nassimhaddad

Source: http://pandaproject.net/docs/determining-the-encoding-of-a-csv-file.html

If you have no way of finding out the correct encoding of the file, then try the following encodings, in this order:

• utf-8
• iso-8859-1 (also known as latin-1) (This is the encoding of all census data and much other data produced by government entities.)
• utf-16

If none of these work the likelihood you are going to determine the encoding without additional information from the source is very low. In theory you may be able to guess the encoding based on the language of the author, however this not a recommended practice.

nassimhaddad

yearweek <- format(as.Date("2010-09-01"), format = "%G%V")

nassimhaddad

launch the shiny server in the background from the linux console (Rscript launch_app.R &),
and then when it says: listening on http://127.0.0.1:8787, you can access it via:

http://IP:8787/p/7815/

(where 7815 is the port specified in the .R file)

nassimhaddad

#'
read.markdown.table <- function(string){
  require(stringr)
  temp <- (strsplit(string, "\n")[[1]])
  temp <- temp[sapply(temp,nchar)>0]
  temp <- strsplit(temp, "\\|")
  temp <- lapply(temp, function(x)str_trim(x[-1]))
  title_row <- temp[[1]]
  temp[[1]]<-NULL

nassimhaddad

#' function that distorts
distort <- function(y, from, to){
  before <- seq(from=1, to=from,length.out  = to)
  after <- seq(from=from+1, to=length(y), length.out  = length(y)-to)
  out <- approx(seq(y),y, xout = c(before, after))
  return(out$y)
}

# example
x <- seq(1,120, by = 1)

nassimhaddad

library(ggvis)
mtcars %>% ggvis(~mpg, fill=~as.factor(cyl)) %>% group_by(cyl) %>% layer_densities()

nassimhaddad

# Install https://github.com/andrie/miniCRAN

require(tools)
require(miniCRAN)
repositorylocation <- "/home/Downloads"
pks <- package_dependencies(c("data.table","ffbase"), db = available.packages(), 
                     which = c("Depends","Imports","LinkingTo"), recursive=TRUE)
pks <- unique(c(do.call(c, pks), c("data.table","ffbase")))
makeRepo(pkgs=pks, path=repositorylocation, type = "source", download = TRUE, writePACKAGES = TRUE)
install.packages(pkgs = "data.table", repos = repositorylocation, type = "source",

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")

nassimhaddad

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

nassimhaddad

%qtconsole