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Introduction to R | R Programming JumpStart

Program in R, Read Data, Access R Packages, Write Functions, Make Graphs, Analyze Data and More

GK# 9250

Course Overview

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R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has also found followers among statisticians, engineers and scientists without computer programming skills who find it easy to use. Its popularity is due to the increasing use of data mining for various goals such as set ad prices, find new drugs more quickly or fine-tune financial models. R has a wide variety of packages for data mining. It's a language that many non-programmers can easily work with, naturally extending a skill set that is common to high-end Excel users. It's the perfect tool for when the analyst has a statistical, numerical, or probabilities-based problem based on real data and they've pushed Excel past its limits.

Schedule

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  • Delivery Format:
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What You'll Learn

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Introduction to R Programming: R Programming JumpStart is a hands-on course covers the manipulation of objects in R including reading data, accessing R packages, writing R functions, and making informative graphs. It includes analyzing data using common statistical models. The course teaches how to use the R software (http://www.r-project.org) both on a command line and in a graphical user interface (GUI).

Outline

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Viewing outline for:

Virtual Classroom Live Outline

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

 

Session: 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

 

Session: 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

Session: Ordered and unordered factors

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

Session: Arrays and matrices

  • Arrays
  • Array indexing. Subsections of an array
  • Index matrices
  • The array() function
  • Mixed vector and array arithmetic. The recycling rule
  • The outer product of two arrays
  • Generalized transpose of an array
  • Matrix facilities
  • Matrix multiplication
  • Linear equations and inversion
  • Eigenvalues and eigenvectors
  • Singular value decomposition and determinants
  • Least squares fitting and the QR decomposition
  • Forming partitioned matrices, cbind() and rbind()
  • The concatenation function, (), with arrays
  • Frequency tables from factors

Session: Lists and data frames

  • Lists
  • Constructing and modifying lists
  • Concatenating lists
  • Data frames
  • Making data frames
  • attach() and detach()
  • Working with data frames
  • Attaching arbitrary lists
  • Managing the search path

 

Session: Reading data from files

  • The read.table()function
  • The scan() function
  • Accessing builtin datasets
  • Loading data from other R packages
  • Editing data

 

Session: Probability distributions

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

 

Session: Grouping, loops and conditional execution

  • Grouped expressions
  • Control statements
  • Conditional execution: if statements
  • Repetitive execution: for loops, repeat and while
  • Session: Writing your own functions
  • Simple examples
  • Defining new binary operators
  • Named arguments and defaults
  • The '...' argument
  • Assignments within functions
  • More advanced examples
  • Efficiency factors in block designs
  • Dropping all names in a printed array
  • Recursive numerical integration
  • Scope
  • Customizing the environment
  • Classes, generic functions and object orientation

 

Session: Statistical models in R

  • Defining statistical models; formulae
  • Contrasts
  • Linear models
  • Generic functions for extracting model information
  • Analysis of variance and model comparison
  • ANOVA tables
  • Updating fitted models
  • Generalized linear models
  • Families
  • The glm() function
  • Nonlinear least squares and maximum likelihood models
  • Least squares
  • Maximum likelihood
  • Some non-standard models

 

Session: Graphical procedures

  • High-level plotting commands
  • The plot() function
  • Displaying multivariate data
  • Display graphics
  • Arguments to high-level plotting functions
  • Low-level plotting commands
  • Mathematical annotation
  • Hershey vector fonts
  • Interacting with graphics
  • Using graphics parameters
  • Permanent changes: The par() function
  • Temporary changes: Arguments to graphics functions
  • Graphics parameters list
  • Graphical elements
  • Axes and tick marks
  • Figure margins
  • Multiple figure environment
  • Device drivers
  • PostScript diagrams for typeset documents
  • Multiple graphics devices
  • Dynamic graphics

 

Session: Packages

  • Standard packages
  • Contributed packages and CRAN
  • Namespaces

Labs

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Viewing labs for:

Virtual Classroom Live Labs

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.

Prerequisites

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Students should have attended the course(s) below, or should have basic skills in these areas:

  • Working with Excel

Who Should Attend

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This is an introductory level programming course.

Attendees for this course should have prior practical hands-on experience with another programming language.  Prior exposure to working with statistics and probability, as well as hands-on working knowledge of Excel would also be helpful but is not required.  We will collaborate with you to design the best solution to ensure your needs are met, whether we customize the material, or devise a different educational path to help your team best prepare for this training. 

Course Delivery

This course is available in the following formats:

Virtual Classroom Live

Experience expert-led online training from the convenience of your home, office or anywhere with an internet connection.

Duration: 2 day

Request this course in a different delivery format.
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