Indicative Syllabus
Module 1
RStudio IDE; R language; Data classification/data types
In this module you will set up the working environment and pass the first big hurdle of importing data and you will learn how to do it in the proper way with a command in R. You will learn how to use RStudio IDE for R from its installation to RStudio customisation and file navigation. You will learn good habits and practice of workflow in an R project. Once you get comfortable with the RStudio working environment you will move on to mastering the key features of R language and you will connect to GitHub.
What you will learn:
- Basic use of R/RStudio console
- Good habits for workflow
- Inputting and importing different data types
- R environment: record keeping
Module 2
Reproducible Reporting
In this module you will learn how to turn your research description and analysis into high quality documents and presentations with R Markdown. You will be designing reproducible reports by automating the reporting process, learning how to take a modern approach to telling your data story. With the knowledge from this lesson you will be able to create reports straight from your R code allowing you to document your analysis and its results as an HTML, pdf, slideshow or Microsoft Word document.
What you will learn:
- Authoring R Markdown Reports
- Embedding R Code
- knitr to compile dynamic R code
- LaTex to incorporate mathematical expressions
- commit, push Rproject on GitHub
Material is released under a Creative Commons Attribution-ShareAlike 4.0 International License.
