Exploring R
Evolution of R
Programming Features of R
R for Machine Learning
R for Data Analysis
Application of R
R vs. Python vs. SAS
R vs. Excel vs.Tableau
Install R base on Windows
Install R Studio on Windows
Install R base on Ubuntu
Install R Studio on Ubuntu
R Starter
First R Program
Working with R Packages
R Workplace and R Sessions
Manage working directory
Customize R studio
RStudio Debugger
RStudio History and Environment variables
R Syntax
R Variables
R Data Types & Structures
R Arithmetic Operators
R Logical Operators
R If Statement
R - If…Else Statement
If…else if…Else Statement
R for loop
R while loop
R repeat loop
R String Construction
R String Manipulation Functions
Creating Character Strings
R Functions
R built-in functions
Working with Vector
R Vector Indexing
R Vector Modification
R Arithmetic Vector Operations
R Lists
Access List elements (List Slicing)
List modification
R Matrix construction
Access Matrix elements
R Matrix Modification
R Matrix Operations
R Array Construction
Accessing Array Elements
Manipulating Array Elements
R Data Frames
Data Extraction
Data Frame Expansion
R Built-in Data frames
R Factors
Manage Factor levels
Factor Functions
R Contingency Tables
R Data Visualization
R – Charts and Graphs
R Density Plot
R Strip Charts
R Boxplots
R Violin Plots
R Bar Charts
R Pie Charts
R Area Plots
R Time Series
Graphics with ggplot2
Ggplot2 Structure
ggplot2 Bar Charts
ggplot2 Pie Chart
ggplot2 Area Plot
ggplot2 Histogram
ggplot2 Scatter Plot
ggplot2 Box Plot
Mean & Median
Standard Deviation
Normal Distribution
Correlation
T-Tests
Chi-Square Test
ANOVA Test
Survival Analysis
Data Pre-processing and Missing Value Analysis
Missing data treatment
Missing value analysis with mice package
Outlier Analysis
Problems with outliers
Outlier Detection
Outlier Treatment
Simple Linear Regression
Mathematical Computation
Linear Regression in R
A complete Simple Regression Analysis
Multiple Linear Regression
Mathematical Analysis
Model Interpretation
A complete Multiple Regression Analysis
Logistic Regression
Mathematical Computation in R
Logistic Regression in R
Heart Risk Analysis using LR
Support Vector Machine
Heart Risk Analysis using SVM
Decision Trees
Random Forest
K means Clustering
Big data Analytics using R-Hadoop
RHADOOP Packages:
rJava: Low-Level R to Java Interface
rhdfs: Integrate R with HDFS
rmr2: MapReduce job in R
plyrmr: Data Manipulation with MapReduce job
rhbase: Integrate HBase with R
Environment setup for RHADOOP
Getting Started with RHADOOP
Modern programming languages that are mainly used to develop enterprise software systems include Java. These platforms have rich functionality to write business logic. However, they are not much efficient when it comes to statistical or mathematical modeling. In the field of modeling, the major contributors are R, Weka, Octave, etc. Out of these, most work as simulation environments; however, R could be used both for simulation as well as for production-level systems. From the above discussion, it is clear that intelligence-based software could not be developed just by using a single technology. To overcome this obstacle, a combination of technologies should be applied. The figure below shows a high-level view of such an intelligent software system and where each technology fits.
From the figure, it is clear that a hybrid system has to be created. In the current scenario, the hybrid system consists of JAVA for business logic programming and R for statistical programming. This shows that we have a need to integrate R with Java. In the following text, we will be showing how to integrate R with Java using rJava library.
1. Install Java
To start, we need Java. We can download the Java Runtime Environment (JRE) and Java Development Kit (JDK). After properly installation of Java we can check executing thecommand:
2. Configure Java Parameters for R
3. R provides thejavareconf utility to configure Java support in R. To prepare the R environment for Java; we can execute this command:
4. Install rJava Package
We can obtain rJava release versions from CRAN. If an Internet connection is available, it will be installed by the command install.packages in an R session. 
5. Configure the Environment Variable CLASSPATH
TheCLASSPATH environment variable must contain the directories with the jar and class files. The class files in this example will be created in /usr/lib/Java/java_1.8.0_77.
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