COURSE OUTLINE

 

R PROGRAMMING

 

Aim of Course:

 

This course will provide a basic introduction to R, and its use in organizing and exploring data. The emphasis is on understanding and working with fundamental R data structures and we will introduce some basic R programming techniques. Once you’ve completed this course you’ll be able to enter, save, retrieve, manipulate, and summarize data using R; you will also have the proper foundation to build your programming skills in R and take advantage of the full power of R.

 

Course Program:

 

SESSION 1: Getting Started with R

  • What is statistical programming?
  • The R package
  • Installation of R
  • The R command line
  • Function calls, symbols, and assignment
  • Packages
  • Getting help on R
  • Basic features of R
  • Calculating with R

 

SESSION 2: Matrices, Array, Lists, and Data Frames

  • Character vectors

 

  • Operations on the logical vectors

 

  • Creating the matrices and operations on it

 

  • Creating the array and operations on it

 

  • Creating the lists and operations on it

 

  • Making data frames

 

  • Working with data frames

 

SESSION3: Getting Data in and out of R

 

 

 

SESSION4: Data Manipulation and Exploration:

  • Variable transformations
  • Creating Dummy variables
  • Data set options (Rename, Label)
  • Keep / Drop Columns
  • Identification and Dealing with the Missing data
  • Sorting the data
  • Handling the Duplicates
  • Joining and Merging (Inner,Left,Right and Cross Join)
  • Calculating Descriptive Statistics
  • Summarize numeric variables
  • Summarize factor variables
  • Transpose Data
  • Aggregated functions using Group by
  • dplyr anddatatable packages for the data manipulation
  • Data preparation using the sqldf package

 

SESSION5: Conditional Statements and Loops:

  • If Else
  • Nested If Else
  • For Loop
  • While Loop

 

SESSION6: Functions:

 

SESSION7: Graphical procedures

  • Pie chart
  • Bar Chart
  • Box plot
  • Scatter plot
  • Multi Scatter plot
  • Word cloud etc.…

 

 SESSION8: Advanced R and Real time analytics examples:

  • Data extraction from the Twitter
  • Text Data handling
  • Positive and Negative word cloud
  • Required packages for the analytics
  • Sentiment analysis using the real time example
  • R code automation
  • Time series analysis with the real time Telecom data
  • Couple of examples with the time series data

 

SESSION9: Integration with R

  • Hadoop with R
  • Tableau with R