1_train.R

library(dplyr)
library(gbm)
library(aws.s3)

set.seed(4321)

setwd("~/survival-titanic/")

titanic <- aws.s3::s3read_using(read.csv, object = paste0("s3://", package_s3_bucket, "/", package_s3_key, "/train.csv")) %>%
    mutate(.,Pclass=factor(Pclass),
                    Survived=factor(Survived),
                    age=ifelse(is.na(Age),35,Age),
                    age = cut(age,c(0,2,5,9,12,15,21,55,65,100)),
                    Title=sapply(Name,function(x) strsplit(as.character(x),'[.,]')[[1]][2]),
                    Title=gsub(' ','',Title),
                    Title =ifelse(Title %in% c('Capt','Col','Don','Sir','Jonkheer','Major'),'Mr',Title),
                    Title =ifelse(Title %in% c('Lady','Ms','theCountess','Mlle','Mme','Ms','Dona'),'Miss',Title),
                    Title = factor(Title),
                    A=factor(grepl('A',Cabin)),
                    B=factor(grepl('B',Cabin)),
                    C=factor(grepl('C',Cabin)),
                    D=factor(grepl('D',Cabin)),
                    E=factor(grepl('E',Cabin)),
                    F=factor(grepl('F',Cabin)),
                    ncabin=nchar(as.character(Cabin)),
                    PC=factor(grepl('PC',Ticket)),
                    STON=factor(grepl('STON',Ticket)),
                    cn = as.numeric(gsub('[[:space:][:alpha:]]','',Cabin)),
                    oe=factor(ifelse(!is.na(cn),cn%%2,-1)),
                    train = sample(c(TRUE,FALSE),
                            size=891,
                            replace=TRUE, 
                            prob=c(.9,.1)   ) )
s3saveRDS(titanic, paste0("s3://", package_s3_bucket, "/", package_s3_key, "/titanic.rds"))
rm(titanic)