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
Analysis of Variance (ANOVA) is a statistical parametric method used for comparing the datasets. In an implementation, it is similar to techniques such as z-test and t-test, where comparison of the means and the relative variance between them is used. However, ANOVA is best applied where more than two populations or samples are meant to be compared.
One-way ANOVA
One-way ANOVA is a hypothesis test that takes into account only one categorical variable or single factor. With the help of F-distribution / p-value, this helps us to compare three or more samples of the means. The Null Hypothesis (H0) represents the equity in all populations means while an Alternative hypothesis is a difference in at least one mean.
Example:
The PlantGrowth dataset contains two variables weight and group with three alternatives. We want to compare the means of the three groups. This ANOVA test concludes that as the p-value is less than the significance level, there is a significant difference among them.
Two-way ANOVA
Two-way ANOVA examines the impact a dependent variable has on two independent variables. It also studies the inter-relationship between independent variables influencing the values of the dependent variable, if any.
Example:
The ToothGrowth Dataset contains three variables, them sup has two classes (OJ, VC), and dose has three classes (0.5, 1, 2). We will test the significance of the variables using a Two-way ANOVA test. The result concludes that as the p values for both are less than the significance level, the variables are significant.
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Study Material