floswald

# load data
hdata <- read.csv("helpdesk log.csv",header=TRUE,sep=",")
hdata$date <- as.Date(hdata$Timestamp,format="%m/%d/%Y")	# Timestamp is in mm/dd/YYYY format

daycount <- table(hdata$date)       # count how many hits per day

# daycount is what I call the "short" vector. It misses some days.

##################################
# daily response wrong graph

floswald

# test polynomial interpolation code by interpolating points on a semi-circle 
# using a different number of points each time.

# contents:
# 1) make.circle(degree) is a function that generates 
#    n=degree+1 data points (x,y) on a semicircle. this is the "data" we 
#    want to interpolate.
# 2) pinterp() makes polynomial interpolation on "data" of degree "degree" 
#    using degree+1 parameters 
# 3) run() is a wrapper that runs the interpolation

floswald

# deferred acceptance algorithm after Gale and Shapley
# coded by florian oswald
# f.oswald@ucl.ac.uk

# Deferred Acceptance Algorithm with male offer
# accepts your preferences or chooses randomly

# to test, use jan's example preferences:
# m.prefs = matrix(data=c(1,2,3,4,4,2,3,1,4,3,1,2,1,4,3,2,1,2,4,5),nrow=4)
# w.prefs=matrix(data=c(2,3,1,4,5,3,1,2,4,5,5,4,1,2,3,1,4,5,2,3),nrow=5)

floswald

# deferred acceptance algorithm after Gale and Shapley

# Deferred Acceptance Algorithm with male offer
# accepts your preferences or chooses randomly

daa <- function(nMen,nWomen,m.prefs=NULL,w.prefs=NULL){
	require(animation)	# load animation package

	if (is.null(m.prefs)){	# if no prefs given, make them randomly
		m.prefs <- replicate(n=nMen,sample(seq(from=1,to=nWomen,by=1)))

floswald

####################################
# R code for a CRRA Utility function 
# Florian Oswald
# u(c) is defined over consumption c \in [-\infty,\infty]
# has automatic switch to log if CRRA = 1
# can produce diagnostic plot
# INPUT: x = vector of consumption values
# params   = list with 3 entries: CRRA(numeric), cutoff(numeric), diag.plot(bool)
#		 	 e.g. utility.params <- list('CRRA' = CRRA, 'cutoff' = cutoff, diag.plot=FALSE)
#			 'cutoff' is a number chosen by the user below which u(c) is quadratically approximated

floswald

Creating interactive web graphs with R: Overview and googleVis tutorial
========================================
```{r results='asis', echo=FALSE, message=FALSE, tidy=FALSE}
library(googleVis)
df <- data.frame(Postcode=c("TF3 4JH", "EC1Y 8LX"),
                 Tip=c("Telford", "RSS"))

## Tree map
T <- gvisTreeMap(Regions,  "Region", "Parent", "Val", "Fac",
                    options=list(width=250, height=150,

floswald

# R script accompanying quasi-concave, concave etc note

if(!require(rgl)) install.packages('rgl')
require(rgl)


# setup data
nx <- 50	# points into x direction
ny <- 50	# points into y

floswald

#LyX 2.0 created this file. For more info see http://www.lyx.org/
\lyxformat 413
\begin_document
\begin_header
\textclass article
\use_default_options true
\begin_modules
knitr
\end_modules
\maintain_unincluded_children false

floswald

library(xlsx)
cdat <- read.xlsx(file="~/datasets/ReinhardRogoff/18_data.xls",sheetName="Aut.Ita.UK.US")
cdat <- cdat[,-c(5,6,7)]
library(reshape)
library(ggplot2)
m <- melt(cdat,c("Year","Country"))
p <- ggplot(m,aes(x=Year,y=value)) + geom_line(aes(color=variable),size=1) + facet_wrap(~Country,ncol=1) + scale_y_continuous(name="Public (Public and Private) debt to GDP (GNP) ratios") + theme(legend.position="top")

floswald

library(inline)
library(Rcpp)

cpp <- 'arma::mat A  = Rcpp::as<arma::mat>(X_);
  arma::vec y(A.n_rows);
	arma::rowvec tmp(A.n_cols);
  for (int i=0; i<A.n_rows; i++) {
    tmp = A.row(i);
    y(i) = tmp.max();
  }