R Programming LiveLessons
Udemy
Course Summary
Fundamentals to Advanced

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Course Description
R Programming: Fundamentals to Advanced is a tour through the most important parts of the statistical programming language R, from the very basics to complex modeling. It covers reading data, programming basics, visualization. data munging, regression, classification, clustering, modern machine learning and more. 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 material in R for Everyone and is a condensed version of the course Mr. Lander teaches at Columbia. Starty by simply installing R and setting up a productive work environment. You will then learn the basics of data and programming and use 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. Keywords: R; R programming; statistics; statistical computation; statistical modeling; predictive analytics; SPSS; SAS; Sweave; UseR; scientific computing; big data

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Course Syllabus
 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 ttests 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 crossvalidation
 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 Kmeans
 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