R-Programming Training

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R-Programming Language Course Content

Introduction to R Languages

  • R as a language
  • Working with data in R

The R ecosystem

  • Why use R?
  • Getting started
  • Installation and setup
  • Packages

Data types

  • Character
  • Factor
  • Integer
  • Float
  • Date and time

Data structures

  • Vectors
  • Matrices
  • Lists
  • Data frames

Data handling

  • Importing data from multiple sources/formats like .csv, .txt, .xlsx, SAS and SPSS files
  • Exporting data to multiple formats
  • Handling data frames: filtering, sorting, merging
  • PLYR package for easy data manipulation


  • Commonly used built in functions
  • Writing user defined functions
  • Installing packages
  • Looping functions
  • The "apply" family of functions
  • Basic visualization

Basic statistics in R

  • Distributions
  • Testing
  • Modeling

Graphics in R

  • Graphics for exploratory data analysis
  • Standard graphic displays

The R environment

  • R in the cloud

Statistical analysis with R

  • Linear models
  • Generalized linear models

Advanced statistical modeling with R

  • Density estimation
  • Survival analysis
  • Classification
  • Clustering

Introduction to Writing R Packages
Integrating with other tools.

  • Tableau
  • Python
  • CPP

Real time problems.
Data manipulation with data.table package.
Interview questions.