Class Details
R Programming Course: Advanced
Course summary
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.
Learning 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
Who this course is for
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 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.
Delivery modes
- Face-to-face, presenter-taught training using your own device
- Online training via the platform Zoom
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.
What you need to do before the course
To participate in this course, you will need R and RStudio installed on your device. They can be downloaded free of charge from:
- Download R here
- Download RStudio here (the free version will suffice)
Face-to-face classes
Please bring your own laptop with all required software pre-installed.
Online classes
For the best experience, we recommend using a computer with a large monitor or dual screens/devices. Small laptop screens can make it difficult to follow the facilitator’s display while working in the software.
Sessions
Location
Face-to-face (Venue TBA, Sydney)
Other information
If your upcoming class is Face-to-face (Venue TBA, Sydney), the class is located at the Camperdown/Darlington campus but the exact classroom and building has not been assigned.
The exact location on campus is confirmed approximately five business days from a class start date and you will then receive a course preparation email with this information.
Meet the facilitators
Paul Yacobellis
Paul Yacobellis has been helping professionals optimise their personal productivity for the past 10 years. He has assisted management and individuals implement time saving, energy maximising techniques. He has lead training sessions that cut out wasteful time, reduce stress, and increase overall well-being. Paul’s techniques range from technical to behavioural.
Paul also has experience in data mining and big data analytics, statistical computing, economic/econometric consulting, spatial statistics and Geographic Information Systems (GIS), transport modelling, and business development. He has consulted with government agencies, private corporations and universities, and has previously taught statistics, mathematics, economics and computer science at the University of Central Florida, Orlando.
Paul holds a Master’s in Statistical Computing (Data Mining track) and a Bachelor of Science in Economics and Mathematics.
When not sharing his passion for enhanced personal productivity, Paul works fulltime as data scientist and enjoys parenthood, long-distance running and reading great books.
