R Programming Training

• Clear understanding of Statistical programming and R environment
• In-depth knowledge of basic features, functions, operators available with R
• Comprehensive information about programming statistical graphics
• Ways of using simulation and numerical optimization
• Extract data from R objects, perform reading and writing of Data, and handle databases
• Use subscripting, character manipulation, and reshaping of data
• Find probability, distributions, regression and correlation
• Significance of sample size and its calculation
• Advance data handling techniques

• Academicians
• PhD scholars
• Survey researcher
• Statistical geneticist
• Risk analyst
• Consultants
• Forecaster

Module 1: Essential to R programming

1: 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

2: 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

3: 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

4: 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

5: 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

6: 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()

7: 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

1: 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

2: 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

 3: 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

4: Dates

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

5: Factors

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

6: Subscripting

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

 7: 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

 8: 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

9:  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 3: Statistical Applications using R programming

1:  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

 2: The R environment

• Session management
  • The workspace
  • Textual output
  • 3 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

 3: 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

 4:  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

 5: 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

 6: Regression and correlation

• Simple linear regression
• Residuals and fitted values
• Prediction and confidence bands
• Correlation
• Pearson correlation
• Spearman’s ρ
• Kendall’s τ

 7: 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

 8: Tabular data

• Single proportions
• Two independent proportions
• k proportions, test for trend
• r × c tables

 9: 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

 10: 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

 11: Multiple Regression

• Plotting multivariate data
• Model specification and output
• Model search

 12: 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

 13: 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

 14: Survival analysis

• Essential concepts
• Survival objects
• Kaplan–Meier estimates
• The log-rank test
• The Cox proportional hazards model

 15:  Rates and Poisson regression

• Basic ideas
  • The Poisson distribution
  • Survival analysis with constant hazard
• Fitting Poisson models
• Computing rates
• Models with piecewise constant intensities

 16: Nonlinear curve fitting

• Basic usage
• Finding starting values
• Self-starting models
• Profiling
• Finer control of the fitting algorithm