Continue to practice data visualization with
ggplot2
Continue to practice data transformation with
dplyr
Integrate 1) and 2) to explore the titanic
dataset
Today, we will continue to combine the data transformation tools
in dplyr
and the data visualization tools in
ggplot2
to explore the patterns and trends in the
titanic
dataset. This dataset contains the information on
passengers aboard the Titanic when it sank in 1912.
github_document
, save it in your
lab
folder as lab5.Rmd
, and work in this
RMarkdown file for the rest of this lab.# Load required packages
library(tidyverse)
library(knitr)
# Read in the data
titanic <- read_csv("https://raw.githubusercontent.com/nt246/NTRES-6100-data-science/master/datasets/Titanic.csv")
# Let's look at the top 5 lines of the dataset
head(titanic, n = 5) %>%
kable()
PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked |
---|---|---|---|---|---|---|---|---|---|---|---|
1 | 0 | 3 | Braund, Mr. Owen Harris | male | 22 | 1 | 0 | A/5 21171 | 7.2500 | NA | S |
2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Thayer) | female | 38 | 1 | 0 | PC 17599 | 71.2833 | C85 | C |
3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26 | 0 | 0 | STON/O2. 3101282 | 7.9250 | NA | S |
4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35 | 1 | 0 | 113803 | 53.1000 | C123 | S |
5 | 0 | 3 | Allen, Mr. William Henry | male | 35 | 0 | 0 | 373450 | 8.0500 | NA | S |
View()
, dim()
,
colnames()
, and ?
. You will see that the
dataset includes the following variables:notes <- read_csv("https://raw.githubusercontent.com/nt246/NTRES-6100-data-science/master/datasets/Notes.csv")
kable(notes)
Variable | Definition | Key |
---|---|---|
PassengerId | Passenger ID | NA |
Survival | Survival | 0 = No, 1 = Yes |
Pclass | Ticket class | 1 = 1st, 2 = 2nd, 3 = 3rd |
Name | Pasenger name | NA |
Sex | Sex | NA |
Age | Age in years | NA |
Sibsp | # of siblings / spouses aboard the Titanic | NA |
Parch | # of parents / children aboard the Titanic | NA |
Ticket | Ticket number | NA |
Fare | Passenger fare | NA |
Cabin | Cabin number | NA |
Embarked | Port of Embarkation | C = Cherbourg, Q = Queenstown, S = Southampton |
Note: Age is fractional if less than 1. If the age is estimated, it is in the form of xx.5
Suggestions:
Make sure that you use figures and/or tables to support your answer.
We provide some possible solutions for each question, but we highly recommend that you don’t look at them unless you are really stuck.
Do not worry if you cannot finish Exercise 1 in 50 minutes. You can keep working on these questions after the break.
# number of passengers in the dataset
nrow(titanic)
## [1] 891
# number of passengers surviving vs. dying
survived_count <- titanic %>%
mutate(survived = ifelse(Survived==0, "no", "yes")) %>%
count(survived) %>%
mutate(percentage = round(n/nrow(titanic)*100,2))
kable(survived_count)
survived | n | percentage |
---|---|---|
no | 549 | 61.62 |
yes | 342 | 38.38 |
# plotting
titanic %>%
mutate(survived = ifelse(Survived==0, "no", "yes")) %>%
ggplot(aes(x = survived)) +
geom_bar(aes(fill = survived)) +
geom_label(data = survived_count, aes(label=str_c(percentage, "%"), y=n/2)) +
coord_flip()
Note: str_c()
is used to add the percentage sign.
# male vs. female
## table
sex_count <- titanic %>%
count(Sex)
kable(sex_count)
Sex | n |
---|---|
female | 314 |
male | 577 |
## plot
sex_count %>%
ggplot(aes(x = Sex, y = n)) +
geom_col(aes(fill = Sex)) +
geom_text(aes(label = n, y = n + 20)) +
ylab("count") +
coord_flip()
# male vs. female broken down by ticket class
## table
sex_class_count <- titanic %>%
group_by(Sex, Pclass) %>%
count()
kable(sex_class_count)
Sex | Pclass | n |
---|---|---|
female | 1 | 94 |
female | 2 | 76 |
female | 3 | 144 |
male | 1 | 122 |
male | 2 | 108 |
male | 3 | 347 |
## plot
sex_class_count %>%
ggplot(aes(x = Sex, y = n)) +
geom_col(aes(fill = Sex)) +
geom_text(aes(label = n, y = n + 20)) +
facet_wrap(~Pclass) +
ylab("count")
# table
sex_survival_count <- titanic %>%
mutate(survived = ifelse(Survived==0, "no", "yes")) %>%
group_by(Sex, survived) %>%
count() %>%
group_by(Sex) %>%
mutate(percentage = round(n/sum(n)*100, 2))
kable(sex_survival_count)
Sex | survived | n | percentage |
---|---|---|---|
female | no | 81 | 25.80 |
female | yes | 233 | 74.20 |
male | no | 468 | 81.11 |
male | yes | 109 | 18.89 |
# plot
sex_survival_count %>%
arrange(Sex, desc(survived)) %>%
group_by(Sex) %>%
mutate(label_y = cumsum(n) - 0.5 * n) %>%
ggplot(aes(x=Sex)) +
geom_col(aes(fill = survived, y=n), color = "black") +
geom_label(aes(label = str_c(percentage, "%"), y = label_y)) +
coord_flip()
Note: the line mutate(label_y = cumsum(n) - 0.5 * n)
is
used to place the labels in the middle of each colored bar.
# age info
## table
age_info_count <- titanic %>%
mutate(age_info = ifelse(is.na(Age), "missing", "available")) %>%
count(age_info)
kable(age_info_count)
age_info | n |
---|---|
available | 714 |
missing | 177 |
## plot
age_info_count %>%
ggplot(aes(x=age_info, y=n)) +
geom_col(aes(fill=age_info)) +
geom_label(aes(y=n+30, label=n)) +
coord_flip()
# age distribution
## summary
titanic %>%
filter(!is.na(Age)) %>%
.$Age %>%
summary()
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.42 20.12 28.00 29.70 38.00 80.00
## Plot
titanic %>%
filter(!is.na(Age)) %>%
ggplot(aes(x=Age)) +
geom_histogram()
titanic %>%
filter(!is.na(Age)) %>%
ggplot(aes(x=Age, fill=Sex)) +
geom_histogram() +
facet_grid(Pclass~Sex)
titanic %>%
mutate(ticket_class = as.character(Pclass)) %>%
filter(!is.na(Age)) %>%
ggplot(aes(x=Age, fill=Sex)) +
geom_density(alpha=0.5) +
facet_grid(ticket_class~.)
Hint: geom_histogram()
and facet_grid()
can
be helpful in answering this question.
titanic %>%
filter(!is.na(Age)) %>%
mutate(survived = ifelse(Survived==0, "no", "yes")) %>%
ggplot(aes(x=Age, fill=survived)) +
geom_histogram(position="stack", color="black") +
facet_grid(Sex~Pclass)
titanic %>%
mutate(n_family=SibSp+Parch) %>%
ggplot(aes(x=n_family)) +
geom_bar() +
scale_x_continuous(breaks = 0:10)
titanic %>%
mutate(n_family=SibSp+Parch, with_family=ifelse(n_family>0, "yes", "no")) %>%
ggplot(aes(x=with_family)) +
geom_bar() +
coord_flip()
titanic %>%
mutate(n_family=SibSp+Parch) %>%
ggplot(aes(x=n_family)) +
geom_bar() +
scale_x_continuous(breaks = 0:10) +
facet_wrap(~Pclass, ncol = 1)
titanic %>%
mutate(n_family = SibSp+Parch, ticket_class = as.character(Pclass)) %>%
ggplot(aes(x = n_family, fill = ticket_class)) +
geom_bar(color = "black", position = "fill") +
scale_x_continuous(breaks = 0:10) +
ylab("proportion") +
coord_flip()
titanic %>%
group_by(Ticket) %>%
mutate(n_ticket=n(), fare_per_ticket = Fare/n_ticket, ticket_class=as.character(Pclass)) %>%
ggplot(aes(x=fare_per_ticket)) +
geom_histogram(bins = 100) +
facet_wrap(~ticket_class, ncol = 1, scales = "free_y")
Share your findings, challenges, and questions with the class.
Suggested activities:
Continue to work on Exercise 1 if you have not finished.
Polish your plots in Exercise 1. Try to put more thought into editing the aesthetics of your figures and tables to make them easier to understand and nicer to look at (e.g. choose the most appropriate geometric object, aesthetic mapping, facetting, position adjustment; add meaningful axis labels, figure titles, legend titles; change the background; be creative; etc.).
Read the example code that we provided in Exercise 1. Make sure that you understand each line, and try to reproduce the output/computations on your own.
Think of other interesting questions you can answer with this dataset and explore different strategies for getting your answer.
Share your findings, challenges, and questions with the class.
END LAB 4