For this assignment, I plan to use the Titanic dataset from the Seaborn public GitHub repository. The dataset is available as a public CSV file, which will allow the R code to load it directly from the internet.
The dataset contains information about 891 Titanic passengers,
including whether each passenger survived, passenger class, sex, age,
fare, family relationships, and embarkation location. The
survived column will be treated as the target variable.
I selected this dataset because I am interested in understanding how passenger characteristics may relate to survival. I loaded the dataset into R, selected a useful subset of columns, renamed abbreviated columns, and replaced numerical category values with understandable labels.
Some passenger information, particularly age and deck, is missing. I will select columns carefully and make sure that the final column names and values are clear and meaningful.
I loaded the original Titanic CSV file directly from its public GitHub URL so that the analysis can be reproduced without using a file stored on my computer.
titanic_data <- read.csv("https://raw.githubusercontent.com/mwaskom/seaborn-data/master/titanic.csv")
head(titanic_data)## survived pclass sex age sibsp parch fare embarked class who
## 1 0 3 male 22 1 0 7.2500 S Third man
## 2 1 1 female 38 1 0 71.2833 C First woman
## 3 1 3 female 26 0 0 7.9250 S Third woman
## 4 1 1 female 35 1 0 53.1000 S First woman
## 5 0 3 male 35 0 0 8.0500 S Third man
## 6 0 3 male NA 0 0 8.4583 Q Third man
## adult_male deck embark_town alive alone
## 1 True Southampton no False
## 2 False C Cherbourg yes False
## 3 False Southampton yes True
## 4 False C Southampton yes False
## 5 True Southampton no True
## 6 True Queenstown no TrueI selected the columns most relevant to understanding passenger survival. I also renamed abbreviated columns and changed numerical category values into labels that are easier to understand.
titanic_clean <- titanic_data[c(
"survived", "pclass", "sex", "age",
"sibsp", "parch", "fare", "embark_town"
)]
names(titanic_clean) <- c(
"survival_status", "passenger_class", "sex", "age",
"siblings_spouses", "parents_children",
"ticket_fare", "embarkation_city"
)
titanic_clean$survival_status <- ifelse(
titanic_clean$survival_status == 1,
"Survived",
"Did Not Survive"
)
titanic_clean$passenger_class <- factor(
titanic_clean$passenger_class,
levels = c(1, 2, 3),
labels = c("First Class", "Second Class", "Third Class")
)
head(titanic_clean)## survival_status passenger_class sex age siblings_spouses parents_children
## 1 Did Not Survive Third Class male 22 1 0
## 2 Survived First Class female 38 1 0
## 3 Survived Third Class female 26 0 0
## 4 Survived First Class female 35 1 0
## 5 Did Not Survive Third Class male 35 0 0
## 6 Did Not Survive Third Class male NA 0 0
## ticket_fare embarkation_city
## 1 7.2500 Southampton
## 2 71.2833 Cherbourg
## 3 7.9250 Southampton
## 4 53.1000 Southampton
## 5 8.0500 Southampton
## 6 8.4583 QueenstownThe Titanic dataset was successfully loaded from a public online source and transformed into a data frame containing eight clearly named columns. The survival and passenger-class values were also changed into labels that are easier to understand. I could extend this work by comparing survival rates across passenger class, sex, and age. Before drawing conclusions, I would also examine how the missing age values might affect the results.
OpenAI. (2026). ChatGPT (GPT-5) [Large language model]. https://chatgpt.com. Accessed September 5, 2026.