Books & Videos

Table of Contents

Chapter: Functions in R

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

Filtering Data

03m 28s

Dropping Data

01m 42s

Merging and Sorting Data

03m 59s

Reshaping Data

02m 42s

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

Adjusting Scales

02m 16s


02m 6s

Adjusting Themes

01m 33s

Combining Plots

02m 4s

Creating Maps

04m 39s

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