Introduction to R Programming | R Programming JumpStart

JumpStart R! Program in R, Read Data, Access R Packages, Write Functions, Make Graphs, Analyze Data & More

Course Code: TTDS6680

Skill Level: Introductory

Duration: 3 Days

Course 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.

Introduction to R Programming 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).

Course Objectives

This course provides indoctrination in the practical use of the umbrella of technologies that are on the leading edge of data science development focused on R and related tools. Working in a hands-on learning environment, led by our expert practitioner, students will learn R and its ecosystem, and where it’s a better a tool than Excel.

This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Our engaging instructors and mentors are highly experienced practitioners who bring years of current "on-the-job" experience into every classroom. Working in a hands-on learning environment, guided by our expert team, attendees will learn about and explore:

R Language and Mathematics
How to work with R Vectors
How to read and write data from files, and how to categorize data in factors
How to work with Dates and perform Date math
How to work with multiple dimensions and DataFrame essentials
Essential Data Science and how to use R with it
Visualization in R
How R can be used in Spark (Optional / Overview)

Need different skills or topics? If your team requires different topics or tools, additional skills or custom approach, this course may be further adjusted to accommodate. We offer additional R Programming, Python, data science, AI / machine learning / deep learning and other related topics that may be blended with this course for a track that best suits your needs. Our team will collaborate with you to understand your needs and will target the course to focus on your specific learning objectives and goals.

Course Prerequisites

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.

Our core R and Python programming, data science, analytics, AI and machine learning training courses provide students with a solid foundation for continued learning based on role, goals, or their areas of specialty.

Please see the Related Courses tab for specific Pre-Requisite courses, Related Courses or Follow On training options. Our team will be happy to help you with recommendations for next steps in your Learning Journey

Course Agenda

Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We’ll work with you to tune this course and level of coverage to target the skills you need most.

Getting Started with R

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

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

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
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

Course Materials

Student Materials: Each participant will receive a Student Guide with course notes, code samples, software tutorials, step-by-step written lab instructions, diagrams and related reference materials and resource links. Students will also receive the project files (or code, if applicable) and solutions required for the hands-on work.

Hands-On Setup Made Simple! Our dedicated tech team will work with you to ensure our ‘easy-access’ cloud-based course environment is accessible, fully-tested and verified as ready to go well in advance of the course start date, ensuring a smooth start to class and effective learning experience for all participants. Please inquire for details and options.

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Excellent detail about the Spring framework which directly relates to my role as a senior engineer. I found the content and style to be very enjoyable, and would encourage me to ask questions where previously I might not have paid much attention, eg. pom version numbers / spring starters being imported. The focus on the different styles of unit testing was also excellent and I would plan to incorporate in day to day work.

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Best programming class I've ever been in, head and shoulders above any thing I took in college as part of a computer science degree. The instructor was thorough and very detailed, he was very good abut explaining all possible scenarios a situation would apply to. The way the albs were structured and the class was conducted was very good for interaction and hand on learning.

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