Fundamentals of R Training in United States of America

• No prior knowledge of R is assumed
• Delegates should already be familiar with basic programming concepts such as variables, scope and functions
• Experience of another scripting language such as Python or Perl would be an advantage

At the end of this course attendees will know:

• Fundamentals of R programming language
• R data types, containers, arrays, flow control and functions
• R text manipulation
• R visualisation packages
• How to perform data exploration and analysis with R

At the end of this course attendees will be able to:

• Write R programs for manipulating and analysing data
• Plot graphs from data sets

Overview of R

• An introduction to the R programming language
• History and capabilities
• Development environments
• Assignment
• Tips

Data: Datatypes and Arithmetic

• An introduction to most common data types in base R
• Simple types
• Composite types
• Arithmetic
• Data manipulation

Containers

• A more detailed look at the most common data types in base R
• Lists, Vectors, Factors, Data Frames
• Converting data types

Higher Dimensions

• A more detailed look at the most common data types in base R
• Matrices and Arrays
• Representing 3 or more dimensions

Databases and IO

• A more detailed look at how to read and write to permanent storage from R
• Various File Types
• Databases

Flow

• A more detailed look at how to control decision making in R for later automation:
• Selection
• Iteration

Functions

• An overview of how to create functions in R
• How to call functions
• The use of variadicity in R
• Design principles for programmers

Text

• An overview of the functions available in stringr for manipulating text.
• A brief introduction to regex.
• Functions for pattern matching and text manipulation

Plotting and Packages

• Plotting in base R
• An overview of the community approved packages: Tidyverse
• Syntax differences
• Overview of packages for Data Import, Transforming and Cleaning
• Overview of packages for report creation and dashboarding
• Overview of packages for Big Data handing and AI
• Extra: Practical use of ggplot2, ggplotly