Merge pull request #130 from lgreski/master

Add articles to R Programming page

.gitignore

@@ -1,3 +1,4 @@
 _site
 .DS_Store
 .Rhistory
+.Rproj.user

capstone.md

@@ -0,0 +1,14 @@
+---
+title: "Capstone"
+permalink: /capstone/
+layout: page
+---
+## Reference Material
+
+- [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.
+
+## Course Project
+
+- [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.
+- [Capstone Strategy](http://bit.ly/2rGcgc6) Describes a general strategy to get through the Capstone: use the simplest approaches possible.
+- [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)
 - ["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.
 - [18 Months of CTA advice](https://thoughtfulbloke.wordpress.com/2015/08/31/hello-world)
+- [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. 
+- [Strategy for Reading Files & APIs / Quiz 2](http://bit.ly/2e4L5oF)
+- [Common Problems: Quiz 2 - sqldf() driver fails to connect](http://bit.ly/2kD2KTY)
+- [Tutorial: Downloading Files](http://bit.ly/2iP2suj) Illustrates various ways of downloading files, including binary and text files. 
 
 ## Comprehensive Notes
 

index.md

@@ -17,6 +17,7 @@ interested in contributing [click here](https://github.com/DataScienceSpecializa
 7. [Regression Models](/regmod/)
 8. [Practical Machine Learning](/pml/)
 9. [Developing Data Products](/ddp/)
+10. [Capstone](/capstone/)
 
 - [Other Resources](/other/)
 - [Curated Pages](/curated/)

rprog.md

@@ -15,6 +15,7 @@ layout: page
 - [Strategy for Coding the Programming Assignments](http://bit.ly/2ddFh9A)
 - [Tutorial for those struggling with Programming Assignment 1](https://github.com/derekfranks/practice_assignment)  
 - [Breaking Down pollutantmean](http://bit.ly/2cHyiCl)
+- [Assignment 1: A More Elegant Solution](http://bit.ly/2kwBBlK)
 - [A SAS Version of pollutantmean?](http://bit.ly/2d3DR4e)
 - [Tutorial for those struggling with Programming Assignment 2](https://github.com/DanieleP/PA2-clarifying_instructions)
 - [Tutorial for those struggling with Programming Assignment 3](https://github.com/DanieleP/PA3-tutorial)
@@ -23,7 +24,7 @@ layout: page
 - [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)
 - [Grading the SHA-1 Hash Code](http://bit.ly/2iUWoB6)
 - [Assignment 2: Demystifying makeVector](http://bit.ly/2bTXXfq)
--  [Assignment 2: makeCacheMatrix as an Object](http://bit.ly/2byUe4e)
+- [Assignment 2: makeCacheMatrix as an Object](http://bit.ly/2byUe4e)
 
 
 ## R Language
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 - [S Objects, R Objects, and Lexical Scoping](http://bit.ly/2dtOSXi)
 - [Common R Mistakes: Overwriting Functions with Data Objects](http://bit.ly/2i3gmoA)
 - [Forms of the Extract Operator](http://bit.ly/2bzLYTL)
+- [Functions to Sort Data Frames](http://bit.ly/2dxItzw)
 - [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*.
+- [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.
+- [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. 
+
 
 
 ## R language cheatsheet