R Programming

R Programming

Released October 2015
Publisher(s): Pearson
ISBN: 0134427211

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Video description

15+ Hours of Video Instruction

R Programming LiveLessons, 2nd Edition, 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, network analysis, web graphics, and techniques for dealing with large data, both in memory and in databases.

Description

This 15-hour video teaches you how to program in R even if you are unfamiliar with statistical techniques. It starts with the basics of using R and progresses into data manipulation and model building. Users learn through hands-on practice with the code and techniques. New material covers chaining commands, faster data manipulation, new ways to read rectangular data into R, testing code, and the hot package Shiny.

Based on a course on R and Big Data taught by the author at Columbia

  • Designed from the ground up to help viewers quickly overcome R’s learning curve
  • Packed with hands-on practice opportunities and realistic, downloadable code examples
  • Presented by an author with unsurpassed experience teaching statistical programming and modeling to novices
  • For every potential R user: programmers, data scientists, DBAs, marketers, quants, scientists, policymakers, and many others

About the Instructor

Jared P. Lander is the Chief Data Scientist of Lander Analytics, the organizer of the New York Open Statistical Programming Meetup (formerly the R Meetup) and an adjunct professor of Statistics at Columbia University. With a masters from Columbia University in statistics and a bachelors from Muhlenberg College in mathematics, he has experience in both academic research and industry. He specializes in data management, multilevel models, machine learning, generalized linear models, data management, visualization, and statistical computing. He is the author of R for Everyone, a book about R Programming geared toward data scientists and non-statisticians alike. Very active in the data community, Jared is a frequent speaker at conferences, universities, and meetups around the world. He is a member of the Strata New York selection committee.

Skill Level

  • Beginner
  • Intermediate
  • Advanced
What You Will Learn
  • Installing R
  • Basic math
  • Working with variables and different data types
  • Matrix algebra
  • data.frames
  • Reading data
  • Data aggregation and manipulation
  • plyr
  • dplyr
  • Making statistical graphs
  • Manipulate text
  • Automatically generate reports and slideshows
  • Display data with popular JavaScript libraries
  • Build Shiny dashboards
  • Build R packages
  • Incorporate C++ for faster code
  • Basic statistics
  • Linear models
  • Generalized linear models
  • Model validation
  • Decision trees
  • Random forests
  • Bootstrap
  • Time series analysis
  • Clustering
  • Network analysis
  • Automatic parameter tuning
  • Bayesian regression using Stan
Who Should Take This Course

Part 1 of the lessons is geared toward people who are new to either R or programming in general.
Part 2 is for R programmers who already have an intermediate level of knowledge such as that gained from Reading R for Everyone or from viewing Part 1.

Course Requirements
  • Basic Programming Skills
Table of Contents

Part 1: R as a Tool

Lesson 1. Getting Started with R
R can only be used after installation, which fortunately is just as simple as installing any other program. In this lesson, you learn about where to download R, how to decide on the best version, how to install it, and you get familiar with its environment, using RStudio as a front end. We also take a look at the package system.

Lesson 2. The Basic Building Blocks in R
R is a flexible and robust programming language, and using it requires understanding how it handles data. We learn about performing basic math in R, storing various types of data in variables such as numeric, integer, character, and time-based and calling functions on the data.

Lesson 3. Advanced Data Structures in R
Like many other languages, R offers more complex storage mechanisms such as vectors, arrays, matrices, and lists. We take a look at those and the data.frame, a special storage type that strongly resembles a spreadsheet and is part of what makes working with data in R such a pleasure.

Lesson 4. Reading Data into R
Data is abundant in the world, so analyzing it is just a matter of getting the data into R. There are many ways of doing so, the most common being reading from a CSV file or database. We cover these techniques, and also importing from other statistical tools, scraping websites, and reading Excel files.

Lesson 5. Making Statistical Graphs
Visualizing data is a crucial part of data science both in the discovery phase and when reporting results. R has long been known for its capability to produce compelling plots, and Hadley Wickham’s ggplot2 package makes it even easier to produce better looking graphics. We cover histograms scatterplots, boxplots, line charts, and more, in both base graphics and ggplot2 and then explore newer packages ggvis and rCharts.

Lesson 6. Basics of Programming
R has all the standard components of a programming language such as writing functions, if statements and loops, all with their own caveats and quirks. We start with the requisite “Hello, World!” function and learn about arguments to functions, the regular if statement and the vectorized version, and how to build loops and why they should be avoided.

Lesson 7. Data Munging
Data scientists often bemoan that 80% of their work is manipulating data. As such, R has many tools for this, which are, contrary to what Python users may say, easy to use. We see how R excels at group operations using apply, lapply, and the plyr package. We also take a look at its facilities for joining, combining, and rearranging data. Then we speed that up with tidyr, data.table, and dplyr.

Lesson 8. In-Depth with dplyr
dplyr has become such an indispensible tool, nearly superseding plyr, that it is worth devoting extra attention to. So we examine its select, filter, mutate, group_by and summarize functions, among others.

Lesson 9. Manipulating Strings
Text data is becoming more pervasive in the world, and fortunately, R provides ways for both combining text and ripping it apart, which we walk through. We also examine R’s extensive regular expression capabilities.

Lesson 10. Reports and Slideshows with knitr
Successfully delivering the results of an analysis can be just as important as the analysis itself, so it is important to communicate them in an effective way. In this lesson, we learn how to use knitr and rmarkdown to write both static and interactive results in the form of pdf documents, websites, HTML5 slideshows, and even Word documents.

Lesson 11. Include HTML Widgets in HTML Documents
Recent years have seen the advance of JavaScript-powered displays of information, and the htmlwidgets package enables R to take advantage of arbitrary JavaScript libraries. In particular, we look at datatable for a tabular display of data, bokeh for rich web plots, and leaflet for powerful mapping.

Lesson 12. Shiny
Built by Rstudio, Shiny is a tool for building interactive data displays and dashboards all within R. This allows the R programmer to convey results in a compelling, user-rich experience in a language he or she is familiar with.

Lesson 13. Package Building
Building packages is a great way to contribute back to the R community, and doing so has never been easier thanks to Hadley Wickham's devtools package. This lesson covers all the requirements for a package and how to go about authoring, testing, and distributing them.

Lesson 14. Rcpp for Faster Code
Sometimes pure R code is not fast enough, and extra speed is required. Rcpp enables R programmers to seamlessly integrate C++ code into their R code. We go over the basics of getting the two languages working together, write some speedy functions in C++, and even integrate C++ into R packages.

Part 2: R for Statistics, Modeling, and Machine Learning

Lesson 15. Basic Statistics
Naturally, R has all the basics when it comes to statistics such as means, variance, correlation, t-tests, and ANOVAs. We look at all the different ways those can be computed.

Lesson 16. Linear Models
The workhorse of statistics is regression and its extensions. This consists of linear models, generalized linear models—including logistic and Poisson regression&mdashand survival models. We look at how to fit these models in R and how to evaluate them using measures such as mean squared error, deviance, and AIC.

Lesson 17. Other Models
Beyond regression there are many other types of models that can be fit to data. Models covered include regularization with the elastic net, Bayesian shrinkage, nonlinear models such as nonlinear least squares, splines and generalized additive models, decision tress, and random forests.

Lesson 18. Time Series
Special care must be taken with data where there is time-based correlation, otherwise known as autocorrelation. We look at some common methods for dealing with time series such as ARIMA, VAR, and GARCH.

Lesson 19. Clustering
A focal point of modern machine learning is clustering, the partitioning of data into groups. We explore three popular methods: K-means, K-medoids, and hierarchical clustering.

Lesson 20. More Machine Learning
Two areas seeing increasing interest are recommendation engines and text mining, which we illustrate with RecommenderLab, RTextTools, and the irlba package for fast matrix factorization.

Lesson 21. Network Analysis
The world is rich with network data that are nicely studied with graphical models. We show you how to analyze and visualize networks using the igraph package.

Lesson 22. Automatic Parameter Tuning with Caret
Machine learning models often have parameters that need tuning, which can significantly affect the quality of the model. The Caret package, by Max Kuhn, makes finding optimal parameter values easy to find.

Lesson 23. Fit a Bayesian Model with RStan
Bayesian data analysis uses simulations to fit both simple and complex models. Andrew Gelman’s new language, Stan, makes this faster and easier than ever before. We explore this by fitting a simple linear regression and varying-intercept multilevel model.

About LiveLessons Video Training

The LiveLessons Video Training series publishes hundreds of hands-on, expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. This professional and personal technology video series features world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, IBM Press, Pearson IT Certification, Prentice Hall, Sams, and Que. Topics include: IT Certification, Programming, Web Development, Mobile Development, Home and Office Technologies, Business and Management, and more. View all LiveLessons on InformIT at: http://www.informit.com/livelessons.

Table of contents

  1. Introduction
    1. Part 1: R as a Tool—Introduction
  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. Lesson 4: Reading Data into R
    1. Learning objectives
    2. 4.1 Read a CSV into R
    3. 4.2 Read an Excel Spreadsheet 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
    9. 4.8 Read XML data
  6. Lesson 5: Making Statistical Graphs
    1. Learning objectives
    2. 5.1 Find the diamonds in the 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
    14. 5.13 Use Web graphics
  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 Apply a function over multiple lists with mapply
    5. 7.4 Perform group summaries with the aggregate function
    6. 7.5 Do group operations with the plyr Package
    7. 7.6 Combine datasets
    8. 7.7 Join datasets
    9. 7.8 Switch storage paradigms
    10. 7.9 Use tidyr
    11. 7.10 Get faster group operations
  9. Lesson 8: In-Depth with dplyr
    1. Learning objectives
    2. 8.1 Use tbl
    3. 8.2 Use select to choose columns
    4. 8.3 Use filter to choose rows
    5. 8.4 Use slice to choose rows
    6. 8.5 Use mutate to change or create columns
    7. 8.6 Use summarize for quick computation on tbl
    8. 8.7 Use group_by to split the data
    9. 8.8 Apply arbitrary functions with do
  10. Lesson 9: Manipulating Strings
    1. Learning objectives
    2. 9.1 Combine strings together
    3. 9.2 Extract text
  11. Lesson 10: Reports and Slideshows with knitr
    1. Learning objectives
    2. 10.1 Understand the basics of LaTeX
    3. 10.2 Weave R code into LaTeX using knitr
    4. 10.3 Understand the basics of Markdown
    5. 10.4 Understand the basics of RMarkdown
    6. 10.5 Weave R code into Markdown using knitr
    7. 10.6 Convert Markdown files to Word
    8. 10.7 Convert Markdown to PDF
    9. 10.8 Create slideshows with RMarkdown
    10. 10.9 Write equations with RMarkdown
  12. Lesson 11: Include HTML Widgets in HTML Documents
    1. Learning objectives
    2. 11.1 Work with datatables of tabular data
    3. 11.2 Use rbokeh
    4. 11.3 Use Leaflet for mapping
  13. Lesson 12: Shiny
    1. Learning objectives
    2. 12.1 Use shiny objects in a markdown document
    3. 12.2 Work with ui.r and server.r files
  14. Lesson 13: Package Building
    1. Learning objectives
    2. 13.1 Understand the folder structure and files in a package
    3. 13.2 Write and document functions
    4. 13.3 Check and build a package
    5. 13.4 Test R code
    6. 13.5 Submit a package to CRAN
  15. Lesson 14: Rcpp for Faster Code
    1. Learning objectives
    2. 14.1 Understand the basics of C++ with R
    3. 14.2 Write a C++ function for R
    4. 14.3 Use Rcpp syntactic sugar
    5. 14.4 Sum in C++
    6. 14.5 Write a package in R
    7. 14.6 Write a package with C++ code
  16. Summary
    1. Part 1: R as a Tool—Summary
  17. Introduction
    1. Part 2: R for Statistics, Modeling and Machine Learning—Introduction
  18. Lesson 15: Basic Statistics
    1. Learning objectives
    2. 15.1 Draw numbers from probability distributions
    3. 15.2 Calculate averages, standard deviations and correlations
    4. 15.3 Compare samples with t-tests and analysis of variance
  19. Lesson 16: Linear Models
    1. Learning objectives
    2. 16.1 Fit simple linear models
    3. 16.2 Explore the data
    4. 16.3 Fit multiple regression models
    5. 16.4 Fit logistic regression
    6. 16.5 Fit Poisson regression
    7. 16.6 Analyze survival data
    8. 16.7 Assess model quality with residuals
    9. 16.8 Compare models
    10. 16.9 Judge accuracy using cross-validation
    11. 16.10 Estimate uncertainty with the bootstrap
    12. 16.11 Choose variables using stepwise selection
  20. Lesson 17: Other Models
    1. Learning objectives
    2. 17.1 Select variables and improve predictions with the elastic net
    3. 17.2 Decrease uncertainty with weakly informative priors
    4. 17.3 Fit nonlinear least squares
    5. 17.4 Use Splines
    6. 17.5 Use GAMs
    7. 17.6 Fit decision trees to make a random forest
  21. Lesson 18: Time Series
    1. Learning objectives
    2. 18.1 Understand ACF and PACF
    3. 18.2 Fit and assess ARIMA models
    4. 18.3 Use VAR for multivariate time series
    5. 18.4 Use GARCH for better volatility modeling
  22. Lesson 19: Clustering
    1. Learning objectives
    2. 19.1 Partition data with k-means
    3. 19.2 Robustly cluster, even with categorical data, with PAM
    4. 19.3 Perform hierarchical clustering
  23. Lesson 20: More Machine Learning
    1. Learning objectives
    2. 20.1 Build a recommendation engine with RecommenderLab
    3. 20.2 Mine text with RTextTools
    4. 20.3 Perform matrix factorization using irlba
  24. Lesson 21: Network Analysis
    1. Learning objectives
    2. 21.1 Get started with igraph
    3. 21.2 Read edgelists
    4. 21.3 Understand common graph metrics
    5. 21.4 Use centrality measures
    6. 21.5 Utilize more graph operations
  25. Lesson 22: Automatic Parameter Tuning with Caret
    1. Learning objectives
    2. 22.1 Establish optimal tree depth for rpart
    3. 22.2 Choose the best number of trees for a random forest
  26. Lesson 23: Fit a Bayesian Model with RStan
    1. Learning objectives
    2. 23.1 Understand the Stan computing paradigm
    3. 23.2 Fit a simple regression model
    4. 23.3 Fit a multilevel model with Stan
  27. Summary
    1. Part 2: R for Statistics, Modeling and Machine Learning—Summary

Product information

  • Title: R Programming
  • Author(s):
  • Release date: October 2015
  • Publisher(s): Pearson
  • ISBN: 0134427211