R Programming Training

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R programming training builds the proficiency in using R programming language for statistical computing and graphics. R, a language and environment, is gaining popularity in getting insight in complex data. The business analyst and other professionals dealing in large amount of data can derive results using the ready-made functions available in R.

R programming training course introduces R environment and basic statistical analysis. It extends the learning curve by teaching techniques used for data manipulation and the overview of basic data structures. Statistical applications using R programming and exploration of data using box plots, histograms, correlation coefficients will also be illustrated.

By the end of R programming classes, you will inculcate the following skillset:

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

Programming background like C, C++, Python will be an added advantage but not mandatory to learn R, but introductory statistics is a prerequisite.

R programming training builds the proficiency in using R programming language for statistical computing and graphics. R, a language and environment, is gaining popularity in getting insight in complex data. The business analyst and other professionals dealing in large amount of data can derive results using the ready-made functions available in R.

R programming training course introduces R environment and basic statistical analysis. It extends the learning curve by teaching techniques used for data manipulation and the overview of basic data structures. Statistical applications using R programming and exploration of data using box plots, histograms, correlation coefficients will also be illustrated.

By the end of R programming classes, you will inculcate the following skillset:

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

Programming background like C, C++, Python will be an added advantage but not mandatory to learn R, but introductory statistics is a prerequisite.

R Programming Training Course Content

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

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