# Load packages
library('ggplot2') # visualization
library('ggthemes') # visualization
library('scales') # visualization
library('dplyr') # data manipulation
library('mice') # imputation
library('randomForest') # classification algorithm
train <- read.csv('../input/train.csv', stringsAsFactors = F)
test  <- read.csv('../input/test.csv', stringsAsFactors = F)
full  <- bind_rows(train, test) # bind training & test data
full$Title <- gsub('(.*, )|(\\..*)', '', full$Name)
rare_title <- c('Dona', 'Lady', 'the Countess','Capt', 'Col', 'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer')
full$Title[full$Title == 'Mlle']        <- 'Miss' 
full$Title[full$Title == 'Ms']          <- 'Miss'
full$Title[full$Title == 'Mme']         <- 'Mrs' 
full$Title[full$Title %in% rare_title]  <- 'Rare Title'
full$Surname <- sapply(full$Name, function(x) strsplit(x, split = '[,.]')[[1]][1])
full$Fsize <- full$SibSp + full$Parch + 1
full$Family <- paste(full$Surname, full$Fsize, sep='_')
full$FsizeD[full$Fsize == 1] <- 'singleton'
full$FsizeD[full$Fsize < 5 & full$Fsize > 1] <- 'small'
full$FsizeD[full$Fsize > 4] <- 'large'
full$Deck<-factor(sapply(full$Cabin, function(x) strsplit(x, NULL)[[1]][1]))
full[c(62, 830), 'Embarked']
embark_fare <- full %>% filter(PassengerId != 62 & PassengerId != 830)
full$Embarked[c(62, 830)] <- 'C'
full$Fare[1044] <- median(full[full$Pclass == '3' & full$Embarked == 'S', ]$Fare, na.rm = TRUE)
factor_vars <- c('PassengerId','Pclass','Sex','Embarked','Title','Surname','Family','FsizeD')
full[factor_vars] <- lapply(full[factor_vars], function(x) as.factor(x))
set.seed(129)
mice_mod <- mice(full[, !names(full) %in% c('PassengerId','Name','Ticket','Cabin','Family','Surname','Survived')], method='rf') 
mice_output <- complete(mice_mod)
par(mfrow=c(1,2))
full$Age <- mice_output$Age
full$Child[full$Age < 18] <- 'Child'
full$Child[full$Age >= 18] <- 'Adult'
full$Mother <- 'Not Mother'
full$Mother[full$Sex == 'female' & full$Parch > 0 & full$Age > 18 & full$Title != 'Miss'] <- 'Mother'
full$Child  <- factor(full$Child)
full$Mother <- factor(full$Mother)
train <- full[1:891,]
test <- full[892:1309,]
set.seed(754)
rf_model <- randomForest(factor(Survived) ~ Pclass + Sex + Age + SibSp + Parch + Fare + Embarked + Title + FsizeD + Child + Mother, data = train)
importance    <- importance(rf_model)
varImportance <- data.frame(Variables = row.names(importance), Importance = round(importance[ ,'MeanDecreaseGini'],2))
rankImportance <- varImportance %>% mutate(Rank = paste0('#',dense_rank(desc(Importance))))
ggplot(rankImportance, aes(x = reorder(Variables, Importance), 
    y = Importance, fill = Importance)) +
  geom_bar(stat='identity') + 
  geom_text(aes(x = Variables, y = 0.5, label = Rank),
    hjust=0, vjust=0.55, size = 4, colour = 'red') +
  labs(x = 'Variables') +
  coord_flip() + 
  theme_few()