# 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.

library(data.table)
library(xgboost)

train=fread('../input/train.csv',select = c("Semana",'Cliente_ID', 'Producto_ID', 'Agencia_ID', 'Ruta_SAK', 'Demanda_uni_equil'))
#test=fread('../input/test.csv',select = c("Semana",'id','Cliente_ID', 'Producto_ID', 'Agencia_ID', 'Ruta_SAK'))

trainnew=train[order('Cliente_ID','Producto_ID',"Semana",'Agencia_ID')]
head(trainnew,100)
#hist(train$Cliente_ID)
#histogram(train$Cliente_ID)
write.csv(trainnew,file='trainnew.csv',row.names=F)