R Programming

(Instant booking on GulfTalent)
Location
Online
Dates
Can be taken anytime
Course Type
Professional Training Course
Accreditation
Yes (Details)
Language
English
Course Fee
$200 $60 only

Course Overview

In this R Programming course, you will master the basics of this beautiful open source language, including factors, lists and data frames. With the knowledge gained in this course, you will be ready to undertake your first very own data analysis. With over 2 million users worldwide R is rapidly becoming the leading programming language in statistics and data science. Every year, the number of R users grows by 40% and an increasing number of organizations are using it in their day-to-day activities. Leverage the power of R by completing this R online course today!

R programming along with a substantial knowledge of statistics can help candidates to have a great career in data Analytics. R is also an widely used tool in many big firms like top Banks, IT, Retail, Healthcare, Pharma, Supply chain and logistics firms. Analyzing large datasets can be done in a shorter period with the help of R programming. There is a huge shortage in the market for professionals with skills in R programming which makes it more interesting to pursue. Since R is a free software it is being widely used which creates a lot of opportunities for professional who are looking to pursue a career in R Programming.

In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.

Who should take this course

Any Graduate/Post graduate willing to learn R Programming. Web developers who want to implement data analysis features in their webpage Everybody interested in statistics and data sciences Researchers who perform data analysis including graphs Professionals working in analytics or related fields

Accreditation

Internationally Accepted Certificate

Course content

Duration of Course:

  • 40+ hours

Topics Covered are:

Module 1: Essential to R programming:

An Introduction to R:

  • History of S and R
  • Introduction to R
  • The R environment
  • What is Statistical Programming?
  • Why use a command line?
  • Your first R session

Introduction to the R language:

  • Starting and quitting R
  • Recording your work
  • Basic features of R
  • Calculating with R
  • Named storage
  • Functions
  • Exact or approximate?
  • R is case-sensitive
  • Listing the objects in the workspace
  • Vectors
  • Extracting elements from vectors
  • Vector arithmetic
  • Simple patterned vectors
  • Missing values and other special values
  • Character vectors
  • Factors
  • More on extracting elements from vectors
  • Matrices and arrays
  • Data frames
  • Dates and times
  • Built-in functions and online help
  • Built-in examples
  • Finding help when you don't know the function name
  • Built-in graphics functions
  • Additional elementary built-in functions
  • Logical vectors and relational operators
  • Boolean algebra
  • Logical operations in R
  • Relational operators
  • Data input and output
  • Changing directories
  • dump() and source()
  • Redirecting R output
  • Saving and retrieving image files
  • Data frames and the read.table function

Programming statistical graphics:

  • High-level plots
  • Bar charts and dot charts
  • Pie charts
  • Histograms
  • Box plots
  • Scatterplots
  • QQ plots
  • Choosing a high-level graphic
  • Low-level graphics functions
  • The plotting region and margins
  • Adding to plots
  • Setting graphical parameters

Programming with R:

  • Flow control
  • The for() loop
  • The if() statement
  • The while() loop
  • Newton's method for root finding
  • The repeat loop, and the break and next statements
  • Managing complexity through functions
  • What are functions?
  • Scope of variables
  • Miscellaneous programming tips
  • Using fix()
  • Documentation using#
  • Some general programming guidelines
  • Top-down design
  • Debugging and maintenance
  • Recognizing that a bug exists
  • Make the bug reproducible
  • Identify the cause of the bug
  • Fixing errors and testing
  • Look for similar errors elsewhere
  • The browser() and debug()functions
  • Efficient programming
  • Learn your tools
  • Use efficient algorithms
  • Measure the time your program takes
  • Be willing to use different tools
  • Optimize with care

Simulation:

  • Monte Carlo simulation
  • Generation of pseudorandom numbers
  • Simulation of other random variables
  • Bernoulli random variables
  • Binomial random variables
  • Poisson random variables
  • Exponential random numbers
  • Normal random variables
  • Monte Carlo integration
  • Advanced simulation methods
  • Rejection sampling
  • Importance sampling

Computational linear algebra:

  • Vectors and matrices in R
  • Constructing matrix objects
  • Accessing matrix elements; row and column names
  • Matrix properties
  • Triangular matrices
  • Matrix arithmetic
  • Matrix multiplication and inversion
  • Matrix inversion
  • The LU decomposition
  • Matrix inversion in R
  • Solving linear systems
  • Eigenvalues and eigenvectors
  • Advanced topics
  • The singular value decomposition of a matrix
  • The Choleski decomposition of a positive definite matrix
  • The QR decomposition of a matrix
  • The condition number of a matrix
  • Outer products
  • Kronecker products
  • apply()

Numerical optimization:

  • The golden section search method
  • Newton- Raphson
  • The Nelder- Mead simplex method
  • Built-in functions
  • Linear programming
  • Solving linear programming problems in R
  • Maximization and other kinds of constraints
  • Special situations
  • Unrestricted variables
  • Integer programming
  • Alternatives to lp()
  • Quadratic programming

Module 2: Data Manipulation Techniques using R programming:

Data in R:

  • Modes and Classes
  • Data Storage in R
  • Testing for Modes and Classes
  • Structure of R Objects
  • Conversion of Objects
  • Missing Values
  • Working with Missing Values

Reading and Writing Data:

  • Reading Vectors and Matrices
  • Data Frames: read.table
  • Comma- and Tab-Delimited Input Files
  • Fixed-Width Input Files
  • Extracting Data from R Objects
  • Connections
  • Reading Large Data Files
  • Generating Data
  • Sequences
  • Random Numbers
  • Permutations
  • Random Permutations
  • Enumerating All Permutations
  • Working with Sequences
  • Spreadsheets
  • The RODBC Package on Windows
  • The gdata Package (All Platforms)
  • Saving and Loading R Data Objects
  • Working with Binary Files
  • Writing R Objects to Files in ASCII Format
  • The write Function
  • The write.table function
  • Reading Data from Other Programs

R and Databases:

  • A Brief Guide to SQL
  • Navigation Commands
  • Basics of SQL
  • Aggregation
  • Joining Two Databases
  • Subqueries
  • Modifying Database Records
  • ODBC
  • Using the RODBC Package
  • The DBI Package
  • Accessing a MySQL Database
  • Performing Queries
  • Normalized Tables
  • Getting Data into MySQL
  • More Complex Aggregations

Dates:

  • as.Date
  • The chron Package
  • POSIX Classes
  • Working with Dates
  • Time Intervals
  • Time Sequences

Factors:

  • Using Factors
  • Numeric Factors
  • Manipulating Factors
  • Creating Factors from Continuous Variables
  • Factors Based on Dates and Times
  • Interactions

Subscripting:

  • Basics of Subscripting
  • Numeric Subscripts
  • Character Subscripts
  • Logical Subscripts
  • Subscripting Matrices and Arrays
  • Specialized Functions for Matrices
  • Lists
  • Subscripting Data Frames

Character Manipulation:

  • Basics of Character Data
  • Displaying and Concatenating Character
  • Working with Parts of Character Values
  • Regular Expressions in R
  • Basics of Regular Expressions
  • Breaking Apart Character Values
  • Using Regular Expressions in R
  • Substitutions and Tagging

Data Aggregation:

  • Table
  • Road Map for Aggregation
  • Mapping a Function to a Vector or List

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