| require(plyr); require(stringr); require(ggplot2); require(lubridate); require(twitteR) | |
| datout_1 <- searchTwitter("I work for the internet", n = 1500, since='2011-11-11', until='2011-12-12') | |
| datout_2 <- searchTwitter("I work for the internet", n = 1500, since='2011-11-13', until='2011-12-14') | |
| datoutdf <- ldply(c(datout_1, datout_2), function(x) x$toDataFrame(), .progress="text") | |
| actual <- grep("I work for the internet", datoutdf[,1], ignore.case=T) | |
| datoutdf2 <- datoutdf[actual,] | |
| datoutdf2$newtime <- round_date(datoutdf2[,4], "hour") |
| from BeautifulSoup import BeautifulSoup | |
| import mechanize | |
| import time | |
| url = "http://www.r-bloggers.com/" | |
| br = mechanize.Browser() | |
| page = br.open(url) |
| require(quantmod) | |
| #get Japanese Yen daily from Fred http://research.stlouisfed.org/fred2 | |
| getSymbols("DEXJPUS",src="FRED") | |
| #get US 10y Yield from Fred | |
| getSymbols("DGS10", src="FRED") | |
| Yen10y <- na.omit(merge(DEXJPUS,DGS10)) | |
| #define colors |
| ''' | |
| @author Michael J Bommarito II | |
| @date Feb 26, 2011 | |
| @license Simplified BSD, (C) 2011. | |
| This script demonstrates how to use Python to archive historical tweets. | |
| ''' | |
| import codecs | |
| import csv |
| ''' | |
| @author Michael J Bommarito II | |
| @date Feb 26, 2011 | |
| @license Simplified BSD, (C) 2011. | |
| This script demonstrates how to use Python to archive historical tweets. | |
| ''' | |
| import codecs | |
| import csv |
| #@author Michael J Bommarito | |
| #@contact michael.bommarito@gmail.com | |
| #@date Feb 20, 2011 | |
| #@ip Simplified BSD, (C) 2011. | |
| # This is a simple example of an R script that will retrieve | |
| # public tweets from a given hashtag. | |
| library(RJSONIO) | |
| # This function loads stored tag data to determine the current max_id. |
| require(quantmod) | |
| #get data from Japan Ministry of Finance website in csv form | |
| url = "http://www.customs.go.jp/toukei/suii/html/data/d41ma.csv" | |
| japantrade <- read.csv(url,skip=2,stringsAsFactors=FALSE) | |
| #start cleaning data and getting in xts form | |
| japantrade.xts <- japantrade[2:NROW(japantrade),] | |
| #remove trailing 0 for future data | |
| japantrade.xts <- japantrade.xts[which(japantrade.xts[,2]!=0),] |
| # calculate the mixing matrix of in igraph graph object 'mygraph', by some vertex attribute 'attrib' | |
| # can change the default use.density=FALSE to return a matrix with raw number of edges rather than density | |
| mixmat <- function(mygraph, attrib, use.density=TRUE) { | |
| require(igraph) | |
| # get unique list of characteristics of the attribute | |
| attlist <- sort(unique(get.vertex.attribute(mygraph,attrib))) | |
| `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/ |
The documents included are the input for knitr. In addition you need to have the tool pandoc installed. I also use a custom beamer template to add the University of Utah \institute command to the template. It also changes the indentation some.
