Introduction to R | R Programming JumpStart

Introduction

• Making R more friendly, R and available GUIs
• The R environment
• Related software and documentation
• R and statistics
• Using R interactively
• An introductory session
• Getting help with functions and features
• R commands, case sensitivity, etc.
• Recall and correction of previous commands
• Executing commands from or diverting output to a file
• Data permanency and removing objects

Simple manipulations, numbers and vectors

• Vectors and assignment
• Vector arithmetic
• Generating regular sequences
• Logical vectors
• Missing values
• Character vectors
• Index vectors, selecting and modifying subsets of a data set
• Other types of objects

Objects, their modes and attributes

• Intrinsic attributes: mode and length
• Changing the length of an object
• Getting and setting attributes
• The class of an object

Ordered and unordered factors

• A specific example
• The function tapply() and ragged arrays
• Ordered factors

Arrays and matrices

• Arrays
• Array indexing. Subsections of an array
• Index matrices
• The array() function
• The outer product of two arrays
• Generalized transpose of an array
• Matrix facilities
• Forming partitioned matrices, cbind() and rbind()
• The concatenation function, (), with arrays
• Frequency tables from factors

Lists and data frames

• Lists
• Constructing and modifying lists
• Data frames

Reading data from files

• The read.table()function
• The scan() function
• Accessing builtin datasets
• Editing data

Probability distributions

• R as a set of statistical tables
• Examining the distribution of a set of data
• One- and two-sample tests

Grouping, loops and conditional execution

• Grouped expressions
• Control statements

Writing your own functions

• Simple examples
• Defining new binary operators
• Named arguments and defaults
• The '...' argument
• Assignments within functions
• More advanced examples
• Scope
• Customizing the environment
• Classes, generic functions and object orientation

Statistical models in R

• Defining statistical models; formulae
• Linear models
• Generic functions for extracting model information
• Analysis of variance and model comparison
• Updating fitted models
• Generalized linear models
• Nonlinear least squares and maximum likelihood models
• Some non-standard models

Graphical procedures

• High-level plotting commands
• Low-level plotting commands
• Interacting with graphics
• Using graphics parameters
• Graphics parameters list
• Device drivers
• Dynamic graphics

Packages

• Standard packages
• Contributed packages and CRAN
• Namespaces

Join an engaging hands-on learning environment, where you’ll:

• Manipulate objects in R and read data
• Access R packages
• Write R functions
• Develop informative graphs
• How to analyze data using common statistical models
• Use R software via the command line and a graphical user interface (GUI)

This course has a 40% hands-on labs to 60% lecture ratio with engaging instruction, demos, group discussions, labs, and project work

This “skills-centric” course is about 50% hands-on lab and 50% lecture, designed to train attendees in core R programming and data analytics skills, coupling the most current, effective techniques with the soundest industry practices. Throughout the course students will be led through a series of progressively advanced topics, where each topic consists of lecture, group discussion, comprehensive hands-on lab exercises, and lab review.

Before attending this course, you should have:

• Hands-on experience with another programming language
• Exposure to working with statistics and probability
• Experience working with Excel

Data Scientist, Data Analyst, Data Architect, Statistician, Data Engineer, Developer, and Database Administrators who need to leverage R for analytics.