1 — Load Required Libraries

This chunk loads the tidyverse package, which helps us display data visualizations

#1
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

2 — Import the Titanic Data

This chunk reads the Titanic Excel file and displays the first few rows.

#2
titanicdata <- readxl::read_excel("TitanicData-1.xlsx")
head(titanicdata)
## # A tibble: 6 × 6
##   pclass survived name                                         sex     age  fare
##   <chr>  <chr>    <chr>                                        <chr> <dbl> <dbl>
## 1 First  Survived Allen, Miss. Elisabeth Walton                fema… 29    211. 
## 2 First  Survived Allison, Master. Hudson Trevor               male   0.92 152. 
## 3 First  Perished Allison, Miss. Helen Loraine                 fema…  2    152. 
## 4 First  Perished Allison, Mr. Hudson Joshua Creighton         male  30    152. 
## 5 First  Perished Allison, Mrs. Hudson J C (Bessie Waldo Dani… fema… 25    152. 
## 6 First  Survived Anderson, Mr. Harry                          male  48     26.6

3 — Summarize Survival by Class

This chunk groups the Titanic data by passenger class and survival status by a percentage

#3
pclass_survival <- titanicdata %>%
  group_by(pclass, survived) %>%
  summarize(n = n()) %>%
  mutate(percent = n / sum(n))
## `summarise()` has grouped output by 'pclass'. You can override using the
## `.groups` argument.
pclass_survival
## # A tibble: 6 × 4
## # Groups:   pclass [3]
##   pclass survived     n percent
##   <chr>  <chr>    <int>   <dbl>
## 1 First  Perished   123   0.381
## 2 First  Survived   200   0.619
## 3 Second Perished   158   0.570
## 4 Second Survived   119   0.430
## 5 Third  Perished   528   0.745
## 6 Third  Survived   181   0.255

4 — Summarize Survival by Class and Gender

This chunk groups the Titanic data by passenger class, gender, and survival status, counts the number in each group, and calculates the percentage for each group.

#4
pclass_sex_survival <- titanicdata %>%
  group_by(pclass, sex, survived) %>%
  summarize(n = n()) %>%
  mutate(percent = n / sum(n))
## `summarise()` has grouped output by 'pclass', 'sex'. You can override using the
## `.groups` argument.

5 — Create Survival Graph

This chunk filters the data to only show passengers who survived as a bar chart

#5
pclass_sex_survival_graph <- pclass_sex_survival %>%
  filter(survived == "Survived") %>%
  ggplot(mapping = aes(x = pclass, y= percent, fill = pclass)) +
    geom_col() +
  facet_grid(~sex)

6 — Customize Graph Appearance

This chunk adds a graph that displays titanic survivial rates in a percentage

#6
pclass_sex_survival_graph +
  labs(title ="Titanic Survival Rates",
       subtitle = "Percent by Gender and Cabin Class",
       caption = "Source: Encyclopedia Titanica") +
  scale_y_continuous(labels = scales::percent) + 
  theme_grey() +
  theme(
    axis.title=element_blank(),
    legend.position = "none"
  )