How to install R/Rstudio short video.
📚 Readings
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This is a list of readings in priority order. All the listed content is free to access online and most are also available in physical copies.
Suggested Texts
| Name | Author | Publisher | Year | Link |
|---|---|---|---|---|
| R for Data Science: Import, tidy, transform, visualize, and model data | Wickham & Grolemund | O'Reilly | 2016 | https://r4ds.had.co.nz |
| ggplot2: Elegant Graphics for Data Analysis | Hadley Wickham | Springer | 2016 | https://ggplot2-book.org/index.html |
| R Markdown: The Definitive Guide | Xie, Allaire & Grolemund | CRC Press | 2021 | https://bookdown.org/yihui/rmarkdown/ |
| Fundamentals of Data Visualization | Claus O. Wilke | O'Reilly | 2020 | https://clauswilke.com/dataviz/ |
| Tidy Modeling with R | Kuhn & Silge | NA | 2021 | https://www.tmwr.org |
📆 Course Schedule
📌
More information will be added here in due course. Please click on the lecture title for more content.
Schedule