Course Information

Data analysis and analytics. Uncover insights and transform your organisation.

Looking to take your data science skills into the stratosphere? Welcome to Advanced R Programming.

Writing elegant R code for data analysis is a critical skill for any modern data scientist, analyst, or programmer. This elegant code can handle advanced data structures like spatial data and regular expressions, and can maximise automation through the functional programming paradigm in R’s apply family of functions.

The advanced R user can also do much more than write code and perform analysis. You can develop custom packages and functions that save time. You can share these time saving techniques with others. You can communicate your findings in clean and tidy ways.

In this course, you will learn how to work with advanced data structures and communicate outside of the R environment. This includes package development, RMarkdown, and database connections. Further, you will learn how to use the Python language within the R environment.

To take this course, we recommend having skills equivalent to the learning outcomes of the R Programming Course: Introduction and R Programming Course: Intermediate. These courses are not a prerequisite but will provide the foundation skills needed for this course.

We encourage you to use the CCE R Programming level self-assessment tool if you are unsure which course level to enrol in.

Aims

This course aims to teach advanced R topics like package development, spatial data, RMarkdown, and package development. These aims are taught through the development of an R package, from the ground up.

Outcomes

By the end of this course, you should be able to:

• develop your own R package
• confidently use advanced formatting types
• share your code and analysis in tidy R markdown documents
• create interactive maps using spatial data
• automate complex functions
• connect to databases in R Studio.

Content

• Regular expressions (regex)
• Spatial data (packages sf, tmap, leaflet)
• Automation through Functionals (packages apply and purr)
• Developing packages in R
• Creating R markdown documents
• Running Python within R
• SQL in R
• Connecting to databases within R/R Studio

Intended audience

Regular users of R, Python, SQL, SAS, SPSS, or other data science programming languages will find the course most beneficial. Managers and leaders will also find Advanced R advantageous for gaining organisation efficiencies.

Prerequisites

As this is an advanced class, some technical programming experience is required. Students who have completed R Programming Course: Introduction and R Programming Course: Intermediate will be well prepared for Advanced R material.

Delivery modes

• Face-to-face, presenter-taught training using your own device
• Online training via the platform Zoom

Face-to-face classes

These classes run in a classroom and you need to bring your own device with R and RStudio installed. You should ensure it is fully charged as access to power is limited. Please note that the University does not carry any responsibility for your lost, stolen, or damaged devices whilst on the University premises.

Online classes

You will need your own device with R and RStudio installed.

Delivery style

This course is taught through a series of concepts, examples, problem exercises, and in class knowledge challenges. The material is presented so that participants of varying backgrounds, skills and abilities can all move together at a brisk but comfortable learning pace.

Materials

A link to access and download the following online course materials (using Dropbox) is provided:

• PowerPoint notes with examples
• all code and script files used throughout the course
• ancillary hand-outs and learning aids.

Before the course

You will need your own device with R and RStudio installed. Both pieces of software are free to download:

• Download R here
• Download RStudio here (the free version will suffice)