# 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.

train <- read_csv("../input/train.csv")
train[1:10,]
#for (i in 1:dim(train)[2]) print(length(unique(train[,i])))

# Travail sur 1% des échantillins
B  =  sample(1:dim(train)[1],10000000)
par(mfrow=c(1, 2))
hist(train[B,5])