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
The package that allows R developer to perform statistical analysis in R via Hadoop MapReduce functionality on a Hadoop cluster. One of the essential prerequisites of installing this package is rJava. We can install rmr2 if rJava is already in the R. rmr2 package is an excellent way to perform data analysis in the Hadoop ecosystem. Its advantages are the flexibility and integration within an R environment. The drawbacks are the need for a deep understanding of the MapReduce paradigm and a large amount of time needed to write code. I think that it's beneficial to customize the algorithms only after having used some current ones first. For instance, the first stage of the analysis may consist of aggregating data through Hive and perform Machine Learning through Mahout. Afterward, rmr2 allows modifying the algorithms in order to improve the performances and fit better the problems. The goals of rmr2 package are to provide map-reduce programmers the easiest, most productive, most elegant way to write map-reduce jobs.
Setting Up Environment:
Before installing the package, we have to set the environment for Hadoop and Java. We can execute the following command to set Hadoop and Java Environment.
Install rmr2 Package:
rmr2 release versions can be obtained from github.com. Assuming an internet connection is available, download the required package and install this from R Install Packages option:
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Study Material