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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
One of R’s power is visualization.Graphics should be key in analyzing the data. We don't understand numbers intuitively like we do images. Graphs are easy to produce for data exploration.R allows practicing a wide variety of statistical and graphical techniques like linear and nonlinear modeling, time-series analysis, classification, standard statistical tests, clustering, etc.
Properties of R Graphics:
We can directly create plots from R code.
Replication and modification are very easy.
Reproducibility
R provides a good variety of packages: ggplot2, plotly, ggedit, graphics
Create first graph:
We can create a graph in R just using the plot function.
In this example, we have assigned a vector of 10 elements in the x variable. In y, we are storing the square value of x. So in y, we have 1, 4, 9, 16,…., 100.
If we plot a graph using x and y, it will display a line like the below graph.
Graph Output:
Plot data:
In this section, we will discuss the plot () function in detail.
Syntax of plot () Function:
The mandatory part is the R objects, based on which we will create our graph. In this example, we are using two vectors x and y as a mandatory part.
In the main, we will define the overall title of the graph. Xlab and ylab are for the title of the x-axis and y-axis, respectively. col is used for the color of the plot, and col.main is for the color of the title. cex.axis is used to control the ratio of the axis label. We will explain type, lty, and pch separately.
type argument:
We have some alternatives for the type argument. If we use "p", the graph will be displayed using point. If we use " l ", it will be a line. If we want to display both points and lines, we have to use "b". I have attached the full list in the below section.
lty argument:
lty represents line type. In plot function, we use seven kinds of lines. We can use the name or associated number to set the value in lty argument.
pch argument:
pch represents numeric values (from 0 to 25), specifying the point symbols (or shapes). We have 25 point shapes, and we can set any of them just using the specific number.
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