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,

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

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",

mbostock

license: gpl-3.0
height: 600
border: no
redirect: https://beta.observablehq.com/@mbostock/d3-choropleth

chrishanretty

library(plyr)
library(zoo)

startyr <- 1992
endyr <- 2012

pg <- read.csv("http://www.parlgov.org/stable/static/data/stable-utf-8/view_cabinet.csv",as.is=T)

## Exclude GDR
pg <- pg[which(pg$country_name_short!="GDR"),]

dsparks

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

# Empty ggplot2 theme
new_theme_empty <- theme_bw()
new_theme_empty$line <- element_blank()
new_theme_empty$rect <- element_blank()
new_theme_empty$strip.text <- element_blank()

dsparks

doInstall <- TRUE
toInstall <- c("ggplot2", "poLCA", "reshape2")
if(doInstall){install.packages(toInstall, repos = "http://cran.us.r-project.org")}
lapply(toInstall, library, character.only = TRUE)

ANES <- read.csv("http://www.oberlin.edu/faculty/cdesante/assets/downloads/ANES.csv")
ANES <- ANES[ANES$year == 2008, -c(1, 11, 17)]  # Limit to just 2008 respondents,
head(ANES)  # remove some non-helpful variables
# Adjust so that 1 is the minimum value for each variable:
ANES <- data.frame(apply(ANES, 2, function(cc){  cc - min(cc, na.rm = T) + 1  }))

jbryer

#' Parse a codebook file with variable and level information.
#' 
#' Parses a codebook file where lines starting at column zero (far left) represet
#' variable information (e.g. name, description, type) and indented lines 
#' (i.e. lines beginning with white space, either tabs or spaces, etc.) represent factor
#' levels and labels. 
#' 
#' Note that white space at the beginning and end of each line is stripped before
#' processing that line.
#' 

chrishanretty

### Takes as input a Google Docs spreadsheet
### Outputs national and regional level estimates of support
### over time

### Load libraries
library(R2WinBUGS)
library(rjags)
library(RCurl)
library(reshape)
library(foreach)

xmarquez