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