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R Programming LiveLessons (Video Training): Fundamentals to Advanced

Video Description

R Programming LiveLessons: Fundamentals to Advanced is a tour through the most important parts of R, the statistical programming language, from the very basics to complex modeling. It covers reading data, programming basics, visualization, data munging, regression, classification, clustering, modern machine learning and more.

About the Author:

Data scientist, Columbia University adjunct Professor, author and organizer of the New York Open Statistical Programming meetup Jared P. Lander presents the 20 percent of R functionality to accomplish 80 percent of most statistics needs. This video is based on the material in R for Everyone and is a condensed version of the course Mr. Lander teaches at Columbia. You start with simply installing R and setting up a productive work environment. You then learn the basics of data and programming using these skills to munge and prepare data for analysis. You then learn visualization, modeling and predicting and close with generating reports and websites and building R packages.

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