MP4 | Video: AVC 960x540 | Audio: AAC 48KHz 2ch | Duration: 15H 11M |
Genre: eLearning | Language: English
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.
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