R has become the go-to environment to support data science and statistical applications across many fields. Researchers write papers in R, engineers develop runtime applications in R, and new statistical methods are first developed in R. Supporting this powerful environment is a complete programming language and a wealth of numerical analysis tools. R has a built-in matrix language that can solve mathematical problems above and beyond statistical regression. This talk will walk through R's tools for matrix manipulation, interpolation, integration, and root finding.

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Numerical Analysis in R
James P. Howard, II
Statistical Programming DC—December 8, 2015
Washington, DC

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• Data Scientist
• Math Nerd
• Public Policy Wonk
• Also, adjunct assistant
professor at UMUC
About Me

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What is scientific computing?
• Numerical analysis
• Doing math with computers
• Results are “good enough”
• Real numbers, stuck in floating
point
What are we doing tonight?
• Some linear algebra
• Some interpolation
• Some integration
• Little bit of root finding
Scientific Computing

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⌘-<TAB> to RStudio
Nobody wants to see a Powerpoint presentation about math

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Facts
• Full suite of numerical tools in
R
• It is comparable to MATLAB in
capability
• It is slower than MATLAB
Opinions
• Certainly more inviting that
MATLAB and Python
• Foundation for creating new
tools
Summary

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Additional Information

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Computational Methods for Numerical Analysis with R
(no picture yet, but some time in 2016)
(watch https://jameshoward.us for some sort of announcement)
Coming Soon