R Programming LiveLessons (Video Training): Fundamentals to Advanced

Table of contents

1. Introduction
   1. Introduction to R Programming LiveLessons 00:04:02
2. Lesson 1: Getting Started with R
   1. Learning objectives 00:00:29
   2. 1.1 Download and install R 00:06:23
   3. 1.2 Work in The R environment 00:18:50
   4. 1.3 Install and load packages 00:04:49
3. Lesson 2: The Basic Building Blocks in R
   1. Learning objectives 00:00:28
   2. 2.1 Use R as a calculator 00:03:43
   3. 2.2 Work with variables 00:04:11
   4. 2.3 Understand the different data types 00:11:33
   5. 2.4 Store data in vectors 00:16:36
   6. 2.5 Call functions 00:04:03
4. Lesson 3: Advanced Data Structures in R
   1. Learning objectives 00:00:25
   2. 3.1 Create and access information in data.frames 00:17:20
   3. 3.2 Create and access information in lists 00:10:57
   4. 3.3 Create and access information in matrices 00:08:02
   5. 3.4 Create and access information in arrays 00:02:00
5. Lesson 4: Reading Data into R
   1. Learning objectives 00:00:24
   2. 4.1 Read a CSV into R 00:05:58
   3. 4.2 Understand that Excel is not easily readable into R 00:01:08
   4. 4.3 Read from databases 00:05:59
   5. 4.4 Read data files from other statistical tools 00:01:17
   6. 4.5 Load binary R files 00:04:40
   7. 4.6 Load data included with R 00:01:49
   8. 4.7 Scrape data from the web 00:02:28
6. Lesson 5: Making Statistical Graphs
   1. Learning objectives 00:00:30
   2. 5.1 Find the diamonds data 00:01:13
   3. 5.2 Make histograms with base graphics 00:01:30
   4. 5.3 Make scatterplots with base graphics 00:02:01
   5. 5.4 Make boxplots with base graphics 00:01:39
   6. 5.5 Get familiar with ggplot2 00:02:30
   7. 5.6 Plot histograms and densities with ggplot2 00:03:52
   8. 5.7 Make scatterplots with ggplot2 00:05:12
   9. 5.8 Make boxplots and violin plots with ggplot2 00:04:25
   10. 5.9 Make line plots 00:08:21
   11. 5.10 Create small multiples 00:04:01
   12. 5.11 Control colors and shapes 00:01:19
   13. 5.12 Add themes to graphs 00:02:18
7. Lesson 6: Basics of Programming
   1. Learning objectives 00:00:28
   2. 6.1 Write the classic 'Hello, World!' example 00:02:05
   3. 6.2 Understand the basics of function arguments 00:10:33
   4. 6.3 Return a value from a function 00:02:47
   5. 6.4 Gain flexibility with do.call 00:03:46
   6. 6.5 Use if statements to control program flow 00:02:08
   7. 6.6 Stagger if statements with else 00:05:33
   8. 6.7 Check multiple statements with switch 00:03:52
   9. 6.8 Run checks on entire vectors 00:05:17
   10. 6.9 Check compound statements 00:05:41
   11. 6.10 Iterate with a for loop 00:06:07
   12. 6.11 Iterate with a while loop 00:01:31
   13. 6.12 Control loops with break and next 00:02:05
8. Lesson 7: Data Munging
   1. Learning objectives 00:00:32
   2. 7.1 Repeat an operation on a matrix using apply 00:04:46
   3. 7.2 Repeat an operation on a list 00:03:05
   4. 7.3 The mapply 00:04:34
   5. 7.4 The aggregate function 00:05:27
   6. 7.5 The plyr package 00:17:18
   7. 7.6 Combine datasets 00:03:51
   8. 7.7 Join datasets 00:05:56
   9. 7.8 Switch storage paradigms 00:05:11
9. Lesson 8: Manipulating Strings
   1. Learning objectives 00:00:21
   2. 8.1 Combine strings together 00:07:28
   3. 8.2 Extract text 00:32:01
10. Lesson 9: Basic Statistics
    1. Learning objectives 00:00:20
    2. 9.1: Draw numbers from probability distributions 00:21:10
    3. 9.2: Calculate averages, standard deviations and correlations 00:16:13
    4. 9.3: Compare samples with t-tests and analysis of variance 00:18:59
11. Lesson 10: Linear Models
    1. Learning objectives 00:00:27
    2. 10.1 Fit simple linear models 00:10:15
    3. 10.2 Explore the data 00:08:33
    4. 10.3 Fit multiple regression models 00:19:17
    5. 10.4 Fit logistic regression 00:10:06
    6. 10.5 Fit Poisson regression 00:07:05
    7. 10.6 Analyze survival data 00:12:01
    8. 10.7 Assess model quality with residuals 00:05:16
    9. 10.8 Compare models 00:07:18
    10. 10.9 Judge accuracy using cross-validation 00:09:06
    11. 10.10 Estimate uncertainty with the bootstrap 00:06:23
    12. 10.11 Choose variables using stepwise selection 00:02:42
12. Lesson 11: Other Models
    1. Learning objectives 00:00:28
    2. 11.1 Select variables and improve predictions with the elastic net 00:14:15
    3. 11.2 Decrease uncertainty with weakly informative priors 00:08:53
    4. 11.3 Fit nonlinear least squares 00:05:16
    5. 11.4 Splines 00:06:49
    6. 11.5 GAMs 00:05:24
    7. 11.6 Fit decision trees to make a random forest 00:06:34
13. Lesson 12: Time Series
    1. Learning objectives 00:00:21
    2. 12.1 Understand ACF and PACF 00:07:16
    3. 12.2 Fit and assess ARIMA models 00:05:14
    4. 12.3 Use VAR for multivariate time series 00:08:06
    5. 12.4 Use GARCH for better volatility modeling 00:09:24
14. Lesson 13: Clustering
    1. Learning objectives 00:00:20
    2. 13.1: Partition data with K-means 00:12:27
    3. 13.2: Robustly cluster, even with categorical data, with PAM 00:02:13
    4. 13.3: Perform hierarchical clustering 00:05:38
15. Lesson 14: Reports and Slideshows with knitr
    1. Learning objectives 00:00:30
    2. 14.1: Understand the basics of LaTeX 00:07:16
    3. 14.2: Weave R code into LaTeX using knitr 00:05:33
    4. 14.3: Understand the basics of Markdown 00:02:45
    5. 14.4: Weave R code into Markdown using knitr 00:02:53
    6. 14.5: Use pandoc to convert from Markdown to HTML5 slideshow 00:07:09
16. Lesson 15: Package Building
    1. Learning objectives 00:00:22
    2. 15.1: Understand the folder structure and files in a package 00:05:25
    3. 15.2: Write and document functions 00:07:32
    4. 15.3: Check and build a package 00:02:10
    5. 15.4: Submit a package to CRAN 00:00:46
17. Summary
    1. Summary of R Programming LiveLessons 00:01:22