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Merge pull request #130 from lgreski/master
Add articles to R Programming page
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.gitignore

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_site
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.DS_Store
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.Rhistory
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.Rproj.user

capstone.md

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---
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title: "Capstone"
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permalink: /capstone/
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layout: page
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---
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## Reference Material
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- [Speech and Language Processing, 3rd Edition](https://web.stanford.edu/~jurafsky/slp3/) Working version of Jurafsky, et. al. book on natural language processing whose content on n-grams is helpful for the capstone.
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## Course Project
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- [n-gram Computations and Computer Capacity](http://bit.ly/2couvxh) Explains the amount of memory required to convert the text files for the course project into n-grams, using the <strong>quanteda</strong> package.
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- [Capstone Strategy](http://bit.ly/2rGcgc6) Describes a general strategy to get through the Capstone: use the simplest approaches possible.
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- [Choosing a Text Analysis Package](http://bit.ly/2qagsPa) Reviews pros and cons of various R packages used for natural language processing, in the context of requirements for the Capstone project.

getclean.md

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- [Codebook template that can be used in the Getting and Cleaning Data project](https://gist.github.com/JorisSchut/dbc1fc0402f28cad9b41)
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- ["Real world" example - reading American Community Survey 2000 PUMS Data:](https://github.com/lgreski/acsexample) Demonstrates how to extract records of a given type from a data file containing multiple record types, and how to use an Excel-based code book to specify arguments for reading a fixed-width file.
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- [18 Months of CTA advice](https://thoughtfulbloke.wordpress.com/2015/08/31/hello-world)
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- [Common Problems: Quiz 1 - Missing Java Runtime](http://bit.ly/2jjtyXM) Explains how to solve the problem of a missing Java Runtime for the question that requires students to process a Microsoft Excel spreadsheet.
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- [Strategy for Reading Files & APIs / Quiz 2](http://bit.ly/2e4L5oF)
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- [Common Problems: Quiz 2 - sqldf() driver fails to connect](http://bit.ly/2kD2KTY)
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- [Tutorial: Downloading Files](http://bit.ly/2iP2suj) Illustrates various ways of downloading files, including binary and text files.
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## Comprehensive Notes
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index.md

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7. [Regression Models](/regmod/)
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8. [Practical Machine Learning](/pml/)
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9. [Developing Data Products](/ddp/)
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10. [Capstone](/capstone/)
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- [Other Resources](/other/)
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- [Curated Pages](/curated/)

rprog.md

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- [Strategy for Coding the Programming Assignments](http://bit.ly/2ddFh9A)
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- [Tutorial for those struggling with Programming Assignment 1](https://github.com/derekfranks/practice_assignment)
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- [Breaking Down pollutantmean](http://bit.ly/2cHyiCl)
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- [Assignment 1: A More Elegant Solution](http://bit.ly/2kwBBlK)
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- [A SAS Version of pollutantmean?](http://bit.ly/2d3DR4e)
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- [Tutorial for those struggling with Programming Assignment 2](https://github.com/DanieleP/PA2-clarifying_instructions)
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- [Tutorial for those struggling with Programming Assignment 3](https://github.com/DanieleP/PA3-tutorial)
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- [Alternative submit script for Programming Assignment 1 that makes submitting more convenient by allowing selection of multiple parts plus prompting if user wants to submit another part before exiting](https://github.com/rchampoux/coursera/blob/master/rprog-scripts-submitscript1.R)
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- [Grading the SHA-1 Hash Code](http://bit.ly/2iUWoB6)
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- [Assignment 2: Demystifying makeVector](http://bit.ly/2bTXXfq)
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- [Assignment 2: makeCacheMatrix as an Object](http://bit.ly/2byUe4e)
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- [Assignment 2: makeCacheMatrix as an Object](http://bit.ly/2byUe4e)
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## R Language
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- [S Objects, R Objects, and Lexical Scoping](http://bit.ly/2dtOSXi)
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- [Common R Mistakes: Overwriting Functions with Data Objects](http://bit.ly/2i3gmoA)
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- [Forms of the Extract Operator](http://bit.ly/2bzLYTL)
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- [Functions to Sort Data Frames](http://bit.ly/2dxItzw)
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- [Creative Use of R: Downloading Course Lectures](http://bit.ly/2bGlI7R) Article illustrating how to use R to automate the download of lectures from *Data Science Specialization* courses, such as *R Programming*. Techniques used in this article are helpful to make research reproducible, as required for courses like *Getting and Cleaning Data* and *Reproducible Research*.
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- [Lexical Scoping and Statistical Computing](http://bit.ly/2cmqAPy) Article by Robert Gentleman and Ross Ihaka at the University of Auckland describing how lexical scoping works, and why it is valuable in statistical computing.
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- [Data Science Job Report 2017: R Passes SAS, But Python Leaves Them Both Behind](http://bit.ly/2oCHulX) Bob Muenchen's take on the job market for various data science langauges.
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## R language cheatsheet

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