# 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')
#head(train)
#head(train)
#str(train)
#str(train)

unique(train$Semana)
#unique(train$Producto_ID)

test<-read_csv('../input/test.csv')
#head(test)
#head(test)
#str(test)
#str(test)
#unique(test$Producto_ID)
unique(test$Semana)
newtrain<-train[train$Semana>8,]
dim(newtrain)

length(unique(newtrain$Cliente_ID))
length(unique(newtrain$Producto_ID))
#product<-read_csv('../input/producto_tabla.csv')
#head(product)

#clients<-read_csv('../input/cliente_tabla.csv')
#head(clients)

#length(unique(clients$Cliente_ID))
#length(unique(product$Producto_ID))
#head(train)
#length(unique(train[,c(5)]))
#length(unique(cbind(train$Producto_ID, train$Cliente_ID)))
#dim(unique(cbind(train$Producto_ID, train$Cliente_ID)))