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Introduction to R for Programmers Training Course & Certification

Introduction to R for Programmers Training Course & Certification


Audience & Prerequisites


Introduction to R for Programmers Course Overview

R is a scripting language for statistical data manipulation and analysis. It was inspired by, and is mostly compatible with, the statistical language S developed by AT&T. The name S, obviously standing for statis- tics, was an allusion to another programming language developed at AT&T with a one-letter name, C. S later was sold to a small firm, which added a GUI interface and named the result S-Plus. R has become more popular than S/S-Plus, both because it’s free and because more people are contributing to it. R is sometimes called ‘GNU S.

Objectives of R Programming Training

  • a public-domain implementation of the widely-regarded S statistical language; R/S is the de facto standard among professional statisticians
  • comparable, and often superior, in power to commercial products in most senses
  • available for Windows, Macs, Linux
  • In addition to enabling statistical operations, it’s a general programming language, so that you can automate your analyses and create new functions
  • object-oriented and functional programming structure
  • your data sets are saved between sessions, so you don’t have to reload each time
  • open-software nature means it’s easy to get help from the user community, and lots of new functions get contributed by users, many of which are prominent statisticians

Prerequisites for R Programming Certification

The only real prerequisite is that you have some programming experience; you need not be an expert pro-grammer, though experts should find the material suitable for their level too.Occasionally there will be some remarks aimed at professional programmers, say about object-oriented programming or Python, but these remarks will not make the treatment inaccessible to those having only a moderate background in programming.


Section 1Overview
Lecture 1History of R
Lecture 2Advantages and disadvantages
Lecture 3Downloading and installing
Lecture 4How to find documentation
Section 2Introduction
Lecture 5Using the R console
Lecture 6Getting help
Lecture 7Learning about the environment
Lecture 8Writing and executing scripts
Lecture 9Saving your work
Section 3Installing Packages
Lecture 10Finding resources
Lecture 11Installing resources
Section 4Data Structures, Variables
Lecture 12Variables and assignment
Lecture 13Data types
Lecture 14Indexing, subsetting
Lecture 15Viewing data and summaries
Lecture 16Naming conventions
Lecture 17Objects
Section 5Getting Data into the R Environment
Lecture 18Built-in data
Lecture 19Reading data from structured text files
Lecture 20Reading data using ODBC
Section 6Control Flow
Lecture 21Truth testing
Lecture 22Branching & Looping
Lecture 23Vectorized calculations
Section 7Functions in Depth
Lecture 24Parameters,Return values
Lecture 25Variable scope
Lecture 26Exception handling
Section 8Handling Dates in R
Lecture 27Date and date-time classes in R
Lecture 28Formatting dates for modeling
Section 9Descriptive Statistics
Lecture 29Continuous data
Lecture 30Categorical data
Section 10Inferential Statistics
Lecture 31Bivariate correlation
Lecture 32T-test and non-parametric equivalents
Lecture 33Chi-squared test
Lecture 34Distribution testing
Lecture 35Power testing
Section 11Group By Calculations
Lecture 36Split apply combine strategy
Lecture 37
Section 12Base Graphics
Lecture 38Base graphics system in R
Lecture 39Scatterplots, histograms, barcharts, box and whiskers, dotplots
Lecture 40Labels, legends, Titles, Axes
Lecture 41Exporting graphics to different formats
Section 13Advanced R Graphics: GGPlot2
Lecture 42Understanding the grammar of graphics
Lecture 43Quick plot function
Lecture 44Building graphics by pieces
Section 14Linear Regression
Lecture 45Linear models
Lecture 46Regression plots
Lecture 47Confounding / Interaction in regression
Lecture 48Scoring new data from models (prediction)