chrishanretty

library(gdata)
library(countrycode)

infile <- "IMFmultipliers.xls"
imf_rep <- read.xls(infile, sheet = 2,
	skip = 3,
	header=F)

names(imf_rep) <- c("Country",
	"gdp_4cast_2011","gdp_4cast_2012",

christophergandrud

#################
# Use R to recreate part of QMV the relative non-proportional hazard estimation graph (fig 2) from Licht (2011) 
# Christopher Gandrud (http://christophergandrud.blogspot.com/)
# 4 October 2012
#################


#### Set Up ####

# Load required libraries

cdesante

###### Ordered Categorical DV: #####
#This uses "importance of aid to blacks" as a DV and race of R as the indicator IV.#
     library(MASS)
     white <- c(1,1,0,1,1,1,1,0,1,1,1,0,1,1,1,1,1,0,1,1,1,1,0,0,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,
1,1,0,1,1,1,1,1,1,1,1,1,0,0,0,1,0,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0,1,1,0,1,1,1,1,1,0,1,1,1,0,1,1,
1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,0,1,0,1,0,1,1,1,1,0,1,0,0,1,0,1,1,1,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,
1,1,1,1,1,1,1,1,1,1,0,1,0,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0,1,1,1,0,1,1,1,1,1,0,1,0,1,1,1,0,0,1,1,1,1,1,1,1,0,1,0,0,1,0,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0,1,1,1,
0,1,1,0,1,1,1,1,1,1,0,0,1,1,0,1,0,1,1,1,1,1,0,1,1,0,1,1,1,1,1,0,1,1,1,0,1,0,1,0,1,1,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,0,0,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,1,
1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,

dsparks

# A simple approach to visually-weighted regression plots

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

# Generate some data:
nn <- 1000
myData <- data.frame(X = rnorm(nn),

dsparks

# Randomly allocating observations into groups, for, e.g. cross-validation
kk <- 10  # Number of partitions, as in "kk-fold cross-validation."

# Here is a data.frame full of good data:
nn <- 1003
myData <- data.frame(matrix(rnorm(nn * 3), ncol = 3))
colnames(myData) <- LETTERS[1:3]

# This does not work:
whichK <- sample(LETTERS[1:kk], nrow(myData), replace = T)

VikParuchuri

term<-c("egypt","jordan","israel","saudi")
term_vec<-foreach(i=1:length(all_score_frames),.combine=rbind) %do%
{
  score_row<-rep(0,length(term))
  for(z in 1:length(score_row))
  {
    sel_score<-all_score_frames[[i]][all_score_frames[[i]]$term==term[z],"score"]
    sel_score[is.na(sel_score)]<-0
    if(length(sel_score)==0)
      sel_score<-0

theconektd

body {
  font-family: Helvetica, arial, sans-serif;
  font-size: 14px;
  line-height: 1.6;
  padding-top: 10px;
  padding-bottom: 10px;
  background-color: white;
  padding: 30px; }

body > *:first-child {

alexstorer

function h = plotDecision(dprime,sdnoise,criterion)
% h = plotDecision(dprime, sdnoise, criterion)
%
% plotDecision makes a plot of two Gaussian functions, inspired by signal
% detection theory.  The inputs are given in terms of standard deviates of
% the signal distribution:
%
% dprime    - d', the distance between the means of the Gaussians
% sdnoise   - $\sigma$, the ratio of the standard deviation of the noise to
%             the standard deviation of the signal.

thomasjensen

##read in the libraries and set the working directory
library(tm)
library(corrplot)
setwd("/path/to/")

##read in the data and subset it to the relevant categories
data <- read.csv("indvandringPolitikken.csv", fileEncoding = "latin1")
data <- data[data$kategori == "Politik" | data$kategori == "Debat" | data$kategori == "Kronikken" | data$kategori == "Leder", ]

##create the corpus and clean it

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')