Programming

ATC-R : Introduction to R Programming

Course Duration

3 Days

Delivery

ILT VILT

Overview

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.

What You'll Learn

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.
This 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).

Who Should Attend

Anyone who wishes to build their proficiency in using R programming language can attend this course. This course is ideal for: Business and IT Professionals, Survey Researcher, Risk Analyst & Data Scientists and Data Engineers

Prerequisites

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.
Good understanding of statistics and working with excel.

Learning Journey

Coming Soon...

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
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 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

Reading data from files

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

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

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

Packages

Standard packages
Contributed packages and CRAN
Namespaces
Summary and Closing Remarks

Instructors

Scott Duffy

Bestselling Azure & TOGAF® Trainer, Microsoft Azure MVP

4.8 (4.1k)

10 Courses

Scott Duffy

Bestselling Azure & TOGAF® Trainer, Microsoft Azure MVP

4.8 (4.1k)

10 Courses

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Why take this course with Trainocate?

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