Chapter 9 A guide to current swirl tutorials

Below are the names of the swirl tutorials which are most relevant to new R users, how to download them and the names of the units within each tutorial

9.1 Regression Models: The basics of regression modeling in R (Team swirl)

Installation:

swirl::install_course("Regression Models")

Units:

1. Introduction
2. Residuals
3. Least Squares Estimation
4. Residual Variation
5. Introduction to Multivariable Regression
6. MultiVar Examples
7. MultiVar Examples2
8. MultiVar Examples3
9. Residuals Diagnostics and Variation
10. Variance Inflation Factors
11. Overfitting and Underfitting
12. Binary Outcomes
13. Count Outcomes

9.2 Statistical Inference: The basics of statistical inference in R (Team swirl)

Installation:

swirl::install_course("Statistical Inference")

Units:

1. Introduction
2. Probability1
3. Probability2
4. ConditionalProbability
5. Expectations
6. Variance
   
7. CommonDistros
   
8. Asymptotics
9. T Confidence Intervals
10. Hypothesis Testing
    
11. P Values
    
12. Power
13. Multiple Testing
    
14. Resampling

9.3 Exploratory Data Analysis: The basics of exploring data in R (Team swirl)

Installation

swirl::install_course("Exploratory Data Analysis")

1. Principles of Analytic Graphs
2. Exploratory Graphs
3. Graphics Devices in R
4. Plotting Systems
5. Base Plotting System
6. Lattice Plotting System
7. Working with Colors
8. GGPlot2 Part1
9. GGPlot2 Part2
10. GGPlot2 Extras
11. Hierarchical Clustering
12. K Means Clustering
13. Dimension Reduction
14. Clustering Example
15. CaseStudy

9.4 Getting and Cleaning Data (Team swirl)

Installation

swirl::install_course("Getting and Cleaning Data")

Units 1: Manipulating Data with dplyr 1: Grouping and Chaining with dplyr 1: Tidying Data with tidyr 1: Dates and Times with lubridate

9.5 Advanced R Programming (Roger Peng)

swirl::install_course("Advanced R Programming")

1. Setting Up Swirl
2. Functions
3. Functional Programming with purrr0

9.6 The R Programming Environment (Roger Peng)

swirl::install_course("The R Programming Environment")

1. Setting Up Swirl
2. Basic Building Blocks
3. Sequences of Numbers
4. Vectors
5. Missing Values
6. Subsetting Vectors
7. Matrices and Data Frames
8. Logic
9. Workspace and Files
10. Reading Tabular Data
11. Looking at Data
12. Data Manipulation
13. Text Manipulation Functions
14. Regular Expressions
15. The stringr Package

9.7 Regular Expressions (Jon Calder)

Installation:

swirl::install_course("Regular Expressions")

Units

1. Introduction
2. Regex in base R
3. Anchors
4. Character Classes
5. Groups and Ranges
6. Quantifiers
7. Applied Examples

9.8 A (very) short introduction to R (Claudia Brauer)

swirl::install_course("A_(very)_short_introduction_to_R")

The author notes: >“Thiscourse is based on a non-interactive tutorial with the same name, which can be downloaded from www.github.com/ClaudiaBrauer/A-very-short-introduction-to-R. The contents are the same (with a few exceptions), so you can open the pdf version alongside to look up how to do something you learned before or browse through the references on the last two pages.”

This tutorial has 3 modules. The first is introductory. The others get into more details tasks, such as sourceing .R file scripts.

9.9 R Programming: The basics of programming in R (team swirl)

1. Basic Building Blocks
   
2. Workspace and Files
3. Sequences of Numbers
   
4. Vectors
5. Missing Values
   
6. Subsetting Vectors
7. Matrices and Data Frames
   
8. Logic
9. Functions
   
10. lapply and sapply
11. vapply and tapply
    
12. Looking at Data
13. Simulation
    
14. Dates and Times
15. Base Graphics

The key vocabulary, concepts and functions covered in this tutorial are:

• programming language
• variable
• +, -, /, and ^
• sqrt()
• abs()
• assignment operator
• <-
• help files
• vector
• c() function (“concatenate”, “combine”)
• element wise operation (“element-by-element”)
• recycling
• vectorized operations (not discussed in those terms)
• up arrow to view command history
• tab completion (“auto-completion”)