R Programming LiveLessons (Video Training): Fundamentals to Advanced

Table of contents

1. Introduction
   1. Introduction to R Programming LiveLessons
2. Lesson 1: Getting Started with R
   1. Learning objectives
   2. 1.1 Download and install R
   3. 1.2 Work in The R environment
   4. 1.3 Install and load packages
3. Lesson 2: The Basic Building Blocks in R
   1. Learning objectives
   2. 2.1 Use R as a calculator
   3. 2.2 Work with variables
   4. 2.3 Understand the different data types
   5. 2.4 Store data in vectors
   6. 2.5 Call functions
4. Lesson 3: Advanced Data Structures in R
   1. Learning objectives
   2. 3.1 Create and access information in data.frames
   3. 3.2 Create and access information in lists
   4. 3.3 Create and access information in matrices
   5. 3.4 Create and access information in arrays
5. Lesson 4: Reading Data into R
   1. Learning objectives
   2. 4.1 Read a CSV into R
   3. 4.2 Understand that Excel is not easily readable into R
   4. 4.3 Read from databases
   5. 4.4 Read data files from other statistical tools
   6. 4.5 Load binary R files
   7. 4.6 Load data included with R
   8. 4.7 Scrape data from the web
6. Lesson 5: Making Statistical Graphs
   1. Learning objectives
   2. 5.1 Find the diamonds data
   3. 5.2 Make histograms with base graphics
   4. 5.3 Make scatterplots with base graphics
   5. 5.4 Make boxplots with base graphics
   6. 5.5 Get familiar with ggplot2
   7. 5.6 Plot histograms and densities with ggplot2
   8. 5.7 Make scatterplots with ggplot2
   9. 5.8 Make boxplots and violin plots with ggplot2
   10. 5.9 Make line plots
   11. 5.10 Create small multiples
   12. 5.11 Control colors and shapes
   13. 5.12 Add themes to graphs
7. Lesson 6: Basics of Programming
   1. Learning objectives
   2. 6.1 Write the classic 'Hello, World!' example
   3. 6.2 Understand the basics of function arguments
   4. 6.3 Return a value from a function
   5. 6.4 Gain flexibility with do.call
   6. 6.5 Use if statements to control program flow
   7. 6.6 Stagger if statements with else
   8. 6.7 Check multiple statements with switch
   9. 6.8 Run checks on entire vectors
   10. 6.9 Check compound statements
   11. 6.10 Iterate with a for loop
   12. 6.11 Iterate with a while loop
   13. 6.12 Control loops with break and next
8. Lesson 7: Data Munging
   1. Learning objectives
   2. 7.1 Repeat an operation on a matrix using apply
   3. 7.2 Repeat an operation on a list
   4. 7.3 The mapply
   5. 7.4 The aggregate function
   6. 7.5 The plyr package
   7. 7.6 Combine datasets
   8. 7.7 Join datasets
   9. 7.8 Switch storage paradigms
9. Lesson 8: Manipulating Strings
   1. Learning objectives
   2. 8.1 Combine strings together
   3. 8.2 Extract text
10. Lesson 9: Basic Statistics
    1. Learning objectives
    2. 9.1: Draw numbers from probability distributions
    3. 9.2: Calculate averages, standard deviations and correlations
    4. 9.3: Compare samples with t-tests and analysis of variance
11. Lesson 10: Linear Models
    1. Learning objectives
    2. 10.1 Fit simple linear models
    3. 10.2 Explore the data
    4. 10.3 Fit multiple regression models
    5. 10.4 Fit logistic regression
    6. 10.5 Fit Poisson regression
    7. 10.6 Analyze survival data
    8. 10.7 Assess model quality with residuals
    9. 10.8 Compare models
    10. 10.9 Judge accuracy using cross-validation
    11. 10.10 Estimate uncertainty with the bootstrap
    12. 10.11 Choose variables using stepwise selection
12. Lesson 11: Other Models
    1. Learning objectives
    2. 11.1 Select variables and improve predictions with the elastic net
    3. 11.2 Decrease uncertainty with weakly informative priors
    4. 11.3 Fit nonlinear least squares
    5. 11.4 Splines
    6. 11.5 GAMs
    7. 11.6 Fit decision trees to make a random forest
13. Lesson 12: Time Series
    1. Learning objectives
    2. 12.1 Understand ACF and PACF
    3. 12.2 Fit and assess ARIMA models
    4. 12.3 Use VAR for multivariate time series
    5. 12.4 Use GARCH for better volatility modeling
14. Lesson 13: Clustering
    1. Learning objectives
    2. 13.1: Partition data with K-means
    3. 13.2: Robustly cluster, even with categorical data, with PAM
    4. 13.3: Perform hierarchical clustering
15. Lesson 14: Reports and Slideshows with knitr
    1. Learning objectives
    2. 14.1: Understand the basics of LaTeX
    3. 14.2: Weave R code into LaTeX using knitr
    4. 14.3: Understand the basics of Markdown
    5. 14.4: Weave R code into Markdown using knitr
    6. 14.5: Use pandoc to convert from Markdown to HTML5 slideshow
16. Lesson 15: Package Building
    1. Learning objectives
    2. 15.1: Understand the folder structure and files in a package
    3. 15.2: Write and document functions
    4. 15.3: Check and build a package
    5. 15.4: Submit a package to CRAN
17. Summary
    1. Summary of R Programming LiveLessons