# This R environment comes with all of CRAN preinstalled, as well as many other helpful packages
# The environment is defined by the kaggle/rstats docker image: https://github.com/kaggle/docker-rstats
# For example, here's several helpful packages to load in 

library(ggplot2) # Data visualization
library(readr) # CSV file I/O, e.g. the read_csv function

# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory

system("ls ../input")

# Any results you write to the current directory are saved as output.

n <- 1000
con <- file("../input/train.csv", open = "r")
head <- readLines(con, 1)
sampdat <- readLines(con, n)
k <- n
while (length(curline <- readLines(con, 1))) {
  k <- k + 1
  if (runif(1) < n/k)
    sampdat[sample(n, 1)] <- curline
}
close(con)
delaysamp <- read.csv(textConnection(c(head, sampdat)))