Indicative Syllabus

In this workshop you will set up the working environment, connect to GitHub and pass the first big hurdle of importing data; 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 files navigation. You will learn good habits and the 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 learn some of the fundamental techniques for data exploration and transformation through the use of the dplyr package. This tidy verse package helps make your exploration intuitive to write and easy to read. You will learn dplyr’s key verbs for data manipulation that will help you uncover and shape the information within the data that is easy to turn into informative plots. You will be introduced to the fundamental principles behind effective data visualisation. Through the use of the grammar of graphics plotting concepts implemented in the ggplot2 package you will be able to create meaningful exploratory plots. You will develop an understanding about the way in which you should be able to think about the necessary data transformations and summaries that can lead to an informative visualisation. We will not stop there. You will be shown how to turn analyses into high quality documents and presentations with R Markdown. With the knowledge from this workshop you will be able to create reproducible 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:

  • Basic use of R/RStudio console
  • Good habits for workflow
  • Inputting and importing different data types
  • R environment: record keeping
  • Data classification
  • Descriptive summary statistics
  • dplyr’s key data manipulation verbs: select, mutate, filter, arrange and summarise/summarize
  • to aggregate data by groups
  • to chain data manipulation operations using the pipe operator
  • basic principles of effective data visualisation
  • to specify ggplot2 building blocks and combine them to create graphical display
  • about the philosophy that guides ggplot2: grammatical elements (layers) and aesthetic mapping
  • visualising data with maps
  • authoring R Markdown reports; embedding R code; LaTex to incorporate mathematical expressions
  • knitr to compile dynamic R code

© 2020 Tatjana Kecojevic