Intermediate R Programming Course

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

Coronavirus (COVID-19) update: arrangement of our courses

Some of our courses have moved to online delivery. We aim to continue to deliver a high-quality educational experience through interactive, online classes in real-time. If you are currently enrolled in a course with us, we will be in touch soon with options for your enrolment.

Please note that for online classes, all course materials (excluding prescribed textbooks) will be shared electronically. No printed resources will be available.

R is an elegant programming language specifically designed for data science, analytics, and statistics. This Intermediate R Programming course guides participants through more advanced data processing, analytics, and visualisation techniques in R.

By the time you leave our course, you will have a set of data manipulation tools, including tidyverse, dplyr, and ggplot at your fingertips. These tools will help you prepare data for more advanced analytics and modelling. You will also learn to perform statistical regression modelling and hypothesis testing on data sets to uncover valuable patterns and insights. Finally, we will cover programming techniques unique to the R, helping you learn to write optimised and efficient code.

Bringing a combination of private, public, and academic professional experience, your instructor will guide you through Intermediate R, showing you step by step how to utilise R for sophisticated data analytics.

*You are not required to have taken the introductory course – Introduction to R Programming. However, you should be familiar with the learning outcomes listed.

Aims

This course aims to provide an in-depth, more advanced coverage of data processing and analysis in the R programming environment. By the end of the day, you should be comfortable processing, manipulating and preparing data for further analysis, visualising data using elegant graphics, computing regression statistics and confidence intervals, and efficiently programming in the R environment.

Outcomes

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

• prepare data
  • work with advanced R data types like data.frames and lists
  • subset data for deep queries
  • use the tidyverse suite of packages for data science
  • utilise the data.table package for faster processing

• perform analytics and statistical inference
  • explore data using ggplot visualisations
  • build and analyse linear regression models
  • run hypothesis tests and confidence intervals
  • produce advanced data visualisations with the ggplot package

• program efficiently in R
  • optimise R-Studio
  • develop for/while loops for data flows
  • vectorise R data frames
  • write efficient and optimised R code
  • utilise custom functions for faster processing.

Content

• The R Statistical Programming Language
• The R Studio Integrated Development Environment (IDE)
• Tidyverse suite of packages for data processing (including dplyr, ggplot2, and more)
• Advanced data types in R
• Data processing and preparation techniques – data subsetting, pipe operators
• R logic flows – for/while loops, vectorizing
• data.table package
• Summary statistic functions
• Linear regression modelling
• Confidence intervals and hypothesis testing
• ggplot for elegant statistical graphics
• Programming efficiency / computational speed checks
• Error handling

Intended audience

Business professionals, managers, IT knowledge workers and lifelong learners looking for a deeper knowledge of data processing and analytics in R will find Intermediate R Programming helpful.

Prerequisites

This course is aimed at professionals with some basic exposure to the R programming language. Participants of this course should be familiar with the learning outcomes of the Introduction to R Programming Course.

Delivery style

Delivered as presenter-taught computer-based training in a computer lab.

This course will be 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

You will be provided with a link to access and download the following course materials:

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

Recommended reading

DeVries, A 2015, R for Dummies, 2nd edition, For Dummies.

Gilespie, C and Lovelace, R, Efficient R Programming, O’Reilly Media.

Jones, O, Maillardet, R, and Robinson, A 2014, Introduction to Scientific Programming and Simulation Using R, 2nd edition, Chapman and Hall/CRC.

Wickem, H 2019, Advanced R, 2nd edition, Chapman and Hall/CRC.

Wickem, H 2017, R for Data Science, O’Reilly Media.

Wickem, H 2016, ggplot2, Springer International Publishing AG.

Additional information

The entire course will be facilitated in a computer lab with R / R Studio pre-installed on all machines. No personal devices are required. Certain R installation procedures are covered, which will follow handouts, for participants needing guidance after the course completes.

USB devices are recommended for those who wish to save and take their work (code scripts, notes, etc) home for later viewing.