How to do k-means clustering with titanic dataset with R?

Installing and loading the libraries:

install.packages(c("ggplot2","caret","rpart","rpart.plot")    #(1)
library(ggplot2)   #(2)
ibrary(caret)   #(3)
library(rpart)  #(4)
library(rpart.plot)  #(5)

Cleaning the missing data

1.Read csv file and store it as dataframe.
2. Replace missing value in Embarked column with mode value.

titanic <- read.csv(titanic, 
                    stringsAsFactors = FALSE)     #(1)
titanic$Embarked[titanic$Embarked == ""] <- "S"    #(2)

Engineering new features:

3.Create a new feature called FamilySize.
4.Make a new feature to track which Age values are missing:

titanic$FamilySize <- 1 + titanic$SibSp + titanic$Parch    #(3) 
titanic$AgeMissing <- ifelse($Age),       #(4)
                             "Y", "N")

Setting up all the factors on the data:

titanic$Survived <- as.factor(titanic$Survived)
titanic$Pclass <- as.factor(titanic$Pclass)
titanic$Sex <- as.factor(titanic$Sex)
titanic$Embarked <- as.factor(titanic$Embarked)
titanic$AgeMissing <- as.factor(titanic$AgeMissing)

Using a very naive (i.e., don’t use this in Production) model for imputing missing ages:

titanic$Age[$Age)] <- median(titanic$Age, 
                                          na.rm = TRUE)

Defining the subset of features that we will use:

features <- c("Pclass", "Sex", "Age",
              "SibSp", "Parch", "Fare", "Embarked",
              "FamilySize", "AgeMissing")

Using the mighty caret package to convert factors todummy variables:

dummy.vars <- dummyVars(~ ., titanic[, features])
titanic.dummy <- predict(dummy.vars, titanic[, features])

Normalizing the titanic.dummy variable for k-means clustering:

titanic.dummy <- scale(titanic.dummy)

Esatablishing variable to store K-means clustering:

clusters.sum.squares <- rep(0.0, 14)

Setting up cluster parameters:

cluster.params <- 2:15

Trying with different parameters:

  1. Set the seed for the reproducibility.
  2. Try with different cluster parameters…
for (i in cluster.params) {
 kmeans.temp <- kmeans(titanic.dummy, centers = i)
 clusters.sum.squares[i - 1] <- sum(kmeans.temp$withinss)

Take a look at our sum of squares.


Plot our scree plot using the mighty ggplot2.

ggplot(NULL, aes(x = cluster.params, y = clusters.sum.squares)) +
  theme_bw() +
  geom_point() +
  geom_line() +
  labs(x = "Number of Clusters",
       y = "Cluster Sum of Squared Distances",
       title = "Titanic Training Data Scree Plot")


Clustering the data using the value from the elbow method:

titanic.kmeans <- kmeans(titanic.dummy, centers = 4)

Adding cluster assignments to our data frame:

titanic$Cluster <- as.factor(titanic.kmeans$cluster)

Visualizing survivability by cluster assignment.:

ggplot(titanic, aes(x = Cluster, fill = Survived)) +
  theme_bw() +
  geom_bar() +
  labs(x = "Cluster Assignment",
       y = "Passenger Count",
       title = "Titanic Training Survivability by Cluster")


Building a single rpart decision tree:

  1. Add cluster fearture to the list of features.
  2. Create single rpart decision tree.
  3. Print out single rpart decision tree.
features <- c(features, "Cluster")        #(9
titanic.rpart <- rpart(Cluster ~ ., data = titanic[, features])    #(10)
prp(titanic.rpart, type = 1)     #(11)


Hi, I dont find the function ise in titanic$AgeMissing ← ise($Age), “Y”, “N”). Thanks

It is supposed to be ifelse($Age), “Y”, “N”)