Titanic Passengers
Yohanes Partogi
2022-10-06
1 Intro
1.1 Titanic
The sinking of the Titanic is one of the most infamous shipwrecks in history.
On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone onboard, resulting in the death of 1502 out of 2224 passengers and crew.
This titanic dataset is taken from the kaggle site.
1.2 Brief
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.1 ✔ stringr 1.4.1
## ✔ readr 2.1.2 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(dplyr)2 Get the Data
titanic <- readr::read_csv('train.csv')## Rows: 891 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): Name, Sex, Ticket, Cabin, Embarked
## dbl (7): PassengerId, Survived, Pclass, Age, SibSp, Parch, Fare
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.dim(titanic)## [1] 891 12Our titanic dataset contains 891 rows and 12 coloumns.
head(titanic)## # A tibble: 6 × 12
## PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin
## <dbl> <dbl> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <chr>
## 1 1 0 3 Braund… male 22 1 0 A/5 2… 7.25 <NA>
## 2 2 1 1 Cuming… fema… 38 1 0 PC 17… 71.3 C85
## 3 3 1 3 Heikki… fema… 26 0 0 STON/… 7.92 <NA>
## 4 4 1 1 Futrel… fema… 35 1 0 113803 53.1 C123
## 5 5 0 3 Allen,… male 35 0 0 373450 8.05 <NA>
## 6 6 0 3 Moran,… male NA 0 0 330877 8.46 <NA>
## # … with 1 more variable: Embarked <chr>Column explanation
- PassangerId = Passanger identity number
- Survived = Survival ; 0 = No, 1 = Yes
- Pclass = Ticket Class ; 1 = 1st, 2 = 2nd, 3 = 3rd
- Name = Name of passanger
- Sex = Sex
- Age = Age in year
- SibSp = Siblings / spouses aboard the Titanic
- Parch = Parents / children aboard the Titanic
- Ticket = Ticket number
- Fare = Passenger fare
- Cabin = Cabin number
- Embarked = Port of Embarkation ; C = Cherbourg, Q = Queenstown, S = Southampton
3 Data Cleansing
3.1 Check the Data Type
str(titanic)## spec_tbl_df [891 × 12] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ PassengerId: num [1:891] 1 2 3 4 5 6 7 8 9 10 ...
## $ Survived : num [1:891] 0 1 1 1 0 0 0 0 1 1 ...
## $ Pclass : num [1:891] 3 1 3 1 3 3 1 3 3 2 ...
## $ Name : chr [1:891] "Braund, Mr. Owen Harris" "Cumings, Mrs. John Bradley (Florence Briggs Thayer)" "Heikkinen, Miss. Laina" "Futrelle, Mrs. Jacques Heath (Lily May Peel)" ...
## $ Sex : chr [1:891] "male" "female" "female" "female" ...
## $ Age : num [1:891] 22 38 26 35 35 NA 54 2 27 14 ...
## $ SibSp : num [1:891] 1 1 0 1 0 0 0 3 0 1 ...
## $ Parch : num [1:891] 0 0 0 0 0 0 0 1 2 0 ...
## $ Ticket : chr [1:891] "A/5 21171" "PC 17599" "STON/O2. 3101282" "113803" ...
## $ Fare : num [1:891] 7.25 71.28 7.92 53.1 8.05 ...
## $ Cabin : chr [1:891] NA "C85" NA "C123" ...
## $ Embarked : chr [1:891] "S" "C" "S" "S" ...
## - attr(*, "spec")=
## .. cols(
## .. PassengerId = col_double(),
## .. Survived = col_double(),
## .. Pclass = col_double(),
## .. Name = col_character(),
## .. Sex = col_character(),
## .. Age = col_double(),
## .. SibSp = col_double(),
## .. Parch = col_double(),
## .. Ticket = col_character(),
## .. Fare = col_double(),
## .. Cabin = col_character(),
## .. Embarked = col_character()
## .. )
## - attr(*, "problems")=<externalptr>From the result above, we find some of data type not in the corect type. We need to convert it into corect type.
titanic[,c("Survived", "Pclass", "Sex", "SibSp", "Parch", "Cabin", "Embarked")] <-
lapply(titanic[,c("Survived", "Pclass", "Sex", "SibSp", "Parch", "Cabin", "Embarked")],
as.factor)
str(titanic)## spec_tbl_df [891 × 12] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ PassengerId: num [1:891] 1 2 3 4 5 6 7 8 9 10 ...
## $ Survived : Factor w/ 2 levels "0","1": 1 2 2 2 1 1 1 1 2 2 ...
## $ Pclass : Factor w/ 3 levels "1","2","3": 3 1 3 1 3 3 1 3 3 2 ...
## $ Name : chr [1:891] "Braund, Mr. Owen Harris" "Cumings, Mrs. John Bradley (Florence Briggs Thayer)" "Heikkinen, Miss. Laina" "Futrelle, Mrs. Jacques Heath (Lily May Peel)" ...
## $ Sex : Factor w/ 2 levels "female","male": 2 1 1 1 2 2 2 2 1 1 ...
## $ Age : num [1:891] 22 38 26 35 35 NA 54 2 27 14 ...
## $ SibSp : Factor w/ 7 levels "0","1","2","3",..: 2 2 1 2 1 1 1 4 1 2 ...
## $ Parch : Factor w/ 7 levels "0","1","2","3",..: 1 1 1 1 1 1 1 2 3 1 ...
## $ Ticket : chr [1:891] "A/5 21171" "PC 17599" "STON/O2. 3101282" "113803" ...
## $ Fare : num [1:891] 7.25 71.28 7.92 53.1 8.05 ...
## $ Cabin : Factor w/ 147 levels "A10","A14","A16",..: NA 82 NA 56 NA NA 130 NA NA NA ...
## $ Embarked : Factor w/ 3 levels "C","Q","S": 3 1 3 3 3 2 3 3 3 1 ...
## - attr(*, "spec")=
## .. cols(
## .. PassengerId = col_double(),
## .. Survived = col_double(),
## .. Pclass = col_double(),
## .. Name = col_character(),
## .. Sex = col_character(),
## .. Age = col_double(),
## .. SibSp = col_double(),
## .. Parch = col_double(),
## .. Ticket = col_character(),
## .. Fare = col_double(),
## .. Cabin = col_character(),
## .. Embarked = col_character()
## .. )
## - attr(*, "problems")=<externalptr>Each of column already changed into desired data type.
Now, we check whether there are missing value from the data.
colSums(is.na(titanic))## PassengerId Survived Pclass Name Sex Age
## 0 0 0 0 0 177
## SibSp Parch Ticket Fare Cabin Embarked
## 0 0 0 0 687 2There are missing values in some columns
Age : 177 missing values
Cabin : 687 missing values
Embarker : 2 missing values
3.2 Handle Missing Value
3.2.1 Replace NaN with Mean
For coulmn Age, we replace the missing value with average of Age
titanic$Age[is.na(titanic$Age)] <- mean(titanic$Age, na.rm = TRUE) 3.2.2 Change NaN into String
For column Cabin, we replace the missing value with “Unknown”
titanic$Cabin <- as.character(titanic$Cabin)
titanic$Cabin[is.na(titanic$Cabin)] <- "Unknown"
titanic$Cabin <- as.factor(titanic$Cabin)3.2.3 Remove NaN row
For column Embarked, we remove row that contain missing value, because the missing value is only two.
titanic <- na.omit(titanic)Make sure no more missing values from the data.
colSums(is.na(titanic))## PassengerId Survived Pclass Name Sex Age
## 0 0 0 0 0 0
## SibSp Parch Ticket Fare Cabin Embarked
## 0 0 0 0 0 0Now, Titanic dataset is ready to be processed and analyzed.
4 Data Information
4.1 Passanger
Total Passangers from Each Embarked
titanic$Embarked <- recode(titanic$Embarked,
"C" = "Cherbourg", "Q" = "Queenstown", "S" = "Southampton")
x <- data.frame(xtabs(~ titanic$Embarked))
x## titanic.Embarked Freq
## 1 Cherbourg 168
## 2 Queenstown 77
## 3 Southampton 644Total Passengers in Each Class
table(titanic$Pclass)##
## 1 2 3
## 214 184 491pie(table(titanic$Pclass))
Average Age of Passengers in Each Class by Gender
aggregate(Age~Pclass+Sex,titanic, mean)## Pclass Sex Age
## 1 1 female 33.79665
## 2 2 female 28.74866
## 3 3 female 24.06849
## 4 1 male 39.28772
## 5 2 male 30.65391
## 6 3 male 27.37215Percentage Women and Men
- Percentage of women and men who survived
xtabs(~ Sex, titanic[titanic$Survived == 1,])['female']/nrow(titanic)*100## female
## 25.98425xtabs(~ Sex, titanic[titanic$Survived == 1,])['male']/nrow(titanic)*100## male
## 12.26097- Percentage of women and men who are not survived
xtabs(~ Sex, titanic[titanic$Survived == 0,])['female']/nrow(titanic)*100## female
## 9.111361xtabs(~ Sex, titanic[titanic$Survived == 0,])['male']/nrow(titanic)*100## male
## 52.64342Correlation beetween Age and Survivded
cor(titanic$Age, as.numeric(titanic$Survived))## [1] -0.074672924.2 Ticket
Average Ticket Prices for Each Class
aggregate(Fare~Pclass, titanic, mean)## Pclass Fare
## 1 1 84.19352
## 2 2 20.66218
## 3 3 13.67555Standard Deviation of Tiket Prices
sd(titanic$Fare)## [1] 49.69755 Insight from the Data
From the above data processing, several conclusions can be drawn
- Total number of passengers is 889 people
- More passengers departing from Southampton than passengers departing from Cherbourg and Queenstown
- There are more female survivers than male passengers
- 1st class ticket prices are higher than other classes with a ticket standard deviation of 49.6975
- There is no correlation between age and survived status