# 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(corrplot)

# 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)
#expedia_train <- fread('../input/train.csv', header=TRUE, select= c("is_booking","orig_destination_distance","hotel_cluster","srch_destination_id"))
#expedia_test <- fread('../input/test.csv', header=TRUE)
destination <- fread("../input/destinations.csv")
destination=data.frame(destination)

dim(destination)
#summary(expedia_test)
print('---------------------------')
print(summary(destination))
print('---------------------------')
print(apply(destination[,-1],2, function(x) sd(x)))
print('---------------------------')
print(apply(destination[,-1],2, function(x) length(unique(x))))
print('---------------------------')
cor.dest <- cor(destination[,-1])
corrplot(cor.dest, method = "circle")