psychemedia

Download and install:

1. R
2. RStudio

Download the dummy csv file from this gist ( https://gist.github.com/psychemedia/9690079 )

In RStudio run:

install.packages("shiny")

ramnathv

example.md: example.Rmd
	./knit

example.ipynb: example.md
	notedown example.md | sed 's/%%r/%%R/' > example.ipynb

jbryer

#' Simplified loading and installing of packages
#'
#' This is a wrapper to \code{\link{require}} and \code{\link{install.packages}}.
#' Specifically, this will first try to load the package(s) and if not found
#' it will install then load the packages. Additionally, if the 
#' \code{update=TRUE} parameter is specified it will check the currently 
#' installed package version with what is available on CRAN (or mirror) and 
#' install the newer version.
#' 
#' @param pkgs a character vector with the names of the packages to load.

tadast

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

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.

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
   }
   

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

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)
}

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~.)