Advanced Programming in R

Date:

07/12/2022 - 08/12/2022

Organised by:

Royal Statistical Society

Presenter:

Dr Jamie Owen

Level:

Advanced (specialised prior knowledge)

Contact:

training@rss.org.uk

Map:

View in Google Maps  (EC1Y 8LX)

Venue:

12 Errol Street, London

Description:

Level: Professional (P)

This training course covers R object-oriented programming (OOP) techniques. The first day discusses what OOP is and the different varieties within R. Beginning with the popular S3 and S4 OOP frameworks, it finishes with the new {R6} package that is used extensively in Shiny applications. On the second day we introduce the {rlang} package as a way of parsing variables from a data set into a function. We then cover {renv} and its uses in managing workflows, by isolating your project’s R dependencies and managing library paths!
 

Learning Outcomes

  • Select the most appropriate form of OOP for their task
  • Leverage encapsulation, polymorphism and inheritance to provide a nice user interface to code
  • Extend the functionality of functions for new object types
  • Use the {rlang} operators !!, !!! and := to pass variables
  • Modify user functions using enquo()
  • Parse and deparse expressions
  • Construct reproducible data analysis workflows with {renv}

Target Audience

This course assumes that participants are comfortable with the fundamentals of R programming. As such the course will be of interest to anyone who uses R, in particular those who want to develop their computer skills to cover more advanced topics. This course would be very useful for participants who do not have a formal background in programming.
 

Delegate Feedback

“I am not scared of R anymore. It was actually fun!”

“The balance between lectures and practicals was good”

“Very clear lectures and hand-outs”

Cost:

£599.76 to £832.32 (including VAT)

Website and registration:

Region:

Greater London

Keywords:

Quantitative Data Handling and Data Analysis, R, OOP , S3 , S4 , rlang , Parse , Deparse , renv

Related publications and presentations:

Quantitative Data Handling and Data Analysis
R

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