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Introduction to R programming

Course Code: 1215
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$2295

Introduction to R programming

Learn to analyze data with common statistical models

Course Code : 1215
Introduction to R programming 0 5 0

$2295

Overview

The Introduction to R programming course helps participants familiarize themselves with concepts like manipulating objects in R, such as, reading data, accessing R packages, writing R functions and creating informative graphs. The course also covers how to analyze data with the help of common statistical models and how to apply the R software on a command line as well as in a Graphical User Interface (GUI). The course combines hands-one exercises and engaging lectures to ensure that the participants get a thorough understanding of the concepts discussed.

Schedule Classes

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Course Delivery

This course is available in the following formats:

Live Classroom
Duration: 4 days

Live Virtual Classroom
Duration: 4 days

What You'll learn

  • R and available GUIs
  • R and statistics
  • Data permanency and removing objects
  • Objects, modes, attributes
  • Arrays
  • Matrices
  • Lists and dataframes
  • R as a set of statistical tables
  • Packages and namespaces

Outline

  • 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
  • Vectors and assignments
  • 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
  • Intrinsic attributes: modes and lengths
  • Changing the length of an object
  • Getting and setting attributes
  • The class of an object
  • A specific example
  • The function tapply() and ragged arrays
  • Ordered factors
  • Arrays
  • Array indexing
  • Sub-sections 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
  • 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
  • The read.table() functions
  • The scan() function
  • Accessing built-in data sets
    • Loading data from other R packages
  • Editing data
  • R as a set of statistical tables
  • Examining the distribution of a set of data
  • One- and two-sample tests
  • Grouped expressions
  • Control statements
    • Conditional execution: IF statements
    • Repetitive execution: FOR loops, REPEAT and WHILE
  • 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
  • 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
  • Non-linear least squares and maximum likelihood models
    • Least squares
    • Maximum likelihood
  • Some non-standard models
  • High-level plotting commands
    • The plot() function
    • Displaying multivariate data
    • Display graphics
    • Arguments to high-level plotting functions
  • Low-level plotting commands
    • Mathematical annotations
    • Hershey vector fonts
  • Interacting with graphics
  • Using graphics parameters
    • Permanent changes: The par() function
    • Temporary changes: Arguments to graphic functions
  • Graphic parameters list
    • Graphic elements
    • Axes and tick marks
    • Figure margins
    • Multiple figure environment
  • Device drivers
    • PostScript diagrams for typeset documents
    • Multiple graphics devices
  • Dynamic graphics
  • Standard packages
  • Contributed packages and CRAN
  • Namespaces
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Prerequisites

Participants need to have basic programming knowledge.

Who Should Attend

The course is highly recommended for –

  • Developers
  • Data scientists
  • UI/UX designers and developers
  • Software engineers
  • Software architects

Interested in this course? Let’s connect!