In Search of the Royal Mail Ship (RMS) Titanic

Chuck Cartledge

July 29, 2017

Contents

1 Introduction 1

2 Discussion 12.1 Sources of data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2.1.1 R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.1.2 R package “titanic” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.1.3 R package “vcdExtra” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.1.4 The crew . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.2 Questions that can be asked . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.3 Presentation of results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3 Conclusion 13

A Plotting commands 20

B Files 21

List of Tables

1 Command line, or pass parameters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 Cross comparison of different partitioning algorithms. . . . . . . . . . . . . . . . . . . . . . . 13

List of Figures

1 The RMS Titanic. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Titanic’s Collapsible Boat D approaches RMS Carpathia. . . . . . . . . . . . . . . . . . . . . 33 Commemorative sailing booklet. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 A Mosaic plot of R Titanic data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 vcdExtra Titanic data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 vcdExtra Titanic data decision tree based on passenger class, age, and number and type of

traveling companions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 vcdExtra Titanic data decision tree based on all available data. . . . . . . . . . . . . . . . . . 98 Missing data map for the “internal” dataset. . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 Missing data map for the “titanic3” dataset. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1210 C50 decision tree. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

i

11 Random Forest decision tree. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1612 rpart decision tree. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1713 J48 (unpruned) decision tree. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1714 J48 (pruned) decision tree. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1815 Accuracy based on training dataset size. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

1 Introduction

The sinking of the Royal Mail Ship (RMS) Titanic on her maiden voyage is a source of constant mystery andromance. Now after more than a century, there are still unanswered questions about the disaster that madeher a part of the English lexicon. Perhaps the simplest question is: how many people (passengers and crew)were on board when she sank, and how many survived? Surprisingly, there is no definitive answer to these,the most simple of questions. Neither from the White Star Line (her owner), nor from the British WreckCommissioner assigned to inquiry into her sinking. In this report, we will enumerate some of the disparatesources, and look at some data that has made its way into the R programming language.

2 Discussion

The RMS Titanic (see Figure 1 on the following page), set sail from South Hampton England for New York,New York on 10 April 1912. She had a fire in her coal stores that started almost 10 days before she sailed,and continued after leaving South Hampton[9]. She had 16 lifeboats, and four Engelhardt “collapsibles” (seeFigure 2 on page 3) that could collectively hold 1,178 people (aka, souls)[5]. When the Titanic set sail,she had approximately 2,224 passengers and crew. Titanic could had a maximum capacity of 3,327 souls.The number of survivors differs between sources; ranging from 706[7, 8] to 712[4]. 711 are reported to havesurvived according to the R library titanic.

Titanic’s sailing was such a special event, that special booklets were created (see Figure 3 on page 4),complete with list of first class passengers2.

2.1 Sources of data

One of the many things that is unusual about the sinking of the Titanic, is that there does not seem to bea definitive list of passengers and crew, let alone a list that identifies those who survived and those who didnot[1]. In the following sections, we will investigate a few of these sources.

2.1.1 R

A summary of the classes of people on the Titanic, and whether or not they survived, is part of the standardR installation in the table Titanic (see Figure 4 on page 5).

“This data set provides information on the fate of passengers on the fatal maiden voyage of theocean liner ’Titanic’, summarized according to economic status (class), sex, age and survival.”

R Staff [6]

2http://www.phillyseaport.org/web_exhibits/mini_exhibits/titanic_passenger_list/titanic_passenger_

list-object-passenger_list.html

http://www.phillyseaport.org/web_exhibits/mini_exhibits/titanic_passenger_list/titanic_passenger_list-object-passenger_list.htmlhttp://www.phillyseaport.org/web_exhibits/mini_exhibits/titanic_passenger_list/titanic_passenger_list-object-passenger_list.html

Figure 1: The RMS Titanic. Photographed 10 April 19121. The Titanic sank 15 April 1912.

2.1.2 R package “titanic”

A collection of 1,309 passenger records organized to support the Kaggle competition3. The total data isdivided into training and testing portions to support machine learning. The data does not have any crewdata.

This data set provides information on the fate of passengers on the fatal maiden voyage of theocean liner “Titanic”, summarized according to economic status (class), sex, age and survival.Whereas the base R Titanic data found by calling data(“Titanic”) is an array resulting fromcross-tabulating 2201 observations, these data sets are the individual non-aggregated observationsand formatted in a machine learning context with a training sample, a testing sample, and twoadditional data sets that can be used for deeper machine learning analysis.

Hendricks [3]

The library has copies of the training and testing dataset used by the Kaggle competition to validate andtest various machine learning algorithms. While the titanic train data set has which passenger survivedor not, the titanic test data set does not. So the only practical way to see how well your approachworked was to submit your R script to Kaggle and the await results. There is sufficient information in thetitanic test data set to reconstruct who lived or died, it may not be worth the effort.

2.1.3 R package “vcdExtra”

A collection of 1,309 passenger records.

Provides additional data sets, methods and documentation to complement the ’vcd’ packagefor Visualizing Categorical Data and the ’gnm’ package for Generalized Nonlinear Models.

3https://www.kaggle.com/c/titanic

2

https://www.kaggle.com/c/titanic

Figure 2: Titanic’s Collapsible Boat D approaches RMS Carpathia.

3

Figure 3: Commemorative sailing booklet.4

Figure 4: A Mosaic plot of R Titanic data. A summary of all personnel on the RMS Titanic broken down bygender, by survival or not, and class. It is interesting to look at the data and consider the adage: “womenand children first.”

5

Friendly et al. [2]

The vcdExtra::Titanicp is a data frame with 1,309 observations on the following 6 variables (see Figure 5on the following page):

• class a factor with levels 1st, 2nd, and 3rd

• survived a factor with levels died, and survived (1 and 2 respectively)

• sex a factor with levels female, and male

• age passenger age in years (or fractions of a year, for children), a numeric vector; age is missing for263 of the passengers

• sibsp number of siblings or spouses aboard, integer: 0:8

• parch number of parents or children aboard, integer: 0:6

In many ways, “vcdExtra::Titanicp” is a union of the “titanic::titanic train” and “titanic::titanic test”datasets, less some of the columnular values. The vcdExtra::Titanicp data supports looking at the data indifferent ways[2] (see Figure 6 on page 8). If we use all the data columns, then the decision tree becomesmuch more interesting

2.1.4 The crew

The previous sections focused on Titanic’s passengers, but the ship also had crew and almost all Titanic listsignore them. I was able to find one site that claimed to have a list of crew members, their job, and whetherthey survived or not. Port Cities Southampton4 claims to be a digital archive of maritime activities for thePort of Southampton, including the departure of the Titanic5. The crew list is broken into several partsbased on last names, and is available for download as PDF files.

An R script was written to parse the crew list files and put the data into a data frame compatible withthe other Titanic datasets.

2.2 Questions that can be asked

Every person that sailed on the Titanic had a long list of attributes that could be used to describe them.These attributes include:

• Class (1st, 2nd, 3rd, crew, other)

• Age,

• Fare,

• Gender,

• Number of traveling family members,

• Number of siblings,

• Place of embarkation, or4http://www.plimsoll.org/StartHere/AboutUs/default.asp5http://www.plimsoll.org/Southampton/Titanic/titaniccrewlist/Default.asp

6

http://www.plimsoll.org/StartHere/AboutUs/default.asphttp://www.plimsoll.org/Southampton/Titanic/titaniccrewlist/Default.asp

Figure 5: vcdExtra Titanic data. The data shows that only three classes of data are present (no crewmembers), that everyone survived or not, that there were only 2 genders identified, and that the attributesof age, sibsp, and parch have the most variability.

7

Figure 6: vcdExtra Titanic data decision tree based on passenger class, age, and number and type of travelingcompanions. Based on the data; if you were not in 3rd class, were, under 16, and in 2nd class, then 157 ofthe 249 people like you died.

8

Figure 7: vcdExtra Titanic data decision tree based on all available data.

9

Table 1: Command line, or pass parameters. The predictions.R R script supports a variety of passparameters, or command line arguments.

Name Switch Default MeaningsourceSelection s internal Which dataset to use.trainingPercentage t 30 Percentage of data (num-

ber of people) to use in thetraining set. The remain-der will be used as the testset.

dataCollection d FALSE Should a long run be madeto collect data based on thedata set selected.

verbose v FALSE Control lots of debugginginformation.

• Nationality of each person (there have been comments that percentage wise Americans survived thannon-Americans because the Americans believed the announcements to abandon ship),

Not all of the datasets included in predictions.R have all attributes. The internal dataset is missing theattribute “embarkation” (see Figure 8 on the following page). While the optional dataset, has all values (seeFigure 9 on page 12).

Each of these attributes can be used to form the question as to who survived and who did not. Not alldatasets have all attributes.

For our investigation, the question is:What is the likelihood that someone survived based on their gender, the number of siblings on board,

the number of people in the traveling family unit, and the group’s traveling class (1st, 2nd, 3rd, and crew).

2.3 Presentation of results

An R script was written to compare the different datasets and to use different partitioning approaches onthe datasets. The script supports a collection of arguments either via the command line, or by passingarguments via the main() function (see Table 1).

predictions.R incorporates four different partitioning algorithms (one algorithm has two variants).They are:

rpart from the rpart library. “Recursive partitioning for classification, regression and survival trees. Animplementation of most of the functionality of the 1984 book by Breiman, Friedman, Olshen andStone.”[?]

C50 from the C50 library. “C5.0 decision trees and rule-based models for pattern recognition.” [?]

Random Forest from the randomForest library. “Classification and regression based on a forest of treesusing random inputs.”[?]

J48 from the RWeka library. “An R interface to Weka (Version 3.9.1). Weka is a collection of machinelearning algorithms for data mining tasks written in Java, containing tools for data pre-processing,classification, regression, clustering, association rules, and visualization.”[?] The J48 algorithm is runin a pruned and unpruned mode.

10

Figure 8: Missing data map for the “internal” dataset.

11

Figure 9: Missing data map for the “titanic3” dataset.

12

Table 2: Cross comparison of different partitioning algorithms. The row names correspond to the cells in aconfusion matrix, with TRUE positives and negatives, and FALSE positives and negatives.

C50 Random Forest rpart J48 (unpruned) J48 (pruned)True + 1031 1031 1031 1031 1031True - 175 175 175 175 175False + 12 12 12 12 12False - 323 323 323 323 323Accuracy 0.7826087 0.7826087 0.7826087 0.7826087 0.7826087Kappa 0.4061712 0.4061712 0.4061712 0.4061712 0.4061712

(see Figure 10on page 15)

(see Figure 11on page 16)

(see Figure 12on page 17)

(see Figure 13on page 17)

(see Figure 14on page 18)

Each algorithm was run using the default program values and verbose = TRUE so that cross algorithmcomparisons can be made (see Table 2). Data on each algorithm is reported:

True + from a confusion matrix, this is the TRUE Positive.

True - from a confusion matrix, this is the TRUE Negative.

False + from a confusion matrix, this is the FALSE Positive.

False - from a confusion matrix, this is the FALSE Negative.

Accuracy the accuracy based on data in the confusion matrix Accuracy = truePositive+trueNegativeallPositive+allNegative

Kappa

While the figures created by the default values are interesting (see Figures 10 on page 15 through 14on page 18), they only provide a snapshot of how their respective algorithms perform based on a singlepartitioning of the data into training and testing subsets. It might be more informative to see how theaccuracy of the algorithms vary as a function of the size of the training dataset. To answer that question,predictions.R was run with dataCollection = TRUE and trainingPercentage = 1. This causes thescript to vary trainingPercentage from 1 to 90%, in 10% steps. The accuracy for each algorithm wascaptured and plotted (see Figure 15 on page 19). Based on the collected data, it appears that trainingpercentages from about 10 to 60 result in all algorithms having nearly identical accuracies. Below 10%, theRandom Forest approach appears best. Above 70%, they all seem to be an overfit the data.

3 Conclusion

A number of interesting things that came out of this investigation, including:

1. The Titanic carried more lifeboats than law required, but far too few to save all personnel (1,178 versus3,327).

2. There does not appear to be an official and agreed to number of people who sailed on the RMS Titanic,and of those who survived or died.

3. The values from in built in R data set (datasets:Titanic)6 is a reasonable approximation, althoughit has limited attributes.

6At the R command prompt: library(help=datasets)

13

4. There are many Titanic people data sets, but some do not contain all classes of people (1st, 2nd, 3rd,and crew).

5. Various passenger and crew lists (with more attributes than the built in Titanic dataset) are available,and can be consolidated into a dataset with more attributes, and still have approximately the samenumber of people.

6. All of the decision tree algorithms tested had comparable results (˜76% accuracy) when the trainingdataset was between 10 and 60% of the entire dataset.

7. Random forest performed most consistently over the widest range of training percentages of all testedalgorithms.

In summary: most of the Titanic personnel datasets are very comparable, and the random forest decisiontree consistently worked the best.

References

[1] RJM Dawson, The ”Unusual Episode” Data Revisited, Journal of Statistics Education 3 (1995), no. 3,1–7.

[2] Michael Friendly, Heather Turner, Achim Zeileis, and Maintainer Michael Friendly, Package ’vcdExtra’,(2016).

[3] Paul Hendricks, Titanic Passenger Survival Data Set, https://github.com/paulhendricks/titanic,2015.

[4] Encyclopedia Titanica Staff, Titanic Survivors, https://www.encyclopedia-titanica.org/titanic-survivors/, 2017.

[5] History Staff, Titanic, http://www.history.com/topics/titanic, 2017.

[6] R Staff, Survival of passengers on the Titanic, package:datasets, 2017.

[7] Titanic Facts Staff, Titanic Survivors, http://www.titanicfacts.net/titanic-survivors.html,2017.

[8] Titanic Universe Staff, How Many People Survived the Titanic Disaster?, http://www.titanicuniverse.com/how-many-people-survived-the-titanic-disaster/1253, 2017.

[9] Wikipedia Staff, British Wreck Commissioner’s inquiry into the sinking of the RMS Titanic,https://en.wikipedia.org/wiki/British_Wreck_Commissioner%27s_inquiry_into_the_sinking_

of_the_RMS_Titanic, 2017.

14

https://github.com/paulhendricks/titanichttps://www.encyclopedia-titanica.org/titanic-survivors/https://www.encyclopedia-titanica.org/titanic-survivors/http://www.history.com/topics/titanichttp://www.titanicfacts.net/titanic-survivors.htmlhttp://www.titanicuniverse.com/how-many-people-survived-the-titanic-disaster/1253http://www.titanicuniverse.com/how-many-people-survived-the-titanic-disaster/1253https://en.wikipedia.org/wiki/British_Wreck_Commissioner%27s_inquiry_into_the_sinking_of_the_RMS_Titanichttps://en.wikipedia.org/wiki/British_Wreck_Commissioner%27s_inquiry_into_the_sinking_of_the_RMS_Titanic

Figure 10: C50 decision tree.

15

Figure 11: Random Forest decision tree.

16

Figure 12: rpart decision tree.

Figure 13: J48 (unpruned) decision tree.

17

Figure 14: J48 (pruned) decision tree.

18

Figure 15: Accuracy based on training dataset size. The horizontal line at 50% represents the accuracy thatwould be achieved based on using an unbiased coin to decide the likelihood of survival.

19

A Plotting commands

A collection of commands to create the images in this report.

‘A Mosaic plot of R Titanic data’ on page 5 –

library(titanic)

library(graphics)

mosaicplot(Titanic, main = "Survival on the Titanic")

‘vcdExtra Titanic data’ on page 7 –

library(vcdExtra)

data(Titanicp)

plot(Titanicp)

‘vcdExtra Titanic data decision tree based on passenger class, age, and number and type of traveling companions’ on page 8–

library(rpart)

library(rpart.plot)

data(Titanicp, package="vcdExtra")

rp0

B Files

A collection of miscellaneous files mentioned in the report.

• titanic3.xls – A consolidated list of passengers.

• predictions.R – An R script to demonstrate the different partitioning approaches and their results.

21

titanic3

pclasssurvivednamesexagesibspparchticketfarecabinembarkedboatbodyhome.dest

11Allen, Miss. Elisabeth Waltonfemale290024160211.3375B5S2St Louis, MO

11Allison, Master. Hudson Trevormale0.916712113781151.5500C22 C26S11Montreal, PQ / Chesterville, ON

10Allison, Miss. Helen Lorainefemale212113781151.5500C22 C26SMontreal, PQ / Chesterville, ON

10Allison, Mr. Hudson Joshua Creightonmale3012113781151.5500C22 C26S135Montreal, PQ / Chesterville, ON

10Allison, Mrs. Hudson J C (Bessie Waldo Daniels)female2512113781151.5500C22 C26SMontreal, PQ / Chesterville, ON

11Anderson, Mr. Harrymale48001995226.5500E12S3New York, NY

11Andrews, Miss. Kornelia Theodosiafemale63101350277.9583D7S10Hudson, NY

10Andrews, Mr. Thomas Jrmale39001120500.0000A36SBelfast, NI

11Appleton, Mrs. Edward Dale (Charlotte Lamson)female53201176951.4792C101SDBayside, Queens, NY

10Artagaveytia, Mr. Ramonmale7100PC 1760949.5042C22Montevideo, Uruguay

10Astor, Col. John Jacobmale4710PC 17757227.5250C62 C64C124New York, NY

11Astor, Mrs. John Jacob (Madeleine Talmadge Force)female1810PC 17757227.5250C62 C64C4New York, NY

11Aubart, Mme. Leontine Paulinefemale2400PC 1747769.3000B35C9Paris, France

11Barber, Miss. Ellen "Nellie"female26001987778.8500S6

11Barkworth, Mr. Algernon Henry Wilsonmale80002704230.0000A23SBHessle, Yorks

10Baumann, Mr. John Dmale00PC 1731825.9250SNew York, NY

10Baxter, Mr. Quigg Edmondmale2401PC 17558247.5208B58 B60CMontreal, PQ

11Baxter, Mrs. James (Helene DeLaudeniere Chaput)female5001PC 17558247.5208B58 B60C6Montreal, PQ

11Bazzani, Miss. Albinafemale32001181376.2917D15C8

10Beattie, Mr. Thomsonmale36001305075.2417C6CAWinnipeg, MN

11Beckwith, Mr. Richard Leonardmale37111175152.5542D35S5New York, NY

11Beckwith, Mrs. Richard Leonard (Sallie Monypeny)female47111175152.5542D35S5New York, NY

11Behr, Mr. Karl Howellmale260011136930.0000C148C5New York, NY

11Bidois, Miss. Rosaliefemale4200PC 17757227.5250C4

11Bird, Miss. Ellenfemale2900PC 17483221.7792C97S8

10Birnbaum, Mr. Jakobmale25001390526.0000C148San Francisco, CA

11Bishop, Mr. Dickinson Hmale25101196791.0792B49C7Dowagiac, MI

11Bishop, Mrs. Dickinson H (Helen Walton)female19101196791.0792B49C7Dowagiac, MI

11Bissette, Miss. Ameliafemale3500PC 17760135.6333C99S8

11Bjornstrom-Steffansson, Mr. Mauritz Hakanmale280011056426.5500C52SDStockholm, Sweden / Washington, DC

10Blackwell, Mr. Stephen Weartmale450011378435.5000TSTrenton, NJ

11Blank, Mr. Henrymale400011227731.0000A31C7Glen Ridge, NJ

11Bonnell, Miss. Carolinefemale300036928164.8667C7S8Youngstown, OH

11Bonnell, Miss. Elizabethfemale580011378326.5500C103S8Birkdale, England Cleveland, Ohio

10Borebank, Mr. John Jamesmale420011048926.5500D22SLondon / Winnipeg, MB

11Bowen, Miss. Grace Scottfemale4500PC 17608262.3750C4Cooperstown, NY

11Bowerman, Miss. Elsie Edithfemale220111350555.0000E33S6St Leonards-on-Sea, England Ohio

11Bradley, Mr. George ("George Arthur Brayton")male0011142726.5500S9Los Angeles, CA

10Brady, Mr. John Bertrammale410011305430.5000A21SPomeroy, WA

10Brandeis, Mr. Emilmale4800PC 1759150.4958B10C208Omaha, NE

10Brewe, Dr. Arthur Jacksonmale0011237939.6000CPhiladelphia, PA

11Brown, Mrs. James Joseph (Margaret Tobin)female4400PC 1761027.7208B4C6Denver, CO

11Brown, Mrs. John Murray (Caroline Lane Lamson)female59201176951.4792C101SDBelmont, MA

11Bucknell, Mrs. William Robert (Emma Eliza Ward)female60001181376.2917D15C8Philadelphia, PA

11Burns, Miss. Elizabeth Margaretfemale410016966134.5000E40C3

10Butt, Major. Archibald Willinghammale450011305026.5500B38SWashington, DC

10Cairns, Mr. Alexandermale0011379831.0000S

11Calderhead, Mr. Edward Penningtonmale4200PC 1747626.2875E24S5New York, NY

11Candee, Mrs. Edward (Helen Churchill Hungerford)female5300PC 1760627.4458C6Washington, DC

11Cardeza, Mr. Thomas Drake Martinezmale3601PC 17755512.3292B51 B53 B55C3Austria-Hungary / Germantown, Philadelphia, PA

11Cardeza, Mrs. James Warburton Martinez (Charlotte Wardle Drake)female5801PC 17755512.3292B51 B53 B55C3Germantown, Philadelphia, PA

10Carlsson, Mr. Frans Olofmale33006955.0000B51 B53 B55SNew York, NY

10Carrau, Mr. Francisco Mmale280011305947.1000SMontevideo, Uruguay

10Carrau, Mr. Jose Pedromale170011305947.1000SMontevideo, Uruguay

11Carter, Master. William Thornton IImale1112113760120.0000B96 B98S4Bryn Mawr, PA

11Carter, Miss. Lucile Polkfemale1412113760120.0000B96 B98S4Bryn Mawr, PA

11Carter, Mr. William Ernestmale3612113760120.0000B96 B98SCBryn Mawr, PA

11Carter, Mrs. William Ernest (Lucile Polk)female3612113760120.0000B96 B98S4Bryn Mawr, PA

10Case, Mr. Howard Brownmale49001992426.0000SAscot, Berkshire / Rochester, NY

11Cassebeer, Mrs. Henry Arthur Jr (Eleanor Genevieve Fosdick)female001777027.7208C5New York, NY

10Cavendish, Mr. Tyrell Williammale36101987778.8500C46S172Little Onn Hall, Staffs

11Cavendish, Mrs. Tyrell William (Julia Florence Siegel)female76101987778.8500C46S6Little Onn Hall, Staffs

10Chaffee, Mr. Herbert Fullermale4610W.E.P. 573461.1750E31SAmenia, ND

11Chaffee, Mrs. Herbert Fuller (Carrie Constance Toogood)female4710W.E.P. 573461.1750E31S4Amenia, ND

11Chambers, Mr. Norman Campbellmale271011380653.1000E8S5New York, NY / Ithaca, NY

11Chambers, Mrs. Norman Campbell (Bertha Griggs)female331011380653.1000E8S5New York, NY / Ithaca, NY

11Chaudanson, Miss. Victorinefemale3600PC 17608262.3750B61C4

11Cherry, Miss. Gladysfemale300011015286.5000B77S8London, England

11Chevre, Mr. Paul Romainemale4500PC 1759429.7000A9C7Paris, France

11Chibnall, Mrs. (Edith Martha Bowerman)female0111350555.0000E33S6St Leonards-on-Sea, England Ohio

10Chisholm, Mr. Roderick Robert Crispinmale001120510.0000SLiverpool, England / Belfast

10Clark, Mr. Walter Millermale271013508136.7792C89CLos Angeles, CA

11Clark, Mrs. Walter Miller (Virginia McDowell)female261013508136.7792C89C4Los Angeles, CA

11Cleaver, Miss. Alicefemale2200113781151.5500S11

10Clifford, Mr. George Quincymale0011046552.0000A14SStoughton, MA

10Colley, Mr. Edward Pomeroymale4700572725.5875E58SVictoria, BC

11Compton, Miss. Sara Rebeccafemale3911PC 1775683.1583E49C14Lakewood, NJ

10Compton, Mr. Alexander Taylor Jrmale3711PC 1775683.1583E52CLakewood, NJ

11Compton, Mrs. Alexander Taylor (Mary Eliza Ingersoll)female6402PC 1775683.1583E45C14Lakewood, NJ

11Cornell, Mrs. Robert Clifford (Malvina Helen Lamson)female55201177025.7000C101S2New York, NY

10Crafton, Mr. John Bertrammale0011379126.5500SRoachdale, IN

10Crosby, Capt. Edward Giffordmale7011WE/P 573571.0000B22S269Milwaukee, WI

11Crosby, Miss. Harriet Rfemale3602WE/P 573571.0000B22S7Milwaukee, WI

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