# 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.
library(data.table)
library(readr)

#Read Town State File
town_state <- read_csv("../input/town_state.csv")
names(town_state)<-c("agency_id","town","state")
count<-table(town_state$state)
barplot(count, main="state", ylab="Count of Agencies",las=2,space=1)

#Read train Dataset
train <- read_csv("../input/train.csv")
#head(train)
summary(train)