sckott

#######################################################
# by Scott Chamberlain <myrmecocystus@gmail.com>
#   for blog post titled "Awesome use case: check my species names and 
#       gimme some distribution data"
#######################################################
# Load packages
install.packages(c("RCurl","stringr","XML","plyr","RJSONIO"))
require(RCurl);require(stringr);require(XML);require(plyr);require(RJSONIO) 

# Clone taxize and rgbif repositories from GitHub

psychemedia

\documentclass[a4paper]{article}
\SweaveOpts{echo=FALSE, keep.source=TRUE}
\usepackage{a4wide}
\usepackage{color}
\usepackage{hyperref}

\begin{document}
\section{Example of  self-documenting data journalism notes}

This is an example of using Sweave to combine code and output from the R statistical programming environment and the LaTeX document processing environment to generate a self-documenting script in which the actual code used to do stats and generate statistical graphics is displayed along the charts it directly produces.

gimoya

# file: jpeg_transparency.R
# purpose: add transparency to jpeg
# author: kay cichini
# arguments: path_to_jpeg, path_to_outfile, alpha (transparency 0-1)
# path_to_outfile defaults to default home directory
# alpha defaults to 0.5
# packages used: jpeg, png
# input: a jpeg image
# output: a png image

gwd999

require(lattice)
require(latticeExtra)
require(reshape2)
require(directlabels)
require(quantmod)
require(PerformanceAnalytics)

getSymbols("^GSPC",from="1900-01-01")

GSPC.monthly <- GSPC[endpoints(GSPC,"months"),4]

timelyportfolio

#analyze asset allocation experience in Spain

require(lattice)
require(latticeExtra)
require(reshape2)
require(directlabels)
require(quantmod)
require(PerformanceAnalytics)
require(RQuantLib)

timelyportfolio

require(PerformanceAnalytics)
require(quantmod)


getSymbols("^GSPC", from = "1900-01-01")
#get return series from closing price
ret.bh <- ROC(GSPC[,4],n = 1,type = "discrete")
#change first value from NA to 0
ret.bh[1,] <- 0

dsparks

# Let's say you want to display a table of model coefficients
# in order of their significance,
# and you want to plot the distribution of model residuals,
# but you don't know how to access these values.
# Use str().

# Generate some random data
NN <- 1000
theData <- data.frame(Alpha = rnorm(NN),
                      Beta = rnorm(NN))

dsparks

# Starting with categorical data, ending with a table of log odds ratios

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

# Canonical example of categorical data
HEC <- melt(HairEyeColor)

gwd999

svmComputeOneForecast = function(
      id,
      data,
      response,
      startPoints,
      endPoints,
      len,
      history=500,
      trace=FALSE,
      kernel="radial",

abarbour

# Add rowwise summary lines to ggplot2::facet_grid style plots.
# Andy Barbour
# geokook.wordpress.com
# December 2012

## clear workspace
rm(list=ls())

library(multitaper)
library(rbenchmark)