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R contains a sea of packages that appeal to all the forms of disciplines like astronomy, biology, etc

R allows its users to develop web-applications using R Shiny

R provides you with several options of advanced data analytics like the development of prediction models, machine learning algorithms, etc

- Those interested in the field of data science
- Those who want to learn R programming from scratch
- Those looking for a robust, structured learning program on R
- Software or Data Engineers interested in learning R Programming

Best-in-class content by leading faculty and industry leaders in the form of videos,

cases and projects, assignments and live sessions.

Getting the Hang of R

- The R Website
- Downloading and Installing R from CRAN
- Installing R on Your Windows Computer
- Installing R on Your Macintosh Computer
- Installing R on Your Linux Computer

Running the R program

Finding Your Way with R

- Getting Help via the CRAN Website and the Internet
- The Help Command in R
- Help for Windows Users
- Help for Macintosh Users
- Help for Linux Users
- Help For All Users
- Anatomy of a Help Item in R

Command Packages

- Standard Command Packages
- What Extra Packages Can Do for You
- How to Get Extra Packages of R Commands
- How to Install Extra Packages for Windows Users
- How to Install Extra Packages for Macintosh Users
- How to Install Extra Packages for Linux Users
- Running and Manipulating Packages
- Loading Packages
- Windows-Specific Package Commands
- Macintosh-Specific Package Commands
- Removing or Unloading Packages

Some Simple Math

- Use R Like a Calculator
- Storing the Results of Calculations

Reading and Getting Data into R

- Using the combine Command for Making Data
- Entering Numerical Items as Data
- Entering Text Items as Data
- Using the scan Command for Making Data
- Entering Text as Data
- Using the Clipboard to Make Data
- Reading a File of Data from a Disk
- Reading Bigger Data Files
- The read.csv() Command
- Alternative Commands for Reading Data in R
- Missing Values in Data Files

Viewing Named Objects

- Viewing Previously Loaded Named-Objects
- Viewing All Objects
- Viewing Only Matching Names
- Removing Objects from R

Types of Data Items

- Number Data
- Text Items
- Converting Between Number and Text Data

The Structure of Data Items

- Vector Items
- Data Frames
- Matrix Objects
- List Objects

Examining Data Structure

Working with History Commands

- Using History Files
- Viewing the Previous Command History
- Saving and Recalling Lists of Commands
- Alternative History Commands in Macintosh OS
- Editing History Files

Saving Your Work in R

- Saving the Workspace on Exit
- Saving Data Files to Disk
- Save Named Objects
- Save Everything
- Reading Data Files from Disk
- Saving Data to Disk as Text Files
- Writing Vector Objects to Disk
- Writing Matrix and Data Frame Objects to Disk
- CONTENTS Writing List Objects to Disk
- Converting List Objects to Data Frames

Manipulating Objects

- Manipulating Vectors
- Selecting and Displaying Parts of a Vector
- Sorting and Rearranging a Vector
- Returning Logical Values from a Vector
- Manipulating Matrix and Data Frames
- Selecting and Displaying Parts of a Matrix or Data Frame
- Sorting and Rearranging a Matrix or Data Frame
- Manipulating Lists

Viewing Objects within Objects

- Looking Inside Complicated Data Objects
- Opening Complicated Data Objects
- Quick Looks at Complicated Data Objects
- Viewing and Setting Names
- Rotating Data Tables

Constructing Data Objects

- Making Lists
- Making Data Frames
- Making Matrix Objects
- Re-ordering Data Frames and Matrix Objects

Forms of Data Objects: Testing and Converting

- Testing to See What Type of Object You Have
- Converting from One Object Form to Another
- Convert a Matrix to a Data Frame
- Convert a Data Frame into a Matrix
- Convert a Data Frame into a List
- Convert a Matrix into a List
- Convert a List to Something Else

Summary Commands

Summarizing Samples

- Summary Statistics for Vectors
- Summary Commands With Single Value Results
- Summary Commands With Multiple Results
- Cumulative Statistics
- Simple Cumulative Commands
- Complex Cumulative Commands
- Summary Statistics for Data Frames
- Generic Summary Commands for Data Frames
- Special Row and Column Summary Commands
- The apply() Command for Summaries on Rows or Columns
- Summary Statistics for Matrix Objects
- Summary Statistics for Lists

Summary Tables

- Making Contingency Tables
- Creating Contingency Tables from Vectors
- Creating Contingency Tables from Complicated Data
- Creating Custom Contingency Tables
- Creating Contingency Tables from Matrix Objects
- Selecting Parts of a Table Object
- Converting an Object into a Table
- Testing for Table Objects
- Complex (Flat) Tables
- Making “Flat” Contingency Tables
- Making Selective “Flat” Contingency Tables
- Testing “Flat” Table Objects
- Summary Commands for Tables
- Cross Tabulation
- Testing Cross-Table (xtabs) Objects
- A Better Class Test
- Recreating Original Data from a Contingency Table

Summary Tables

Looking at the Distribution of Data

- Stem and Leaf Plot
- Histograms
- Density Function
- Using the Density Function to Draw a Graph
- Adding Density Lines to Existing Graphs
- Types of Data Distribution
- The Normal Distribution
- Other Distributions
- Random Number Generation and Control
- Random Numbers and Sampling
- The Shapiro-Wilk Test for Normality
- The Kolmogorov-Smirnov Test
- Quantile-Quantile Plots
- A Basic Normal Quantile-Quantile Plot
- Adding a Straight Line to a QQ Plot
- Plotting the Distribution of One Sample Against Another

Using the Student’s t-test

- Two-Sample t-Test with Unequal Variance
- Two-Sample t-Test with Equal Variance
- One-Sample t-Testing 183 Using Directional Hypotheses
- Formula Syntax and Subsetting Samples in the t-Test

The Wilcoxon U-Test (Mann-Whitney)

- Two-Sample U-Test
- One-Sample U-Test
- Using Directional Hypotheses
- Formula Syntax and Subsetting Samples in the U-test

Paired t- and U-Tests

Correlation and Covariance

- Simple Correlation
- Covariance
- Significance Testing in Correlation Tests
- Formula Syntax

Tests for Association

- Multiple Categories: Chi-Squared Tests
- Monte Carlo Simulation
- Yates’ Correction for 2 n 2 Tables
- Single Category: Goodness of Fit Tests

Box-whisker Plots

- Basic Boxplots
- Customizing Boxplots
- Horizontal Boxplots

Scatter Plots

- Basic Scatter Plots
- Adding Axis Labels
- Plotting Symbols
- Setting Axis Limits
- Using Formula Syntax
- Adding Lines of Best-Fit to Scatter Plots

Pairs Plots (Multiple Correlation Plots)

Line Charts

- Line Charts Using Numeric Data
- Line Charts Using Categorical Data

Pie Charts

Cleveland Dot Charts

Bar Charts

- Single-Category Bar Charts
- Multiple Category Bar Charts
- Stacked Bar Charts
- Grouped Bar Charts
- Horizontal Bars
- Bar Charts from Summary Data

Copy Graphics to Other Applications

- Use Copy/Paste to Copy Graphs
- Save a Graphic to Disk
- Windows
- Macintosh
- Linux

Examples of Using Formula Syntax for Basic Tests

Formula Notation in Graphics

Analysis of Variance (ANOVA)

- One-Way ANOVA
- Stacking the Data before Running Analysis of Variance
- Running aov() Commands
- Simple Post-hoc Testing
- Extracting Means from aov() Models
- Two-Way ANOVA
- More about Post-hoc Testing
- Graphical Summary of ANOVA
- Graphical Summary of Post-hoc Testing
- Extracting Means and Summary Statistics
- Model Tables
- Table Commands
- Interaction Plots
- More Complex ANOVA Models
- Other Options for aov()
- Replications and Balance

Creating Data for Complex Analysis

- Data Frames
- Matrix Objects
- Creating and Setting Factor Data
- Making Replicate Treatment Factors
- Adding Rows or Columns

Summarizing Data

- Simple Column and Row Summaries
- Complex Summary Functions
- The rowsum() Command
- The apply() Command
- Using tapply() to Summarize Using a Grouping Variable
- The aggregate() Command

Simple Linear Regression

- Linear Model Results Objects
- Coefficients
- Fitted Values
- Residuals
- Formula
- Best-Fit Line
- Similarity between lm() and aov() >

Multiple Regression

- Formulae and Linear Models
- Model Building
- Adding Terms with Forward Stepwise Regression
- Removing Terms with Backwards Deletion
- Comparing Models

Curvilinear Regression

- Logarithmic Regression
- Polynomial Regression

Plotting Linear Models and Curve Fitting

- Best-Fit Lines
- Adding Line of Best-Fit with abline()
- Calculating Lines with fitted()
- Producing Smooth Curves using spline()
- Confidence Intervals on Fitted Lines

Summarizing Regression Models

- Diagnostic Plots
- Summary of Fit

Adding Elements to Existing Plots

- Linear Model Results Objects
- Error Bars 364 Using the segments() Command for Error Bars
- Using the arrows() Command to Add Error Bars
- Adding Legends to Graphs
- Color Palettes
- Placing a Legend on an Existing Plot
- Adding Text to Graphs
- Making Superscript and Subscript Axis Titles
- Orienting the Axis Labels
- Making Extra Space in the Margin for Labels
- Setting Text and Label Sizes
- Adding Text to the Plot Area
- Adding Text in the Plot Margins
- Creating Mathematical Expressions
- Adding Points to an Existing Graph
- Adding Various Sorts of Lines to Graphs
- Adding Straight Lines as Gridlines or Best-Fit Lines
- Making Curved Lines to Add to Graphs
- Plotting Mathematical Expressions
- Adding Short Segments of Lines to an Existing Plot
- Adding Arrows to an Existing Graph

Matrix Plots (Multiple Series on One Graph)

Multiple Plots in One Window

- Splitting the Plot Window into Equal Sections
- Splitting the Plot Window into Unequal Sections

Exporting Graphs

- Using Copy and Paste to Move a Graph
- Saving a Graph to a File
- Windows
- Macintosh
- Linux
- Using the Device Driver to Save a Graph to Disk
- PNG Device Driver
- PDF Device Driver
- Copying a Graph from Screen to Disk File
- Making a New Graph Directly to a Disk File

Copy and Paste Scripts

- Make Your Own Help File as Plaintext
- Using Annotations with the # Character

Creating Simple Functions

- One-Line Functions
- Using Default Values in Functions
- Simple Customized Functions with Multiple Lines
- Storing Customized Functions

Making Source Code

- Displaying the Results of Customized Functions and Scripts
- Displaying Messages as Part of Script Output
- Simple Screen Text
- Display a Message and Wait for User Intervention

Case study 1 - wildfire activity in the western united states

- Exercise 1 Have the number of wildfires increased or decreased in the past few decades?
- Exercise 2 Has the acreage burned increased over time?
- Exercise 3 is the size of individual wildfire increased over time?
- Exercise 4 has the length of the fire season increased over time?
- Exercise 5 does the average wildfire size differ by federal organization?

Case study 2- single family residential home and rental value

- Exercise 1 what is the trend for home value in the Austin metro area
- Exercise 2 what is the trend for rental rates in the Austin metro area?
- Exercise 3 determining the price-rent ratio for the Austin metropolitan area
- Exercise 4 comparing residential home values in Austin to other texas and u.s. Metropolitan areas

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In this project we will analyze the Uber Pickups in New York City dataset. This is more of a data visualization project that will guide you towards using the ggplot2 library for understanding the data and for developing an intuition for understanding the customers who avail the trips

Exploratory data analysis on battles and character deaths in the Game of Thrones series using R

Understanding customer buying patterns using a Groceries dataset.Implementing Apriori algorithms using R and data visualization using arules and arulesViz packages

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- Comprehensive Hands-on Coverage on Basic and Advanced R
- Learn Data Visualization using R
- Explore more with Exploratory Data Analysis Techniques

R is a programming language that is designed and used mainly in the statistics, data science, and scientific communities. R has... Read More

Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in.... Read More

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