# 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(readr)
library(dplyr)
#library(ggplot2)
#library(scales)
#library(treemap)

train <- read_csv("../input/train.csv")
test <- read_csv("../input/test.csv")

length(unique(train$Agencia_ID))
length(unique(test$Agencia_ID))



train_by_Agencia<-train %>% group_by(Agencia_ID) %>%summarise(count=n())
train_by_Agencia
write.csv(train_by_Agencia, file = "train_by_Agencia.csv", row.names = FALSE)