####
R Functions and Arguments

06m 25s

####
Understanding Environments

02m 58s

####
Working with Lexical Scoping

02m 49s

####
Understanding Closure

02m 17s

####
Performing Lazy Evaluation

01m 56s

####
Creating Infix Operators

02m 51s

####
Using the Replacement Function

02m 17s

####
Handling Errors in a Function

04m 30s

####
The Debugging Function

04m 5s

### Chapter:
Data Extracting, Transforming, and Loading

####
Downloading Open Data

02m 14s

####
Reading and Writing CSV Files

01m 13s

####
Scanning Text Files

02m 21s

####
Working with Excel Files

01m 55s

####
Reading Data from Databases

04m 3s

####
Scraping Web Data

05m 17s

### Chapter:
Data Pre-Processing and Preparation

####
Renaming the Data Variable

02m 27s

####
Converting Data Types

02m 35s

####
Working with Date Format

02m 55s

####
Adding New Records

02m 9s

####
Merging and Sorting Data

03m 59s

####
Detecting Missing Data

03m 14s

####
Imputing Missing Data

04m 3s

### Chapter:
Data Manipulation

####
Enhancing a data.frame with a data.table

04m 49s

####
Managing Data with data.table

04m 14s

####
Performing Fast Aggregation with data.table

02m 9s

####
Merging large Datasets with a data.table

02m 41s

####
Subsetting and Slicing Data with dplyr

02m 8s

####
Sampling Data with dplyr

01m 25s

####
Selecting Columns with dplyr

02m 40s

####
Chaining Operations in dplyr

02m 9s

####
Arranging Rows with dplyr

01m 22s

####
Eliminating Duplicated Rows with dplyr

01m 39s

####
Adding New Columns with dplyr

01m 14s

####
Summarizing Data with dplyr

01m 54s

####
Merging Data with dplyr

02m 11s

### Chapter:
Visualizing Data with ggplot2

####
Creating Basic Plots with ggplot2

04m 15s

####
Changing Aesthetics Mapping

03m 9s

####
Introducing Geometric Objects

03m 13s

####
Performing Transformations

03m 27s

### Chapter:
Making Interactive Reports

####
Creating R Markdown Reports

02m 47s

####
Learning the Markdown Syntax

03m 14s

####
Embedding R Code Chunks

02m 18s

####
Creating Interactive Graphics with ggvis

02m 39s

####
Understanding Basic Syntax and Grammar

01m 57s

####
Controlling Axes and Legends and Using Scales

02m 55s

####
Adding Interactivity to a ggvis Plot

03m 40s

####
Creating an R Shiny Document

02m 15s

####
Publishing an R Shiny Report

02m 28s

### Chapter:
Simulation from Probability Distributions

####
Generating Random Samples

02m 51s

####
Understanding Uniform Distributions

01m 38s

####
Generating Binomial Random Variates

02m 30s

####
Generating Poisson Random Variates

02m 6s

####
Sampling from a Normal Distribution

04m 7s

####
Sampling from a Chi-Squared Distribution

01m 59s

####
Understanding Student's t- Distribution

02m 11s

####
Sampling from a Dataset

01m 52s

####
Simulating the Stochastic Process

02m 29s

### Chapter:
Statistical Inference in R

####
Getting Confidence Intervals

05m 54s

####
Performing Z-tests

03m 12s

####
Performing Student's t-Tests

02m 15s

####
Conducting Exact Binomial Tests

02m 9s

####
Performing Kolmogorov-Smirnov Tests

02m 16s

####
Working with the Pearson's Chi-Squared Tests

01m 40s

####
Understanding the Wilcoxon Rank Sum and Signed Rank Tests

01m 48s

####
Conducting One-way ANOVA

02m 39s

####
Performing Two-way ANOVA

03m 1s

### Chapter:
Rule and Pattern Mining with R

####
Transforming Data into Transactions

05m 11s

####
Displaying Transactions and Associations

03m 2s

####
Mining Associations with the Apriori Rule

04m 18s

####
Pruning Redundant Rules

02m 14s

####
Visualizing Association Rules

02m 35s

####
Mining Frequent Itemsets with Eclat

03m 8s

####
Creating Transactions with Temporal Information

02m 52s

####
Mining Frequent Sequential Patterns with cSPADE

02m 42s

### Chapter:
Time Series Mining with R

####
Creating Time Series Data

05m 11s

####
Plotting a Time Series Object

02m 26s

####
Decomposing Time Series

02m 11s

####
Smoothing Time Series

05m 21s

####
Forecasting Time Series

02m 30s

####
Selecting an ARIMA Model

03m 18s

####
Creating an ARIMA Model

02m 19s

####
Forecasting with an ARIMA Model

02m 11s

####
Predicting Stock Prices with an ARIMA Model

04m 24s

### Chapter:
Supervised Machine Learning

####
Fitting a Linear Regression Model with lm

05m 34s

####
Summarizing Linear Model Fits

02m 53s

####
Using Linear Regression to Predict Unknown Values

03m 57s

####
Measuring the Performance of the Regression Model

03m 23s

####
Performing a Multiple Regression Analysis

04m 17s

####
Selecting the Best-Fitted Regression Model with Stepwise Regression

02m 42s

####
Applying the Gaussian Model for Generalized Linear Regression

02m 19s

####
Performing a Logistic Regression Analysis

04m 30s

####
Building a Classification Model with Recursive Partitioning Trees

03m 58s

####
Visualizing Recursive Partitioning Tree

02m 14s

####
Measuring Model Performance with a Confusion Matrix

01m 38s

####
Measuring Prediction Performance Using ROCR

03m 46s

### Chapter:
Unsupervised Machine Learning

####
Clustering Data with Hierarchical Clustering

06m 10s

####
Cutting Tree into Clusters

01m 44s

####
Clustering Data with the k-means Method

02m 9s

####
Clustering Data with the Density-=Based Method

03m 11s

####
Extracting Silhouette Information From Clustering

01m 50s

####
Comparing Clustering Methods

02m 12s

####
Recognizing Digits Using the Density-Based Clustering Method

01m 52s

####
Grouping Similar Text Documents with k-means Clustering Method

02m 14s

####
Performing Dimension Reduction with Principal Component Analysis (PCA)

02m 51s

####
Determining the Number of Principal Components Using a Scree Plot

01m 50s

####
Determining the Number of Principal Components Using the Kaiser Method

01m 19s

####
Visualizing Multivariate Data using a biplot

02m 54s