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
A contingency table is especially useful when a large number of observations need to be condensed into a smaller format, whereas a complex (flat) table is a type of contingency table used when constructing only one table, as opposed to several.
We can manipulate, alter, and produce table objects using the table() command to summarize a data sample, including vector, matrix, list, and data frame objects. We can also create a few particular kinds of table objects, like contingency tables and complex (flat) contingency tables using table() command.
Example:
We have two variables am (0 and 1) and cyl (4, 6, 8) in mtcars dataset. We can create a contingency table just using table () command mentioning the variables. This table will represent the relationship of each sample of the variable with all the samples of other variables. In this example, for 0 am, we have 3 4 cyl, 4 6cyl, and 12 8 cyl.
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Study Material