Books & Videos

Table of Contents

Chapter: Introduction

Part 1: R as a Tool--Introduction

03m 23s

Chapter: Lesson 1: Getting Started with R

Learning objectives

00m 28s

1.1 Download and Install R

06m 23s

1.2 Work in the R Environment

18m 50s

1.3 Install and load packages

04m 49s

Chapter: Lesson 2: The Basic Building Blocks in R

Learning objectives

00m 26s

2.1 Use R as a calculator

03m 43s

2.2 Work with variables

04m 11s

2.3 Understand the different data types

11m 32s

2.4 Store data in vectors

16m 36s

2.5 Call functions

04m 3s

Chapter: Lesson 3: Advanced Data Structures in R

Learning objectives

00m 25s

3.1 Create and access information in data.frames

17m 20s

3.2 Create and access information in lists

10m 57s

3.3 Create and access information in matrices

08m 1s

Chapter: Lesson 4: Reading Data into R

Learning objectives

00m 26s

4.1 Read a CSV into R

05m 58s

4.2 Read an Excel Spreadsheet into R

04m 38s

4.3 Read from databases

05m 59s

4.4 Read data files from other statistical tools

01m 17s

4.5 Load binary R files

04m 40s

4.6 Load data included with R

01m 48s

4.7 Scrape data from the web

02m 28s

4.8 Read XML data

27m 23s

Chapter: Lesson 5: Making Statistical Graphs

Learning objectives

00m 34s

5.1 Find the diamonds in the data

01m 13s

5.2 Make histograms with base graphics

01m 29s

5.3 Make scatterplots with base graphics

02m 1s

5.4 Make boxplots with base graphics

01m 39s

5.5 Get familiar with ggplot2

02m 30s

5.6 Plot histograms and densities with ggplot2

03m 51s

5.7 Make scatterplots with ggplot2

05m 12s

5.8 Make boxplots and violin plots with ggplot2

04m 24s

5.9 Make line plots

08m 21s

5.10 Create small multiples

04m 1s

5.11 Control colors and shapes

01m 18s

5.12 Add themes to graphs

02m 18s

5.13 Use Web graphics

29m 48s

Chapter: Lesson 6: Basics of Programming

Learning objectives

00m 26s

6.1 Write the classic "Hello, World!" example

02m 4s

6.2 Understand the basics of function arguments

10m 32s

6.3 Return a value from a function

02m 47s

6.4 Gain flexibility with

03m 46s

6.5 Use "if" statements to control program flow

02m 8s

6.6 Stagger "if" statements with "else"

05m 33s

6.7 Check multiple statements with switch

03m 51s

6.8 Run checks on entire vectors

05m 17s

6.9 Check compound statements

05m 40s

6.10 Iterate with a for loop

06m 7s

6.11 Iterate with a while loop

01m 30s

6.12 Control loops with break and next

02m 5s

Chapter: Lesson 7: Data Munging

Learning objectives

00m 35s

7.1 Repeat an operation on a matrix using apply

04m 45s

7.2 Repeat an operation on a list

03m 5s

7.3 Apply a function over multiple lists with mapply

04m 34s

7.4 Perform group summaries with the aggregate function

05m 26s

7.5 Do group operations with the plyr Package

17m 18s

7.6 Combine datasets

03m 51s

7.7 Join datasets

05m 56s

7.8 Switch storage paradigms

05m 11s

7.9 Use tidyr

02m 50s

7.10 Get faster group operations

22m 2s

Chapter: Lesson 8: In-Depth with dplyr

Learning objectives

00m 22s

8.1 Use tbl

01m 48s

8.2 Use select to choose columns

03m 8s

8.3 Use filter to choose rows

03m 38s

8.4 Use slice to choose rows

01m 8s

8.5 Use mutate to change or create columns

02m 39s

8.6 Use summarize for quick computation on tbl

01m 34s

8.7 Use group_by to split the data

02m 35s

8.8 Apply arbitrary functions with do

06m 50s

Chapter: Lesson 9: Manipulating Strings

Learning objectives

00m 20s

9.1 Combine strings together

07m 28s

9.2 Extract text

32m 0s

Chapter: Lesson 10: Reports and Slideshows with knitr

Learning objectives

00m 29s

10.1 Understand the basics of LaTeX

07m 16s

10.2 Weave R code into LaTeX using knitr

05m 33s

10.3 Understand the basics of Markdown

02m 45s

10.4 Understand the basics of RMarkdown

04m 55s

10.5 Weave R code into Markdown using knitr

02m 53s

10.6 Convert Markdown files to Word

01m 29s

10.7 Convert Markdown to PDF

01m 25s

10.8 Create slideshows with RMarkdown

03m 9s

10.9 Write equations with RMarkdown

07m 13s

Chapter: Lesson 11: Include HTML Widgets in HTML Documents

Learning objectives

00m 28s

11.1 Work with datatables of tabular data

06m 9s

11.2 Use rbokeh

08m 31s

11.3 Use Leaflet for mapping

07m 11s

Chapter: Lesson 12: Shiny

Learning objectives

00m 22s

12.1 Use shiny objects in a markdown document

13m 56s

12.2 Work with ui.r and server.r files

08m 21s

Chapter: Lesson 13: Package Building

Learning objectives

00m 23s

13.1 Understand the folder structure and files in a package

05m 25s

13.2 Write and document functions

07m 32s

13.3 Check and build a package

02m 9s

13.4 Test R code

06m 58s

13.5 Submit a package to CRAN

00m 46s

Chapter: Lesson 14: Rcpp for Faster Code

Learning objectives

00m 29s

14.1 Understand the basics of C++ with R

01m 47s

14.2 Write a C++ function for R

04m 35s

14.3 Use Rcpp syntactic sugar

05m 51s

14.4 Sum in C++

05m 35s

14.5 Write a package in R

09m 37s

14.6 Write a package with C++ code

06m 4s

Chapter: Summary

Part 1: R as a Tool--Summary

01m 2s

Chapter: Introduction

Part 2: R for Statistics, Modeling and Machine Learning--Introduction

02m 13s

Chapter: Lesson 15: Basic Statistics

Learning objectives

00m 20s

15.1 Draw numbers from probability distributions

21m 10s

15.2 Calculate averages, standard deviations and correlations

16m 13s

15.3 Compare samples with t-tests and analysis of variance

18m 58s

Chapter: Lesson 16: Linear Models

Learning objectives

00m 28s

16.1 Fit simple linear models

10m 15s

16.2 Explore the data

08m 33s

16.3 Fit multiple regression models

19m 16s

16.4 Fit logistic regression

10m 6s

16.5 Fit Poisson regression

07m 5s

16.6 Analyze survival data

12m 1s

16.7 Assess model quality with residuals

05m 15s

16.8 Compare models

07m 18s

16.9 Judge accuracy using cross-validation

09m 6s

16.10 Estimate uncertainty with the bootstrap

06m 23s

16.11 Choose variables using stepwise selection

02m 42s

Chapter: Lesson 17: Other Models

Learning objectives

00m 27s

17.1 Select variables and improve predictions with the elastic net

14m 14s

17.2 Decrease uncertainty with weakly informative priors

08m 53s

17.3 Fit nonlinear least squares

05m 16s

17.4 Use Splines

06m 48s

17.5 Use GAMs

05m 24s

17.6 Fit decision trees to make a random forest

06m 34s

Chapter: Lesson 18: Time Series

Learning objectives

00m 20s

18.1 Understand ACF and PACF

07m 15s

18.2 Fit and assess ARIMA models

05m 13s

18.3 Use VAR for multivariate time series

08m 6s

18.4 Use GARCH for better volatility modeling

09m 24s

Chapter: Lesson 19: Clustering

Learning objectives

00m 20s

19.1 Partition data with k-means

12m 26s

19.2 Robustly cluster, even with categorical data, with PAM

02m 13s

19.3 Perform hierarchical clustering

05m 38s

Chapter: Lesson 20: More Machine Learning

Learning objectives

00m 21s

20.1 Build a recommendation engine with RecommenderLab

13m 13s

20.2 Mine text with RTextTools

09m 13s

20.3 Perform matrix factorization using irlba

04m 4s

Chapter: Lesson 21: Network Analysis

Learning objectives

00m 18s

21.1 Get started with igraph

08m 16s

21.2 Read edgelists

07m 11s

21.3 Understand common graph metrics

10m 12s

21.4 Use centrality measures

05m 59s

21.5 Utilize more graph operations

04m 15s

Chapter: Lesson 22: Automatic Parameter Tuning with Caret

Learning objectives

00m 19s

22.1 Establish optimal tree depth for rpart

06m 18s

22.2 Choose the best number of trees for a random forest

03m 35s

Chapter: Lesson 23: Fit a Bayesian Model with RStan

Learning objectives

00m 25s

23.1 Understand the Stan computing paradigm

01m 33s

23.2 Fit a simple regression model

06m 53s

23.3 Fit a multilevel model with Stan

06m 42s

Chapter: Summary

Part 2: R for Statistics, Modeling and Machine Learning--Summary

00m 49s