jbryer

source('https://raw.github.com/gist/1606595/269d61dfcc7930f5275a212e11f3c43771ab2591/GoogleReader.R')

rbloggers = getRSSFeed(feedURL="http://r-bloggers.com/feed",
			   email="GOOGLE READER EMAIL",
			   passwd="GOOGLE READER PASSWORD",
			   posts=5000)
entries = rbloggers[which(names(rbloggers) == "entry")]
length(entries)
saveXML(rbloggers, file='rbloggers.xml')

simecek

# go to 'https://developers.facebook.com/tools/explorer' to get your access token
access_token <- "******************* INPUT YOUR ACCESS TOKEN ******************************"

require(RCurl)
require(rjson)

# Facebook json function copied from original (Romain Francois) post
facebook <-  function( path = "me", access_token, options){
    if( !missing(options) ){
        options <- sprintf( "?%s", paste( names(options), "=", unlist(options), collapse = "&", sep = "" ) )

timelyportfolio

#further explore Japanese trade data by region
#website is http://www.customs.go.jp/toukei/suii/html/time_e.htm
#format is http://www.customs.go.jp/toukei/suii/html/data/d42ma001.csv
#and the filename increments from 001 to 008 for the geographic areas

require(quantmod)

numgeog <- 8  #there are really 9 but the last is "special area"

urls <- paste("http://www.customs.go.jp/toukei/suii/html/data/d42ma00",

jeromyanglim

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.

jeromyanglim

`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. 

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### 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/

fototo

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

iolloyd

* Script to record and tag spotify tracks, by Lloyd Moore *)
(* Make sure you are already recording in Audio Hijack Pro with a session called 'spotifySession' *)

tell application "Spotify"
	set currentTrack to (current track)
	set trackName to (name of currentTrack)
	tell application "Audio Hijack Pro"
	    set theSession to my getSession()
	end tell
	repeat

dsparks

# Letting plyr do the work for us.
doInstall <- TRUE  # Change to FALSE if you don't want packages installed.
toInstall <- c("foreign", "plyr", "Hmisc", "ggplot2")
if(doInstall){install.packages(toInstall, repos = "http://cran.r-project.org")}
lapply(toInstall, library, character.only = TRUE)

# See: http://voteview.org/dwnominate.asp
dwNominate <- read.dta("ftp://voteview.com/junkord/HL01111E21_PRES.DTA")

# Make a re-coded party variable

fototo

# Cross-matrix distances and different measurement options, with "proxy"
doInstall <- TRUE  # Change to FALSE if you don't want packages installed.
toInstall <- c("proxy", "MASS", "Zelig")
if(doInstall){install.packages(toInstall, repos = "http://cran.us.r-project.org")}
lapply(toInstall, library, character.only = TRUE)

# Invent two data frames
voterIdealPoints <- data.frame(matrix(rnorm(26*2), ncol = 2))
rownames(voterIdealPoints) <- letters

fototo

# Simple ggplot2 heatmap
# with colorBrewer "spectral" palette

doInstall <- TRUE  # Change to FALSE if you don't want packages installed.
toInstall <- c("ggplot2", "reshape2", "RColorBrewer")
if(doInstall){install.packages(toInstall, repos = "http://cran.us.r-project.org")}
lapply(toInstall, library, character.only = TRUE)

# Generate a random matrix
# This can be any type of numeric matrix,