R Package participants will learn

• base r
• plyr
• dplyr
• stringr
• ggplot2
• xml2
• foreign
• xlsx
• RmySQL
• shiny
• jsonlite

Detail Description of course:

Basic Introduction to R 

• Introduction to R
• Drawback of using R

 Getting help 

• help ()
• Mailing List
• R Web Page
• ? Operator
• ?? Operator
• Hands on Exercise 

Structure of program in R 

• Using R console
• Scripting in R 

Packages: 

• Type of packages
• Introduction to R Base Packages
• Introduction User Created Package
• Brief introduction to some user created packages
• Package Installation
• Hands on Exercise 

Basic Data type 

• Integer
• Numeric
• Character
• Logical
• Complex
• Special data type 

Advance data objects 

• Vector
• List
• Matrices
• Array
• Table
• Data Frame
• Naming row and column of data frame and matrix
• Hand on Exercise 

Simple Statistic In R 

• Mean
• Median
• Mode
• Covariance
• Correlation
• Pearson
• Spearman
• Interpreting Correlation 

Loops and conditional 

• Use of loop and conditionals
• Structure of conditionals
• if statement
• if, else statement
• if ,else if , else statement
• while loop
• for loop
• Repeat
• Hand on Exercise 

IO in R 

• General file structure.
• csv files
• excel files
• JSON
• XML

Advanced loop 

• apply ()
• sapply ()
• laaply ()
• tapply ()
• by ()
• Hands on exercise 

Data Manipulation with plyr and dplyr 

• Introduction to plyr and its components.
• xxply function of plyr
• Introduction to dplyr
• Data manipulation with dplyr 

Date and Time in R 

• Introduction to date and times.
• Problem with date and time.
• Introduction to lubridate.
• Date and time manipulation 

String Manipulation in R 

• Basic of String
• Understanding String operations.
• Important String Operations
• String split
• String Substitution.
• Sub Strings finding.
• Finding pattern
• Regular Expression in R
• Introduction to StringR packages
• Stringr functions in detail
• Hands on Exercise 

Function in R 

• Introduction to function in R
• Structure of function
• Returning a value from a function
• Returning complex data type from a function
• Recursion
• Hands on exercise 

Some mathematical functions 

• Finding minimum maximum
• Trigonometric function
• Exponential function
• Logarithm calculation
• Finding absolute value
• Factorial function
• Cumulative mathematical functions
• Pmin ()
• Pmax ()
• Round ()
• Floor ()
• ceiling ()
• sqrt () 

Set Operations in R 

• Defining set
• Set properties
• Union
• Intersection
• Subtraction 

Graphics in R: 

• Use of graphs and chart
• Basic elements of graph
• Graphics in R base package
• par()
• plot()
• Basic elements of graph generation
• ggplot2 package
• Grammar of graphics
• Layered structure of ggplot2
• Basic elements of ggplot2
• qplot()
• ggplot()
• Some chart use and creation with Base R and ggplot2 package
• Bar chart
• Stacked Bar Chart
• Histogram
• Scatter plot
• bubble chart
• Pie chart
• quantile quantile plot
• Box Plot
• Area Plot
• Multiple plots
• Line graph (Time Series Plotting)
• Writing plot to files
• Hands on Exercise 

R connection with Database 

• Introduction to RDBMS
• Introduction to MySql
• R packages to connect to database
• Data analysis of data from database
• Hands on Exercise 

Debugging in R 

• Introduction to Debugging
• Some useful function to debug
• browser()
• debug()
• undebug()
• debugonce()
• trace()
• untrace()
• setBreakPoint()
• Hands On Exercise 

Shiny introduction 

• Introduction to Shiny.
• Concept of client and Server
• Shiny application
• Shiny application main components.
• Creating first Shiny application. 

Shiny widgets 

• Introduction to Widgets.
• Widgets in Shiny
• Control Widgets.
• Different control widgets and their applications.
• Understanding Page Layouts 

Data and R Script integration in Shiny 

• Data integration
• R Script integration 

Reactivity 

• Introduction to Reactive expression
• Reactive expression behavior
• Creating reactive variables
• Accessing reactive variables 

HTML and Shiny 

• HTML tags in Shiny
• HTML templates in Shiny 

Linear Regression:

• Introduction to simple linear regression.
• Business use cases of Linear regression.
• Assumptions of simple linear regression.
• Parameter calculation.
• Function lm()
• Multiple linear regression.
• F-test on coefficient selections.
• Step up and step down methods.
• Other methods of independent variables selection.
• Package leaps in R.
• Validation of linear regression assumptions
• Problem of multicollinearity.
• Qualitative independent variables.
• Lasso and Ridge regression.
• Inference from results.

Classification:

• Introduction to classification.
• Business use cases of classification.
• Approach of classification. 

Logistic regression:

• Introduction to logistic regression.
• Mathematical development of logistic model.
• Result interpretation
• Classification evaluation metrics introduction.
• Result evaluation.
•  R function glm() 

Classification Evaluation metrics :

• Confusion metrics.
• Sensitivity.
• Specificity.
• ROC curve.
• Area under curve.
• Package caret

Decision tree :

• Introduction to decision tree.
• Classification and regression tree.
• Splitting algorithms
• ID3
• C4.5
• CART
• Tree pruning
• R package rpart
• R package tree
• Inference of results

Ensemble learning :

• Introduction to ensemble learning.
• Random forest
• R library randomForest

Bayes Classification :

• Introduction to Bayes theorem.
• Naive Bayes classification.
• R package e1071 

Neural Networks :

• Introduction to Neural network.
• Basic idea about brain.
• Perceptrons
• Activation Functions
• Multilayer Perceptrons
• Feed Forward networks
• Error back propagation algorithm
• R package nnet

Clustering :

• Introduction of clustering.
• Business use cases of Clustering.
• Clustering approach
• Partitioning algorithms
• Hierarchy algorithms
• Density based
• Introduction to R package “cluster” and other clustering methods in R base package.
• K means clustering
• K medoides (PAM)
• Hierarchical clustering
• BIRCH
• DBSCAN
• Comparison of different clustering algorithms and model evaluations
• R package cluster

Market Basket Analysis :

• Introduction to market basket analysis.
• Business use cases for market basket analysis
• Apriori Algorithm
• FP Growth algorithms
• R package arules 

Text Analysis in R:

• Introduction to text analysis.
• Introduction to R library “tm”.
• Business use cases of Text analysis.
• Approaches to do text analysis.
• Word Clouds.
• R package word clouds

Recommendation system:

• Introduction to recommender system.
• SVD and other matrix factorizations.
• Classification and Recommendation.
• Matrix factorization and Recommendation 

Introduction to deep learning