fototo

## example-r-markdown.rmd
This post examines the features of [R Markdown](http://www.rstudio.org/docs/authoring/using_markdown)
using [knitr](http://yihui.name/knitr/) in Rstudio 0.96.
This combination of tools provides an exciting improvement in usability for
[reproducible analysis](http://stats.stackexchange.com/a/15006/183).
Specifically, this post
(1) discusses getting started with R Markdown and `knitr` in  Rstudio 0.96;
(2) provides a basic example of producing console output and plots using R Markdown;
(3) highlights several code chunk options such as caching and controlling how input and output is displayed;
(4) demonstrates use of standard Markdown notation as well as the extended features of formulas and tables; and
(5) discusses the implications of R Markdown.

## caschools-analysis.rmd
`r opts_chunk$set(cache=TRUE)`

This is a quick set of analyses of the California Test Score dataset.  The post was produced using R Markdown in RStudio 0.96.  The main purpose of this post is to provide a case study of using R Markdown to prepare a quick reproducible report.  It provides examples of using plots, output, in-line R code, and markdown. The post is designed to be read along side the R Markdown source code, which is available as a gist on github.

<!-- more -->

### Preliminaries
* This post builds on my earlier post which provided a guide for [Getting Started with R Markdown, knitr, and RStudio 0.96](jeromyanglim.blogspot.com/2012/05/getting-started-with-r-markdown-knitr.html)
* The dataset analysed comes from the `AER` package which is an accompaniment to the book [Applied Econometrics with R](http://www.amazon.com/Applied-Econometrics-R-Use/dp/0387773169) written by [Christian Kleiber](http://wwz.unibas.ch/personen/profil/person/kleiber/) and [Achim Zeileis](http://eeecon.uibk.ac.at/~zeileis/

## performance summary.r
require(knitr)
knit2pdf("performance summary.rnw")

## GreatCircleFlights.R
library(maps)
library(geosphere)
library(plyr)
library(ggplot2)
library(sp)

airports <- read.csv("http://www.stanford.edu/~cengel/cgi-bin/anthrospace/wp-content/uploads/2012/03/airports.csv", as.is=TRUE, header=TRUE)
flights <- read.csv("http://www.stanford.edu/~cengel/cgi-bin/anthrospace/wp-content/uploads/2012/03/PEK-openflights-export-2012-03-19.csv", as.is=TRUE, header=TRUE)

# aggregate nunber of flights

## currencyplot.r
require(quantmod)
require(fAssets)

#get asian currency data from the FED FRED data series
getSymbols("DEXKOUS",src="FRED") #load Korea
getSymbols("DEXMAUS",src="FRED") #load Malaysia
getSymbols("DEXSIUS",src="FRED") #load Singapore
getSymbols("DEXTAUS",src="FRED") #load Taiwan
getSymbols("DEXCHUS",src="FRED") #load China
getSymbols("DEXJPUS",src="FRED") #load Japan

## french industry animated correlation gif.r
#get very helpful Ken French data
#for this project we will look at Industry Portfolios
#http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/48_Industry_Portfolios_daily.zip

require(latticeExtra)
require(animation)
require(PerformanceAnalytics)
require(quantmod)

#my.url will be the location of the zip file with the data

## horizon plot of 48 industries with dendrogram order.r
require(fAssets)
require(latticeExtra)
require(quantmod)
require(PerformanceAnalytics)

#my.url will be the location of the zip file with the data
my.url="http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/48_Industry_Portfolios_daily.zip"
#this will be the temp file set up for the zip file
my.tempfile<-paste(tempdir(),"\\frenchindustry.zip",sep="")
#my.usefile is the name of the txt file with the data

## why trend is not your friend on french industries.r
#get very helpful Ken French data
#for this project we will look at Industry Portfolios
#http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/48_Industry_Portfolios_daily.zip

require(latticeExtra)
require(PerformanceAnalytics)
require(quantmod)

#my.url will be the location of the zip file with the data
my.url="http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/48_Industry_Portfolios_daily.zip"

## horizon plot of french 48 industry portfolio.r
#get very helpful Ken French data
#for this project we will look at Industry Portfolios
#http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/48_Industry_Portfolios_daily.zip

require(latticeExtra)
require(PerformanceAnalytics)
require(quantmod)

#my.url will be the location of the zip file with the data
my.url="http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/48_Industry_Portfolios_daily.zip"

## chartTimeSeries and chartsPerformanceSummary.r


require(PerformanceAnalytics)

#replicate example provided in chart.TimeSeries with plot.xts

# These are start and end dates, formatted as xts ranges.
 ## http://www.nber.org-cycles.html
 cycles.begin.dates<-c("1857-06",
                 "1860-10",
	This post examines the features of [R Markdown](http://www.rstudio.org/docs/authoring/using_markdown)
	using [knitr](http://yihui.name/knitr/) in Rstudio 0.96.
	This combination of tools provides an exciting improvement in usability for
	[reproducible analysis](http://stats.stackexchange.com/a/15006/183).
	Specifically, this post
	(1) discusses getting started with R Markdown and `knitr` in Rstudio 0.96;
	(2) provides a basic example of producing console output and plots using R Markdown;
	(3) highlights several code chunk options such as caching and controlling how input and output is displayed;
	(4) demonstrates use of standard Markdown notation as well as the extended features of formulas and tables; and
	(5) discusses the implications of R Markdown.
	`r opts_chunk$set(cache=TRUE)`

	This is a quick set of analyses of the California Test Score dataset. The post was produced using R Markdown in RStudio 0.96. The main purpose of this post is to provide a case study of using R Markdown to prepare a quick reproducible report. It provides examples of using plots, output, in-line R code, and markdown. The post is designed to be read along side the R Markdown source code, which is available as a gist on github.

	<!-- more -->

	### Preliminaries
	* This post builds on my earlier post which provided a guide for [Getting Started with R Markdown, knitr, and RStudio 0.96](jeromyanglim.blogspot.com/2012/05/getting-started-with-r-markdown-knitr.html)
	* The dataset analysed comes from the `AER` package which is an accompaniment to the book [Applied Econometrics with R](http://www.amazon.com/Applied-Econometrics-R-Use/dp/0387773169) written by [Christian Kleiber](http://wwz.unibas.ch/personen/profil/person/kleiber/) and [Achim Zeileis](http://eeecon.uibk.ac.at/~zeileis/
	library(maps)
	library(geosphere)
	library(plyr)
	library(ggplot2)
	library(sp)

	airports <- read.csv("http://www.stanford.edu/~cengel/cgi-bin/anthrospace/wp-content/uploads/2012/03/airports.csv", as.is=TRUE, header=TRUE)
	flights <- read.csv("http://www.stanford.edu/~cengel/cgi-bin/anthrospace/wp-content/uploads/2012/03/PEK-openflights-export-2012-03-19.csv", as.is=TRUE, header=TRUE)

	# aggregate nunber of flights
	require(quantmod)
	require(fAssets)

	#get asian currency data from the FED FRED data series
	getSymbols("DEXKOUS",src="FRED") #load Korea
	getSymbols("DEXMAUS",src="FRED") #load Malaysia
	getSymbols("DEXSIUS",src="FRED") #load Singapore
	getSymbols("DEXTAUS",src="FRED") #load Taiwan
	getSymbols("DEXCHUS",src="FRED") #load China
	getSymbols("DEXJPUS",src="FRED") #load Japan
	#get very helpful Ken French data
	#for this project we will look at Industry Portfolios
	#http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/48_Industry_Portfolios_daily.zip

	require(latticeExtra)
	require(animation)
	require(PerformanceAnalytics)
	require(quantmod)

	#my.url will be the location of the zip file with the data


	require(PerformanceAnalytics)

	#replicate example provided in chart.TimeSeries with plot.xts

	# These are start and end dates, formatted as xts ranges.
	## http://www.nber.org-cycles.html
	cycles.begin.dates<-c("1857-06",
	"1860-10",