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The Comprehensive Statistics and Data Science with R Course

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Learn how to use R for data science tasks, all about R data structures, functions and visualizations, and statistics.

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- 19.5 hours on-demand video
- 1 Supplemental Resource
- Full lifetime access
- Access on mobile and TV

- Certificate of Completion

What Will I Learn?

- Students will understand what R is, and how to input and output data files into their R sessions.
- Students will know how to manipulate numbers and vectors, and will understand objects and classes.
- Students will understand how to create data structures in R: vectors; arrays and matrices; lists and data frames.
- Students will know how to use R as a statistical environment following many examples.
- Students will understand how to create, estimate and interpret ANOVA, regression, GLM and GAM statistical models with many examples of each.
- Students will learn how to create statistical and other visualizations using both the base and ggplot graphics capabilities in R.

Requirements

- Students must install R and RStudio (free software) but ample instructions are provided.

Description

This course, ** The Comprehensive Statistics and Data Science with R Course**, is mostly based on the authoritative documentation in the online "An Introduction to R" manual produced with each new R release by the Comprehensive R Archive Network (CRAN) development core team. These are the people who actually write, test, produce and release the R code to the general public by way of the CRAN mirrors. It is a rich and detailed 10-session course which covers much of the content in the contemporary 105-page CRAN manual. The ten sessions follow the outline in the

1. Introduction to R; Inputting data into R

2. Simple manipulation of numbers and vectors

3. Objects, their modes and attributes

4. Arrays and matrices

5. Lists and data frames

6. Writing user-defined functions

7. Working with R as a statistical environment

8. Statistical models and formulae; ANOVA and regression

9. GLMs and GAMs

10. Creating statistical and other visualizations with R

It is a comprehensive and decidedly "hands-on" course. You are taught how to actually use R and R script to create everything that you see on-screen in the course videos. Everything is included with the course materials: all software; slides; R scripts; data sets; exercises and solutions; in fact, everything that you see utilized in any of the 200+ course videos are included with the downloadable course materials.

The course is structured for both the novice R user, as well as for the more experienced R user who seeks a refresher course in the benefits, tools and capabilities that exist in R as a software suite appropriate for statistical analysis and manipulation. The first half of the course is suited for novice R users and guides one through "hands-on" practice to master the input and output of data, as well as all of the major and important objects and data structures that are used within the R environment. The second half of the course is a detailed "hands-on" transcript for using R for statistical analysis including detailed data-driven examples of ANOVA, regression, and generalized linear and additive models. Finally, the course concludes with a multitude of "hands-on" instructional videos on how to create elegant and elaborate statistical (and other) graphics visualizations using both the base and gglot visualization packages in R.

The course is very useful for any quantitative analysis professional who wishes to "come up to speed" on the use of R quickly. It would also be useful for any graduate student or college or university faculty member who also seeks to master these data analysis skills using the popular R package.

Who is the target audience?

- This course will benefit anyone wishing to learn R and especially those who seek an in-depth "hands-on" tutorial on performing statistical analyses with R.
- The course is useful for graduate students, college and university faculty, and working quantitative analysis professionals.

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Curriculum For This Course

219 Lectures

19:40:46
+
–

Introduction to R and Inputting Data into R
12 Lectures
01:18:47

+
–

Manipulating Numbers and Vectors
17 Lectures
01:21:38

Introduction to Section 2

05:52

Vectors and Assignment (part 3)

05:37

Vector Arithmetic (part 1)

05:26

Vector Arithmetic (part 2)

04:41

Vector Arithmetic (part 3)

05:49

Vector Arithmetic (part 4)

04:02

Vector Arithmetic (part 5)

05:58

Generating Regular Sequences

04:35

Logical Vectors

04:24

More Missing Values; Character Vectors

04:28

Index Vectors (part 1)

05:51

Index Vectors (part 2)

06:57

Index Vectors (part 3)

04:40

Index Vectors (part 4)

02:05

Session 2 Exercises

01:13

+
–

Objects and Classes: Their Modes and Attributes
17 Lectures
01:24:14

Solutions to Session 2 Exercises (part 1)

05:32

Solutions to Session 2 Exercises (part 2)

06:07

Solutions to Session 2 Exercises (part 3)

04:23

Preview
04:45

Preview
02:28

Strings

02:24

Factors

05:53

Logical and Missing

05:07

Vectors

03:40

Vectorization and Recycling

05:05

Basic Data Structures in R (slides, part 1)

04:54

Basic Data Structures (slides, part 2)

05:28

Basic Data Structures (slides, part 3)

05:54

Objects: Script Examples (part 1)

05:22

Objects: Script Examples (part 2)

06:02

Objects: Script Examples (part 3)

05:53

Objects: Script Examples (part 4)

05:17

+
–

Arrays and Matrices
22 Lectures
01:55:23

Session 4 Exercises

00:54

More on Factors

05:39

Function tapply() and Ragged Arrays

07:20

Arrays

05:43

Arrays and Matrices (part 1)

05:12

Arrays and Matrices (part 2)

05:06

Warpbreaks Data (part 1)

05:28

Warpbreaks Data (part 2)

05:40

More about Matrices (part 1)

05:13

More about Matrices (part 2)

05:31

More about Matrices (part 3)

06:22

More about Matrices (part 4)

05:10

More about Matrices (part 5)

04:35

More about Matrices (part 6)

05:33

Creating Matrices (part 1)

05:41

Creating Matrices (part 2)

04:28

Row Names and Column Names

05:35

More on Array Function

05:13

Outer Product of Two Arrays

05:48

+
–

List and Data Frame Structures
19 Lectures
01:34:36

Introduction to Lists

05:55

Accessing List Components (part 1)

05:51

Accessing List Components (part 2)

05:41

More List Dissection (part 1)

05:11

More List Dissection (part 2)

04:06

More About Lists

03:35

What are Data Frames

05:17

Characteristics of Data Frames

04:57

A Data Frame is a List

04:48

Data Frames are Lists

03:25

Manipulating Data Frames (part 3)

05:10

Manipulating Data Frames (part 4)

06:13

Manipulating Data Frames (part 5)

04:18

Manipulating Data Frames (part 6)

04:13

Manipulating Data Frames (part 7)

04:27

+
–

User-Defined Functions
27 Lectures
02:13:31

Exercise Solutions (part 1)

05:10

Exercise Solutions (part 2)

05:20

Exercise Solutions (part 3)

05:57

Exercise Solutions (part 4)

04:58

Exercise Solutions (part 5)

05:20

Exercise Solutions (part 6)

06:00

Exercise Solutions (part 7)

05:09

Exercise Solutions (part 8)

05:38

Introduction to Writing Functions in R

02:27

Writing Functions (slides, part 1)

04:09

Writing Functions (slides, part 2)

04:17

Two Sample t-test (part 1)

04:54

Two Sample t-test (part 2)

05:03

Finish t-test Example; Named Arguments and Defaults

04:59

Many Means and More

04:48

More Functions Examples (part 1)

04:48

More Functions Examples (part 2)

05:18

Superassigment Examples (part 3)

04:57

Superassignment Examples (part 4)

04:47

Optional Arguments Example (part 5)

06:08

More Functions Examples (part 8)

04:31

Still More Examples (part 9)

04:14

Exercises for User Defined Functions Section

04:11

+
–

Working with R as a Statistical Environment
28 Lectures
02:21:14

User-Defined Functions Exercise 1a. Solution (part 1)

02:58

User-Defined Functions Exercise 1a. Solution (part 2)

05:16

User-Defined Functions Exercise 1b. Solution

06:24

User-Defined Functions Exercise 2 Solution (part 1)

04:22

User-Defined Functions Exercise 2 Solution (part 2)

04:48

Introduction to R as a Statistical Environment

06:58

Basic Operations (part 1)

03:51

Basic Operations (part 2)

04:22

Basic Operations (part 3)

04:45

Prussian Horsekicks and Functions (part 1)

05:28

Prussian Horsekicks and Functions (part 2)

05:19

Functions; Vectors and Matrices (part 1)

05:57

Functions; Vectors and Matrices (part 2)

05:37

Functions; Vectors and Matrices (part 3)

05:48

Data Frames and Histograms (part 1)

05:19

Data Frames and Histograms (part 2)

05:57

Attaching and Working with Data Frames (part 1)

04:33

Attaching and Working with Data Frames (part 2)

04:16

Attaching and Working with Data Frames (part 3)

05:12

Entering Data Manually (part 1)

05:43

Entering Data Manually (part 2)

05:15

Entering Data Manually (part 3)

04:23

Exercises for Working with R as a Statistical Environment

05:26

Exercises Solutions (part 1)

03:48

Exercises Solutions (part 2)

05:00

Exercises Solutions (part 3)

04:41

+
–

Statistical Models and Formulae, ANOVA and Regression
27 Lectures
02:07:00

Statistical Modeling Operators in R (part 1)

05:33

Statistical Modeling Operators in R (part 2)

05:51

Statistical Modeling Operators in R (part 3)

05:04

Analysis of Variance (ANOVA) (slides, part 1)

04:58

ANOVA (slides, part 2)

05:22

ANOVA (slides, part 3)

04:06

ANOVA Scripts (part 1)

05:03

ANOVA Scripts (part 2)

05:13

ANOVA Scripts (part 3)

05:13

ANOVA Scripts (part 4)

05:21

ANOVA Scripts (part 5)

04:01

ANOVA Scripts (part 6)

03:49

What is Linear Modeling ? (slides, part 3)

02:57

What is Linear Modeling ? (slides, part 4)

04:14

Regression Domains (slides, part 1)

04:23

Regression Domains (slides, part 2)

04:45

Regression Scripts (part 3)

04:56

Regression Scripts (part 4)

06:08

Regression Scripts (part 5)

04:27

Regression Scripts (part 6)

05:00

Regression Scripts (part 7)

03:21

Regression Scripts (part 8)

04:07

Linear Modeling Exercise

01:48

+
–

Generalized Linear Models (GLMs) and Generalized Additive Models (GAMS)
19 Lectures
01:33:52

What are GLMs ? (slides, part 3)

05:40

What are GLMs ? (slides, part 4)

05:24

What are GLMs ? (slides, part 5)

04:48

What are GLMs ? (slides, part 6)

04:21

GLM: ESR Study (part 1)

06:01

GLM: ESR Study (part 2)

04:47

GLM: ESR Study (part 3)

04:49

GLM: Womens' Role in Society (part 3)

04:15

GLM: Colonic Polyps (part 1)

04:26

GLM: Colonic Polyps (part 2)

05:51

GAMs and Smoothers (slides, part 1)

04:41

GAMs and Smoothers (slides, part 2)

03:44

Smoothers: Olympic Data (part 1)

05:11

Smoothers and GAMs (part 2)

07:12

Smoothers and GAMs (part 3)

05:07

+
–

Creating Visualizations with R
31 Lectures
03:50:31

Preview
06:29

Still More Graphics Features

07:16

More on Plotting Characters

07:05

More on Plotting and Features and an Exercise

06:46

Exercise Solution and More on Base Graphics

09:43

More Base Features Compared to ggplot

08:17

Adding Text to Plots (part 1)

08:45

Adding Text to Plots (part 2)

09:31

Adding Shapes to Plots Interactively (part 1)

07:22

Adding Shapes to Plots Interactively (part 2)

07:39

Adding Shapes to Plots Interactively (part 3)

08:05

Adding Nonlinear Fits to Plots (part 3)

05:50

Adding Nonlinear Fits to Plots (part 4)

05:26

Adding Nonlinear Fits to Plots (part 5)

07:21

Boxplots (part 1)

05:55

Boxplots (part 2)

07:25

Boxplots (part 3)

07:23

Boxplots (part 4)

10:21

Histograms

06:54

Time Series and Piechart

06:07

Stripchart and Pairs Plot

06:58

Exercise Solution and Shingles

07:02

Shingles, Coplot, and Interaction Plots

06:35

Box and Whiskers Plot and Design Plot

08:14

Interaction and XYPlots

06:25

Effects Sizes

05:34

Bubble and Sunflower Plots

07:53

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