nassimhaddad

# source: http://bigdata-analyst.com/best-way-to-add-a-footnote-to-a-plot-created-with-ggplot2.html

library(gridExtra)
g <- arrangeGrob(p, sub = textGrob("Footnote", x = 0, hjust = -0.1, vjust=0.1, gp = gpar(fontface = "italic", fontsize = 18)))

nassimhaddad

myfun <- function(){
  out <- list()
  out$x <- LETTERS
  out$y <- "hello world"
  class(out) <- "vtable"
  out
} 
print.vtable <- function(x){
  cat("object of class vtable")
  print(str(x$x))

nassimhaddad

address<- 'ftp://user:pw@sftp2.server.com' 

items <- strsplit(getURL(address, .opts=curlOptions(ftplistonly=TRUE)), "\n")[[1]] 
filename <- items[3]

bin = getBinaryURL(paste0(address, "/",filename))
writeBin(bin, filename)

nassimhaddad

// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console

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  with open(fname,'r') as fin:
    A = fin.readlines()

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as.Date(string, "%Y")

# for a list of options, see table in
# http://blog.mollietaylor.com/2013/08/date-formats-in-r.html

nassimhaddad

input <- c("kitten", "dog")
candidates <- c("sitting", "sittingAround", "doggy")

get_best_match <- function(input, candidates, repeatInput = FALSE){
  distM <- adist(as.character(input), as.character(candidates))
  if (repeatInput){
    out <- data.frame(input,
                      bestMatch = candidates[apply(distM, 1, which.min)],
                      distance = apply(distM, 1, min))
  } else{

nassimhaddad

%qtconsole

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interactiveScatter <- function(xvar, yvar, data, 
                               tooltip = c(xvar, yvar), title = "",
                               lmline = TRUE){
  require(rCharts)
  
  x <- xvar
  y <- yvar
  
  tooltipString <- paste0("#!function(item){return ",

nassimhaddad

# code copied from:
# http://heuristically.wordpress.com/2009/12/23/compare-performance-machine-learning-classifiers-r/

# load the ROCR package which draws the ROC curves
require(ROCR)
 
# create an ROCR prediction object from rpart() probabilities
x.rp.prob.rocr <- prediction(x.rp.prob[,2], BreastCancer[ind == 2,'Class'])
# prepare an ROCR performance object for ROC curve (tpr=true positive rate, fpr=false positive rate)
x.rp.perf <- performance(x.rp.prob.rocr, "tpr","fpr")