# 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(Matrix)
library(caret)
# 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")
# sample_submission.csv
# test_categorical.csv
# test_date.csv
# test_numeric.csv
# train_categorical.csv
# train_date.csv
# train_numeric.csv
# train_date <- read.csv('../input/train_date.csv', stringsAsFactors = F)
# train_categorical <- read.csv('../input/train_categorical.csv')
# train_numeric <- read.csv('../input/train_numeric.csv', stringsAsFactors = F)

# test  <- read.csv('../input/test.csv', stringsAsFactors = F)
# full  <- bind_rows(train, test) # bind training & test data
# Any results you write to the current directory are saved as output.
dt <- fread("../input/train_numeric.csv",
            drop = "Id",
            nrows = 200000,
            showProgress = F)
str(dt)