schaunwheeler

# The MIT License (MIT)
#
# Copyright (c) 2012 Schaun Jacob Wheeler
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:

Ram-N

rm(list=ls())
library(ggplot2)

df <- read.csv("~/RStats/data/ResearchGrants2013.csv", stringsAsFactors=F, strip.white=T)
df <- df[,-6] #get rid of the trailing blank
dim(df)

#quick check
unique(df$Research.Category)
table(df$Research.Category)

mollietaylor

library(OIdata)
library(RColorBrewer)
library(classInt)

# load state data from OIdata package:
data(state)

# set constants:
nclr <- 8 # number of bins
min <- 0 # theoretical minimum

hrbrmstr

library(ggplot)

nosql.df <- read.csv("nosql.csv", header=TRUE)
nosql.df$Database <- factor(nosql.df$Database,
                            levels=c("MongoDB","Cassandra","Redis","HBase","CouchDB",
                                     "Neo4j","Riak","MarkLogic","Couchbase","DynamoDB"))

gg <- ggplot(data=nosql.df, aes(x=Quarter, y=Index))
gg <- gg + geom_point(aes(color=Quarter), size=3)
gg <- gg + facet_grid(Database~.)

ramnathv

This is a short demo of how to use brew and knitr in combination with each other to get the best of the templating facilities in brew and the literate programming functions in knitr. The main idea is to write a function brew_knit

# Preprocess template using brew and then run knit on the output
brew_knit <- function(template, params, ...){
 brew::brew(template, envir = list2env(params))
 input = gsub(".Rnwe", '.Rnw', template)
 knitr::knit(input)
}

dlmace

require(shiny);
require(caret);
require(e1071);
require(randomForest);
require(nnet);
require(glmnet);

#code for plotting nnets, taken from: http://beckmw.wordpress.com/2013/03/04/visualizing-neural-networks-from-the-nnet-package/
# the formula of the output of train is off and doesn't work correctly though.
#Function for plotting nnets, not working right now

ivannp

require(quantmod)

prepare.indicator = function(close, indicator, roc.n, normalize=FALSE, func=mean) {
   rets = ROC(close, type="discrete", n=roc.n)
   
   if(normalize) {
      # Normalize the returns to daily
      rets = ((1 + rets) ^ (1/roc.n)) - 1
   }
   

ramnathv

This gist consists of a makefile and a script that can automate the creation of PDFs from Slidify generated HTML slides. The script assumes that the makefile and the script makepdf.js are in the same directory as the Rmd file. The makepdf.js script uses casperjs, a very useful utility to automate tasks that require browser interaction. You may follow the installation instructions on the casperjs website, to make sure that you have all the dependencies correctly installed before trying this out.

mages

set.seed(12345)
dat <- data.frame(category=rep(c("Food", "Drinks"),20),
                  product=rep(c("Cheese", "Wine", "Bread", "Beer"), 10),
                  year=sort(rep(c(2004:2013),4)),
                  value=sort(rnorm(40)))

dat <- dat[with(dat, order(category, year)),]

library(lattice)
# Change some of the default lattice settings

tadast