Indicative Syllabus

Day 1

RStudio IDE; R language; Data classification and summary statistics; Introduction to Visualization Principles

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 files 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 be introduced to fundamental principles behind effective data visualisation.

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
  • Basic principles of effective data visualisation

Day 2

Data Wrangling and Visualising Data

In this module you will 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. Through the use of grammar of graphics plotting concepts implemented in the ggplot2 package, you will be able create meaningful exploratory plots. You will develop understanding about the way in which you should be able to think about necessary data transformations and summaries that can lead to an informative visualisation. You will learn how to create static maps and interactive maps with geolocated data by using the most popular packages in the R GIS community: simple features and leaflet.

What you will learn:

  • 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
  • to specify ggplot2 building blocks and combine them to create graphical display
  • about the philosophy that guides ggplot2: grammatical elements (layers) and aesthetic mappings.
  • visualising data with maps

Day 3

Automated Reporting and Introduction to Shiny

In this module you will learn how to turn your analyses 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. After you gain fundamental knowledge of markdown and knitr, you will learn to create interactive web-graphics using Shiny R package.

What you will learn:

  • Authoring R Markdown Reports
  • Embedding R Code
  • knitr to compile dynamic R code
  • LaTex to incorporate mathematical expressions
  • create dynamic graphics using Shiny‘s reactive features
  • build and deploy Shiny app

© 2019 Tatjana Kecojevic