Code
# combine test and train datasets
library(titanic)
library(dplyr)
titanic <- bind_rows(titanic_train, titanic_test) 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.
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 SCode
tail(titanic,3) # look at the last 3 rows of the dataset 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 CCode
table(titanic$Survived)
0 1
549 342 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$Name), ]
print(astor %>% select(Name, Survived)) Name Survived
701 Astor, Mrs. John Jacob (Madeleine Talmadge Force) 1
1094 Astor, Col. John Jacob NACol. Astor was not known to have survived nor was he confirmed as a non-survivor. The NA means he was not recovered. His wife, however, did survive.
Average Age
Code
#create a new variable that is the average age. the na.rm=TRUE tells R to skip missing values.
xage <- round(mean(titanic$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$Survived.f)
Did Not Survive Survived Unsure
female 81 233 152
male 468 109 266Code
# use this to create the values for inline code
female_survived <- nrow(titanic[titanic$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
# insert code here. note the way this is worded. USe column proportions
ctable(titanic$Sex,titanic$Survived.f, prop="c")Cross-Tabulation, Column Proportions
Sex * Survived.f
Data Frame: titanic
-------- ------------ ----------------- -------------- -------------- ---------------
Survived.f Did Not Survive Survived Unsure Total
Sex
female 81 ( 14.8%) 233 ( 68.1%) 152 ( 36.4%) 466 ( 35.6%)
male 468 ( 85.2%) 109 ( 31.9%) 266 ( 63.6%) 843 ( 64.4%)
Total 549 (100.0%) 342 (100.0%) 418 (100.0%) 1309 (100.0%)
-------- ------------ ----------------- -------------- -------------- ---------------Code
# rather do this by hand so you can use inline code
total_males <- sum(titanic$Sex == "male")
male_survived <- nrow(titanic[titanic$Sex == "male" & titanic$Survived.f == "Survived", ])
percentage_male_survived <- round((male_survived / total_males) * 100, digits=1)Among all male passengers only 12.9% survived. This percentage does not exclude the unsure male passengers.
6. Percent of all Titanic passengers were females who perished
Code
ctable(titanic$Sex,titanic$Survived.f, prop="r")Cross-Tabulation, Row Proportions
Sex * Survived.f
Data Frame: titanic
-------- ------------ ----------------- ------------- ------------- ---------------
Survived.f Did Not Survive Survived Unsure Total
Sex
female 81 (17.4%) 233 (50.0%) 152 (32.6%) 466 (100.0%)
male 468 (55.5%) 109 (12.9%) 266 (31.6%) 843 (100.0%)
Total 549 (41.9%) 342 (26.1%) 418 (31.9%) 1309 (100.0%)
-------- ------------ ----------------- ------------- ------------- ---------------Code
females_perished <- sum(titanic$Survived.f == 'Did Not Survive' & titanic$Sex == "female")
total_passengers <- nrow(titanic)
percentage_females_perished <- (females_perished / total_passengers) * 100Of the 1309 passengers, only 81. That is just 6.1879297% of passengers.
7. Distribution of passengers by fare class and embark location?
Note that passengers embarked at one of three locations.
S: Southampton, England
C: Cherbourg, France
Q: Queenstown, Ireland
Code
# fix missing labels and convert to a factor
titanic$Embarked <- ifelse(titanic$Embarked=="","Unknown",titanic$Embarked)
titanic$Embarked.f <- as.factor(titanic$Embarked)
# insert code here to complete.
# Create a table to show the distribution of passengers by fare class and embarkation location
fare_embark_table <- table(titanic$Pclass, titanic$Embarked.f)
fare_embark_table
C Q S Unknown
1 141 3 177 2
2 28 7 242 0
3 101 113 495 0In first class, 141 departed from Cherbourg, 3 departed from Queenstown, 177 from Southampton and 2 had unknown departure locations. Second class had 28 embark from Cherbourg, 7 embark from Queenstown, and 242 from Southampton. Lastly, third class, the largest class, had 101 depart from Cherbourg, 113 from Queenstown, and 495 depart from Southampton.
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 Gender
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.
::: {.cell}
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.
Create and interpret two charts that address embark location and fare class.
Include both the intent, the chart, and the interpretation.
Be prepared to share your charts with the class.
Dodged Bar Chart comparing embark location and fare class
Code
pip2 <- titanic %>%
group_by(Pclass, Embarked.f) %>%
summarise(Count = n(), .groups = 'drop') %>%
mutate(Total = sum(Count), Percentage = (Count / Total) * 100)
pip2# A tibble: 10 × 5
Pclass Embarked.f Count Total Percentage
<int> <fct> <int> <int> <dbl>
1 1 C 141 1309 10.8
2 1 Q 3 1309 0.229
3 1 S 177 1309 13.5
4 1 Unknown 2 1309 0.153
5 2 C 28 1309 2.14
6 2 Q 7 1309 0.535
7 2 S 242 1309 18.5
8 3 C 101 1309 7.72
9 3 Q 113 1309 8.63
10 3 S 495 1309 37.8 Code
ggplot(pip2, aes(x = Pclass, y = Percentage, fill = Embarked.f)) +
geom_bar(stat = "identity", position = position_dodge(width = 0.9)) +
geom_text(aes(label = sprintf("%.0f%%", Percentage),
group = Embarked.f),
position = position_dodge(width = 0.9), vjust = -0.25) +
scale_y_continuous(labels = scales::percent_format()) +
labs(x = "Class Status", y = "Percentage", fill = "Embarked Location",
title = "Percentage of Survival by Gender on the Titanic",
subtitle = "Nearly 38% of all passengers embarked from Southampton, England") +
theme(axis.text.y = element_blank()) 
I used a dodged bar chart to utilize percentages and the easy to interpret visuals it provides. This chart would show which class status had the most passengers and where most of those passenger got aboard the Titanic.
The dodged bar chart indicates there is a missing value in the embarked location of a first class passenger. No first class passengers departed from Queenstown, Ireland. 11 percent of first class passengers departed from Cherbourg, France and 14 percent departed from Southampton, England. In second class, 1 percent departed from Queenstown, Ireland, 2 percent embarked from Cherbourg, and 18 percent from Southampton. Lastly, the third class is the largest class with 55 percent of all passengers being in this group. 8 percent got on in Cherbourg, 9 percent in Queenstown, and 38 percent in Southampton.
Dodged Bar Chart comparing embark location and fare class
Code
fare_embark_df <- as.data.frame(fare_embark_table)
colnames(fare_embark_df) <- c("Pclass", "Embarked", "Count")
pip3 <- titanic %>%
group_by(Pclass, Embarked.f) %>%
summarise(Count = n(), .groups = 'drop') %>%
mutate(Total = sum(Count))
ggplot(pip3, aes(x = Pclass, y = Count, fill = Embarked.f)) +
geom_bar(stat = "identity", position = position_dodge(width = 0.9)) +
geom_text(aes(label = Count, group = Embarked.f),
position = position_dodge(width = 0.9), vjust = -0.25) +
labs(x = "Class Status", y = "Count", fill = "Embarked Location",
title = "Count of passengers and embark location",
subtitle = "495 3rd class passengers barked in Southampton") +
theme(axis.text.y = element_blank())
This stacked bar chart has the number of passengers in each class, and the location they embarked from. The 3rd class passengers make up the most of the passengers on the Titanic with 709 passengers with the majority (495) departing from Southampton. The 2nd class had 277 total passengers with the most (242) departing from Southampton. Lastly, the 1st class passengers mostly departed from Southampton as well with 177. First class had 323 passengers and two passengers who departed from an unknown location.