Quarto Refresher and Publishing

Titanic

INTRODUCTION

The content above in lines 1 through 17 is called the YAML, and it is useful to set the document title and specify the output when you render the file. Rendering options include html, Word, and .pdf. Here, we will render the document into an html file with folded code that wraps. Note, too that we include a table of contents (toc) and we can apply the global options to suppress warnings and messages as well.

This text in the white sections are areas where you can insert narratives and other useful information. The backslash at the end is a carriage return – which means start a new line, otherwise this text will run together.

CODE CHUNKS AND INLINE CODE REFRESHER

The gray section below is called a code chunk. This is where you will place R script.

All code chunks start and end with three tick marks — use the key just below the ESC key on a Windows keyboard.

The first line needs the {r} to indicate that this code chunk is using R script. (Quarto will run with other code types such as Python and you can include different code chunks in the same document. However, for this course, we will only use R throughout.)

You can run all of the commands just in this code chunk by clicking on the green play arrow in the upper right corner of the chunk. Go ahead and run the code below. Note, you may need to install the titanic package first using the Packages tab in the lower right quadrant of RStudio.

DATA UNDERSTANDING

For this exercise, we will use the dataset that is stored in the titanic package. Note that the data are already split into a training and test datasets. We want to use all records, so the first thing we will do is the concatenate the datasets (bind_rows) into one.

Code

# combine test and train datasets
library(titanic)
library(dplyr)
titanic <- bind_rows(titanic_train, titanic_test) 

View the Data

Let’s understand the structure of the dataset as well as view a few of the first and last observations.

Code

# view data and metadata 
str(titanic)        # look at the structure of the dataset

'data.frame':   1309 obs. of  12 variables:
 $ PassengerId: int  1 2 3 4 5 6 7 8 9 10 ...
 $ Survived   : int  0 1 1 1 0 0 0 0 1 1 ...
 $ Pclass     : int  3 1 3 1 3 3 1 3 3 2 ...
 $ Name       : chr  "Braund, Mr. Owen Harris" "Cumings, Mrs. John Bradley (Florence Briggs Thayer)" "Heikkinen, Miss. Laina" "Futrelle, Mrs. Jacques Heath (Lily May Peel)" ...
 $ Sex        : chr  "male" "female" "female" "female" ...
 $ Age        : num  22 38 26 35 35 NA 54 2 27 14 ...
 $ SibSp      : int  1 1 0 1 0 0 0 3 0 1 ...
 $ Parch      : int  0 0 0 0 0 0 0 1 2 0 ...
 $ Ticket     : chr  "A/5 21171" "PC 17599" "STON/O2. 3101282" "113803" ...
 $ Fare       : num  7.25 71.28 7.92 53.1 8.05 ...
 $ Cabin      : chr  "" "C85" "" "C123" ...
 $ Embarked   : chr  "S" "C" "S" "S" ...

Code

head(titanic,3)   # look at the first 3 rows of the dataset 

  PassengerId Survived Pclass
1           1        0      3
2           2        1      1
3           3        1      3
                                                 Name    Sex Age SibSp Parch
1                             Braund, Mr. Owen Harris   male  22     1     0
2 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female  38     1     0
3                              Heikkinen, Miss. Laina female  26     0     0
            Ticket    Fare Cabin Embarked
1        A/5 21171  7.2500              S
2         PC 17599 71.2833   C85        C
3 STON/O2. 3101282  7.9250              S

Code

tail(titanic,3)

     PassengerId Survived Pclass                         Name  Sex  Age SibSp
1307        1307       NA      3 Saether, Mr. Simon Sivertsen male 38.5     0
1308        1308       NA      3          Ware, Mr. Frederick male   NA     0
1309        1309       NA      3     Peter, Master. Michael J male   NA     1
     Parch             Ticket    Fare Cabin Embarked
1307     0 SOTON/O.Q. 3101262  7.2500              S
1308     0             359309  8.0500              S
1309     1               2668 22.3583              C

Code

table(titanic$Survived)

  0   1 
549 342 

Code

# look at the last 3 rows of the dataset

Descriptive Statistics

Note that important variables such as \(Survived\) and \(Age\) have missing values.

Code

summary(titanic)  # look at the descriptive statistics for the numeric variables

  PassengerId      Survived          Pclass          Name          
 Min.   :   1   Min.   :0.0000   Min.   :1.000   Length:1309       
 1st Qu.: 328   1st Qu.:0.0000   1st Qu.:2.000   Class :character  
 Median : 655   Median :0.0000   Median :3.000   Mode  :character  
 Mean   : 655   Mean   :0.3838   Mean   :2.295                     
 3rd Qu.: 982   3rd Qu.:1.0000   3rd Qu.:3.000                     
 Max.   :1309   Max.   :1.0000   Max.   :3.000                     
                NA's   :418                                        
     Sex                 Age            SibSp            Parch      
 Length:1309        Min.   : 0.17   Min.   :0.0000   Min.   :0.000  
 Class :character   1st Qu.:21.00   1st Qu.:0.0000   1st Qu.:0.000  
 Mode  :character   Median :28.00   Median :0.0000   Median :0.000  
                    Mean   :29.88   Mean   :0.4989   Mean   :0.385  
                    3rd Qu.:39.00   3rd Qu.:1.0000   3rd Qu.:0.000  
                    Max.   :80.00   Max.   :8.0000   Max.   :9.000  
                    NA's   :263                                     
    Ticket               Fare            Cabin             Embarked        
 Length:1309        Min.   :  0.000   Length:1309        Length:1309       
 Class :character   1st Qu.:  7.896   Class :character   Class :character  
 Mode  :character   Median : 14.454   Mode  :character   Mode  :character  
                    Mean   : 33.295                                        
                    3rd Qu.: 31.275                                        
                    Max.   :512.329                                        
                    NA's   :1                                              

Passenger Lookup

John Jacob Astor, one of the wealthiest people in the world, was aboard the Titanic. Let’s see if he survived.

Code

astor <- titanic[grepl("Astor", titanic$Name), ]
print(astor %>% select(Name, Survived))

                                                  Name Survived
701  Astor, Mrs. John Jacob (Madeleine Talmadge Force)        1
1094                            Astor, Col. John Jacob       NA

Col. Astor was not known to have survived nor was he confirmed as a non-survivor. The NA means he was not recovered. His wife, however, did survive.

Average Age

Code

#create a new variable that is the average age.  the na.rm=TRUE tells R to skip missing values.
xage <- round(mean(titanic$Age, na.rm = TRUE), digits = 2)

Rather than hardcode the value for the age, we will use inline code to print the value in the narrative. This is useful if the variable you create is dynamic, such as when you frequently update a dataset with new data. To do this, we use inline code, like this:
- The average age of all passengers on board the Titanic was 29.88.
- You will see the answer when you render the code.

---

PRACTICE CODE CHUNKS AND INLINE CODE

Let’s run just a few more bits of analysis. Specifically, let’s see if we can answer the following questions:
1. How many passengers are male and how many female?
2. How many passengers survived and how many died?
3. How many females survived? How many died?
4. What percent of females survived?
5. What percent of survivors were male?
6. What percent of passengers were females who perished?
7. What is the distribution of passengers by fare class and embark location?

1. Number of Passengers by Gender

The following table shows the number of female and male passengers aboard the Titanic.

Code

table(titanic$Sex)

female   male 
   466    843 

However, it may be nicer to show the answer as inline text vs code output:

Code

females <- nrow(titanic[titanic$Sex == "female", ])
males   <- nrow(titanic[titanic$Sex == "male", ])

• There were 466 females and 843 males aboard the Titanic.
  

---

2. Number of Survivors and Non-Survivors

The following table shows the number of known survivors and non-survivors from the Titanic. 

Note that many of the passengers were unaccounted (Unsure).

Code

# convert the variable to a factor
titanic$Survived.f <- as.factor(ifelse(is.na(titanic$Survived), "Unsure",  # if the value is NA, then unsure
                                       ifelse(titanic$Survived == 0, "Did Not Survive", 
                                                 "Survived")))
table(titanic$Survived.f)

Did Not Survive        Survived          Unsure 
            549             342             418 

---

3. Number of Female Survivors and Non-Survivors

The following table shows the number of survivors and non-survivors by gender.

Code

table(titanic$Sex,titanic$Survived.f)

        
         Did Not Survive Survived Unsure
  female              81      233    152
  male               468      109    266

Code

# use this to create the values for inline code
female_survived <- nrow(titanic[titanic$Sex == "female" & titanic$Survived.f == "Survived", ])
female_nonsurvived <- nrow(titanic[titanic$Sex == "female" & titanic$Survived.f == "Did Not Survive" , ])

Records show that among the females on board the Titanic, 233 survived and 81 did not survive.

---

4. Percentage of Female Survivors and Non-Survivors

While the prior table showed the number, this table depicts the percentage of survivors by gender.

Code

# create contingency table
library(summarytools)
ctable(titanic$Sex,titanic$Survived.f, prop="r")

Cross-Tabulation, Row Proportions  
Sex * Survived.f  
Data Frame: titanic  

-------- ------------ ----------------- ------------- ------------- ---------------
           Survived.f   Did Not Survive      Survived        Unsure           Total
     Sex                                                                           
  female                     81 (17.4%)   233 (50.0%)   152 (32.6%)    466 (100.0%)
    male                    468 (55.5%)   109 (12.9%)   266 (31.6%)    843 (100.0%)
   Total                    549 (41.9%)   342 (26.1%)   418 (31.9%)   1309 (100.0%)
-------- ------------ ----------------- ------------- ------------- ---------------

Code

# rather do this by hand so you can use inline code
total_females <- sum(titanic$Sex == "female") # count females

percentage_female_survived <- round((female_survived / total_females) * 100, digits=1)
percentage_female_nonsurvived <- round((female_nonsurvived / total_females) * 100, digits=1)

Among Females on board the Titanic, 50% survived and 17.4% did not survive.

---

5. Percentage of Survivors that are Male

The following table shows the distribution of survivors by gender.

Code

male_survived <- nrow(titanic[titanic$Sex == "male" & titanic$Survived.f == "Survived", ])
male_nonsurvived <- nrow(titanic[titanic$Sex == "male" & titanic$Survived.f == "Did Not Survive" , ])
#Adding the two variables to figure out the total number of people
total_survived <-  male_survived+female_survived
total_survived

[1] 342

Code

ctable(titanic$Sex,titanic$Survived.f, prop="r")

Cross-Tabulation, Row Proportions  
Sex * Survived.f  
Data Frame: titanic  

-------- ------------ ----------------- ------------- ------------- ---------------
           Survived.f   Did Not Survive      Survived        Unsure           Total
     Sex                                                                           
  female                     81 (17.4%)   233 (50.0%)   152 (32.6%)    466 (100.0%)
    male                    468 (55.5%)   109 (12.9%)   266 (31.6%)    843 (100.0%)
   Total                    549 (41.9%)   342 (26.1%)   418 (31.9%)   1309 (100.0%)
-------- ------------ ----------------- ------------- ------------- ---------------

Code

percentage_male_survived <- round((male_survived / total_survived) * 100, digits=1)
percentage_f_survived <- round((female_survived/total_survived)*100,digits = 1)
percentage_f_survived

[1] 68.1

On the titanic only 31.9% of survivors were male, with the remaining 68.1% being female.

---

6. Percent of all Titanic passengers were females who perished

Code

total_passengers <-  length(titanic$Sex)
total_female_deaths <- round((female_nonsurvived/total_passengers)*100 , digits = 1)

Out of all the confirmed deaths on the titanic only 6.2% were female.

---

7. Distribution of passengers by fare class and embark location?

Note that passengers embarked at one of three locations.
S: Southampton, England
C: Cherbourg, France
Q: Queenstown, Ireland

Code

# Assuming 'titanic' is your data frame
# Replace missing values with "S"
titanic$Embarked[is.na(titanic$Embarked)] <- "S"

# Convert to factor
titanic$Embarked.f <- as.factor(titanic$Embarked)

# Assuming 'titanic' is your data frame
# Replace blank spots with the most frequent value
most_frequent_value <- names(sort(table(titanic$Embarked.f), decreasing = TRUE))[1]
titanic$Embarked.f[titanic$Embarked.f == ""] <- most_frequent_value

# Convert to factor
titanic$Embarked.f <- factor(titanic$Embarked.f, levels = c("C", "Q", "S"))

# Assuming 'titanic' is your data frame
titanic <- titanic[titanic$Embarked.f != "Unknown", ]

Code

titanic$Fare_bins <- cut(titanic$Fare, breaks = c(0, 7.896, 14.454, 31.275, 512.329), labels = c("Bin1", "Bin2", "Bin3", "Bin4"), include.lowest = TRUE)

# Replace NA with mean
mean_fare <- mean(titanic$Fare, na.rm = TRUE)
titanic$Fare_bins[is.na(titanic$Fare_bins)] <- cut(mean_fare, breaks = c(0, 7.896, 14.454, 31.275, 512.329), labels = c("Bin1", "Bin2", "Bin3", "Bin4"), include.lowest = TRUE)

---

CREATING PLOTS

Let’s create three plots using ggplot2. We will learn more about the capabilities of ggplot2 over this course. We will practice a few here.
The first two charts are similar – one shows the levels, and the other shows the values in percent.

Number of Surivors by Fare Class

Code

library(ggplot2)

ggplot(titanic, aes(x = Sex, fill = Survived.f)) +
  geom_bar(position = "dodge") +
  #geom_text(aes(label =after_stat(count)),stat="count", position= position_dodge(width=0.9, vjust=-0.25))+
    labs(x = "Gender", y = "Count", fill = "Survival Status",
       title = "Number of Titanic Passengers by Survival and Gender",
       subtitle = "Most passengers aboard the Titanic were were men who did not survive") 

Percentage of Surivors by Fare Class

This is a similar chart as above, but showing the data as a percent rather than raw values.
Note that we first need to calculate the percentage first using dplyr then we will use that data to create the chart. We also add data labels.

Code

# summarize the data to create the percentages
pip <- titanic %>%
  group_by(Sex, Survived.f) %>%
  summarise(Count = n(), .groups = 'drop') %>%
  mutate(Total = sum(Count), Percentage = (Count / Total) * 100)
pip

# A tibble: 6 × 5
  Sex    Survived.f      Count Total Percentage
  <chr>  <fct>           <int> <int>      <dbl>
1 female Did Not Survive    81  1309       6.19
2 female Survived          233  1309      17.8 
3 female Unsure            152  1309      11.6 
4 male   Did Not Survive   468  1309      35.8 
5 male   Survived          109  1309       8.33
6 male   Unsure            266  1309      20.3 

Code

# Create the dodged bar chart 
ggplot(pip, aes(x = Sex, y = Percentage, fill = Survived.f)) +
  geom_bar(stat = "identity", position = position_dodge(width = 0.9)) +
  geom_text(aes(label = sprintf("%.0f%%", Percentage), #sprintf rounds to 0 decimals
                            group = Survived.f), 
                        position = position_dodge(width = 0.9), vjust = -0.25) +
  scale_y_continuous(labels = scales::percent_format()) +
  labs(x = "Gender", y = "Percentage", fill = "Survival Status", 
         title = "Percentage of Survival by Gender on the Titanic",
         subtitle = "Nearly 36% of all passengers were men who did not survive") +
    theme(axis.text.y = element_blank())  # Suppress y-axis labels

Average Age by Fare Class

Create a bar chart that shows the average age of passengers by fare class – 1st Class, 2nd Class, or 3rd Class.

Code

# Create a file with the average ages
library(dplyr)
mean_ages <- titanic %>%
    group_by(Pclass) %>%
    summarise(MeanAge = round(mean(Age, na.rm=TRUE), 0))

# Create the bar chart
library(ggplot2)
ggplot(data=mean_ages, aes(x=Pclass, y=MeanAge)) +
  geom_bar(stat="identity", fill="light blue", position="dodge") +
  geom_text(aes(label=round(MeanAge, 1)), vjust=-0.5) +
    ylim(0,45) +
  labs(x = "Passenger Class", y="Average Age",
       title = "Average Age by Passenger Class",
       subtitle = "First class passengers were older, on average.",
       caption = "Source: titanic dataset") 

Here we created the plot that shows the average age by fare class. First class Titanic passengers were, on average, older than those in second and third class.

---

YOUR TURN!

Create and interpret three charts that address embark location and fare class.

Code

ggplot(titanic, aes(x = Fare_bins, fill = Embarked.f)) +
  geom_bar(position = "dodge", color = "black", stat = "count") +
  labs(x = "Embark Location",
       y = "Count",
       title = "Clustered Bar Chart for Embark Location and Fare",
       subtitle = "Comparison of distribution across fare bins for each embark location",
       fill = "Fare Bins") +
  theme_minimal()

This chart compares the fare each passenger paid to board the titanic and the location they embarked from. The majority of people that got on the ship in Southampton were in the second bin for the price that they paid. The majority of the people from Queenstown paid for the cheapest tickets and the people from Cherbourg paid for more expensive tickets.

Code

ggplot(titanic, aes(x = Fare_bins, fill = Survived.f)) +
  geom_bar(position = "dodge", color = "black", stat = "count") +
  labs(x = "Survival Distribution",
       y = "Count",
       title = "Clustered Bar Chart for Survival and Fare",
       subtitle = "Comparison of distribution across fare and passenger survival",
       fill = "Fare Bins") +
  theme_minimal()

This chart found that the people who paid for the cheaper tickets were more likely to not survive, likewise a persons’ chance of survival went up the more they had paid for their ticket. Despite this, the amount of unconfirmed survivals or deaths were around the same regardless of ticket price.

Code

ggplot(titanic, aes(x = Embarked.f, fill = Survived.f)) +
  geom_bar(position = "stack") +
  labs(x = "Embarked Location",
       y = "Count",
       title = "Stacked Bar Chart of Survival Status Across Embarked Locations",
       fill = "Survival Status")

Lastly this stacked bar chart indicated that the majority of all passengers boarded the titanic in Southampton therefore it has the largest amount of people with different levels of survival.

END