# 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
library(data.table)
library(plyr)

# 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<-fread('../input/train.csv', header = T, sep = ',')

str(train)
names(train)
attach(train)

client<-fread('../input/cliente_tabla.csv', header = T, sep = ',')
train<-merge(client, train, by = "Cliente_ID")

product<-fread('../input/producto_tabla.csv', header = T, sep = ',')
train<-merge(product, train, by = "Producto_ID")

town<-fread('../input/town_state.csv', header = T, sep = ',')
train<-merge(town, train, by = "Agencia_ID")