R Programming Training Institute in Delhi - R Programming Course in Delhi

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
• 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
• Mapping a function to a matrix or array
• Mapping a Function Based on Groups
• There shape Package
• Loops in R
• Reshaping Data
• Modifying Data Frame Variables
• Recoding Variables
• The recode Function
• Reshaping Data Frames
• The reshape Package
• Combining Data Frames
• Under the Hood of merge

Module 4: Statistical Applications using R programming

• Basics
• First steps
• An overgrown calculator
• Assignments
• Vectorized arithmetic
• Procedures
• Graphics
• R language essentials
• Expressions and objects
• Functions and arguments
• Vectors
• Quoting and escape sequences
• Missing values
• Functions that create vectors
• Matrices and arrays
• Factors
• Lists
• Data frames
• Indexing
• Conditional selection
• Indexing of data frames
• Grouped data and data frames
• Implicit loops
• Sorting
• The R Environment
• Session management
• The workspace
• Textual output
• 4 Scripting
• Getting help
• Packages
• Built-in data
• attach and detach
• subset, transform, and within
• The graphics subsystem
• Plot layout
• Building a plot from pieces
• Using par
• Combining plots
• R programming
• Flow control
• Classes and generic functions
• Data entry
• Reading from a text file
• Further details on read.table
• The data editor
• Interfacing to other programs
• Probability and distributions
• Random sampling
• Probability calculations and combinatorics
• Discrete distributions
• Continuous distributions
• The built-in distributions in R
• Densities
• Cumulative distribution functions
• Quantiles
• Random numbers
• Descriptive statistics and graphics
• Summary statistics for a single group
• Graphical display of distributions
• Histograms
• Empirical cumulative distribution
• Q–Q plots
• Boxplots
• Summary statistics by groups
• Graphics for grouped data
• Histograms
• Parallel boxplots
• Stripcharts
• Tables
• Generating tables
• Marginal tables and relative frequency
• Graphical display of tables
• Barplots
• Dotcharts
• Piecharts
• One- and two-sample tests
• One-sample t test
• Wilcoxon signed-rank test
• Two-sample t test
• Comparison of variances
• Two-sample Wilcoxon test
• The paired t test
• The matched-pairs Wilcoxon test
• Regression and correlation
• Simple linear regression
• Residuals and fitted values
• Prediction and confidence bands
• Correlation
• Pearson correlation
• Spearman’s ?
• Kendall’s ?
• Analysis of variance and the Kruskal–Wallis test
• One-way analysis of variance
• Pairwise comparisons and multiple testing
• Relaxing the variance assumption
• Graphical presentation
• Bartlett’s test
• Kruskal–Wallis test
• Two-way analysis of variance
• Graphics for repeated measurements
• The Friedman test
• The ANOVA table in regression analysis
• Tabular data
• Single proportions
• Two independent proportions
• k proportions, test for trend
• r × c tables
• Power and the computation of sample size
• The principles of power calculations
• Power of one-sample and paired t tests
• Power of two-sample t test
• Approximate methods
• Power of comparisons of proportions
• Two-sample problems
• One-sample problems and paired tests
• Comparison of proportions
• Advanced data handling
• Recoding variables
• The cut function
• Manipulating factor levels
• Working with dates
• Recoding multiple variables
• Conditional calculations
• Combining and restructuring data frames
• Appending frames
• Merging data frames
• Reshaping data frames
• Per-group and per-case procedures
• Time splitting
• Multiple Regression
• Plotting multivariate data
• Model specification and output
• Model search
• Linear models
• Polynomial regression
• Regression through the origin
• Design matrices and dummy variables
• Linearity over groups
• Interactions
• Two-way ANOVA with replication
• Analysis of covariance
• Graphical description
• Comparison of regression lines
• Diagnostics
• Logistic regression
• Generalized linear models
• Logistic regression on tabular data
• The analysis of deviance table
• Connection to test for trend
• Likelihood profiling
• Presentation as odds-ratio estimates
• Logistic regression using raw data
• Prediction
• Model checking
• Survival analysis
• Essential concepts
• Survival objects
• Kaplan–Meier estimates
• The log-rank test
• The Cox proportional hazards model
• Rates and Poisson regression
• Basic ideas
• The Poisson distribution
• Survival analysis with constant hazard
• Fitting Poisson models
• Computing rates
• Models with piecewise constant intensities
• Nonlinear curve fitting
• Basic usage
• Finding starting values
• Self-starting models
• Profiling
• Finer control of the fitting algorithm

• Students get the well-defined R Programming training in Delhi, and after completion of the R Programming course, they are placed in recognized organizations.
• Our certified trainers having 5 to 15 years of industrial experience facilitate students with a regular, weekend or customized R Programming training in Delhi in accordance with their career requirements.
• Trainers mentor their students in major project training, minor project training, live project preparation, and interview preparation.
• Our established R Programming training institute has smart classrooms and smart labs equipped with the latest servers, routers, switches, nodes, projectors, live racks, Wi-Fi connectivity, digital pads, and other networking devices.
• Students are free to access the labs for an unlimited number of hours as per their own preferred timings. In addition, students can avail Extra Time Slots (E.T.S.) for practical, absolutely free.
• APTRON provides free Study Material, Books, PDFs, Video Trainings, Video Lectures, Sample Questions, Exam Preparation, Interview Questions, HR screening questions, Lab Guides and Projects.
• There is no extra fee charged from the students who need to retake any R Programming class or require One-on-One attention from the instructors.
• During the R Programming training, we also facilitate students with free of cost personality development sessions. These sessions comprise of spoken English, group discussions, mock interviews, presentation skills.
• Hostel facilities available at Rs.4,500/- per month for R Programming training in Delhi.
• Our R Programming certification is globally recognized. We accept Cheque, Cash, Credit Card, Debit card, Net Banking payment modes.