TypeClassroom Training
Time3 Days
B100 A, South city 1, Near Signature Towers, Gurgaon, Haryana, 122001, IN
Monday, Tuesday, Wednesday, Thursday, Friday09:30 – 18:30
Introduction to R for Programmers Training Course & Certification

Introduction to R for Programmers Training Course & Certification


Audience & Prerequisites

Course Outline

Schedule & Fees


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.

Course Outline                                                  Duration: 3 Days

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

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