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R Toolbox > About the course > Indicative Syllabus

- - Module 1
    - - 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.
  - Module 2
    - - 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.
  - Module 3
    - - In this module 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 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.
  - Module 4
    - - 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.
  - Module 5
    - - In this workshop you will learn how to create interactive web applications (apps) straight from R. You will be introduced to the basic structure of a Shiny app and you will learn how to build a user interface for your app. You will be introduced to the range of input widgets and reactive outputs that would give your Shiny app a “live” quality! Shiny allows you to present your data story in a new and innovative way that will help you carry out data analysis, visualisation and communication with a wider audience.
  - Module 6
    - - In this module you will learn how to create dynamic R Markdown documents to build static websites allowing you to use R code to render the results of your analysis. The blogdown through the use of R Markdown allows technical writing allowing you to add graphs, tables, LaTeX equations, theorems, citations, and references. This makes blogdown an perfect tool for designing websites to communicate your R data story telling or just awesome general-purpose websites. After you create your awesome website using the blogdown and HUGO template you will push it onto your GitHub and deploy on Netlify all free of charge.

Indicative Syllabus

Module 1

RStudio IDE; R language; Data classification and summary statistics

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

Module 2

Data Wrangling

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.

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

Module 3

Visualising Data

In this module 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 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:

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

Module 4

Automated 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

Module 5

Introduction to Shiny

In this workshop you will learn how to create interactive web applications (apps) straight from R. You will be introduced to the basic structure of a Shiny app and you will learn how to build a user interface for your app. You will be introduced to the range of input widgets and reactive outputs that would give your Shiny app a “live” quality! Shiny allows you to present your data story in a new and innovative way that will help you carry out data analysis, visualisation and communication with a wider audience.

What you will learn:

build a user interface for Shiny app
create dynamic graphics using Shiny‘s reactive features
deploy Shiny app

Module 6

Build a website with blogdown in R

In this module you will learn how to create dynamic R Markdown documents to build static websites allowing you to use R code to render the results of your analysis. The blogdown through the use of R Markdown allows technical writing allowing you to add graphs, tables, LaTeX equations, theorems, citations, and references. This makes blogdown an perfect tool for designing websites to communicate your R data story telling or just awesome general-purpose websites. After you create your awesome website using the blogdown and HUGO template you will push it onto your GitHub and deploy on Netlify all free of charge.

What you will learn:

Apply HUGO theme to create a website using the blogdown
Perosnalise the website
Deploy site from your computer to the Internet
Use GitHub for version control
Use Netlify for continuous deployment
Updating your website: serving site, push to GitHub, deploy