# 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")

quit()

# ------------------------ LOADING INPUTS -------------------------------- #

# Tables show which ad on each display_id was clicked or not.
clicks_train <- read.csv("../input/clicks_train.csv")
clicks_test  <- read.csv("../input/clicks_test.csv") # make predictions 'clicked'

# Information about each display_id: uuid, document, time, platform, location 
events <- read.csv("../input/events.csv")

# Datails about documents: 
#       category, entity, topic with confidence levels, 
#       source, publisher (+publish_time), 
docs_cats   <- read.csv("../input/documents_categories.csv")
docs_ents   <- read.csv("../input/documents_entities.csv")
docs_meta   <- read.csv("../input/documents_meta.csv")
docs_topics <- read.csv("../input/documents_topics.csv")

# Table is a the log of users visiting documents: 
#       uuid, document, time, location, source
page_views <- read.csv("../input/page_views.csv")
page_views_sample <- read.csv("../input/page_views_sample.csv")

# Details about ads: document, campaign, advertiser
promoted_content <- read.csv("../input/promoted_content.csv")


sample_submission <- read.csv("../input/sample_submission.csv")


nrow(click_train)
ncol(click_train)

colnames(click_train)



# --------------------- DATA UNDERSTANDING ------------------------------- #



# Any results you write to the current directory are saved as output.