Programming in R Training in Ireland

Delegates must be existing R users who have attended:

• R for Data Handling (QADHR) or Python Programming (QAPYTH3)

• or have a similar level of knowledge in another programming language.

• Increase your knowledge of the core R language, data types, and collections in R
• Use RStudio to create R Scripts and identify the range of other software solution types
• Understand why R is considered an easy language for Data Analysts and plan to create packages with a range of R Users in mind
• Control the flow of your R code with selection and iteration structures and functions
• Create your own subroutines and class structures using R
• Learn about different programming paradigms
• Develop data engineering and software development solutions using base R
• Lift the hood on community packages, developer tools, and code submission guidelines
• Practice with simple and intermediate difficulty exercises and an extension project to test a simple financial strategy with time series objects

Introduction to R for Programmers

• Investigate the R programming language and typical usage
• Explore R Studio including accessibility, the purpose of each window, and types of R files including R Scripts, Quarto, Markdown, and Shiny
• Create an R Script and use base R and package functionality to write to the console, save R Objects in the environment, install and load packages, and plot static and animated data visualisations.

Atomic Datatypes and Fundamental Operations

• Store single elements as each of the six base R datatypes: logical, integer, numeric, complex, character, raw.
• Identify the differences between NULL, NA, NaN, and Inf.
• Convert datatypes and use arithmetic and relational operators.

Heterogeneous Datatype Containers and Addressing

• Identify containers that can contain a mixture of datatypes
• Create and address from a List using a variety of operations
• Create and address from a Data Frame using a variety of operations

Homogeneous Datatype Containers, Casting, and Piecewise Operations

• Identify containers that have a consistent datatype throughout
• Create vectors, factors, matrices, and arrays and convert to vectors
• Demonstrate piecewise operations in R and use arithmetic, relational, and logical operators

Selection

• Identify selection structures for use in flow control with R
• Use Boolean logic to control the flow of a program
• Select the appropriate structure for solving a problem which requires choices to be automated

Iteration

• Identify loop structures that can be used to repeat selected code sections
• Practice using definite and indefinite repetition structures with break and next to control the flow
• Select the appropriate iterative structure for problems which require repeated actions to be automated

Creating Subroutines

• Identify how functions and procedures are defined in R and the need for checking trustworthiness of code sources
• Explore the variety of ways functions can be called including tag abbreviations and base R pipes
• Create functions including with the use of variadic

Programming Paradigms

• Create R code that solves a problem using Imperative then Object Oriented Programming (OOP) paradigms.
• Identify the features of a class, and differences between S3, S4, R5, and R6 objects in R
• Identify key terminology for OOP programming including object, class, instantiation, encapsulation, inheritance, and polymorphism – and view R examples demonstrating each.
• Create a solution to a problem using both imperative and object oriented (R5) paradigms.

Development Practices

• Identify community standards such as naming conventions, commenting, code structure, formatting and style, function documentation, and unit testing.
• Identify error handling methods in R including custom error handling
• Identify packages to enable testing in R
• Create an exemplar function by following development best practices

Performance Enhancement

• Identify how to measure the time taken and memory used by an R program
• Compare the effect of vectorisation on performance and assess the use of ifelse(), and the apply family of functions

Developer Tools and Packaging

• Save and load RData and environments
• Identify the functionality of devtools
• Deconstruct and critique a selection of functions from published packages
• Examine the submission guidelines for CRAN

Practice Project – Strategy Testing

• Additional Practice if time allows
• Create a solution in R to compare financial trading strategies using imperative, object oriented, or object oriented with inheritance.

Related learning

Data Science Learning Pathways can be selected by choosing either Python or R and a Cloud Platform certification:

• QAIDSDP Introduction to Data Science for Data Professionals
• Sourcing and handling data:
  • QADHPYTHON Data Handling with Python
  • QADHR Data Handling with R
  • QAPDHAI Python Data Handling with AI APIs
• Statistics for Data Analysis:
  • QASDAPY Statistics for Data Analysis with Python
  • QASDAR Statistics for Data Analysis with R
• Programming and Software Development skills:
  • QAPYTH3 Python Programming
  • QARPROG R Programming
• Machine Learning Development:
  • QADSMLP Data Science and Machine Learning with Python
  • QADSMLR Data Science and Machine Learning with R
• Forecasting:
  • QATSFP Time Series and Forecasting with Python
  • QATSFR Time Series and Forecasting with R

Suggested Certifications:

• MDP100 Designing and Implementing a Data Science Solution on Azure (DP-100)
• AMWSMLP Machine Learning Pipelines on AWS
• GCPMLGC Machine Learning on Google Cloud